Authors - Bhavana Chalkalwar, Reena S. Satpute Abstract - In today’s world Artificial Intelligence is rapidly growing in modern tech industry which enhances the power of new IT services. Artificial Intelligence helps to Education, Government And Private Services for better future work. The new way of teaching and providing instruction requires an adaptive approach rather than the traditional way of teaching to meet the wide variety of student learners. This change in education is being led by technological advances in areas such as machine learning, natural language processing, and intelligent tutoring systems. This paper will examine Artificial Intelligence's role in education, including the various applications available and what potential benefits and challenges each might present, as well as how the use of Artificial Intelligence in education as a whole may affect education moving forward. Many of the current systems using AI today allow for customized arrangements through the use of automated assessment tools, intelligent tutoring, and adaptive algorithms. With the use of chatbots and virtual assistants, the amount of student interaction and the speed at which students can obtain information will increase as well as hold instructors accountable for identifying learning gaps. In addition, AI tools have an enormous impact on what happens inside the classroom and in institutional management. These tools will help streamline many of the non-teaching duties or administrative assignments assigned to educators, improve how resources are distributed, and better equip school administrators and policymakers to make decisions related to education. Additionally, AI tools can be used in research practices that are unique to institutions of higher education, such as automating literature reviews, analysing trend data within a compilation of data, and contributing to predictive modelling. However, the use of AI in education has created significant ethical, technical, and sociological concerns.
Authors - Aanchal Khandalkar, Reena S. Satpute Abstract - Mobile app developments exploded lately, and it’s not hard to see why. Things like 5G, AI, machine learning, and Mobile Edge Computing aren’t just making headlines they’re actually changing how apps work. Now, apps are smarter, more personalized, and packed with features that can show up overnight. Sounds amazing, but the flip side is tough: people expect everything instantly. They want quick responses, apps that basically read their minds, and zero downtime, no matter where they are. Honestly, building apps these days is anything but simple. Hardware is a real pain for developers. Phones just don’t have the power of regular computers. You get less memory, slower processors, and, of course, batteries that bail on you before you even realize. So, developers have to work magic keep the app running smoothly without draining the battery, or else people just uninstall. And with so many apps depending on outside libraries and analytics tools, there’s a whole new pile of problems. Sure, these tools help, but they open the door to security risks. Not every team has someone who lives and breathes security, so it’s easy for privacy issues or sketchy code to sneak in. All of that puts some cracks in the process and makes building solid apps a lot trickier. Such fragmentation exists on platform that developing AI/ML application for it, on the Android platform in particular, turns out to be extremely painful to integrate on. Then it becomes a question of tradeoffs from developers’ perspective-how the usage of cloud services versus local device processing fits, on each having its share of difficulties regarding scale, speed, power and security. In this paper, we go through the workflow and delve deeper into a comparative study on native application development.
Authors - Khushi Tijare, Reena S. Satpute Abstract - Bio-signals such as eye movements, electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and pupil dilation are real-time reactions to language cues that provide more data than static text representations used in classical NLP. The aim of this study is to examine how bio signals could be employed in deep natural language processing (NLP) to enhance task effectiveness and interpretability during reading comprehension, sentiment analysis, named entity recognition (NER), and syntax parsing. The following sections will describe the important aspects of design, including. Pre-processing steps for EEG and eye-tracking datasets. Model architecture types such as feature injection, multi-task learning based on auxiliary tasks, attention mechanisms, and multimodal transformers. Experimental design and metrics used for model evaluation. Ethical considerations regarding the use of cognitive signals. All design decisions made by the authors have been verified through experiments involving open access benchmark datasets like ZuCo, ZuCo 2.0, and recently developed EEG datasets.
Authors - Luong Tran Thi, Nguyen Van Long, Bac T. Nguyen, Hiep L Thi Abstract - Maximum Distance Separable (MDS) matrices play a crucial role in the design of diffusion layers in modern symmetric cryptographic primitives such as block ciphers, hash functions, and lightweight cryptographic schemes. Owing to their ability to achieve the optimal level of diffusion as measured by the branch number criterion, MDS matrices significantly enhance resistance against differential and linear cryptanalysis. However, the practical deployment of MDS matrices often faces challenges due to high computational cost, a large number of XOR operations, and substantial hardware resource requirements. Therefore, the construction of implementation-efficient MDS matrices has become an important research direction in modern cryptographic design. This paper presents a comprehensive survey of methods for constructing and optimizing MDS matrices with a focus on reducing implementation complexity in both software and hardware environments. Specifically, classical construction methods based on Cauchy, Vandermonde, and Reed–Solomon structures are reviewed, along with structured matrices such as circulant, recursive, and involutory forms. In addition, optimization techniques targeting XOR count, circuit depth, and memory usage are analyzed, and several open research directions in the design of efficient diffusion layers for modern cryptographic systems are discussed.
Authors - Hiep. L. Thi Abstract - Recent advances in quantum computing threaten the cryptographic foundations of blockchain systems. While existing surveys analyze quantum resistant blockchain architectures at the system level, wallet-layer migration and threshold post-quantum signing mechanisms remain underexplored. This paper proposes a post-quantum secure cryptocurrency wallet architecture based on code based threshold signatures. We introduce a formal quantum-adversarial wallet model, design a modular wallet framework, provide a security reduction argu ment under syndrome decoding hardness, and outline a migration-compatible de ployment strategy
Authors - Hiep. L. Thi Abstract - Secret-sharing is a fundamental cryptographic primitive that enables the secure distribution of sensitive information among multiple participants while guaranteeing correctness and privacy. In this paper, we present a comprehensive study of secret-sharing schemes from both information-theoretic and computational perspectives. We begin by reviewing classical threshold constructions, including Shamir’s polynomial-based scheme and its algebraic interpretation via linear codes. The notions of correctness and perfect secrecy are formalized using entropy, and the role of access structures in characterizing authorized subsets is emphasized. We then examine ideal secret-sharing schemes, where each share has the same size as the secret, and highlight their deep connection with representable matroids. In particular, we discuss how matroid representability over finite fields characterizes the existence of ideal linear secret-sharing schemes, thereby linking combinatorial independence with cryptographic access control. Additional structural results concerning field dependence, minimal non-ideal access structures, and connections to linear codes are also addressed. The paper further explores computational secret-sharing, which relaxes perfect privacy to computational indistinguishability under standard cryptographic assumptions. We describe constructions based on encryption and threshold key sharing, as well as realizations derived from monotone circuits. A central theme is the separation between information-theoretic and computational models: while certain access structures require exponential share size in the information-theoretic setting, they admit polynomial-size shares under computational assumptions. Finally, we discuss algebraic methods underlying secret-sharing, including polynomial interpolation and linear coding techniques, and illustrate how these tools support efficient distributed protocols such as secure multiparty computation and threshold cryptography. Overall, the paper provides a unified treatment of structural, algebraic, and computational aspects of secret-sharing, highlighting its foundational role in modern distributed cryptographic systems.
Authors - Polinati Vinod Babu , M.V.P Chandra Sekhara Rao , Kolagotla Venkateswara Reddy , Manukonda Ravi Chandra , C P Pavan Kumar Hota , Kurumalla Suresh Abstract - The web services today are flooded with automated bots, distributed request floods and IP based attacks. Traditional firewalls are based on either a static signature rule or an opaque machine learning model that is not very transparent. We introduce ShieldNet, a lightweight Web Application Firewall (WAF), which is developed in FastAPI and Redis as a state storage. The explainable real-time request filtering performed by ShieldNet is on the basis of a deterministic decision pipeline, a combination of IP reputation scoring (Redis backed, counts on infractions), SlowAPI based rate limiting, Detection of bot user agent, Up-to-date use of open access threat feeds (spamhaus, abuse.ch, etc.). Request identities can be provided as either permitted (HTTP 200), rate limited (429) or blocked (403); all of which can be traced through the middleware layers. Synthetic load testing and feed validation reveal low latency, high throughput and high detection fidelity. The system has good performance, scalability and interpretability balance thus it can be used in real-time web security implementation.
Authors - Apolinar P. Datu, Pamela V. Zuniga, Chona S. Lajom, Jobert D. Bravo, Renen Paul M. Viado, Ma. Yvonne Czarina C. Angcaya, Maricris Punzalan Abstract - Higher education institutions play a critical role in advancing sustainable development, particularly through effective and inclusive communication practices supported by digital technologies. This study examined the role of Information and Communication Technology (ICT) standards in shaping effective communication strategies for sustainable development in higher education. Using a quantitative descriptive research design, data were collected through a structured questionnaire administered to thirty (30) higher education personnel, including faculty members, administrators, and ICT staff aged 22 years and above. A 4-point Likert scale was utilized to measure levels of ICT standards implementation, communication effectiveness, accessibility and inclusivity, system interoperability, and stakeholder engagement. The findings revealed a high level of ICT standards implementation across participating institutions. Results further showed that ICT standards strongly influence the clarity, reliability, timeliness, and organization of communication strategies. Standardized ICT practices were also found to enhance accessibility and inclusivity in digital communication, support system interoperability, and improve stakeholder engagement in sustainability-related initiatives. Overall, the study highlights that ICT standards serve as essential enablers of coherent, inclusive, and sustainable communication in higher education. The findings underscore the importance of strengthening ICT standards to support institutional sustainability goals and foster meaningful stakeholder participation.
Authors - A Aruna kumari, Sri Vishnu Prabhu Gudavalli, Tamminana Visweswari Abstract - This project presents a hybrid architecture, which combines sequential and non-sequential models, to detect diabetic retinopathy (DR) in retinal fundus image data using deep learning. The model has a non-sequential backbone feature extraction layer, a pre-trained ResNet50, built using the Functional API of Keras to allow flexibility and skip connections. On top of that, a functional custom sequential classifier head is added, comprising of, e.g., GlobalAveragePooling2D, Dense, and Dropout layers, also constructed in a functional manner to ensure the architecture is coherent. The classifier is used to make a binary prediction: DR or not. The model was trained using the EyePACS data using different techniques of image preprocessing, including resizing, normalization, and augmentation. The resulting architecture is completely accurate and exhibits good generalization on unseen data, which indicates the effectiveness of transfer learning in combination with deep sequential classifiers. This bifurcated architecture is understandable and performance-wise attractive to be used in clinical decision support in diabetic retinopathy screening.
Authors - Masaki Murakami, Atsuhiro Goto Abstract - Post-quantum cryptography (PQC) migration in the financial sector is a resource allocation problem shaped by network interdependence. This study develops a prototype dynamic simulation model that incrementally introduces cyber-risk internalization: starting from a baseline without cyber-risk channels, adding firm-level direct cyber loss, and finally incorporating systemic cyber loss propagated through the financial network. The paper quantifies how the scope of cyber-risk internalization determines migration outcomes under a shared investmentallocation framework. Results show that firm-level internalization accelerates early migration but remains insufficient for full sector-wide coverage, whereas systemic-risk internalization can close the late-stage adoption gap under sufficiently supportive policy conditions. This distinction shows that policy support for PQC migration should not be understood only as cost reduction, but also as a mechanism for internalizing network externalities. Through nested comparisons of these model variants, this study demonstrates how each risk channel distinctly shifts migration outcomes.
Authors - Akbar Doniyorovich, Doniyor Gulomov Zaynobidin o'g'li, Dilshoda Akramova, Xushmamatova Aminakhon Rustam qizi, Tukhtaeva Shakhnoza Gaybulla kizi, Abdimurodova Shakhnoza Anvar qizi Abstract - This article discusses the theoretical foundations and рractical directions of innova tive didactic methods aimed at develoрing the comрetence of using artificial intelligence (AI) in рedagogical activities. The study analyzes the рroblem of forming teachers' digital literacy, technological readiness, and ability to integrate intellectual technologies into the educational рrocess. The article рroрoses methods aimed at increasing the innovative comрetence of teach ers based on a didactic aррroach and a steр-by-steр learning model develoрed to achieve effec tive results in the use of AI tools in education. These methods include aррroaches such as рroblem-based learning, рroject work, reflexive analysis, and collaborative learning with AI as sistants. According to the results of the study, the systematic introduction of artificial intelli gence technologies into the educational рrocess develoрs the information-analytical, methodo logical, creative, and reflexive comрetence of teachers. The use of innovative didactic methods increases the effectiveness of teacher activity, individualizes the educational рrocess, enhances analytical thinking, and a creative aррroach. The scientific results рresented in the article serve as a scientific and methodological basis for the рrocess of modernizing the рedagogical education system, imрlementing the conceрt of digital рedagogy into рractice, and рreрaring future teachers to work with artificial intelligence technologies.
Authors - Apolinar P. Datu, Jobert D. Bravo, Reine Joshua L. Cruz, Helga Marie B. Cabarle, Najera R. Umpar, Minsoware S. Bacolod Abstract - In today’s rapidly evolving Information and Communication Technology (ICT) landscape, digital tools have become essential in shaping how research is presented, shared, and collaboratively developed. This study aimed to quantitatively examine the collaborative benefits of digital tools and their impact on user engagement during research presentations. Using a quantitative descriptive–comparative research design, data were collected from thirty (30) respondents consisting of students, educators, ICT professionals, and administrative staff who regularly engage in ICT-based collaborative activities. A structured survey questionnaire was used to gather numerical data on the frequency of tool usage, levels of collaboration, and user engagement. Descriptive statistics such as frequency, percentage, and weighted mean were employed to analyze the data. The findings revealed that digital collaboration tools are frequently used in ICT-related activities and are strongly associated with improved coordination, communication, and teamwork. Respondents reported high levels of collaboration and engagement, particularly when using interactive features such as real-time communication, shared document editing, and feedback mechanisms. These features were found to enhance motivation, focus, and willingness to participate actively in research-related tasks. Overall, the study highlights the importance of intentional and effective integration of digital tools in research presentations to foster meaningful collaboration and sustained engagement in ICT-based academic and professional environments.
Authors - Wonseong Kim Abstract - Geopolitical risk now tends to surface in information environments well before it shows up as physical disruption or moves in market prices, and often before any policy response. Current geopolitical risk indices track the salience of news, and a separate body of work on disinformation detection looks for signals of manipulation. What neither line of work does is connect manipulated discourse to the channels through which food, energy, supply chain, sanctions, and macro-financial risks are actually transmitted. To address this gap, the paper develops a DISARM-informed large language model framework for narrative-based monitoring of disinformation-driven geopolitical risks. The framework is organised as a four-layer architecture that brings together observable manipulation signals, the classification of narrative function, mapping onto risk domains, and the construction of indicators. From these layers it derives interpretable indicators that capture manipulated risk discourse, gaps in framing, concentration of narratives, and transmission across domains. Our central claim is a methodological one: once observable manipulation, narrative function, and risk-domain mapping are represented together, unstructured multilingual media can be turned into auditable early-warning signals. We intend the result as a decision-support instrument for sustainable security monitoring, not as a means of attribution or causal estimation.
Authors - Nassour Annour Saad, Mahamat Issa Hassan, Marayi Choroma, Mahamat Atteib Ibrahim Doutoum, Djaury Dadjia Abstract - Pediatric type 1 diabetes (T1D) remains a major public health priority, especially in low-resource settings where presenting diabetic ketoacidosis is still common. This systematic review (2020–2025), conducted under PRISMA 2020 and complemented by TRIPOD/TRIPOD+AIinspired criteria for predictive models, synthesizes AI/ML work on early screening and risk stratification in children. Islet autoantibodies and genetic risk scores improve discrimination, but the literature shows substantial AUC variability depending on sample size, calibration, and validation design (single split versus repeated or family-level validation). Ensemble models often outperform classical approaches with multimodal data. We emphasize external validation, class-imbalance handling, and reproducible pipelines. The main gap remains the absence of models simultaneously integrating autoantibodies, HLA/GRS, and C-peptide.
Authors - Imane Bari, Abdellatif Aziki, Zineb Alaoui Abstract - This study analyses the relationship between digital operational risk management and the financial performance of industrial firms in the Agadir region of Morocco and investigates the role of artificial intelligence in risk governance. Using a quantitative survey of 50 industrial firms and linear regression with principal component analysis, the results show that a structured digital risk management framework, covering identification, assessment, and mitigation of threats, is positively and significantly associated with financial performance (R = 58.7%, F = 3.518, p < 0.05). Several constraints are identified, including skill shortages, limited technological resources, and insufficient digital governance culture. The study further shows that AI-based tools, through automated anomaly detection and predictive analysis, strengthen the effectiveness of risk management frameworks. These findings support the integration of AI as a technical component of operational risk governance in industrial settings.
Authors - Libero Nigro, Franco Cicirelli Abstract - This paper builds on the Hartigan-Wong (HW) algorithm for unsupervised clustering. Although basic HW comes with an intrinsic high computational cost, it is known to be a better solution than K-Means, because it is less likely to get stuck around a sub-optimal solution of the data space. The paper, in particular, proposes a variant of HW, named Evolutionary HW (E-HW), which embodies genetic concepts and favors the achievement of more accurate clustering. E-HW depends on the use of a population of candidate solutions (centroid configurations), preliminarily created. E-HW is fed by a solution extracted from the population, which gets refined (crossed) and possibly replaced (mutated) following the basic HW operations. New generations of the population then come into existence. E-HW can be repeated a certain number of times, after that, experimental results highlight that the population favors the emergence of a solution close to the optimal one. To smooth out the computational burden, many operations of E-HW are implemented in parallel Java, so as to exploit the computing benefits of modern multi-core machines. The paper demonstrates the effectiveness of E-HW by using a collection of benchmark datasets, and the clustering results are compared with those achieved by competitor algorithms.
Authors - Ilgar G. Aliyev, Konul Gafarbayli, Firangiz Mammadrzayeva Abstract - Modern parallel gas pipeline systems require intelligent and operationally reliable emergency-management mechanisms capable of distinguishing real leakage events from normal technological transients under real-time operating conditions. Although IoT- and SCADA-based monitoring technologies are widely used in modern gas transmission infrastructures, most existing systems primarily rely on threshold-based supervision or empirical data-driven methods, which often lack physical interpretability and analytical decision-making capability. This paper proposes an intelligent IoT-driven emergency-management and analytical decision-making framework for parallel gas pipelines based on the integration of digital monitoring technologies with analytical gas-dynamic modeling. The proposed cyber-physical architecture combines wireless pressure sensors, SCADA-assisted supervisory control, synchronized shut-off valves, and analytical decision algorithms to ensure real-time identification, localization, and mitigation of emergency operating modes. Analytical criteria are developed for distinguishing emergency and technological pressure variations, estimating emergency detection time, localizing the leakage coordinate, and determining the optimal activation time of interconnecting pipeline valves. The proposed framework enables rapid isolation of damaged pipeline sections while ensuring adaptive gas redistribution through intact parallel lines. Unlike conventional monitoring-based approaches, the developed methodology transforms emergency control into an analytically justified intelligent supervision mechanism capable of minimizing gas losses, preventing cascade disturbances, and improving operational sustainability. The integration of IoT-based sensing with analytical decision-making additionally improves compatibility with Industry 4.0 and digital twin concepts for future smart gas transmission infrastructures.
Authors - Kirill Kalichkin, Tatiana Gritskevich Abstract - The study is devoted to the analysis of vibration agnostics problems as a method of preventive control in the design of wheel sets of railway locomotives. The study examines vibration agnostics as a preventative control method for designing individual mechanisms and components of railway locomotive wheel sets designed for long-term, safe operation. Currently, the main issue with the mechanical drives of wheel-motor unit assemblies and motor-anchor bearing assemblies in railway locomotive wheel sets remains increased vibration during highfrequency operation. The authors analyze the prediction of potential defects using digital twins, the goal of which is to enable engineers to accurately predict solutions when similar defect signs are detected during operation of different digital twin scenarios. This enables the development of preventative measures to prevent accidents at the early stages of wheel set defect development.
Authors - Emmanuel Tuyishimire Abstract - The fourth Industrial Revolution(4IR), together with the COVID-19 pandemic have made a loud call for digitizing diagnosis processes. The world is now convinced that it is imperative to digitize the diagnosis of long standing diseases such as malaria for more efficient treatment and control. It has been seen that malaria control would benefit a lot from digitising its diagnosis processes such as data gathering. We propose, in this paper, the architecture of a digital data collection system and how it is used to gather data for malaria awareness. The system is formally specified using Z notation, and based on the capability of the system, the malaria determinants are defined and their retrieving mechanisms are discussed.
Authors - Sara Sadiq Jawad, Dheyaa Jasim Kadhim Abstract - Software-defined networks (SDNs) suffer from dynamic congestion due to the nature of the data traffic they transmit. This includes both aggregated mobile network activity (such as video streaming, social media access, browsing, messaging, and mobile app usage) and real backbone internet traffic traces (which contain diverse packet flows from broadband services, cloud computing systems, downloads, server connections, and large-scale network transactions). This congestion reduces service quality and leads to poor resource allocation. Therefore, traffic prediction is considered a smart and efficient transition for SDNs. This paper proposes a deep learning-based predictive system for forecasting incoming traffic using two types of data (periodic Milano and bursty MAWI dataset). It also examines the impact of periodic and bursty traffic types on the prediction models used by the proposed system; and on the integration mechanisms used to translate predictions into actionable data. The results show that periodic Milano traffic requires temporal learning, while bursty MAWI traffic requires clipping, alignment, log scale, and robust prediction. Using MAWI traffic also required alignment between the training and evaluation phases through the application of cross-domain adaptation, unlike the Milano traffic which showed a direct response. The proposed system also demonstrated improved throughput, reduced congestion, and more stable decision-making.
Authors - Quoc-Anh Nguyen, Thanh-Nghi Doan, Huu-Hoa Nguyen Abstract - Crop productivity has been and continues to be influenced by both beneficial and harmful insect species. The classification of these insects plays a critical role in identifying threats and implementing crop protection measures. This paper presents a multimodal insect dataset for multimodal classification, utilizing both images and supplementary textual descriptions. The dataset is enriched to provide comprehensive information about various insect species. The study illustrates an integrated approach for feature extraction, similarity analysis, and insect classification. Furthermore, the research also introduces a model interpretation mechanism for the deep learning-based feature extraction process.
Authors - Petros Papagiannis, George Pallaris, Pantelitsa Leonidou Abstract - Predicting student academic performance presents a persistent challenge for higher education institutions. This paper presents a machine learning study at Cyprus College, Cyprus, using 311 studentcourse records across three semesters from 13 Computer Science courses. Four assessment components—midterm examination, final examination, assignments, and participation—alongside absence counts for 95 unique students are used as features. Seven algorithms are evaluated—Random Forest, XGBoost, SVM, Logistic Regression, K-Nearest Neighbours, Decision Tree, and Naive Bayes—using stratified five-fold cross-validation across three tasks: regression, binary pass/fail classification, and multiclass grade band prediction. SHAP analysis (applied to the full feature set) identifies feature contributions, while early-warning experiments exclude the final examination score to simulate mid-semester prediction. Results show that midterm and assignment scores predict final outcomes with R2=0.746 before the final examination, whilst Random Forest and XGBoost achieve 97.4% pass/fail accuracy. Participation contributes zero predictive signal despite a 10% grade weighting, with direct implications for assessment design at small higher education institutions.
Authors - Sukuse Abe Abstract - According to Einstein’s theory of relativity, the space we inhabit is distorted. Grigori Perelman solved the geometrization conjecture, which states that this space can be classified into eight spaces. Of these eight types, the classification of hyperbolic manifolds remains unresolved. Solving the volume conjecture would greatly advance the classification of hyperbolic manifolds. While the volume conjecture is one of the open problems in knot theory for knots in general, we successfully prove it for the family of oriented twist knots. The proof use the method of steepest descent and the theory of functions of several complex variables on the basis of colored Jones polynomials.
Authors - O. Aina, C.J. Van Staden, P. Makgato-Khunou Abstract - The role of leadership in the acceptance and integration of mobile technology for teaching and learning at a Private Higher Education Institution (PHEI) in South Africa have been explored. Mobile technologies have become widely used, but their application in higher education has been sporadic. The attitude towards mobile technology for teaching and learning are conditioned by the institutional environment which is in turn influenced by the leadership’s adoption. The study applied unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theories. A quantitative case study design was used with academic and institutional leaders. The study established a significant positive relationship between the construct of leadership perception and the acceptance of mobile technology for teaching and learning. The study concludes that leadership influences perceptions about acceptance of mobile technology for teaching and that enablers for the integration depend on the presence of appropriate communication channels and enabling conditions.
Authors - Dzhansel Abtula, Stanka Hadzhikoleva, Emil Hadzhikolev, Iliana Ivanova, Elitsa Dubarova Abstract - The present study examines the potential of generative language models as tools for discourse analysis of war-related language in media texts. The study is based on a corpus of texts processed by three generative AI models using an identical prompt that defines a multi-stage analytical procedure. This procedure includes the extraction of war-related lexical units, semantic classification, functional analysis of evaluative and ideological features, compilation of a thematic glossary, analysis of metaphors, identification of discursive strategies, and genre determination of the texts. The generated analyses are evaluated through a structured questionnaire based on a five-point Likert scale, completed by an expert with an academic background. The study aims to systematically assess and compare the quality of discourse analyses produced by different language models under controlled conditions. The results indicate that all models generate structurally coherent and terminologically consistent analyses, but differ significantly in interpretative depth and contextual sensitivity. These findings support the need for a hybrid approach that combines automated analysis with human expertise to ensure accurate interpretation of implicit meanings, ideological nuances, and context-dependent discourse features.
Authors - Makomborero Murwira, Dane Brown Abstract - The proliferation of realistic synthetic speech poses significant threats to information integrity and public safety through financial fraud and misinformation campaigns. While traditional countermeasures based on Gaussian Mixture Models have proven effective against earlygeneration deepfakes, they struggle to generalise to sophisticated attacks produced by contemporary neural synthesis. This paper investigates enhanced audio deepfake detection through Light Convolutional Neural Networks (LCNNs) combined with Linear Frequency Cepstral Coefficients (LFCCs) and advanced training strategies. This study systematically evaluates the impact of margin-based loss functions (Cosface and A-Softmax) and FreqAugment data augmentation on model robustness and generalisation capability. Validated on the ASVspoof 2019 Logical Access dataset, the optimised LCNN model achieves an Equal Error Rate (EER) of 5.40% on the evaluation set containing thirteen unseen attack types, representing a 33% relative improvement over the baseline LFCCGMM countermeasure (8.09% EER). The combination of Cosface loss with FreqAugment demonstrates superior performance compared to ASoftmax configurations, reducing false negatives substantially. Per-attack analysis reveals robust performance across diverse spoofing techniques, though vulnerabilities to advanced neural waveform manipulation methods remain. The proposed framework provides a practical, deployable solution for audio deepfake detection in real-world security-critical applications.
Authors - Ida Bagus Dwipayana , Naniek Utami Handayani, Singgih Saptadi Abstract - The global construction sector is under increasing pressure to shift towards sustainable operational models, particularly in heavy materials processing like asphalt production. This study provides empirical, case-studybased insights regarding sustainable procurement practices at the Asphalt Mixing Plant (AMP), run by PT. Jasamarga Tollroad Maintenance in Karawang, West Java, Indonesia. Utilizing six semesters of the Environmental Management Monitoring Effort (UKL-UPL) data from Semester 1 (S1) 2021 to S1 2025, this research assesses the environmental performance of three integrated innovations: (1) a gas-fired burner system substituting for fuel oil, (2) multi-layered noise reduction technologies, and (3) collection and water vapor-based treatment of stone dust to optimize novelty soil fertility systems. Stack emissions analysis found significantly lower levels of CO at 184 mg/m3 and NOx at 212 mg/m3 than Indonesian regulation enables KEP-13/MENLH/3/1995, while ambient air was consistently compliant during monitoring over nine periods. Outdoor sound levels in the upwind and downwind locations were 65.6 dB (A) and 56.8 dB (A), respectively, both below the exposure limit of 70dB(A) specified by SK Men LH KEP-48/MenLH/11/1996, Sensitivity analysis between four fuel-technology scenarios shows a combination approach achieving a 61.9% CO emission index reduction, 55.4% NOX index reduction, and SO2 index is reduced by 78%, relative to diesel fuel basis. This study finds a significant research gap with respect to operationalizable sustainable procurement criteria combining air quality, noise, and waste valorization in an integrated framework at the AMP operational level. The results offer theoretically underpinned and empirically verified procurement criteria, which are directly applicable to the international construction industry, thus advancing green supply chain management, principles of circular economy as well as pathways towards net-zero construction.
Authors - Cossi Blaise Avoussoukpo, Amara Camara, Babou Dione Abstract - Digital scholarly infrastructures increasingly shape academic visibility by supporting the creation, organisation, dissemination, and discovery of research outputs. Existing studies have advanced important dimensions of this domain, including bibliometric evaluation, persistent identiers, metadata standards, and indexing systems. However, they primarily examine these components independently and provide only a limited understanding of how their interactions inuence visibility in contemporary research ecosystems. This paper introduces the Academic Visibility Pipeline Model (AVPM). This conceptual framework conceptualises academic visibility as a sequence of interconnected stages linking research production, metadata structuring, scholarly indexing, identity and aliation aggregation, dissemination, and discovery. By integrating socio-technical coordination, interoperability, and cascading failure, the AVPM explains how visibility emerges and how upstream constraints inuence downstream outcomes. An illustrative application to the Guinea academic visibility ecosystem demonstrates the explanatory value of the framework by identifying structural bottlenecks and tracing their eects across the visibility pipeline. The proposed framework extends conventional publication- and citation-centred perspectives and provides a process-oriented foundation for institutional strategy, research governance, and digital transformation in emerging research ecosystems.
Authors - Duy Nguyen Ngoc, Hiep Nghia Phan Abstract - Large Language Models (LLMs) have recently shown strong capabilities in automatic text summarization. However, applying these models to lowresource languages such as Vietnamese remains challenging due to limited training resources and language-specific characteristics. In this work, we ex-amine whether prompt optimization can improve Vietnamese summarization quality without modifying model parameters. This paper introduces a Vietnamese-aware Prompt Optimization Framework that refines prompt instructions by combining task-specific guidance, role-based prompting, linguistic con-straints, and iterative feedback. The generated summaries are assessed using both automatic evaluation metrics and human judgments to examine the ef-fectiveness of different prompt designs. We evaluate the proposed approach on a benchmark Vietnamese multi-aspect opinion dataset using several commercial and open-source LLMs, including GPT-4, Claude 3, Gemini 1.5, and PhoGPT, and compare their performance with Vietnamese pre-trained summarization models such as ViT5 and BARTpho. Our experiments show that refining prompts consistently improves summary quality across the evaluated models. In particular, GPT-4 with the optimized prompt achieves an 8.7% increase in ROUGE-L and receives higher human evaluation scores for fluency and factual consistency than the standard prompting setting.
Authors - Tien-Dao Luu, Viet Truong Xuan, Doan Hoang Phuc Nguyen, Nghi Huynh Quang, Do Chau Giang Nguyen, Nghia Nguyen Khoi, Huu Hoa Nguyen Abstract - The Mekong Delta agroecosystem faces compounding climate stressors and acute data fragmentation. While agricultural data exists online, it remains largely unstructured and unverified. This study introduces WikiCrop-AI, an integrated data-processing and machinelearning framework designed to consolidate heterogeneous agronomic information. The architecture comprises three interconnected modules: an Agricultural Notebook utilizing a Retrieval-Augmented Generation pipeline for multi-format data ingestion, a browser-based computational environment (WikiLab) for reproducible workflows, and a client-side analytics module for multivariate clustering. Evaluation of the ingestion pipeline yielded a String Similarity Score of 0.99 for structured text extraction. Furthermore, generative outputs assessed via the Automatic LLMs Citation Evaluation framework achieved average scores of 0.94 for both Citation Recall and Precision, alongside a Claim Recall of 0.92, indicating reliable knowledge synthesis under the tested conditions. A hierarchical clustering case study on 22 soybean cultivars further illustrates the platform’s utility in supporting non-programming local agronomists. Ultimately, WikiCrop-AI provides a decentralized infrastructure to translate scattered digital resources into actionable, verified agricultural intelligence. The complete open-source code for the WikiCrop-AI ecosystem is publicly accessible on GitHub at: https://github.com/mekonglab-vn/ WikicropAI.
Authors - Ioannis Patias, Koutaro Hachiya Abstract - The rapid growth of compute-intensive applications has intensified the need for selecting appropriate hardware accelerators. This paper presents a workload-to-architecture framework that explains when GPUs, FPGAs, or ASICs are most suitable, based on four determinants: parallelism granularity, memory behavior, dataflow regularity, and specialization depth. We organize representative workload classes—dense linear algebra, sparse/irregular algorithms, streaming signal processing, bit-level control workloads, and deep learning (training/inference)—and discuss how each class aligns with the execution and memory models of the three accelerator families. Our analysis highlights that GPUs excel in throughput-oriented, data-regular workloads; FPGAs provide deterministic latency via spatial pipelining and customized data paths; and ASICs achieve the best performance-per-watt for stable, high-volume tasks. The resulting framework provides practical guidance for accelerator selection and motivates heterogeneous system design.
Authors - Ronky Amber-Doh, Benjamin Ghansah, Winfred Larkotey, Stephen Opoku Oppong, Ezekiel Okoe, Olivia Osei-Tutu, Emmanuel Prah, Ephrem Kwaa-Aidoo Abstract - This paper introduces a novel framework, ExplainoGraph, that integrates square loss optimization with explainable artificial intelligence methods for knowledge graph–based recommender systems. Prior studies show that knowledge graph embeddings significantly enhance recommendation accuracy; however, they suffer from limited interpretability, thereby constraining user trust and system transparency. To address this gap, we introduce ExplainoGraph, which embeds explainability directly into the recommendation process through interpretable scoring functions and feature attribution procedures that provide meaningful insights into model decisions. Again, the framework incorporates ripple set propagation to effectively model user preferences, particularly in sparse data environments where traditional methods are suboptimal. Extensive experiments conducted on multiple benchmark datasets demonstrate that Explaino-Graph consistently outperforms the state-of-the-art baselines used across key evaluation metrics, including Precision@K, Recall@K, F1-score, and normalized discounted cumulative gain (NDCG)
Authors - Sayyora Qulmatova Abstract - This study uses machine learning models such as Multi-Linear Regression (MLR) and Holt-Winters Exponential Smoothing to model and forecast agricultural production indicators in Uzbekistan. The dataset consists of key agricultural indicators such as gross agricultural output, milk production, egg production, honey production, vegetables, fruits, and livestock products (in live weight). To improve model performance and ensure comparability, various data preprocessing methods such as StandardScaler, MinMaxScaler, RobustScaler, and Normalizer were used. The forecasting accuracy of each model was evaluated using standard error metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percent- age error (MAPE). Empirical results show that the MLR model provides stable and interpretable forecasts, especially when combined with appropriate scaling methods. The Holt-Winters model exhibits strong performance for time series with consistent trends, but shows limitations when applied to variable data. The MLP model effectively captures nonlinear relationships and complex time patterns, although its performance is sensitive to data preprocessing, with MinMaxScaler generally yielding superior results. Overall, the results show that no model is universally optimal; instead, the choice of forecasting technique should be based on the characteristics of the data. The proposed modeling framework contributes to increasing the accuracy and reliability of agricultural forecasts and can support evidence-based policy planning and decision-making in the agricultural sector of Uzbekistan.
Authors - Salma Chlaikhy, Adil Chakhtouna, Abdellah Adib Abstract - We propose a neural encoding framework that predicts continuous ECoG signals from self-supervised Data2Vec speech representations using multivariate Ridge regression. Evaluated on 18 auditory cortical electrodes from nine participants, the model achieves a mean Pearson correlation of r ≈ 0.39 under pooled cross-validation and r = 0.29±0.018 under leave-one-subject-out (LOSO) evaluation, reaching approximately 57% of the noise ceiling. Results confirm that self-supervised speech representations capture stimulus-driven cortical dynamics, highlighting their promise for neural signal modeling and brain–computer interface research.
Authors - Raita Rollande Abstract - This paper addresses the research question: How can an effectively organized and managed adult education reskilling and upskilling process help bridge the skills gap in today’s labor market? In an era of rapid technological development and changing business needs, the demand for continuous workforce reskilling and up-skilling has become increasingly critical. This study examines a private sector-driven adult education model that prioritizes speed, quality, and flexibility in response to industry demands. TestDevLab is a fast-growing company specializing in software quality assurance. Due to the lack of industry-specific specialists from traditional higher education institutions, TestDevLab has developed an in-house training solution where experienced engineers train new specialists while also upskilling existing employees. To address these challenges systematically, the company established TDL School. This dedicated training institution has designed the Model for Rapid and High-Quality Reskilling and Up-skilling tailored to mid-sized businesses. This article presents the TDL School model, exploring innovative training approaches, curriculum development, and company collaboration to highlight best practices for effective workforce development. The findings demonstrate that a well-structured, professionally organized learning process enhances employability, strengthens industry competitive-ness, and fosters lifelong learning. By sharing this model, the study aims to pro-vide a scalable framework that other companies of similar size can adopt to ad-dress their workforce challenges. This research contributes to the ongoing discussion on adaptive education models and their role in bridging the skills gap in today’s labor market.
Authors - Isaac Baffour Senkyire, Benjamin Ghansah, Emmanuel Freeman Abstract - With the rapid development of deep learning, CNN-based medical im-age segmentation algorithms have been successful. However, study on the pancreas in 3D CT and MRI images is limited due to the excess use of computer memory and the complexity of the pancreas. In this paper, we present a memory-efficient cascaded 3D network for pancreas segmentation in CT and MRI. We develop a novel Lightweight 3D Bond (L3D-Bond) Layer to reduce filter size, and maintain performance while lowering memory usage, and a novel Light-weight 3D Asymmetric Corollary Atrous Spatial Pyramid Pooling Module (L3D-aCASPP) that captures multi-scale 3D context with lower computational cost. Our experiments were done using the public NIH pancreas segmentation dataset, MRI pancreas segmentation dataset, and MSD spleen segmentation dataset achieving a competitive segmentation performance of 80.12 DSC on the NIH dataset with parameters less than 0.5 million.
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - Inadequate Resource Issues (IRI) are one of the challenges in Strategic Management (SM). This study concentrated on applying System Dynamics (SD) modeling to solve IRI. Strategic standard tools like SWOT analysis, PESTEL analysis, and the Resource-Based View have proven effective in addressing IRI; unfortunately, developments like digital transformation, long-term sustainability, and the rise of emerging market multinational corporations are poised to shape the future of SM in these regions. These traditional methods are no longer coping with new technology; hence, the authors implemented a new SD model to tackle the IRI in SM. Additionally, most strategic managers are incapable of developing an SD model due to mathematical and scientific complexity. Although SD is a reliable technique for handling complex issues in management, most managers reject SD because of the implementation’s need for scientific and mathematical requirements. To solve IRI mathematically and make scientific predictions about what would happen if variables were altered in the upcoming five years (2025–2035) and the impact on customers, the study created an SD model.
Authors - Arosha de Silva Abstract - The COVID-19 pandemic disrupted educational systems worldwide and required schools to adopt online learning within a short period. In Sri Lanka, secondary school teachers encountered numerous difficulties while adapting to virtual teaching environments. This study examines the challenges experienced by teachers when conducting online instruction during the pandemic. A mixed-methods approach was adopted, combining qualitative interviews with quantitative survey data collected from secondary school teachers and educational professionals. The findings revealed that teacher motivation, technological infrastructure, and increased workload significantly influenced the effectiveness of online teaching. Difficulties related to internet access, digital resources, and professional demands affected teachers’ ability to deliver lessons efficiently. The study highlights the importance of institutional support, professional training, and improved access to technology in strengthening online education. The findings may assist policymakers and educational institutions in developing effective strategies to support online and blended learning initiatives in the future.
Authors - Bharg Trivedi, Chaitaili Chandankhede Abstract - There is a continuous change in Android malware because it is obfuscated, polymorphic, and structural. Such changing methods diminish the performance of conventional signature-based detection methods. In an effort to defeat this challenge, the present paper provides a model that uses CNN and GNN models. It is an integration of spatial byteplot representations and structural call graph representations to successfully identify Android malware. Our study was based on a dataset of 1,159 real Android applications, and the used extraction technique was based on the static features. The CNN element of the structure Recognized robust spatial attributes of the grayscale images of the byteplot data with a ResNet-50 network. Meanwhile, the GNN component of the structure used a GraphSAGE network to derive structural representations of automatically generated function call graphs. The fused representations are combined into a 2304 dimensional feature vector. It is also optimized by making use of different methods such as Mutual Information. In this study, an Extreme Gradient Boosting Classifier on the fused representations to achieve successful Android malware detection. The assessment indicates that the framework attains a classification accuracy of more than 99% with businesses across the cross-validation holding the same accuracy.
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - Knowledge management (KM) is an essential company training process that incorporates four sequential factors: non-knowledgeable professionals, training to become knowledgeable professionals, new knowledgeable professionals, and knowledgeable and experienced professionals. Because the aforementioned variables are interconnected and make it extremely difficult to produce a measured solution, they must be thoroughly analyzed using mathematical formulas and reliable techniques. These issues impact businesses of all sizes, necessitating a versatile instrument for flexible KM analysis. Additionally, most KM managers dislike SD modeling due to its complexity, especially those without scientific training. This study recommended using system dynamic (SD) modeling rather than conventional tools to address the aforementioned issues. The use of SD modeling stems from three factors: (a) the examination of complicated dependencies; (b) the requirement for mathematical formulas; and (c) the graphical results in contrast to traditional methods. The study’s SD model included the four sequential factors and their relationships. KM managers should focus especially on the graph’s data when necessary modifications are needed.
Authors - A Aruna kumari, Tamminana Visweswari Abstract - Lung cancer is a fatal illness that causes several deaths worldwide and detection of lung cancer remains a challenge for medical professionals. Detection of cancer in early stages is difficult as the size of the tumor is very small making it difficult for medical professionals to detect. Cancer detected in the early stages can be treated with proper techniques which can save the lives of the patients. Due to excessive information in the CT scans, MRIs, X-rays, and PET scans the manual detection of lung tumor becomes extremely difficult. The methodology helps in detecting the presence of cancerous tissues in the lungs and predicting which stage of lung cancer is present. The methodology mainly includes image preprocessing, training the model, extracting features using deep learning algorithms and classifying the stage of cancer present as Normal, Benign, Malignant Stage 1, Malignant Stage 2, and Malignant Stage 3. Proper image processing techniques like image augmentation, image normalization and image resizing are applied on the IQ-OTH/NCCD dataset for extracting the necessary features which will be used while training the model. A hybrid model is created by combining two deep learning models, the Xception and MobileNetV2 architectures which can accurately distinguish between the different lung cancer stages and predict the stage of cancer. The performance metrices which include accuracy, precision, recall, f1-score and confusion matrix were also calculated to determine the accuracy of the proposed hybrid model. The proposed model helps in accurate and reliable diagnosis of lung cancer at early stages.
Authors - Erika Haydee Rubio-Camara, Oscar May Tzuc, Elsy Maria Rosales-Uc, Fran-cisco Gilberto Herrera-Chale, Roman A. Canul-Turriza, M. Jimenez Torres Abstract - Mechanical vibration energy harvesting has emerged as a promising strategy for supporting sustainable energy generation in industrial environments, where machinery and transportation systems continuously produce recoverable vibrational energy. This study presents the development and evaluation of predictive models based on deep multilayer perceptrons (DMLP) and Convolutional Neural Networks (CNNs) for estimating the energy potential associated with mechanical vibrations under industrial operating conditions. A simulation frame-work was implemented using experimentally reported operational ranges, including vibration frequencies between 10 and 50 Hz, amplitudes from 0.01 to 0.03 m, and temperatures between 25 and 45 °C. The analysis considered piezoelectric, electromagnetic, and triboelectric harvesting mechanisms to evaluate model adaptability under different scenarios. The predictive framework was implemented using TensorFlow and validated through a 10-fold cross-validation strategy combined with hyperparameter optimization. Results indicate that both architectures achieve high predictive capability for estimating harvested energy; however, CNN models consistently outperformed Deep MLP models, obtaining lower prediction errors and higher stability across validation folds. The superior performance of CNNs is associated with their ability to capture localized patterns and structured relationships within vibration-related data. The proposed method-ology demonstrates the feasibility of integrating artificial intelligence techniques into vibration-based energy harvesting systems for industrial applications. Furthermore, the study provides a computational framework for evaluating operational conditions, optimizing harvesting performance, and supporting the design of sustainable self-powered monitoring systems.
Authors - Mariia Nazarkevych, Vasyl Lytvyn, Oleg Stechkevych, Hanna Nazarkevych, Roman Cholkan, Danyil Korotych Abstract - An information technology for adaptive enterprise management using weak signals has been developed, which is based on the collected information about the environment, the assessment of factors affecting the enterprise, the calculation of the indicator of the impact on the enterprise based on integral dependence, the method of detecting weak signals and predicting the state of the enterprise, which provides high sensitivity taking into account changes in the environment and increases the efficiency of enterprise management. A method of recognizing weak signals is shown, which, by comparing the permissible value with the difference between the found and predicted values of the indicator of the impact on the smart enterprise based on integral dependence, provides early detection of threats or opportunities for the smart enterprise. It is proposed to develop a smart enterprise management system using weak signals based on an integrated approach and in accordance with the following principles: systematicity; integration of computer, communication and software components; modularity; openness; compatibility; variable equipment composition.
Authors - Marlon Kulatunga, Kaavya Raigambandarage, Senali Guruge, Themiya Alwis, Amila Nuwan Senarathne, Kavinga Yapa Abeywardena Abstract - Contemporary Kubernetes deployments suffer from two fundamental shortcomings: admission control mechanisms apply static rule sets without accounting for namespace operational context, and content inspection services governed by RFC 3507 remain disconnected from the orchestration layer. This work presents an integrated four-module security framework that jointly addresses both deficiencies. A probabilistic namespace characterisation algorithm employing seven weighted indicators achieves 96.7% accuracy in determining deployment tiers, even when metadata labels are absent or deliberately misleading. A compliance-driven policy orchestrator aligned with CIS Kubernetes Benchmark controls and PCI-DSS v4.0 requirements translates a unified constraint representation into artefacts for both OPA Gatekeeper and Kyverno, attaining 99.2% cross-engine decision parity. An environment-responsive traffic manager generates tier-specific Istio routing configurations, while a custom Kubernetes operator governs content scanning pod lifecycles through a multi-dimensional wellness metric that captures security-relevant signals invisible to conventional autoscalers. Evaluation on a five-node K3s cluster demonstrates full compliance coverage across 93 benchmark controls and 28 regulatory mandates, sub-five-second failover under all disruption scenarios, and correct detection of degraded scanning capability that CPU and memory metrics alone would overlook.
Authors - Ronewa Gilbert NTHATHENI, Tumiso THULARE Abstract - The rapid advancement of digital technologies has transformed the relationship between governments and citizens, creating new opportunities for participatory governance through e-government initiatives. This study evaluates the effectiveness of online engagement platforms in promoting democratic governance in South Africa. Using a scoping review methodology, the research examines the benefits, challenges, and contextual dynamics shaping citizen participation through digital platforms. Findings suggest that while online engagement tools enhance transparency, accountability, and access to information, their effectiveness is constrained by structural barriers such as the digital divide, limited institutional capacity, and low digital literacy. The study concludes that the success of e-participation initiatives depends on inclusive design, infrastructure investment, and meaningful government responsiveness. Recommendations are provided to strengthen digital governance and improve citizen engagement outcomes.
Authors - Supriya Narad Abstract - Agricultural economies are predominantly relevant in developing countries, wherein the farmers have to struggle operating under the impact of several constraints posed by crop diseases. Among food crops, the potato is a major one with vulnerable destructive diseases like Early Blight and Late Blight, capable of destroying the yield if detected late. Old methods of visual inspection are time-consuming and sometimes erroneous because of laxity, human error, and lack of expertise. With this research, an automated intelligent disease detection system is devised, making use of image processing and deep learning, Arduino, specifically Convolutional Neural Networks (CNNs). The model was trained using potato leaf images from the Plant Village dataset, which are improved using various preprocessing techniques, including color space conversion, image augmentation, and image resizing. The proposed CNN architecture achieved a high rate of classification accuracy of 97.2% in distinguishing healthy leaves vs. Early blight and Late blight infected leaves. Lightweight, reliable, and fast, it supports implementation on mobile or handheld devices in low-resource environments, thus giving farmers the ability to use them for timely diagnostics. The system has good prospects for scaling up for other crops and disease types in future versions.
Authors - Khadidje OUSMANE KOSSI, Mandicou BA, Bachar Haggar SALIM, Simon Antoine SARR, Maboury DIAO, Alassane BAH Abstract - Heart disease in athletes remains a significant challenge in sports cardiology and an important public health concern, particularly among young competitive individuals at risk of sudden cardiac events. Although pre-participation screening programs are widely implemented, diagnostic uncertainty persists, especially in distinguishing physiological cardiac remodeling from pathological cardiomyopathy. This complexity results from the interaction of genetic predisposition, structural adaptation, electrophysiological variability, and cumulative training exposure. Using the PRISMA framework, this study presents a systematic review of research published between 2015 and 2025 to evaluate the application of artificial intelligence (AI) in the diagnosis and monitoring of cardiovascular diseases in athletes. The analysis reveals that most studies rely on unimodal, monocentric, and retrospective designs, often based on limited datasets and lacking external validation. Despite high reported performance metrics, performance degradation of 5–10% in external cohorts is frequently observed. Furthermore, explainability techniques are inconsistently applied, and real-world clinical integration remains limited. Only a small number of studies adopt multimodal approaches integrating electrophysiological, imaging, biological, and training-related data. These limitations restrict the clinical translation of AI models. Future research should prioritize multicenter, diverse, and explainable multimodal frameworks to support reliable cardiovascular risk stratification and return-to-play decision making.
Authors - Oluwaranti A. Omowami Abstract - Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational injuries in construction, driven by heavy lifting, awkward postures, repetitive motion, and whole-body vibration. Traditional ergonomic risk assessment methods are retrospective and unable to capture the dynamic conditions of construction sites. Wearable sensor technologies offer a real-time, objective alternative. This structured narrative review examines the implementation, accuracy, and occupational health outcomes of wearable sensor systems applied to ergonomic risk monitoring among construction workers. A structured review of peer-reviewed literature from 2017 to 2024 identified six sensor categories: inertial measurement units (IMUs), wearable insole pressure systems, surface electromyography (sEMG), electrodermal activity (EDA) sensors, heart rate monitors, and smartphone embedded sensors. Reported posture classification accuracy reached up to 99.01% under controlled conditions using deep learning classifiers. Key implementation barriers include sensor discomfort, motion artifacts, worker acceptance, data privacy and cybersecurity concerns, and the multi-employer structure of construction. A consistent gap exists between laboratory validation accuracy and real-world field performance. Occupational health outcome studies remain limited. Future priorities include longitudinal field validation and integration with behavior-based safety frameworks.
Authors - Samira Boulahbel-Bachari, Hind Dib-Slamani Abstract - This study examines digital governance transformation trajectories across fourteen Middle East and North Africa (MENA) countries between 2010 and 2024. Rather than classifying countries as simple “leaders” or “laggards,” it adopts a multidimensional framework covering digital governance, digital infrastructure, inclusion, institutional capacity, and economic capacity. Entropy weighting derives indicator weights, TOPSIS ranks countries according to their proximity to the best observed transformation profile, while hierarchical clustering identifies shared trajectory patterns. Robustness is assessed through VIKOR and principal component analysis. The results reveal marked regional heterogeneity. Saudi Arabia leads the ranking, followed by Türkiye, Oman, and the United Arab Emirates, while Morocco shows a balanced trajectory despite more limited economic resources. Other countries display differentiated progress across connectivity, online services, and institutional conditions, with Tunisia and Lebanon occupying the lowest relative positions. The findings show that progress in aggregate e-government scores does not necessarily reflect coherent digital governance development across all dimensions. The study advances a trajectory-based view of digital governance and offers a practical basis for regional benchmarking and policy prioritization in heterogeneous contexts.
Authors - Mohammad Arafat Ullah Abstract - Fault Detection and Classification (FDC) plays a critical role in semiconductor manufacturing by identifying defective wafers before subsequent processing stages, thereby reducing manufacturing cost, material waste, and production time. Traditional Statistical Process Control (SPC)-based FDC systems are widely used in semiconductor fabrication; however, machine learning techniques can significantly improve defect detection and process monitoring efficiency. In this research, the SECOM semiconductor manufacturing dataset collected from Kaggle was analyzed using multiple machine learning approaches. Several classification techniques including custom Support Vector Machine (SVM), kernel-based SVM, custom K-Nearest Neighbor (KNN), and Random Forest were implemented and compared for defective wafer detection. In addition, pseudo time-series semiconductor signals were reconstructed from static process features. Exponentially Weighted Moving Average (EWMA) smoothing and temporal feature extraction were then applied for signal-based fault analysis. Experimental results show that SVM-based approaches achieved strong classification performance on the SECOM dataset, while temporal signal reconstruction provided additional insight into semiconductor process behavior. The study presents a comparative analysis between conventional feature-based learning and reconstructed temporal feature-based learning for semiconductor fault detection applications.
Authors - M. A. M. P. Wanigaratne, K. B. H. M. T. T. Bandaranayake, C. S. Mohottala, J. V. Pannilage Abstract - Windows Subsystem for Linux (WSL) enables Linux command-line workflows to run directly on Windows endpoints, but this hybrid execution model creates security visibility and interpretation challenges. Host-side monitoring can identify that WSL was launched, but it may not provide sufficient Linux-side command context for threat investigation. This paper presents an explainable WSL command threat detection approach that combines machine learning-based risk scoring with Retrieval-Augmented Large Language Model (LLM) reasoning. The machine learning layer uses wrapper-aware and structure-aware command features to classify WSL-style command activity and convert model output into operational risk scores. The reasoning layer processes suspicious and malicious events using retrieved cybersecurity knowledge to generate analyst-readable explanations, MITRE ATT&CK mappings, confidence reasoning, and suggested defensive actions. The ML component was evaluated using a hybrid command dataset containing 8,028 samples, while the reasoning component was evaluated using 120 sanitized command level scenarios. Results show that the Calibrated SVM achieved 0.96 accuracy and 0.96 malicious class F1-score. Retrieval-augmented reasoning improved MITRE ATT&CK mapping accuracy from 52% to 87% and reduced hallucinated statements from 31% to 12%. The results indicate that combining ML risk scoring with grounded LLM reasoning can improve both alert prioritization and analyst understanding for WSL enabled endpoints.
Authors - Pavel E. Zhukov Abstract - The paper analyzes the problem of growth of public debt in developed countries with the compound interest approach, initially proposed with the Sargent-Wallace model. It is concluded that since 2002, when central banks began to apply the New Keynesian Model in monetary policy, governments have been widely using deficit financing of fiscal expenditures in order to stimulate economic growth. Based on the experience of 2002-2025, the parameters of the exponential growth of the debt-to-GDP ratio and the dangerous values of the budget deficit are assessed. General conclusions are made about the ineffectiveness of the dominant model of fiscal policy and the need to revise it, as well as the need to consider the growth of the money supply in monetary policy. General recommendations proposed. First: it is obvious that the United States and Japan have to introduce the VAT and do not increase customs duties. Second: In order to accelerate economic growth, it is necessary to shift fiscal policy priorities from the development of infrastructure and social programs to R&D, which will increase labor productivity. Third: Generally, all the social programs have to be audited. Specific for the United States, health insurance reform and limiting the growth of the budget deficit due to the Medicare and Medicaid programs are urgently needed. Fourth: Perhaps "national" companies with a high degree of localization should be stimulated with tax incentives for corporate income tax and shareholder income tax.
Authors - Unnati Parmar, Jatin Modh Abstract - Due to the high degree of infusion and morphology, Gujarati is regarded as a low-resource language in the Natural Language Processing field. The key reason why Gujarati can be classified as such is the lack of computing tools and annotated digitized corpus. Every single dialect of the language has its own morphological, lexical, and orthographic peculiarities since the language is extremely diverse. It includes the most diversified dialect – Kutchi – alongside Kathiawadi, Surti, Charotari, and Pattani dialects. The current paper focuses on the evolution in the sphere of Gujarati dialect identification via texts from 2021 till 2025. Morpheme segmentation, parts of speech identification, regional idioms identification, and neural machine translation model adaptation will be analyzed throughout this paper. This study looks at the transition from traditional grammar-based systems to modern deep learning algorithms. Performance metrics from the latest literature are used to identify research gaps. They include excellent results in the detection of idioms and high F1-scores for morphological tagging. Performance metrics of various models, such as transformers and Bidirectional Long Short-Term Memory network, are compared with DFA techniques. A framework for hybrid language models, combining both linguistics and neural networks, is proposed in the conclusion section of this literature review. Neural network models have been found to offer significant improvements in morphology when compared to traditional methods. In this paper, we address an inadequacy in the processing of informal language through the identification of disparities in resources between geographic variations. We propose a combination model that maintains geographic identity in modern-day computerized environments.
Authors - Athanasios Angelakis, Gabriele De Vito, Eleni-Myrto Trifylli, Filomena Ferrucci Abstract - Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely used as a first-line non-invasive test (NIT), but its fixed formula may underuse non-linear diagnostic information contained in age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count (PLT). We evaluated whether machine-learning-enhanced NITs (MLE-NITs) can improve advanced fibrosis detection while preserving the clinically accessible FIB-4 variable space. We used three biopsy-validated MASLD cohorts from China, Malaysia, and India (n = 784). The Chinese cohort was split into 486 training and 54 internal validation/tuning patients; final performance was reported only on the Malaysian (n = 147) and Indian (n = 97) external cohorts. Models used five variables: age, FIB-4, AST, PLT, and ALT. We compared FIB-4 with a shallow-deep neural network (s-DNN), TabPFN, and gpt-4o-2024-08-06 in zero-shot and fine-tuned settings. FIB-4 achieved external thresholded ROC-AUCs of 0.75 and 0.60 in Malaysia and India, respectively. TabPFN achieved 0.69 and 0.66, fine-tuned GPT-4o achieved 0.75 and 0.63, and the s-DNN achieved 0.77 and 0.67. The s-DNN contained only 354 trainable parameters, compared with 7,244,554 parameters for TabPFN, and provided the most balanced fixed-threshold operating profile. External diagnostics showed s-DNN Brier scores of 0.18 and 0.22, with AST and FIB-4 as dominant permutation-importance variables. Exploratory decision-curve analysis showed cohort-dependent clinical utility, favoring TabPFN in Malaysia and s-DNN in India.
Authors - Harshala Shingne, Shwetambari Borade, Dhanashree Hadsul, Aditya D. Nandgirwar, Pranali Pawar, Rupali Vairagade Abstract - Increasing proliferation of networked systems have compounded the necessity to seek effective and non-invasive intrusion detection solutions. The conventional intrusion detecting systems (IDS) are mainly centralized into data aggregation scheme that introduces essential constraints pertaining to data exposure, scalability, and robustness of the system itself. Partially in reaction to this, this paper presents a privacy conscious federated intrusion detection design that allows collinear model training by many network participants in the absence of exchanging raw data. This framework exploits federated learning to create a global intrusion detecting model by continually aggregating local-trained updates, thus retaining the data locality and ownership. In order to achieve high privacy assurances, there are inbuilt secure aggregation mechanisms and perturbation-based mechanisms that accomplish this by avoiding the leakage of sensitive information during model sharing. Additionally, an adaptive-aggregating strategy is proposed that can effectively manipulate non identically distributed data of the participants, as well as improving the generalization process of the global model. Extensive testing on test sets of benchmark intrusion detection has shown that the proposed framework has very high detection rates and much less privacy risk and communication overhead than the traditional centralized techniques. The findings confirm that the framework has the capability of offering a scalable, secure and efficient intrusion detection solution in distributed networks.
Authors - Shashank Mallesh, Anithadevi M D, Srinidhi G A, Chandana Sreenivas Abstract - Hospital surge capacity management remains a critical challenge in healthcare systems, particularly during pandemic events. This research presents a novel Adaptive Multi-Objective Capacity Management (AMCM) framework that integrates Long Short-Term Memory (LSTM) networks with Multi-Objective Particle Swarm Optimization (MOPSO) to optimize bed allocation, staffing schedules, and equipment distribution. The framework simultaneously minimizes patient wait times, operational costs, and resource wastage while maximizing bed utilization efficiency. Comprehensive evaluation against state-of-the-art algorithms including Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and traditional Mixed Integer Linear Programming (MILP) demonstrates superior performance across all metrics. The proposed method achieves 94.7% bed utilization accuracy, reduces emergency department wait times by 42.3%, and decreases surge-related costs by 38.9% compared to conventional approaches. Validated on real-world COVID-19 hospital data spanning 18 months across five major health systems, the AMCM framework provides healthcare administrators with an intelligent decision support system for proactive capacity planning and dynamic resource allocation.
Authors - Nwagu Chima Ajanwachuku, Onyemaobi Bethram Chibuzo, Nwafor Franca Amaka, Divine Nnodim Oluchi Abstract - Tertiary institutions across the world are now adopting artificial intelligence-based academic detection systems to aid in detecting different forms of academic misconduct. For these detection systems, there is more focus on technical performance metrics such as detection accuracy, precision and recall and little focus on whether these systems validly measure the complex construct of academic misconduct. This study aims to examine how AI-based academic integrity detection systems operationalise, measure, and validate academic misconduct in higher education, focusing on construct operationalisation, measurement accuracy, and construct validity, through a systematic literature review. We conducted a systematic review and retrieved articles from ACM Digital Library, IEEE Xplore, Web of Science, and Google Scholar. Of 793 articles, 56 were selected using the PRISMA framework, and the findings were synthesised narratively. The 56 studies focused on plagiarism detection, AI-generated text detection, authorship verification, behavioural monitoring, biometric authentication, and multimodal detection systems. Across all the studies we considered, detection systems mainly measured observable digital signals. 55 of 56 studies showed evidence of construct misalignment between the measured signal and the claimed misconduct construct. Most studies we considered treated similarity as plagiarism, AI-generated probability as dishonesty, and behavioural anomalies as cheating, even though these signals could not capture intent, differentiate between acceptable collaboration and collusion, or account for disclosure practices or alignment with institutional policy. Also, we observed that most studies validated detection systems using technical metrics such as accuracy, precision, recall, and F1 score, and just a few directly addressed construct validity, bias, or robustness.
Authors - Dang Trung Thanh, Nguyen Huynh Anh Tuyet Abstract - The objective of this project is to apply GPS devices in combination with GIS software to build image interpretation keys for spatial data management. The research content includes: collecting satellite imagery data and documents; conducting field surveys and collecting GPS coordinates for 149 sample points; and building image interpretation keys for geographical objects. The main research method combines GPS field surveys, remote sensing image interpretation, and the application of GIS software such as QGIS and Google My Maps to process, analyze, and manage spatial data. In addition, the project utilizes methods of document collection, statistics, and comparison to ensure the accuracy and scientific validity of the research results. The project results: A set of image interpretation keys was developed for several key geographical objects in the study area, including: water bodies (27 samples), transportation (33 samples), agricultural land (27 samples), residential construction land (28 samples), and vacant land (34 samples). The research has contributed to demonstrating the effective application of GPS combined with GIS and remote sensing in surveying, mapping, and managing geographic information on current land use. The research and development direction is: Integrating artificial intelligence (AI) and machine learning (Deep Learning) into the image interpretation process
Authors - Dang Trung Thanh, Nguyen Huynh Anh Tuyet Abstract - This study evaluates the implementation of the 2024 Land Law regarding land-use conversion in Thuan An Ward, Ho Chi Minh City, Vietnam. The research aims to assess the changes introduced by the new legal framework and examine its practical impacts on land-use conversion procedures at the local level. The study employed a combination of document analysis and a questionnaire survey of 100 respondents, including local government officials and citizens. The collected data were analyzed using descriptive statistics and comparative methods. The results indicate that the 2024 Land Law has improved the landuse conversion process by simplifying administrative procedures, reducing processing time, and increasing transparency. Survey findings show that most respondents considered the new regulations easy to understand, the processing time efficient, and the procedural costs reasonable. Overall public satisfaction increased from 65% under the 2013 Land Law to 85% under the 2024 Land Law. Nevertheless, several challenges remain, including incomplete digital land databases, limited public understanding of some legal provisions, and issues related to the implementation of market-oriented land pricing. These findings provide practical evidence for improving land-use conversion management and support the effective implementation of the 2024 Land Law at the local level.
Authors - Rebecca Hufkie, Dane Brown Abstract - Protein structure determines function, yet experimental determination methods remain costly and slow. This study presents an optimised transformer-based system for predicting protein sidechain angles and reconstructing 3D structures directly from sequence data. Trained on the SidechainNet CASP12 dataset comprising 25,044 proteins, the systematically refined model achieves 0.244 radians RMSE for angle prediction and 1.413 ̊A RMSD for structural accuracy, representing a 70% improvement over the baseline. Incorporating backbone angles, secondary structure, and evolutionary information reduces RMSD from 1.861 ̊A to 1.413 ̊A compared to sequence-only inputs. On challenging CASP12 free-modelling targets, the system scores 84 to 86 GDC. This performance is competitive with leading methods while maintaining computational efficiency through single-sequence prediction without multiple sequence alignment generation. Results indicate that specific architectural choices, including deeper networks, GELU activation, and increased embedding dimensions, combined with robust dropout and weight decay, enable highly accurate structure prediction from limited training data. This demonstrates that carefully constrained models can capture complex biological folding patterns efficiently without massive computational overhead.
Authors - The Quan Trong, Nguyen Trong Nhan Abstract - An autocorrelation receiver can be employed in surveillance and communication systems to identify the type and operating mode of radiation sources in the absence of a priori signal information. In this work, the autocorrelation receiver is defined as a system comprising a broadband analog front-end with frequency conversion to the intermediate frequency range and a narrowband processing unit based on autocorrelation. The performance of signal processing is governed by the received pulse duration and the length of the fast Fourier transform (FFT) window. The study provides an estimate of the signal-to-noise ratio (SNR) required to achieve a specified probability of correct classification of simple radio pulses at a fixed false alarm rate. The results show that increasing the pulse duration while maintaining a fixed FFT window (i.e., reducing the ratio of FFT window length to pulse duration) decreases the required SNR. Consequently, the probability of correct classification is improved under these conditions. Furthermore, high receiver efficiency is achieved when the ratio of the FFT window length to the pulse duration is kept below 10. If the number of FFT samples is fixed, further improvement in classification performance requires increasing both the sampling frequency and the processing rate.
Authors - Uphaar Goyal, Chirag Patadia, Nitinkumar Leuva Abstract - Modern software development environments depend on cloudnative infrastructure, automated CI/CD pipelines, and distributed DevSecOps workflows. These environments improve delivery speed but expand the attack surface through privilege escalation, credential compromise, insider threats, and misconfigured pipeline permissions. Traditional Role-Based Access Control (RBAC) improves least-privilege enforcement, but static RBAC does not adequately respond to changing runtime context such as business hours, network trust, multi-factor authentication status, and deployment pipeline state. This paper proposes an enhanced cybersecurity framework that integrates RBAC, contextaware policy enforcement, and Agentic AI-based autonomous auditing. The proposed agent observes access events, learns behavioral patterns, detects anomalous requests, and generates explainable audit evidence without replacing deterministic access control. Experimental evaluation in a simulated CI/CD environment shows that the RBAC with Agentic AI framework achieves 98.21% accuracy, improves anomaly detection compared with traditional access-control models, and maintains low enforcement latency under increasing concurrency. The results indicate that Agentic AI can strengthen secure software development by adding adaptive audit intelligence while preserving the predictability and administrative clarity of RBAC.
Authors - Thokozani Nkosinathi Hlubi, Nazeer Joseph Abstract - Digital transformation is reshaping the auditing profession by introducing advanced digital tools, automation, and data‑driven processes that redefine how audits are planned, executed, and evaluated. This study examines the effect of digital transformation on auditing processes, focusing on how digital tools, automation technologies, and shifting skill requirements influence audit effectiveness and efficiency. Using a qualitative research design, Rich Picture workshops were conducted with practicing auditors to explore how emerging technologies are integrated into real-world audit environments. The findings reveal three key themes. First, digital tools enhance real‑time access to information, improve collaboration, and deepen auditors’ understanding of complex IT environments. Second, automation significantly improves audit effectiveness by streamlining routine tasks, supporting anomaly detection, and enabling more robust risk assessments—while still requiring professional judgment. Third, efficiency gains emerge through time savings, resource optimization, and evolving competency requirements, underscoring the need for continuous upskilling. Building on these insights, the study proposes a four‑stage implementation framework consisting of strategic alignment, workforce development, workflow redesign, and ethical governance safeguards. The research contributes to both theory and practice by demonstrating how digital transformation reshapes audit work and offering a structured roadmap for organizations seeking to modernize their audit functions responsibly and sustainably.
Authors - Zumna Usman, Madiha Khalid, Weiwei Jiang, Momina Shaheen, Umar Mujahid, Muhammad Najam-ul-Islam Abstract - The Internet of Things (IoT) networks operate under strict resource constraints having limited computational capability, memory, bandwidth, and energy, while still being required to combine essential security goals such as confidentiality and mutual authentication with efficiency, particularly in Radio-Frequency Identification (RFID)-based systems. For this reason, Ultra-Lightweight authentication protocols are commonly used, where traditional cryptographic techniques are often too demanding. The Random Rearrangement Block Matrix-Based Ultra- Lightweight RFID Authentication Protocol (RUAP) was introduced to strengthen security. In this paper, RUAP is analyzed and shown not to eliminate fundamental weaknesses. By applying a probabilistic disclosure attack, it is shown that public messages leak exploitable statistical information, making it possible to fully recover the identifier in reduced configurations and to recover about 71.77% of a 96-bit identifier. It is further shown that RUAP’s asymmetric key update mechanism allows adversaries to trigger desynchronization, resulting in denial of service.
Authors - Rajiv Ghai, Anil Kumar Bisht, Akash Sanghi Abstract - Agricultural supply chains face a fundamental tension consumers require transparency for provenance verification while commercial stakeholders demand confidentiality of pricing, buyer identities and quality scores. This paper introduces SelEncChain-Agri, a selective field encryption (SFE) framework that resolves these conflicting demands on a single fully decentralised Solana blockchain. Supply chain data is partitioned into public fields stored in plaintext and confidential fields encrypted with ECIES combined with Multi-Party Key Encapsulation (MP-KEM). Authorised parties decrypt fields using existing Solana wallet keypairs via a formally specified Ed25519-to-X25519 key derivation (libsodium convention), requiring no additional key material or trusted third party. Three technical contributions are made: (1) SFE field encryption with per-capsule independently randomised nonces preventing GCM keystream reuse (2) SFE decryption and (3) a key revocation protocol providing post-revocation forward secrecy. Security analysis under the Dolev-Yao model provides a constructionlevel IND-CPA argument under the DDH assumption on Curve25519. Comparative evaluation shows SelEncChain-Agri satisfies all eight stakeholder requirements from the agricultural blockchain literature, versus at most seven for dualchain alternatives. A Solana devnet prototype confirms functional correctness: end-to-end latency was 3,691 ms for batch creation and 2,111 ms for event submission; client-side SFE overhead was 74.59 ms (Python), with estimated pure cryptographic cost of 3.3 ms (Rust/WebAssembly). Keywords: Agricultural supply chain · Anchor framework · DPDP Act 2023 · ECIES · Food traceability · Key revocation · MP-KEM · Privacy-preserving · RFC 7748 · Selective field encryption · Solana blockchain
Authors - Austin jojo Jallah, Reena Satpute Abstract - As the transformer language models were developed, it was possible to introduce one of the significant technological revolutions in conversational AI. Basically, the understanding and production of human language has been enhanced by transformers. Transformer models make it possible to build dialogue systems that provide natural, complex, and context-sensitive communication to develop further customer support technologies, healthcare technologies, and virtual assistance services. In the current paper, this paper will assess advanced transformer systems, BERT, GPT, and T5, as well as their variants, assessing their performance capabilities in the use of dialogue. All the models are evaluated in terms of performance quality, which is judged by its fluency production and also gauges of coherence and contextual accuracy and its general ability to pro-cess computations. This review talks about the prompt engineering approaches and the human feedback-enhanced learning through reinforcement (RLHF) and the adapter transfer learning methods, to improve the flexibility and quality of the model. The paper presents the fresh trends in Conversational AI with multi-modal learning as well as retrieval-enhanced generation and factuality-based coherence knowledge application. Nevertheless, transformer-based models have three key weaknesses, among which, there are bias and hallucinations, and the computing requirements are very high. These weaknesses are analyzed and we discuss the potential remedies that involve symbolic deep learning combinations and efficient compression methods, such as quantification and pruning. We have found out the extent to which each system can do and the things that they cannot accomplish, and hence can help to determine the right applications of each of the frameworks. We introduce an evaluation comparison as a scholarly resource to the practitioners of dialogue system development so they can perform better with moral and effective models of operations. The article concentrates on the devel-opment of transformer-based conversational AI and its estimated impact on hu-man-computer dialogue systems
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - The financial management involves outstanding debts, trade receivables, deduction charges, economic value, generating capacity, and stockpiling accumulation. These six variables mentioned above must be carefully examined using mathematical formulas and trustworthy tools because they are interrelated, which makes it very difficult to establish a solution. These challenges affect companies of all sizes, requiring a flexible tool for adaptable financial management analysis. To mitigate the aforementioned problems, this study suggested system dynamic (SD) modeling to handle the problem instead of using traditional tools. The SD modeling is used because of (a) complex dependencies analysis, (b) the need for mathematical formulas, and (c) the graphical outputs compared to traditional tools. The study constructed an SD model with six variables and their interconnections. When adjustments are required, financial managers should pay particular attention to the out-of-graph data.
Authors - Mariusz Szynkiewicz Abstract - The protection of information resources is one of the central issues in contemporary information science, and one also significant from an IT perspective – particularly in the context of cybersecurity. In this article, I propose one possible approach to addressing this challenge. The proposal concerns the protection of information resources in a broad sense: from the stage of information acquisition and creation through to its distribution. In the following sections, I outline the main assumptions of a comprehensive model for the protection of information resources, discussed in the context of building the information resilience of participants in digitised information exchange processes, and in relation to issues associated with the concept of cyber hygiene. The central thesis of the article rests on the assumption that effective protection of information resources is possible only on the basis of an integrated model addressing the following procedures: (a) validation – the assessment of the level and value of a given resource; (b) threat identification – the logical level, scale, and types of vulnerability; (c) detailed analysis of the type of abuse – qualitative diagnosis; (d) selection of techniques and methods for counteracting a given class of attack – the methodological level; and (e) selection of possible corrective and preventive measures – the elements of cyber hygiene.
Authors - S. Jimenez-Garcia, V. Zorrilla-Munoz, G. Martinez-Navarrete, N. Garcia-Aracil, A.M. Peiro-Peiro Abstract - This paper presents an integrated framework that combines digital screening, ergonomic assessment, humanoid robot benchmarking and immersive virtual reality (VR) training to support the prevention of musculoskeletal disorders and burnout in nursing professionals. The framework is grounded in occupational health data from Spanish nursing professionals and incorporates sex/gender and anthropometric differences as design variables. A descriptive cross-sectional analysis was performed on 316 professionals from the European Health Survey in Spain, filtered by occupation, and complemented with the PROBEREN project approach. Two high-demand activities in Internal Medicine and Infectious Diseases units were selected: hygiene, comfort care, pressure ulcer prevention and postural changes in bedridden patients; and intrahospital transfers under isolation or clinical support. The sample showed a strong proportion of women (86.4%), mean age of 45.97 years, chronic health problems in 54.7%, prescribed medication use in 51.9%, recent physical pain in 46.8% and pain interfering with daily activities in 31.6%. These findings support a transition from descriptive profiling to proactive prevention. The proposed ecosystem links early screening, capture of expert movements, biomechanical comparison with a humanoid robot, personalized VR training and longitudinal reassessment. Future pilot validation should evaluate usability, VR-related fatigue, ergonomic risk reduction, burnout, pain and implementation barriers in real clinical settings.
Authors - Aleksander Karastoyanov Abstract - Reinforcement learning (RL) has been widely proposed for adaptive virtual machine (VM) right-sizing in cloud environments, yet most published work reports results from a single random seed with a fixed reward formulation, conditions that may not reflect genuine generalization. This paper addresses both limitations through a systematic multi-seed, multi-reward evaluation of a Proximal Policy Optimization (PPO) agent applied to VM right-sizing on the Alibaba Cluster Trace 2018. Fifteen independent training runs (three reward configurations × five seeds) are conducted on a ten-VM simulation environment. The key finding is that reward formulation, not the RL algorithm per se, is the dominant determinant of SLA compliance: the unified reward variant (Config A) achieves a mean SLA violation rate of 1.1% (±2.11) across five seeds, statistically comparable to a Threshold baseline (2.43%), while dimension-aware variants expose a critical instability in memory-saturated environments. A one-sample t-test yields t = −1.40, p = 0.23, confirming that neither superiority nor inferiority relative to Threshold can be claimed, and motivating the need for larger seed sets and alternative reward designs in future work. Three empirically grounded reward design principles are derived for practitioners deploying RL-based resource managers in high-memory-pressure infrastructure.
Authors - Soham Paithankar, Supriya Narad Abstract - Agriculture is a major contributor to India economy and supports the livelihood of a large population, however, traditional farming practices largely depend on manual observation, weather information, which often results in resource utilization and reduced crop productivity. The increasing variability of climate condition further challenges. The integration of internet of things (IoT) and software technologies provides an effective approach and data-driven decision by enabling real-time monitoring and data-driven decision making. This paper presents such as temperature, humidity, and soil moisture using field sensor data. The collected information is processed and stored using Python based software and compared with real-time weather information obtained through a weather API. A dashboard interface developed using Flask and streamlit visually presents sensor data, weather data, and comparative result to support informed decision-making system. The system demonstrates how low-cost IoT devices combine with software platform can improve agriculture monitoring, optimize resource usage, and support sustainable farming practice, this study highlights the potential of IoT and software integration in transforming the potential of IoT. agriculture into an intelligent data-driven system suitable for developing region such as India.
Authors - Emmanuel Opoku Debrah, Sunet Eybers, Corne J. van Staden Abstract - Student Information and Management Systems (SIMS) are increasingly being implemented in higher education institutions (HEIs) in developing countries. However, with these mandatory systems, there is limited evidence of user satisfaction. This systematic literature review investigates the influence of technological, organizational, and environmental (TOE) factors on user satisfaction with the use of mandatory SIMS in HEIs in developing countries, with a focus on Ghana. A search was conducted across six databases and Google Scholar, focusing on the past decade (2015-2025). Two reviewers independently evaluated the studies' quality using the Mixed Methods Appraisal Tool (MMAT 2018). The average consensus MMAT score was 4.08/5 (7 High, 14 Moderate, 5 Lower quality). A total of 1,382 records were screened and considered for duplicates and applicability. The result was 27 empirical studies. A narrative synthesis supported by thematic mapping identified the most frequently reported determinants: system quality, reliability, and performance (n=18); organizational support, IT capacity, and management readiness (n=12); perceived usefulness, ease of use, and user experience (n=10); ICT infrastructure and connectivity (n=9); and information quality (n=6). The synthesis suggests that TOE factors are not independent: technological advantages can be offset by organizational weaknesses, and environmental factors set an upper limit to satisfaction. This suggests that Ghanaian HEIs focus on integrated investments in user training, technical support, and infrastructure rather than just system upgrades.
Authors - Lucian Julio Felix da Costa, Claudio de Souza Baptista, Andre Luiz Firmino Alves Abstract - Auditing public procurement processes is essential to ensure transparency, accountability, and efficiency in the management of public funds. However, the increasing complexity of procurement procedures poses significant challenges for auditors, particularly regarding the timely detection of pricing irregularities. This paper presents a software tool for price comparison designed to support the identification of overpricing and underpricing in public works procurement. The proposed solution leverages semantic retrieval and historical price comparison techniques to analyze procurement data and integrate up-to-date market information. Additionally, the application provides interactive visualizations and semantic retrieval mechanisms to support auditors during procurement price analysis activities. The expected contribution of this study lies in improving the effectiveness and accuracy of procurement oversight, strengthening financial analysis processes, and contributing to the prevention and deterrence of fraudulent practices among bidders.
Authors - Vanderson dos Santos Araujo, Eliane Tamara Lima Oliveira, Pedro Manoel Hermínio Alves, Andre Luiz Firmino Alves, Claudio de Souza Baptista Abstract - Auditing public tenders requires analyzing lengthy documents to verify compliance with tender notices, a time-consuming task prone to human error. This article empirically evaluates the use of Large Language Models (LLMs) to assist with auditing tender notices. A total of 50 official tender notices and 736 audit instances were analyzed, comparing three context-provisioning strategies: expanded context windows, integrated file retrieval, and a custom Retrieval-Augmented Generation (RAG) pipeline. The results show that no single approach is superior across all scenarios. Models with long windows performed better at confirming explicit conformities, whereas retrieval-based strategies demonstrated greater sensitivity to potential non-conformities due to omissions. The analysis also indicates that the type of question strongly influences performance, especially for interpretive questions or those that rely on the absence of documentary evidence. As a key contribution, the study demonstrates that the effectiveness of AI-assisted auditing depends on the combination of the contextualization strategy, the quality of the retrieved context, and the formulation of the questions, reinforcing the role of LLMs as tools to support the auditor.
Authors - Ahmed Belgaid Abstract - Digital transformation has profoundly altered the organization of work and reinforced the importance of mastering digital tools for employability and productivity. In this context, this study highlights the challenges of digital accessibility for employees with visual impairments and its application through an analysis of the new WCAG standards. The aim of our analysis is to demonstrate that digital accessibility consists of guaranteeing an inclusive digital transformation; it is not limited to simply ac-quiring digital solutions or adapting existing ones. It is a comprehensive preparation process and an integrated approach involving various stakeholders within the company.
Authors - BENABDELLAH Nouhaila, CHRAIBI Abdeslam, BENRREZZOUQ Rhizlane Abstract - The importance of smart logistics dashboards as key tools for digital transformation in manufacturing companies is becoming more recognized. They enable real-time visibility, in-tegrate various data sources, and allow for analysis. However, there is limited research on how these dashboards affect decision quality and operational performance in emerging countries. This paper explores the impact of smart logistics dashboards on data-driven decision making (DDDM) and operational performance in Moroccan manufacturing firms. We conducted a quantitative survey among logistics and operations managers and examined a proposed concep-tual framework using PLS-SEM. The findings showed that the capabilities provided by smart dashboards significantly improve decision quality through better data integration and real-time analytics. Additionally, DDDM plays a key role in the relationship between smart dashboards and operational performance. These results are important for the field of smart logistics and digital transformation and have valuable practical implications for management in manufactur-ing firms in emerging countries.
Authors - Francisco Castro Murillo, Teresita de Jesus Alvarez Robles, Andres Sandoval Bringas, Monica Carreno Leon, Francisco Javier Alvarez Rodriguez Abstract - Web accessibility for visually impaired users remains a critical challenge in HCI. While technical compliance with WCAG is well-established, knowledge of User Experience (UX) evaluation methods tailored to non-visual interaction is fragmented. This paper presents a systematic mapping of the literature (2016–2026), analyzing 18 high-quality studies selected from 134 records retrieved from IEEE Xplore, ACM DL, SpringerLink, and ScienceDirect. Results show that user testing is the predominant method, often combined with standardized questionnaires like SUS and NASA-TLX. However, critical gaps persist: small sample sizes, inconsistent participant reporting, and a lack of metrics designed for non-visual interaction. This study contributes a taxonomy of visually impaired user profiles and identifies the technical requirements for building an inclusive web usability laboratory. By bridging the gap between technical auditing and real-world user satisfaction, this work provides a roadmap for the development of the MEUX LAB, ensuring more equitable digital evaluation environments.
Authors - Vikas Pandey, Sanasam Chanu Inunganbi Abstract - Plagiarism has become a serious problem in universities, research organizations, and professional workplaces, affecting academic integrity and the originality of work. While cloud-based detection tools are widely used, they share a fundamental problem that rarely gets discussed openly: every document is required to be submitted and handed over to a third-party server. For unpublished research, legal drafts, or any sensitive material, this trade-off is not acceptable. The proposed method, named ZERO, takes a different approach and runs entirely on the local machine, watching the clipboard quietly in the background and scoring text against a local TF-IDF corpus in under 200 milliseconds, with no uploads, no accounts, and no data leaving the device. An optional web scanning module is available when broader source coverage is needed. On top of the similarity score, ZERO provides a word-level risk heatmap, a sentence-by-sentence originality breakdown, a stylometric module called Writing DNA, and a scan history timeline. Testing on 60 hand-labelled samples showed that a recalibrated scoring curve brings the average score on original technical writing down from 34.7% to 9.8%, while keeping verbatim-copy detection at 95%. API credentials are stored in the OS keychain, and inter-process communication is locked to a strict channel whitelist, making the application well-suited for confidential and pre-publication work.
Authors - Ali Raheman, Asad Khan, Tejas Bhagat, Fazal Raheman Abstract - The emergence of quantum computing challenges digital infrastructure to achieve both quantumresilient security and energy-efficient performance. This is particularly critical for decentralized finance (DeFi), where layered software stacks and exposed authentication mechanisms increase complexity, overhead, and attack-surface exposure. This paper proposes Quantum Ledger Technology (QLT), a blockchain-agnostic architectural framework that integrates Zero Vulnerability Computing (ZVC), Solid-State Software-on-a-Chip (3SoC), and Quantum-Resilient User-Evasive Cryptographic Authentication (QRUECA). Together, these mechanisms reduce software-mediated trust, minimize exposed authentication surfaces, and shift trust enforcement toward hardware-rooted execution environments. A hypothesis-driven evaluation framework is introduced to assess whether architectural simplification can reduce authentication latency, memory usage, energy overhead, and attack-surface complexity while preserving functional equivalence with existing blockchain systems. Preliminary results indicate that hardware-rooted authentication and reduced trusted software complexity can provide a promising foundation for scalable, energy-aware, and quantum-resilient digital infrastructure. The proposed framework aligns with the goals of secure, sustainable, and intelligent future computing systems.
Authors - Shamsa AlNasri, Muna Ali AlShamsi, Mariam AlNuaimi, Hanae Ouahhabi, Gurdal Ertek Abstract - This study presents an analytics framework for analyzing and benchmarking sales transactions data of e-commerce products across multiple countries. The framework consists of an integrated multi-faceted application of a carefully selected portfolio of data analytics techniques. Specifically, the framework combines (a) statistical distribution fitting to well-known probability distributions (Gamma, Normal, Weibull, Lognormal), (b) box plot analysis followed by statistical hypothesis testing (Kruskal-Wallis and Dunn tests) and visualization of pairwise comparison results, and (c) text mining (Latent Dirichlet Allocation (LDA) and word clouds). Although many studies in the literature report on the analysis of e-commerce product sales, this is the first study that combines the mentioned techniques within a multi-faceted yet also unified approach. The results obtained for a case study on the Gulf Cooperation Council (GCC) countries reveal regional differences in consumer behavior, pricing, and preferences. The insights obtained can be used to improve the marketing and engagement of the selected case with the selected products and countries. However, more importantly, the primary contribution of the study is the generalizable analytics framework presented that can be adopted and applied to any product set and country selection with similar data attributes.
Authors - Deepak Mane, Ashwanth Nair, Nihar Gundale, Om Khamkar, Tanmay Kulkarni, Ranjeet Bidwe, Amol Kamble, Suraj Sawant Abstract - There many areas in which the organization can make use of technologies that will make decision making easy. ai (artificial intelligence) is one of the most useful and innovative technologies that is used in many fields of organization to help in business management and decision making. in the recent years, HR department has became very important in the organization, since the quality and skills of the worker in directly proportional to the performance of the organization. After ai is being used in many ways in the organization like in marketing and sales department, now its starting to guide HR department for employee related decision. The purpose of using ai in HR department in to support decision that are based on objective data analysis, not on subjective aspects. The goal of this work is to analyse influence on employee attrition and objective factors. In order to identify the main causes that contribute to a workers decision to leave a company, and identify the employee that about to leave a company. After training, the obtained model for the prediction of employs attrition is tested on real dataset provided by IBM analytics, which has 35 features and about 1500 samples. Results are obtained in terms of classical metrics and the algorithm that produced the best results of the dataset is the gaussian naïve bayes classifier. It has best recall rate of 0.54, since it measures the ability of classifier to achieves an overall false negative rate equal to 4.5% of the total observations.
Authors - Deepak Mane, Ashwanth Nair, Nihar Gundale, Om Khamkar, Tanmay Kulkarni, Ranjeet Bidwe, Amol Kamble, Suraj Sawant Abstract - Accurate emotion recognition remains a significant challenge in affective computing, particularly when relying on unimodal approaches such as facial expression analysis. These systems are inherently limited because individuals can deliberately mask their emotions, and visually similar expressions such as fear and surprise often lead to misclassification. Such limitations highlight the need for more robust methods that incorporate complementary sources of information. The proposed system uses a multimodal framework which combines the Circumplex Model of Affect through its visual and physiological cues to achieve better reliability. The FER-2013 dataset provides data for a Convolutional Neural Network which estimates emotional valence based on facial expressions captured through standard camera systems. The MAX30102 photoplethysmography sensor measures heart rate and heart rate variability through its connection with an Arduino to determine emotional arousal. The rule-based fusion engine combines these modalities to determine the final emotional state which it then categorizes into joy, stress, anxiety, and calmness. The system uses physiological data to clarify between emotional states which appear similar and it also identifies hidden emotional states which facial expressions cannot express. The system offers health monitoring, human computer interaction, and psychological assessment fields a dependable and efficient solution.
Authors - Caio Tertuliano Ribeiro, Lilian Berton Abstract - This paper studies a Disparate Impact (DI)-oriented variant of fairness-aware hyperparameter optimization for XGBoost in banking and credit settings. The method combines Design of Experiments (DoE), Response Surface Methodology (RSM), and NBI-style sampling to jointly tune XGBoost hyperparameters, the decision threshold, and the positive-class weight under a fixed evaluation budget. Unlike the preliminary composite-fairness draft, the final experiment optimizes a DI-only objective while keeping Statistical Parity Difference (SPD), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD) as audit metrics. Experiments on Bank Marketing, German Credit, and Default of Credit Card Clients with 30 replicas per dataset show that the proposed utopia selector is competitive with evaluation-matched random search and consistently superior to the XGBoost default configuration. Relative to the default baseline, it improves Balanced Accuracy by +0.059, +0.020, and +0.019, while reducing the DI gap by -0.375, -0.174, and -0.141, respectively. The main takeaway is that DI-only optimization provides a finance-oriented and reproducible way to navigate fairness–performance trade-offs rather than a universal domination claim over random search.
Authors - Syeda Fatima Rafique, Mohammed Abobaker Baobaid, Majid Shaher Ebrahim Tayfour, Hamed Marhoun Khamis Alsaedi, Ibrahim Alfaki, Gurdal Ertek Abstract - With the invention of Large Language Models (LLMs) and the development and increased usage of generative AI platforms, “vibe coding” (AI-assisted coding, coding with AI assistants) has become an integral part of software development, documentation, and maintenance. Although there are multiple detailed studies on the experiences of developers with vibe coding for software development, no earlier work was encountered on the vibe coding of analytics dashboards in particular. However, in an era of exponential growth in data volume, variety, and velocity, data analytics, and in particular, analytics dashboards, are highly relevant and can serve as competitive leverage for every organization. This paper is the first attempt in the literature to answer the following research question: “What are the practical project experiences of developers during vibe coding of analytics dashboards, especially in terms of challenges faced?” In this paper, experiences in two case study projects on analytics dashboard development are shared as lessons learned to guide developers, product managers, and project managers.
Authors - Daniel H. M. Marques, Luiz A. P. Silva, Andson M. Balieiro, Mohammed B. Alshawki, Alexandre J. R. Serres, Dalton C. G. Valadares Abstract - The evolution towards 5G and Beyond (5G/B5G) standardization, and even the development of 5G, can be accelerated by using accessible, high-fidelity emulation environments to validate emerging network architectures. For instance, Network Slicing is an aspect that can especially benefit from these tools. However, defining an emulation platform that is compatible with research objectives can be challenging. So, this work aims to compare two different Mobile Network emulation setups: one using Open5GS to emulate the Core Network and UERANSIM for the implementation of the Radio Access Network (RAN) and User Equipment (UE), and another using an OpenAirInterface (OAI) End-to- End implementation. Furthermore, this work aims to fill relevant gaps in the academic literature, addressing implementation obstacles at a granular level and the architectural trade-offs necessary to stabilize these environments. To that end, we identified and resolved operational friction points such as kernel-level GPRS Tunneling Protocol User Plane (GTP-U) conflicts, slice identity alignment, and Physical layer (PHY) timing sensitivities in virtualized radio frequency simulators through two deployment frameworks. Our analysis shows that, while the Open5GS/UERANSIM stack offers better agility for Core Network prototyping, OAI offers a more flexible and feature-rich framework, suitable for researching advanced RAN features, albeit with greater configuration complexity. By documenting troubleshooting protocols and architectural comparisons, this work serves as a practical guide for researchers migrating from 5G simulation to 5G/B5G-ready emulation test environments.
Authors - Aditya D. Nandgirwar, Deepika Burte, Rashmi Malvankar, Rupali Vairagade, Shwetambari Borade, Sandeep M. Chitalkar Abstract - Another significant development in intelligent system development is the notion of Agentic Artificial Intelligence (Agentic AI): as passively generative models continue becoming acts-oriented entities capable of perceiving their environment, thinking about goals, and planning and executing more complex tasks without necessarily involving humans. Unlike the traditional artificial intelligence systems, whose main input is the fixed input, agentic AI systems respond to objectives, are adaptive in their decision-making and are able to interact with other tools, environments and fellow agents. Thus agentic systems are increasingly being used in diverse technical disciplines, including software development and cybersecurity, healthcare, finances, robotics and enterprise automation. The paper provides a thorough description of agentic AI, its theoretical basis, architectural design components, implementation plans, security issues and applications. We take a look at intelligent agent development and explain fundamental concepts in the designing of intelligent agents such as the reasoning, the strategy to plan, the management of memory, and collaboration of intelligent agents. Moreover, we examine leading agentic AI systems and platforms that facilitates development and coordination of autonomous systems.
Authors - A.N. Gachahi, L.W. Gachahi Abstract - Antennas are fundamentally important in all modern wireless communication systems. Their design directly influences the performance, efficiency, and size of wireless devices. A core limitation of existing antenna designs is their reliance on spatial resonance where antenna size is proportional to a fraction of the wavelength of the signal they are intended to transmit or receive. Here, we present the feasibility of using single-layer capacitors (SLCs) as radiating elements for antenna applications. The SLC approach exploits displacement currents and the temporal resonance inherent to capacitive structures. A theoretical framework is established using Maxwell’s equations and circuit-level analysis to explain how electromagnetic fields arise around SLCs. An experimental setup using an array of 90 ceramic capacitors is constructed, and a magnetic field sensor is used to measure radiated fields across a range of frequencies. The antenna is then modeled in MATLAB, and simulations are performed to evaluate radiation patterns and impedance characteristics. Results confirm field generation consistent with theoretical predictions. Integration into ESP32-C3 Wi-Fi modules is demonstrated. Impedance mismatch is identified as a key limitation and addressed through a resistive matching network, improving signal consistency under varying distance conditions. The theoretical, experimental and simulation results in this set-up confirm that SLCs can serve as efficient, compact antenna elements, with practical implications for RF systems where size, cost, and integration constraints are critical.
Authors - PARVIZ FIRUDIN OQLU KAZIMI, KAZIM ASAD OQLU KAZIMLI Abstract - Objective: The targeted modeling of social information in global, regional, and local projects, and the use of information influence for both progressive and aggressive purposes, constitutes an activity in the field of information engineering. It is important to study the multifaceted and complex scientific and theoretical foundations of this activity and discuss them in a broad academic community. Theoretical Foundations: It is inappropriate to limit information engineering to technical, technological, and software issues; it is important to study the scientific, theoretical, and experimental aspects of information influence at various levels using modern technologies. Research Methods: This study presents considerations regarding methods for targeted information modeling in education, culture, and information-intensive fields. The proposed considerations are intended to standardize a number of processes, identify aggressive elements in some areas, and, in some cases, consider innovative information modeling. The proposed concept can be compared with a number of sociological analytical models. However, for the first time, it is proposed that the essence of information, the study of the aspects of thesauri influence, and the application of modern technologies will gain greater relevance. This study addresses the application of our theoretical work, known as "information
Authors - Vishakha Shinde, Himangi Pande Abstract - The rapid growth of digital communication platforms, cloud multimedia sharing and AI-driven visual systems has increased the demand for secure image ownership verification and multimedia authentication. Conventional watermarking approaches often suffer from limited robustness against compression, geometric distortion and adversarial attacks. Recent advances in deep learning and cryptographic protection mechanisms have enabled the development of intelligent hybrid watermarking frameworks with improved robustness, adaptive embedding and secure ownership verification. This paper presents a comprehensive review of secure image watermarking techniques integrating deep learning architectures and cryptographic security schemes. The study analyzes CNN-, autoencoder-, GAN- and diffusion-based watermarking frameworks along with encryption-assisted watermark embedding strategies, attack resilience mechanisms and lightweight watermarking systems for edge– IoT environments. Benchmark datasets, performance metrics, loss functions and quantitative comparisons of existing frameworks are also discussed. The comparative analysis indicates that hybrid deep learning–cryptographic watermarking methods significantly improve robustness, authentication reliability and resistance against signal-processing, geometric and adversarial attacks. However, computational complexity, scalability and real-time deployment constraints remain major challenges. Finally, the paper discusses future research
Authors - Meghali Kalyankar, Prashant Lahane Abstract - As deepfake generation technologies have rapidly advanced, establishing authenticity for multimedia content on digital platforms has become a major challenge[1]. Current deepfake detection approaches primarily rely on unimodal analysis and face challenges in encoding joint AV inconsistencies, temporal consistency, and adversarial attacks. To overcome these problems, a novel Multimodal Attention and Adversarial Deepfake Network (MMAD-Net) framework for robust audiovisual deepfake detection is proposed. The proposed solution adopts a framework that combines fine-grained visual feature extraction provided by VideoMAE v2 [2], audio representation learning provided by HuBERT [3] and temporal dependency modeling provided by TimeSformer [4] to model the fine-grained spatial and temporal inconsistency in manipulated media. In addition, a Multimodal CoAttention Transformer (MCAT) is used for better cross-modal interaction between audio and visual streams, and a hybrid HOA-COA optimization scheme optimizes discriminative feature representations to ensure better feature separation and remove redundancy. Adversarial Consistency Training (ACT) is embedded in the learning process to enhance adversarial robustness against adversarial perturbation and unseen adversarial manipulation. FakeAVCeleb and Celeb-DF are used for testing the proposed model with several performance metrics. Experimental results show that MMAD-Net can provide stable and general detection performance while maintaining a high level of robustness for current multimodal deepfake detection methods.
Authors - Ritesh Kumar, S. Rajaprakash Abstract - The society we live in today has seen the accumulation of knowledge via social media platforms such as Twitter and Facebook, which are expanding at a tremendous rate on a daily basis. The victims of these social media platforms are users who tweet or post on a variety of issues from any location in the globe via the usage of the internet. Tweets are used to assess both positive and negative mes-sages in order to produce polarity scores and also to have the ability to anticipate future trends. Twitter is a source from which these polarity scores may be collected; nevertheless, the information about polarity scores is kept confidential. The information will be easily compromised, and the fluctuations in the score will result in incalculable consequences, such as affecting the global economic position, the brands of corporations, and therefore the reputations of businesses. The installation of Salsa, which offers faster encryption due to the district round and greater data security, was something that Daniel Bernstein intended to do in order to address these issues. In this work, a fresh approach is provided by changing the Salsa20/4 algorithm in order to further strengthen the security of the polarity scores, which is a vital necessity in the society that we live in today. The proposed method is RRCF has two encryption stages. Stage 1 is comprised of column operations, whereas stage 2 is comprised of four procedures. Finding the greatest common factor of the pain text that has been provided is the initial step in the procedure. Identifying the time period in the pain text is the second step in the procedure. The outcome of the second step is used in the third phase, which is to create a pair of values. The application of the pair values and the swapping of the cell values in the given matrix is the fourth step. In comparison to the Salsa20/4 technique, the suggested methodology has a much higher level of security.
Authors - Boago Seropola, George Anderson Abstract - When it comes to healthcare, the implementation of machine learning (ML) and deep learning models requires a shift away from blackbox methodologies and toward frameworks that are transparent and auditable. This is necessary in order to guarantee ethical governance and patient safety. In this study, an integrated XAI-CRISP-DM framework is proposed. This methodology incorporates post-hoc Explainable Artificial Intelligence (XAI) into the iterative stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). Additionally, the research places an emphasis on continual post-deployment oversight. Within the context of HIV/AIDS risk classification in Botswana, we analyse the interpretability of non-linear decision boundaries in LightGBM and Multi- Layer Perceptron (MLP) models. This evaluation is carried out with the assistance of SHAP and LIME. For the purpose of quantitatively validating the clinical significance of socio-demographic characteristics using the publicly available dataset, the fifth Botswana AIDS Impact Survey 2021 (BAIS V), this study makes use of impact score and explanatory specificity. Results demonstrate that LIME and SHAP effectively decompose complex model outputs into human-readable feature weights, identifying key drivers such as sexual-activity-before-15 and condom use. Through the tracking of model integrity and concept drift in dynamic clinical situations, the incorporation of a monitoring stage guarantees that continuous accountability is maintained. The results of this study suggest that the XAI-CRISP-DM framework is an essential methodological standard for resource-constrained environments such as Botswana. This framework ensures that data-driven healthcare solutions are not only high-performing but also statistically reliable, equitable, and ethically sound.
Authors - Ana Laura Lezama Sanchez, Mireya Tovar Vidal Abstract - In this paper, we present the automatic classification of autoimmune skin diseases using deep convolutional neural networks. Hence in this study we conducted within the context of supervised classification of dermatological images, with the objective of designing and implementing a model capable of distinguishing among five clinical classes like lupus, psoriasis, vitiligo, lichen planus and healthy skin. Therefore, a deep convolutional neural network-based system, trained and evaluated on a labeled dataset of clinical images, is proposed. The model was evaluated using the metrics precision, recall, F1 and accuracy. The results obtained indicated that the accuracy was 70%, demonstrating the model’s ability to learn relevant discriminattive features. The best performance was observed in the healthy skin and vitiligo classes, with F1 of 0.82 and 0.80, respectively, indicating high identification capacity. On the other hand, the psoriasis and lichen planus classes showed moderate performance, with F1 values of 0.63 and 0.58, respectively. The lupus class exhibited the lowest performance, with an F1 of 0.46, reflecting the complexity of its visual variability and its similarity to other conditions.
Authors - Katherine Garcia-Velez, Daniel Maldonado Abstract - This paper examines the trajectory of digital transformation in Ecuador's public sector between 2021 and 2025. Its objective is to analyze how the country moved from an initial strategic orientation to a legal and public policy consolidation of digital transformation. Methodologically, the study adopts a qualitative approach based on documentary analysis and diachronic comparison of official instruments: the Digital Agenda 2021-2022, the Digital Transformation Agenda 2022-2025, the Organic Law for Digital and Audiovisual Trans-formation (2023), and the Digital Transformation Public Policy 2025-2030. The analysis is grounded in the idea of the progressive institutionalization of digital reform and compares the evolution of these instruments in terms of their nature, scope, and institutional implications. The findings show a four-phase sequence: agenda setting, strategic coordination, legal consolidation, and programmatic consolidation. The study concludes that Ecuador progressed from strategically oriented instruments toward a binding legal framework and a national public pol-icy. However, the existence of this institutional architecture does not, by itself, imply homogeneous results in terms of performance or service quality, so its effective implementation remains an open empirical field.
Authors - C. Bagath Basha, S. Rajaprakash, K. Karthik, Panjala Vijay Goud, Macharla Rakesh, M Ramana Kumar Abstract - The privacy and security of sensitive information is a major concern in the social assistance sector due to the widespread use of Internet of Things technologies. This study presents a secure sharing algorithm for IoT social assistance data based on blockchain and smart contracts. It aims to address the inherent hazards of conventional centralized administration, such as data loss and manipulation. This paper propose a security method and this method has four process. There are four steps to the new method. First, change the text from plain text to “ASCII code” (A). The second step is to take the A values and make pairs. Then, swap the cells in the matrix so that the even pair numbers start from the 0th cell value and go to the end of the matrix. The third step is to use the “ASCII code” as C to find the prime number. To do the fourth step, you need to use Equation 1. Take the numbers from A1 and pair them up. Then, change the cells in the matrix The message is ultimately received in its original format via the process of decryption, which is thought of as the inverse of this conversion. The proposed technique provides a higher level of security when compared to more conventional encryption methods.
Authors - Rory Lewis Abstract - This work presents a formal supervisory framework for detecting and intervening in large-scale AI misbehavior using neuromorphic sentience, supported by probabilistic guarantees. As generative and adaptive artificial intelligence systems become foundational to human decision-making, scientific discovery, and national infrastructure, ensuring their reliable and safe operation has emerged as a critical challenge. Existing approaches rely primarily on external, reactive monitoring and are insufficient for models operating at machine speed. More fundamentally, closed computational systems cannot reliably represent or act upon their own epistemic limits, creating an inherent blind spot in autonomous operation. To address this limitation, a control architecture is introduced in which an independent neuromorphic module supervises internal AI dynamics through event-driven processing and dendritic integration. Operating without a global clock, the system continuously monitors activation patterns, attention shifts, and inter-module interactions in real time, enabling low-latency and energy-efficient detection of transient and distributed signatures of instability that are inaccessible to conventional approaches. Using probabilistic inference over these signals, the framework identifies early indicators of hallucination, instability, and unintended coordination prior to output generation. Formal lemmas establish mathematical bounds on the probability of undetected misbehavior under realistic operating conditions. Finally, the framework is integrated with a human governance model in which democratically defined thresholds regulate the balance between AI capability and societal safety.
Authors - Md Manirul Islam, Md. Mushfiqur Rahman , Sazzad Hossain Abstract - Moving IoT-edge systems to post-quantum security is not as simple as replacing one algorithm with another. Different parts of the system have different security needs. Short-lived telemetry, control messages, firmware updates, and trust records should not all be protected in the same way. Device limits are also very different: a tiny leaf sensor, a capable actuator, a gateway, and a cloud service do not have the same memory, energy, or bandwidth budget. This paper presents an adaptive hybrid classical-post-quantum cryptographic framework for such heterogeneous environments. We make four main contributions. First, we define a four-tier system and threat model for resource-constrained IoT-edge deployments. Second, we describe profile selection as a practical decision problem that balances security, latency, energy, memory, and bandwidth. Third, we propose a small profile catalog, P0 to P4, that maps classical, hybrid, and PQ-first choices to real deployment roles. Fourth, we evaluate the framework through a benchmark-grounded experimental emulation using current standards and published device measurements. The main idea is simple: apply the strongest protection where compromise would hurt most, while keeping weak devices usable in the real world.
Authors - Imane Bari, Mounir Oubenyahya, Abdellatif Aziki, Fouad Achemchem Abstract - Climatic conditions and water scarcity in the Souss Massa region of Morocco are causing a significant decline in fodder supply and threatening the continuity of dairy farming. In response to these structural constraints, dairy cooperatives have adopted hydroponic green fodder (HGF) cultivation, an approach that reduces water consumption and frees agricultural land for higher-value crops. This study analyses the financial performance and structural effects of HGF adoption in two dairy cooperatives selected as pilot cases. Using a descriptive and explanatory methodology based on accounting and financial records collected over 4 to 6 years, combined with a non-parametric econometric model, the study assesses production costs, financial viability indicators, and the effect of innovation on key financial ratios. Results confirm the financial viability of the HGF investment and document its temporary effects on financial independence and self-financing capacity, while highlighting the social dimension of this innovation in preserving livestock farmers’ livelihoods.
Authors - Swetha P. , Maniratnam, Prasad B Honnavalli Abstract - The dark web has been identified as a major source of organizational risk, facilitating underground markets for stolen credentials, proprietary data, and pre-attack threat intelligence. An organization lacking visibility in these underground marketplaces faces attacks entirely beyond conventional monitoring solutions. This paper introduces a realtime dark web monitoring system integrating anonymous browsing via Tor, Selenium WebDriver, and open-source Wazuh Security Information and Event Management (SIEM) to create a unified threat intelligence solution. The system performs continuous keyword searching for organizational name references and autonomously browses authenticated dark web marketplaces for content extraction. Identified events are serialized as structured JSON messages and ingested into Wazuh via custom decoder and rule definitions, providing actionable alerts on the analyst dashboard within seconds. The system has been evaluated over 24-hour continuous sessions on two live dark web markets, achieving 95% keyword detection accuracy, 1.4 seconds average alert latency to dashboard, and stable long-duration browser operation without application crashes. The system requires no commercial licensing, is fully configurable to organizational targets, and features native SIEM integration, making it a viable and accessible solution compared to proprietary dark web intelligence services.
Authors - Pam Cole Abstract - As artificial intelligence transitions from passive tools to autonomous agents, governance frameworks designed for slower systems are being asked to contain behavior they were not engineered to address. Two converging forces widen the resulting gap. Synthetic Trust describes the industrialization of unearned credibility, where AI systems simulate socio-emotional cues to suppress verification. Ungoverned Acceleration describes autonomous execution at a pace that outruns institutional oversight. Drawing on cross-industry research covering sixteen major AI systems, peer-reviewed findings on emotional manipulation, and documented incidents across sectors, this paper proposes the A.W.A.R.E. Governance Flywheel, organized around Identity, Trust, and Control spokes with Visibility, Attribution, and Containment as operational mechanisms, bound by Accountability. A comparative analysis of six frameworks across six continents shows no widely adopted framework currently operationalizes agentic governance at this specificity. The paper extends the framework with operational metrics, industry applications, adoption barriers, and three testable propositions.
Authors - Yasmine AGOUN, Cheikh SALMI, Nour El-Houda SENOUSSI Abstract - The rapid growth of the Internet of Things (IoT) has made cybersecurity prone to several vulnerabilities, highlighting the need for a semantic and well-organized structure for cybersecurity knowledge to ensure reliable threat detection and mitigation. In this paper, we propose IoTSecOnto, a largely automated pipeline for building a security-centric Internet of Things (IoT) ontology. The pipeline combines automated security literature mining, text analytics, natural language processing, Large Language Models (LLMs), and formal concept analysis. This approach reveals domain-specific concepts and relations and organizes them into a coherent hierarchy. A human-assisted review phase is needed to ensure the reliability and the accuracy of the derived security knowledge. In addition, the ontology is designed to be flexible and can be improved over time. IoTSecOnto is implemented using OWL 2, RDFLib, SPARQL, and SHACL constraints. We used a Mirai botnet and ontology quality metrics to demonstrate its effectiveness. The obtained results confirm the ability of IoTSecOnto to support knowledge sharing, automated reasoning, and improved threat analysis across diverse IoT settings.
Authors - Sanae.Chakir, Adil.Bekraoui, El moukhtar Zemmouri, Hassan.Majdoubi, Mhamed. Mouqallid Abstract - For cuttings to successfully root indoor environmental conditions in a greenhouse are essential. This article examines the efficacy of two predictive models, linear regression and artificial neural networks, in predicting the parameters associated with rooting plant cuttings. For evaluation the analysis uses the RMSE MAPE and R² indices. According to the results artificial neural networks perform better than linear regression in terms of prediction accuracy. By utilizing these insights, farmers can use artificial neural network models to implement optimal control strategies which will al-low them to accurately predict indoor variables and ultimately increase crop productivity.
Authors - Miroslav Stefanov, Stoyan Denchev, Kristiyan Stefanov Abstract - Large Language Models (LLMs) are increasingly used in educational settings, but they are not inherently constrained to the boundaries of specific instructional materials. This can lead to unsupported claims, external knowledge leakage, and reduced instructional precision. This study proposes and evaluates a lesson-bounded LLM architecture for reliable educational AI systems. The architecture combines retrieval-augmented generation, context restriction, structured response control, explicit refusal behavior, and post-hoc confidence calibration. Using a multi-domain instructional dataset and a benchmark of inscope and out-of-scope questions, the proposed system is compared against an unconstrained baseline LLM. Results show strong retrieval discrimination and boundary control, with high Area Under the Receiver Operating Characteristic Curve, high Average Precision, strong refusal recall, low out-of-scope answer rate, reduced verbosity, and improved support-based instructional density. Calibration analysis further shows that raw retrieval scores are not reliable probability estimates, but Platt scaling substantially improves confidence reliability. These findings suggest that lesson-bounded architectural constraints can improve the controllability, auditability, and reliability of intelligent educational systems while highlighting the need for stronger factuality evaluation and confidence interpretation.
Authors - Hatem Yousif Alkhonini, Fethi Fkih Abstract - This study evaluates five post hoc explanation methods using XAI-Bench under controlled settings with ground-truth explanations. Results show significant differences in robustness, with MAPLE outperforming Shapley-based methods under high correlation. Feature correlation impacts explanation quality more than the choice of method, and performance degrades with increasing dimensionality-especially for LIME. Exact methods become infeasible beyond d=10, and robust evaluation requires multi-seed replication.
Authors - Maria Jose Cantos Cedeno, Kevin Michael Mero Ramirez Abstract - The spread of disinformation through social media, messaging apps, and other digital channels is a growing problem in Ecuador, due to the limitations of manual fact-checking processes in the face of the high volume of information. In this context, Transformer-based models are presented as high-potential solutions for detecting fake news across various domains and languages. The objective is to comparatively evaluate Transformer architectures pre-trained using fine-tuning techniques for the automatic classification of fake and real news in the Ecuadorian context. The CRISP-DM methodological framework was applied to guide the development of deep learning models. A balanced dataset of 5,000 news items in Ecuadorian Spanish was constructed, equally distributed between real and fake news. Data processing was carried out through a 12-stage sequential pipeline to reduce noise, prevent data leakage, and preserve relevant semantic features of Spanish. Furthermore, the models were trained using homogeneous hyperparameters and evaluated using various metrics employed in the scientific field. As a result, all models exceeded 89% accuracy; BETO achieved the highest precision, mBERT the best recall, and DistilBERT the highest computational efficiency. It is concluded that Transformer architectures proved to be scalable, effective, and viable solutions for the automatic detection of fake news in Ecuador.
Authors - Arsalan Vahi Abstract - The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation, there is a significant gap in addressing different security aspects, such as design principles, specific to the IoT environment. This study aims to bridge this gap. This research presents an examination with focusing on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a concise overview of lightweight ciphers with emphasizing on their security challenges which is an essential consideration for real-time and resource-constrained applications.
Authors - Amine El Ameri, Ahmed Drissi Abstract - Quantum computing threatens TLS through the Harvest Now, Decrypt Later (HNDL) attack: adversaries record encrypted traffic today and decrypt it once quantum capabilities mature. Existing approaches integrate post-quantum key encapsulation mechanisms directly into the TLS handshake; while cryptographically sound, they inflate the first handshake message and cause IP fragmentation of the ClientHello record that some middleboxes reject, leaving these solutions undeployable on today’s Internet. This paper proposes PH-PQ-TLS, a post-quantum key establishment framework for TLS 1.3 that adds post-quantum confidentiality while preserving the standard handshake, thereby avoiding fragmentation and middlebox incompatibilities. The framework requires no redesign of TLS, uses only standardized TLS mechanics, and is crypto-agile by design. We provide a full Go implementation and evaluate its overhead against a standard TLS 1.3 baseline. The ClientHello record measures 289 bytes, well below the minimum IPv6 Maximum Transmission Unit of 1280 bytes, whereas a hybrid ML-KEM-768 ClientHello reaches roughly 1473 bytes and exceeds that threshold. The post-handshake upgrade adds 1.18 ms of latency per full connection, a one-time cost amortized over the session lifetime and avoided on Pre-Shared Key resumed connections. These results show that HNDL protection does not require redesigning TLS, replacing the Web PKI, or accepting deployment failures, offering a practical, incremental path toward quantum-resilient TLS on today’s infrastructure.
Authors - Sayuni Dewapriya, Pehan Gunasekara, Shenal Peiris, Charith Herath, Kavinga Yapa Abeywardena, Ayesha Wijesooriya Abstract - DNS is often trusted within modern network environments, making it a common channel for covert communication, malware activity, and infrastructure abuse. This paper presents a near-real-time AI/ML-based DNS threat detection framework designed to identify suspicious DNS behaviour and transform raw network activity into actionable security events. The proposed approach combines machine learning, behavioural analysis, flow-based detection, event aggregation, risk scoring, and contextual threat intelligence to improve visibility across plaintext DNS and DNS-over-HTTPS traffic patterns. The framework supports practical security operations by reducing raw alert noise and producing structured outputs suitable for dashboard monitoring and SIEM-based investigation. Evaluation using public datasets, generated attack traffic, and live DNS traffic demonstrates that the framework can support effective DNS threat monitoring, alert prioritisation, and SOC-level analysis.
Authors - Marcos Paulo Jeronimo Francisco, Carlos Hideo Arima, Napoleao Verardi Galegale, Joshua Onome Imoniana Abstract - The increasing complexity of digital systems and the growing sophistication of cyber threats have intensified the need for proactive security assessment methods. Threat Modeling is a structured practice for identifying potential vulnerabilities, attack paths and mitigation strategies during the software development lifecycle. However, its manual application is often time-consuming, subjective and dependent on scarce cybersecurity expertise. In this context, Large Language Models (LLMs) may support security teams by generating threat hypotheses, classifying risks and recommending controls. This study evaluates the effectiveness of three LLM-based tools — ChatGPT, Gemini and Manus — in cybersecurity threat modeling for a real-world backend information system. A controlled computational experiment was conducted using standardized prompts applied to the three models, with three independent executions per prompt. The evaluation considered five dimensions: threat identification coverage, technical depth of analysis, quality of risk classification, assertiveness of control recommendations and result consistency. To consolidate the comparison, a Final Effectiveness Metric (FEM) was proposed. The results show different performance profiles among the evaluated models. Manus achieved the highest FEM score, with stronger threat coverage, technical depth, control recommendations and consistency. ChatGPT presented intermediate performance, with structured and detailed analyses, while Gemini showed lower threat coverage, but satisfactory technical reasoning in specific tasks. The findings also indicate that LLMs can enhance threat modeling activities by expanding analytical capacity and supporting DevSecOps practices. Nevertheless, their outputs require human validation, especially regarding risk classification, framework alignment and false positive analysis.
Authors - Jeanne Roux Ngo Bilong, Python Ndekou Tandong Paul, Bakary Kone, Dethie Dione, Ibrahima Toure, Boris Sourou ZANNOU, Hamidou Dathe, Mamadou Diarra, Olga Ngangmo Kengni, Mamadou Thiam, Cheikh Amed Diloma Gabriel Traore Abstract - Breast cancer is a non-communicable disease that causes thousands of deaths each year worldwide. Early detection of breast cancer in women is a public health priority in developing countries. Based on data collected from patients’ breasts, machine learning models can help predict the risk of developing breast cancer.We used three machine learning algorithms (SVM, decision tree, and random forest) for predicting the risk of developing breast cancer, taking into account the physiological factors of breasts. Data collected from 568 patients was used to train the machine learning algorithms. An evaluation of the performance of the three algorithms showed that the random forest algorithm had the highest F1 score, which led to the selection of this algorithm for creating a computer application to diagnose breast cancer risk. The results of this algorithm show 97% accuracy with 94% recall in predicting high breast cancer risk and an F1 score of 96%. The result obtained indicates the model’s excellent ability to correctly predict high cancer risk across the entire risk probability spectrum. The good performance of the model using the Random Forest algorithm could be useful as first-level medical support for breast cancer screening. The developed model is capable of providing the probability of the risk of developing breast cancer for each female patient.
Authors - Sergey Kubinski, Emil Hadzhikolev Abstract - This paper presents a Smart Fitness Assistant system for generating personalized workout and dietary recommendations using machine learning and domain-informed physiological feature engineering. The proposed approach incorporates indicators such as Basal Metabolic Rate, Total Daily Energy Expenditure, and target caloric intake derived from user data. The recommendation task is formulated as a multi-class classification problem for exercise and diet planning. Decision Tree, Multilayer Perceptron, and Random Forest models are evaluated using both baseline and enriched feature sets. Experimental results demonstrate that the inclusion of physiological features improves predictive performance, with Random Forest achieving the highest accuracy. The developed system is implemented within a modular software architecture that supports user interaction, recommendation generation, data management, and progress tracking.
Authors - Madhulika Gajjala Abstract - The fast-paced development in digital transformation strategies has considerably transformed human resource management systems towards cloudbased, smart and efficient workforces management tools. As current HR systems face various problems like data repository fragmentation, poor connectivity options, slow synchronization processes, scalability issues, and lack of advanced workforce intelligence, the current study aims to develop an AFDHRP framework, which would comprise a CSIL, AOIE, WIMM, and ADHOU components. In addition, this research proposes the implementation of Cloud-Integrated HR Data Synchronization and Interoperability (CHDSI) algorithm and Adaptive Workforce Intelligence and HR Optimization (AWIHO) algorithm into an innovative solution. A comparison was made with the two state-of-the-art models, Deep-Hill and Deep Learning-Based ERP Optimization System, on several performance metrics including human resource integration accuracy, interoperability efficiency, synchronization reliability, workforce intelligence metric, decision support capability, cloud scalability, API ecosystem performance, and FutureReady HR Platform Effectiveness Metric. According to the results, the AFDHRP performed excellently scoring an accuracy of 99.5%, 99.2% for interoperability efficiency, 99.4% for synchronization reliability, and 99.8% for overall platform effectiveness.
Authors - Jose Hernandez Cortaza, Arturo Corona Ferreira, Pablo Payro Campos Abstract - The research adopts a qualitative approach through a descriptive-explanatory case study, supported by source triangulation, integrating direct observation and expert judgment in the requirements engineering process, with the purpose of identifying critical points in the traditional digital document certification workflow at a Mexican public higher education institution. Based on this approach, a digital transformation model was designed alongside a system architecture aimed at guaranteeing the integrity, decentralization, and verifiability of digital documents, integrating Blockchain, IPFS, and smart contracts, which enabled a multilayer verification scheme. The results demonstrate that the proposed system allows multilayer verification, prevents document duplication, and strengthens transparency and trust in academic issuance and certification processes. In terms of performance, the system presents an estimated Gas per transaction of 327,423, with an average processing time of 17.94ms, during which the entire document issuance and certification process is fully executed. The main contribution of this work is the proposal of a replicable digital transformation model that combines emerging technologies (Blockchain, IPFS, and smart contracts) with a qualitative organizational analysis, providing a practical framework for the secure management of academic documents in public higher education institutions, significantly contributing to the improvement of document management. This model can be adopted and adapted by institutions seeking to strengthen the integrity and interoperability of digital issuance and certification processes.
Authors - Shayma W. Nourildean, Yousra Abd Mohammed, Nahida Naji Kadhim Abstract - The growth of the Internet of Things (IoT) equipment has changed many industrial and social applications, but it has made the IoT network vulnerable to a wide range of malicious activities by increasing the number of potential attack points. Traditional IDS has a problem with scalability, adaptability and accuracy when facing new and complex cyber threats. In this study, a strong ensemble machine learning framework that integrates Decision Tree (DT), Random Forest (RF), and XGBoost through confidence-based soft voting, had been validated across individual datasets. The proposed system (DTXG-RF) uses different forces and weaknesses of these algorithms to improve the accuracy of the detection and reduce the chances of causing it a false alarm. Two Benchmark IoT data sets, CIC-IoT2023and IoTID20, were used. These data sets showed a wide range of scenarios in the real IoT attack. To reduce overfitting risk and data leakage, strict train–test separation, stratified splitting, and pipeline-based preprocessing were enforced, and additional cross-validation experiments were conducted to verify model stability across folds and datasets. Assessment results showed that DTXG-RF ensemble voting model consistently improves traditional machine learning models such as DT, XGBOOST, KN, logistic regression, Nave Bayes and Catboost. The model accuracy of CIC -IoT-2023 and IOTID20 were 94.03% and 99.99%, respectively, with AUCs of 0.95025 and 0.9994. These results indicated that the ensemble IDS was lightweight and could achieve high detection accuracy with low latency and memory overheads, which is also suitable for low-latency IoT edge deployment.
Authors: Nellylyn Moyo, Naume Sonhera Abstract: The adoption of generative artificial intelligence (GenAI) has quickly evolved from its experimental stage to a question of institutional governance in higher education. Although initial approaches to the integration of GenAI in universities have been characterized by ad hoc trial-and-error methods and inconsistent policies, higher education organizations are now taking a more systematic approach to the regulation and implementation of GenAI for educational, research, and administrative purposes. This study provides a conceptual critical literature review of the approaches being adopted by universities toward GenAI, emphasizing trends in the global context and in Africa and South African higher education. The study demonstrates that GenAI readiness should not be viewed solely in terms of willingness to use AI technologies but rather as a multifaceted construct. Institutional governance, on the other hand, has evolved to incorporate responses related to issues of academic integrity, redesigning assessments, AI literacy, requirements for disclosure, development of staff, protection of data, and responsible-use guidelines. Nevertheless, such an evolution is patchy, reactive, and very much dependent on institutional capacities, disciplinary differences, and available resources. In the case of African higher education, the possibilities generated by the advent of GenAI have been heavily determined by structural limitations, such as digital inequality, uneven infrastructures, immature policies, and lack of capacity building measures. South Africa emerges as a particularly interesting example because of the emergence of governance responses in a space characterised by inequalities related to access, digital literacy, and institutional capacity. The study argues for an approach to GenAI adoption readiness that would consider the issue at three interdependent levels: individual readiness, organisational readiness, and structural readiness. Such an approach would enable a more contextually sensitive perspective on responsible GenAI adoption.
Authors: Nellylyn Moyo, Sello Prince Sekwatlakwatla, Tranos Zuva Abstract: GenAI has created considerable opportunities and challenges within institutions of higher education. While GenAI tools could help with teaching, learning, assessment, feedback, academic writing, research, and administrative functions, the uptake of these technologies cannot merely be viewed from a perspective of accessibility. In this study, an approach towards institutional readiness for adopting GenAI within higher education institutions is provided by examining infrastructure, governance, training, readiness for ethical adoption, reassessment, and fit-to-use practices. Peer-reviewed literature written between 2020 and 2026 is reviewed here to show that institutional readiness for GenAI adoption must be viewed as an organisational capability rather than as a static technology readiness state. The conclusion can be drawn that while reliable digital infrastructure is an indispensable requirement, institutional readiness also requires governance maturity, clarity of policy, integrity, protection of data, professional development of faculty members, AI literacy of students, as well as rethinking of assessments. The readiness concept is additionally influenced by factors such as digital inequality, varying capacities of institutions, limited resources, and policies in the higher education systems in Africa and South Africa. Fit to use alignment and practice-policy alignment are two concepts that may be useful in determining how well integrated the use of GenAI technology is into educational processes. GenAI can only be used responsibly if there is proper alignment among various aspects, including technology, policy, ethics, and context.
Authors: Manuela Moreno-Arcila, Luz Marcela Restrepo-Tamayo, Gloria Piedad Gasca-Hurtado Abstract. Productivity in software development teams is a multidimensional phenomenon that cannot be reduced to delivery metrics or technical activity indicators. The social and human factors that influence collective performance are consistently overlooked in the measurement frameworks used in practice. The lack of a tool that integrates social, human, and process dimensions with execution results in a structured way prevents a holistic and actionable measurement of software teams' productive capacity. The lack of a tool that systematically integrates social, human, and process dimensions with execution results prevents a holistic and actionable assessment of software teams' productivity. The PCI was designed following the Design Science Research (DSR) paradigm, through a process consisting of two interconnected components: 1) the design and content validation of the Team Capacity Measurement Instrument (TCMI), through expert judgment and calculation of the Content Validity Coefficient (CVC), and 2) the conceptual construction of the index, including the definition of socio-technical dimensions, standardization of variables, conceptual weighting, aggregation, and interpretation using bands and alerts by dimension. The PCI generates a standardized composite score on a [0–100] scale using a weighted aggregation formula with theoretically justified weights, accompanied by a classification scheme that divides the system’s health into four bands and a mechanism for generating actionable alerts by dimension.
