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 - 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 - 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 - 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: 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.
