Authors - Ubaydullo Vakhabovich Gafurov, Nargiza Ruzibaeva, Khujayar Musurmonovich Shennayev, Nosir Mahmudovich Mahmudov, Saodat Sadriddinova, Alisher Bakberganovich Sherov, Nilufar Karimova Abstract - The sustainable development of tourism enterprises increasingly depends on their capacity to implement innovative activities supported by efficient financial mechanisms. However, traditional financing models often fail to provide sufficient flexibility and accessibility for innovation-driven tourism firms, particularly small and medium-sized enterprises. This study proposes a digital financial ecosystem model designed to enhance the financing of innovative activities in tourism enterprises through digitalization instruments. The research integrates concepts of digital finance, FinTech platforms, data-driven credit assessment, and smart contract mechanisms into a unified systemic framework. Using a system dynamics modeling approach combined with structural analysis, the study develops a conceptual and quantitative model linking digital financial infrastructure, access to alternative funding sources, and innovation performance indicators. Empirical validation is conducted using survey data collected from tourism enterprises and analyzed through structural equation modeling. The results demonstrate that digital financial ecosystem components significantly improve funding accessibility, reduce transaction costs, and increase innovation intensity. The findings highlight the mediating role of digital maturity in strengthening the relationship between financial accessibility and innovation outcomes. The proposed model contributes to the theoretical development of digital transformation in tourism finance and offers practical implications for policymakers and enterprise managers. The study provides a scalable framework for enhancing innovation financing in digitally transforming tourism markets.
Authors - Heidi Koivisto, Sari Ahonen Abstract - The integration of AI-assisted code generation tools in software development has the potential to significantly improve productivity and code quality. This paper presents a comparative study of several leading AI tools, including GitHub Copilot, Amazon Q, GitLab Duo, and Claude Code, in the context of software quality assurance (QA). These tools are evaluated based on their ability to generate test cases for a sample application, focusing on metrics such as test coverage, accuracy, and maintainability. Our findings provide insights into the strengths and limitations of each tool, offering guidance to practitioners seeking to take advantage of AI in their QA processes. The results indicate that while AI tools can accelerate test case generation and improve coverage, careful consideration is needed to ensure the generated tests are relevant and maintainable in the long term.
Authors - Avanti Chokhare, Reena Satpute Abstract - With the world's population moving from rural to urban areas, urban infrastructure including, but not limited to, transportation systems, energy re-sources and public service delivery are all strained in their ability to cope with additional residents. Urban management techniques that have historically worked in managing urban growth are no longer sufficient to meet the demands of com-plex modern cities. The Internet of Things (IoT), a transformative technology that allows real-time data collection, enables smarter decision-making and automates many of the services within urban areas through a variety of connected sensors, devices and digital platforms. The purpose of this study is to analyze how IoT will enable the creation of sustainable and efficient smart cities by encompassing multiple transportation management systems, energy efficiency improvements, and environmental monitoring, health, education and digital governance administrative systems. Additionally, the study investigates additional enabling technologies such as Artificial Intelligence (AI), Edge and Cloud Computing, Block-chain and other technologies that can improve the overall functionality of the IoT. Finally, this paper investigates key challenges associated with the implementation of IoT applications in urban areas, including security, reliability and data privacy issues. It is clear that the implementation of standardized frameworks, scalable architecture and phased implementation strategies will be key components in the successful implementation of smart cities. Responsible adoption of IoT technology will lead to improved environmental sustainability, operational efficiency and citizen quality of life in urban areas.
Authors - Joseph Afriyie, Stephen Opoku Oppong, Benjamin Ghansah, Daniel Kobina Danso Essel, Dickson Keddy Wornyo, Ephrem Kwaa Aidoo Abstract - The COVID-19 pandemic forced educational institutions to adopt online learning, which resulted in expanded digital spaces that cybercriminals used to launch phishing attacks against students, faculty, and institutional systems. This research article provides a comprehen-sive literature review that evaluates machine learning techniques for phishing detection in online educational settings. The PRISMA guidelines were used to select 40 studies from 2013 to 2023 after researchers examined publications retrieved from IEEE Xplore, SpringerLink, and Google Scholar. The review analyzes various digital education ecosystems through its examination of algorithmic methods and datasets, performance evaluation metrics, and detection framework de-signs that universities use to defend against phishing attacks. The research shows that Random Forest and Gradient Boosting, together with deep learning methods, which include Convolutional Neural Networks, Long Short-Term Memory networks, and Recurrent Neural Networks, deliver superior detection performance reaching over 90% accuracy in most scenarios. Educational in-stitutions encounter three primary challenges, which include implementing real-time systems to combat emerging phishing techniques, ensuring dataset compatibility with various environments, and managing their restricted resource availability. The study establishes that institutions need to create technical detection frameworks that work together with user training programs to establish better institutional protection measures.
Authors - Bullet Tiwari, Reena Satput Abstract - The Internet of Things (IoT) systems are becoming numerous, trans-forming our virtual world by continually gathering information, linking equipment, and automating most of the spaces. However, the size, decentralization, and dispersal of the IoT networks cause grave droughts with reliability, security, and workability. The current rule-based surveillance tools are not able to handle the dynamism and volume of data generated by drastically many IoT devices. The paper describes a machine learning (ML) system that predicts the performance of applications, issues, and predicts failures of devices in IoT settings. It compares the methods of supervised, unsupervised, and deep learning and examines their performance in the constraints of computing power, delay and energy. It talks about the trade-offs between centralized and decentralized learning in which clouds and edges are used respectively to decide on the most suitable deployment. The framework has security, privacy, and sustainability design issues, as well. The suggested solution is expected to enhance IoT resiliency with the help of predictive intelligence to manage issues before they happen and increase the overall stability of the industry, healthcare, and smart city environments.
Authors - Roseline Oluwaseun Ogundokun, Rotimi-Williams Bello, Pius Adewale Owolawi, Chunling Tu, Etienne A. van Wyk Abstract - Extramarital affairs can undermine trust and lead to relational break-down, yet the ability to anticipate risk factors remains limited. Recent studies have used deep learning approaches to predict infidelity, achieving high classification accuracy but at the cost of interpretability and resource requirements. This article proposes a novel research to examine whether simple, interpretable mod-els, k-nearest neighbours (KNN), linear regression (LR) and support vector regression (SVR), can predict the amount of time individuals spend in extramarital affairs using readily available socio-demographic and relational features. Using the well-known affairs dataset comprising 6,366 observations and nine variables, we apply feature engineering, cross-validation training and regression-based evaluation to compare model performance. Our findings indicate that although KNN outperforms LR and SVR in terms of accuracy, all models struggle to capture variance; mean squared error (MSE) values remain high, and the coefficient of determination (𝑅2) values close to zero. We discuss the implications for predictive counselling and outline future research directions.
Authors - Yad Sabah Hussein, Raid W. Daoud, hab Abdulrahman Satam, Mohammad Fakhreldin Abstract - This systematic review investigates the design strategies and performance outcomes of AI-powered robotic systems in warehouse automation. The study synthesizes recent advances in robotic navigation, object recognition, task scheduling, and multi-robot coordination, with particular emphasis on machine learning and reinforcement learning techniques that allow robots to adapt to dynamic environments and optimize decision-making in real time. Performance evaluation across the literature reveals significant improvements in throughput, accuracy, and flexibility when AI-driven robotics are deployed. Robots equipped with advanced sensors and computer vision systems demonstrate enhanced capabilities in obstacle avoidance, inventory management, and autonomous material handling. Integration with cloud computing and Internet of Things (IoT) infra-structures further strengthens interoperability and enables predictive analytics for supply chain optimization. Key performance metrics such as energy efficiency, error reduction, and scalability are analyzed to highlight the comparative ad-vantages of AI-enhanced solutions over conventional automation. Despite these advances, challenges remain in ensuring safety, interoperability among heteroge-neous systems, and cost-effective scalability. High implementation costs, cyber-security risks, and the absence of standardized protocols are identified as barriers to widespread adoption. The review concludes that hybrid approaches—combining learning-based adaptability with formal safety guarantees—represent a promising direction for future research. Moreover, sustainable design practices and energy-efficient robotics are essential to align warehouse automation with broader environmental goals. This review provides a consolidated perspective on the state of AI and robotics in warehouse automation, offering insights for re-searchers, practitioners, and industry leaders seeking to design resilient, intelligent, and sustainable warehouse systems.
Authors - Johanes Fernandes Andry, Hendy Tannady, Glisina Dwinoor Rembulan, Ongky Alex Sander, Guan Nan Abstract - Fast paced change in digital transformation, government institutions have been forced not only to embrace IT but to also govern it strategically for alignment with organizational goals. Unfortunately, most organizations still grapple with several major problems including lack of alignment between business and IT and unreliable configuration data management. In light of this, this research proposes an effective IT governance strategy that will address the out-lined challenges through utilization of the COBIT 2019 approach. Based on the results, the organization is moderately mature in terms of compliance and risk awareness but still needs improvement in the alignment between its IT and business objectives and the validation of configuration data. Moreover, organizational resistance and lack of user involvement were found to be among the most important obstacles to successful implementation. According to the findings, the application of COBIT 2019 framework in the process of governance is likely to lead to better governance capacity, higher quality of information, and improved decision-making processes.
Authors - Xinyi ZHU, Han Wang, Jun WU Abstract - In rainy-day images, rain patterns exhibit complex morphologies and significant variations in scale, and they tend to overlap with background textures and edge structures, posing significant challenges for rain removal tasks based on a single image. Addressing the shortcomings of existing methods in modeling complex rain patterns, enhancing key regions, and utilizing complementary information across channels, this paper proposes a multi-scale attention and multi-channel fusion image rain removal method tailored for complex rain pattern scenarios. First, we construct a parallel multi-scale feature extraction module that uses standard convolutions and dilated convolutions with varying dilation rates to capture fine-scale local rain patterns, mesoscale rain patterns, and large-scale contextual information, thereby enhancing the network's ability to perceive rain patterns across multiple scales. Second, an adaptive attention optimization module is designed to re-calibrate multi-scale features across both channel and spatial dimensions, enabling the model to focus more on areas with dense rain patterns, edge structures, and effective texture information. Finally, a multi-channel feature interaction and fusion mechanism is introduced. Through channel segmentation, cross-channel interaction, and residual fusion, this mechanism enhances information complementarity between different feature subspaces, thereby improving the structural preservation and visual naturalness of the restored results. Experimental results on the Rain100H, Rain100L, Rain12, and SPA-Data datasets demonstrate that our method achieves superior rain removal performance across multiple test scenarios, exhibiting particularly strong generalization capabilities in real-world rainy conditions.
Authors - Jatmiko Yogopriyatno, Nursanty, Yorry Hardayani Abstract - Ambiguity in public decision-making constitutes a structural impediment to effective e-governance, manifesting as procedural uncertainty, inconsistent treatment of analogous cases, and unclear authority allocation. This study examines the Integrated Aspiration and Minutes Management Information System (SMART MURA) developed by the Regional Legislative Council (DPRD) of Musi Rawas Regency, Indonesia, as a sociotechnical solution for resolving decision ambiguity through stakeholder consensus-based participatory design. Employing design science research (DSR) methodology, the study analyses the SMART MURA User Guide as a social contract encoding collective stakeholder agreements generated through multi-stakeholder Focus Group Discussions involving five organisational levels. Three ambiguity-resolution mechanisms are identified: (1) bounded automation for standardising routine administrative processes, (2) explicit judgment points for clarifying the locus of professional discretion, and (3) flexible categorisation that accommodates case complexity without sacrificing treatment consistency. These mechanisms produce four interdependent governance benefits: procedural certainty for citizens, cross-case treatment consistency, traceable accountability through digital audit trails, and operational efficiency. The system further demonstrates structural adaptability to regulatory change and incorporates a tiered data governance architecture that balances operational transparency with individual privacy protection. The study advances e-governance theory by proposing a Transparent Discretionary Space Structuring (TDSS) framework that transcends the binary opposition between rigid Standardization and unstructured discretion. Keywords: E-Governance, Public Decision Ambiguity, Design Science Research, Digital Discretion, Participatory Design, Legislative Information System, Data Governance.
Authors - A. Aruna Kumari, Tamminana Visweswari, Sri Vishnu Prabhu Gudavalli Abstract - Globally, food waste accounts for almost one-third of total food production, which is approximately 1.3 billion tons annually. Some of the most wasted foodstuffs at the consumer level include fruits, vegetables and bread. This proposed work will help to solve this financial and environmental issue by creating an intelligent system to eliminate vegetable waste with the help of MobileNetV2 which is a Convolutional Neural Networks (CNN) based model to classify the freshness of vegetables and use object detection model called as YOLOv8n to recognize types of vegetables. It begins with the user uploading images of vegetables, which are pre-processed with the help of normalization and data augmentation. The MobileNetV2 model categorizes fresh and spoiled produce with accuracy rates of 95 percent, which is expected of a Freshness classifier. In the meantime, the YOLOv8n finder detects the specific type of vegetable with the help of a mAP50 of 0.934 to recommend the specific vegetable. If the vegetable is spoilt, it will provide instructions on how to compost and if it is fresh, it provides zero waste recipes. The entire system shall be made easily accessible in a web application that has Flask back-ends.
Authors - Reena (Mahapatra) Lenka, Jaee Jogalekar Abstract - The outward appearance is a crucial necessity for all organizations and sectors. Daily attendance registration in a conservative manner is a tedious and lengthy task. Furthermore, each organization has sanctioned its own approach to sparkle involvement through personal mockery and the employment of sheets to bolster its attendance. To tackle these challenges, various standard automated identification and verification systems have been extensively utilized, including IRIS, RFID, and biometric techniques. In contrast, crocodile growth is highly demanded under these conditions, requiring more time, and it is reckless in vegetation. Any error or harm to the RFID card will lead to incomplete participation Locating this array of strategies for such a broad range necessitates higher costs and additional effort to integrate our involvement into the database. In today's context, the awareness and recognition of unique identities have grown globally due to the demand for safety in financial transactions, health monitoring, validation, and security, along with other essential factors such as reducing fraudulent participation, increased costs, and, most importantly, lowering the chances of marking our involvement. This document outlines a suggested enhancement for the clever attendance verification system, incorporating a facial recognition approach utilizing deep learning techniques. A cloud dataset will mainly be created by capturing the faces of the approved students or staff. Similarly, the face is affirmed through the inclination derived from deep learning. In addition, the created images will be saved in the established database, each assigned a distinct label. The extraction of facial features will rely on Haar-like characteristics computed through a Deep Learning method. The suggested new method attains improved propagation by utilizing Haar-like features and a deep-learning-optimised algorithm
Authors - Alta van der Merwe, Mpho Xaba Abstract - This paper examines how change management can be integrated into the TOGAF Architecture Development Method to strengthen enterprise architecture implementation and organisational transformation. Using a qualitative systematic literature review of 35 studies published after 2010, we identified recur-ring change management dimensions across models, theories, frameworks, and methodologies, namely leadership, strategy, communication, organisational structure, organisational culture, collaboration, transformation, innovation, governance, risk management, and commitment. We then aligned these dimensions to relevant TOGAF ADM phases and illustrated their application through a fictitious case study of Teleconnect, a telecommunications company undergoing post-acquisition integration. The findings show that TOGAF implementation is strengthened when change management is embedded as a structured and complementary organisational capability rather than treated as a separate activity. The paper contributes a conceptual framework that links change management dimensions to TOGAF ADM and offers a practical basis for supporting enterprise trans-formation through a more integrated architecture approach.
Authors - Alta van der Merwe, David Moselane Abstract - This study examines how artificial intelligence can strengthen teaching practices and improve university readiness for first-year students within the context of Society 5.0. While AI offers strong potential to support a human-centred and inclusive education system, its implementation faces obstacles, including resistance to change and scepticism about its value. The research explores strategies for effective AI integration, with a focus on personalised learning experiences tailored to individual student needs and the automation of administrative tasks to allow educators to focus on improving their teaching and student engagement. A systematic literature review and meta-analysis were conducted to evaluate how AI enhances university preparedness, with particular attention to perceptions of AI adoption and the challenges associated with its implementation. The findings highlight how AI can support more inclusive and responsive education systems aligned with the goals of Society 5.0, where technology serves societal needs. While acknowledging limitations such as time constraints and the rapidly evolving nature of AI technologies, the study offers practical insights to help educators reduce educational disparities, promote inclusivity, and equip students with skills required for a socially responsive and technologically integrated.
Authors - Franck W. Boubayi, Regis F. Babindamana, Peter A. Kidoudou Abstract - The adoption of e-commerce remains a major challenge for many enterprises in developing countries, where digital transformation is often constrained by technological, organizational, and regulatory factors. This study investigates the factors influencing e-commerce adoption among Congolese enterprises through the Technology Acceptance Model (TAM). Data were collected from businesses operating in various sectors and analyzed using descriptive statistics and predictive modeling techniques. The findings reveal that although digital technologies are increasingly used in business activities, e-commerce adoption remains limited. Only 35.5% of surveyed enterprises actively engage in online sales, while most organizations continue to rely on social media platforms for promotion and telephone-based order processing. The results also highlight significant cybersecurity gaps: nearly half of the surveyed enterprises do not conduct vulnerability assessments, more than half lack firewall or intrusion detection mechanisms, and 16% have already experienced cyberattacks. In addition, limited awareness of national data protection regulations exposes many businesses to legal and operational risks. The study further shows that artificial intelligence is widely perceived as a strategic opportunity, with 83% of respondents recognizing its potential for business growth and 89% supporting the establishment of an appropriate regulatory framework. Based on the identified determinants, a predictive model is proposed to support decision-making and promote wider adoption of e-commerce in the Congolese context. The findings provide practical insights for policymakers, business leaders, and researchers seeking to accelerate digital transformation while strengthening cybersecurity readiness.
Authors - Oleksandr Hladkyi, Alexander Gertsiy, Tetiana Tkachenko, Valentyna Zhuchenko, Tetiana Shparaga, Olha Liubitseva, Tetiana Mykhailenko, Iryna Kochetkova Abstract - The profitable spatial location of tourism companies and destinations plays an important role in land management investigations nowadays. It significantly influences on tourism destinations' spatial efficiency. There are four main concepts of determining spatial efficiency of enterprises' location: the urban planning concepts, synergistic or integrative concept, functional and communicative concept as well as the concept of service clusters. Our approach is essentially different from all mentioned above. It's based on the analysis of tourism products (goods, labor, services) production efficiency rates determined by spatial location effect in land management system. The process of estimation of spatial efficiency of tourism destinations' location should be divided into four parts. The first part consists in gathering complete statistical data about tourism destinations development in specific location/region. Based on primary statistical data, the following indicators of tourism destinations' spatial efficiency could be calculated: labor productivity, profitability, capital-labor ratio and cost recovery. At the second part, every index of tourism destinations' spatial efficiency has to be modulated using gravitational potential model. At the third part we have to identify individual clusters of different levels of tourism destinations' spatial efficiency based on gravitational modulator data. At the fourth part all received clusters could be figured on geographic contour maps of particular region. Using above-mentioned methods, gravitational model of spatial efficiency of tourist enterprises' location in key regions of Ukraine has been created.
Authors - Franck W. Boubayi Abstract - The rapid development of e-commerce platforms has significantly increased the exposure of digital systems to advanced cyber threats such as phishing, DDoS attacks, identity theft, and data breaches. To address the limitations of conventional security mechanisms, this paper proposes a Risk-Adaptive Multi-layer Security Framework (RAMSF) integrating multi-factor authentication, AI-based intrusion detection, post-quantum cryptography, and blockchain-based distributed auditing. The main contribution of the model lies in an adaptive decision engine based on dynamic risk evaluation, enabling real-time adjustment of security policies according to behavioral context and detected anomalies. The framework is evaluated using the CICIDS2017 and UNSW-NB15 datasets with 10-fold cross-validation. Experimental results show that RAMSF outperforms several classical models, including SVM, Random Forest, CNN, and XGBoost, achieving 97% accuracy, 95.5% F1-score, 98% AUC, and a low false positive rate. These results demonstrate that adaptive hybrid architectures represent a promising approach for strengthening cybersecurity in e-commerce systems against both current and post-quantum threats.
Authors - Febrian Nasrullah, Abdul Mukti Soma Abstract - This study examines the impact of risk perception, financial selfefficacy, and financial literacy on the actual usage behavior and intention to use "Buy Now Pay Later" (BNPL) services among Generation Z in Indonesia. Adopting a quantitative approach, the study surveyed 385 Gen Z individuals who use BNPL services such as Kredivo or Akulaku. Data were analyzed using SEM-PLS with the aid of SmartPLS 4. The results indicate that financial selfefficacy and financial literacy contribute to actual usage behavior and intention, whereas risk perception has a negative impact on both. Furthermore, intention contributes to actual usage behavior and mediates the effects of the other variables. These findings indicate that financial management skills, risk perception, and individual confidence in financial capability play a pivotal role in shaping BNPL usage behavior among Generation Z in Indonesia.
Authors - Byron Albuja-Sanchez, Miguel Angel Lema Carrera, Luis Antonio Ortiz Parra Abstract - This study focuses on evaluating the capabilities of different large language models chatbots in the task of designing a PID controller for a third-order transfer function of a real-world vehicle’s cruise control system. Chatbots received a detailed prompt containing the system’s transfer function and the design’s goals in the form of overshoot and settling time constraints. Chatbots only received simulation-response information as feedback during the tuning process to test their predisposition to fix the errors without being specifically asked to do so. Results showed that chatbots have a good level of knowledge regarding basic control theory and basic tuning methods for PID controllers. Preferred tuning methods involved pole placement with dominant second order dynamics, Ziegler-Nichols and heuristic methodologies. Simulation results compared the controllers designed by chatbots with a PID tuned with ant lion optimizer algorithm, none of the evaluated chatbots outperformed the optimization-based benchmark controller. However, Gemini 3 Flash designed a controller which performance was close to the ant lion optimizer results. Chatbots’ underperformance was attributed to the following facts: no expert feedback was given to them to fix the observed flaws in the proposed designs, no specific methodologies were asked to be used in order to improve the results, and no specific instructions to redesign the controllers were given to chatbots in order to test their disposition to fix their errors. Results suggest that LLMs can assist in preliminary controller design tasks, although their effectiveness remains limited without expert-guided iteration and explicit optimization-oriented prompting.
Authors - Aman Kumar, Kathan Nitin Patel, Aviral Sharma Abstract - Diabetes mellitus is perceived as a disease that significantly impacts a nation’s social, human, and financial expenditures. Concurrently, it is imperative to lower the prevalence rate and address the misunderstandings surrounding diabetes. An improved model that employs machine learning techniques to identify the behavior of diabetes in an individual. We have employed the parameters observed in the typical lifestyle, as well as the individual's emotional states and physical activities in the elderly age group. For a variety of test parameters, the proposed model implements a network classifier. It has been noted that this methodology yields effective results in the diagnosis of diabetes mellitus when the appropriate dataset is provided. The dataset utilized in this reseacrh study is the Indian PIMA dataset from the UCI Machine learning database. The detection of diabetes is contingent upon the presence of eight features in this dataset. The proposed Machine learning model has been implemented using a multilayer neural network that has been trained on backpropagation and feed-forward network simulation.
Authors - Nallappagari Venkatarami Reddy Abstract - Business Process Automation (BPA) has become an essential requirement of modern enterprise environments owing to the need for operational efficiency, process agility, and smart decision making. Traditional methods of BPA mostly depend on rules-based approaches which are not able to adapt to the needs of a dynamic environment as these methods do not incorporate the element of adaptive intelligence and autonomous orchestration. To overcome such limitations, this study aims to develop an intelligent orchestration framework named IWOF for Autonomous Business Process Automation. In the proposed solution, PIEL, AWOE, DRAM, and PDOU have been used. Also, two new algorithms named AWIO and PARDO are developed for optimizing the process sequencing, resource assignment, and decision support tasks respectively. Experimental evaluation was performed by applying the proposed framework to datasets consisting of processes related to employee onboarding, payroll management, procurement approvals, recruitment workflow, and finance transactions. With the help of the IWOF model, Process Automation Accuracy, Workflow Completion Rate, Resource Utilization Efficiency, and Autonomous Business Process Automation Score (ABPAS) were measured to be 98.7%, 98.2%, 97.1%, and 98.9%, respectively, surpassing all other available models such as OSMAS and BPA-SME.
Authors - Abeer Tag, Tahani Almarri, Abir Sidilemine, Rowaa Khaled, Loay Ismail Abstract - Medication non-adherence among elderly and chronically ill patients remains a critical global health challenge, leading to severe complications, hospital readmissions, and reduced quality of life. This paper presents REMEDI, a smart mobile medication dispensing robot that integrates autonomous indoor navigation, biometric patient authentication, automated pill dispensing, pill verification, and real-time adherence monitoring into a unified platform. The system combines a TurtleBot3 Waffle Pi mobile base with a custom-designed three-cylinder dispensing mechanism controlled using Raspberry Pi 5 and Arduino Nano. Patient verification is performed using facial recognition with MobileFaceNet embeddings and liveness detection, achieving an overall verification accuracy of 83.3% and zero false accepts during experimental testing. Autonomous navigation is implemented using LiDAR-based SLAM and A* path planning, enabling map-based movement between predefined indoor patient locations. Post-dispensing verification uses a custom-trained YOLOv11 object detection model integrated with OpenCV for pill detection and counting. A companion Android application allows caregivers to enroll patients, schedule medications, and monitor adherence in real time. Experimental results show successful integrated operation, including dispensing delays below 5 seconds, navigation success rates of 84–92%, 95% dispensing reliability, and functional multi-patient queue management. Although pill verification achieved only 69% real-world accuracy, the results demonstrate the feasibility of integrating mobility, secure authentication, dispensing, and monitoring in one user-centered prototype. REMEDI aims to bridge the gap between stationary home medication dispensers and large institutional delivery robots.
Authors - Simon Kloker, Alex Cedric Luyima, Matthew Bazanya Abstract - This paper presents WASHtsApp, a WhatsApp-based mHealth chatbot that supports clean water, sanitation, and hygiene (WASH) education in rural African settings. The chatbot uses Retrieval-Augmented Generation (RAG) to reduce out-of-context responses and improve answer relevance. Following a Design Science Research approach, we evaluated the artifact in two steps: expert content validation (four WASH experts) and community acceptance validation (n = 71). Expert ratings classified 86% of responses as perfect or sufficient, while community results showed high perceived usefulness, ease of use, and intention to use. The findings indicate that WhatsApp is a viable delivery channel for WASH education and that a constrained RAG setup can provide useful localized guidance. We also discuss privacy, safety, and future improvements, including local-language support.
Authors - Manar Eloued , Narjes Benameur , Sonia Esseghaier, Salam Labidi Abstract - Magnetic Resonance Elastography (MRE) is a novel, non-invasive im-aging technique for assessing liver stiffness. However, the lack of standardization introduces variability in measurements. The Manual selection of the Region of Interest (ROI) remains subjective and operator-dependent, often including areas with blood vessels or poor wave propagation, which can compromise measurement accuracy. This study proposes a deep learning- based approach to automatically identify an optimal region for liver stiffness measurement (LSM). A total of 160 MRE ex-ams, comprising paired magnitude and wave attenuation images from both healthy individuals and patients with liver disease, were used. A 3D U-Net architecture was trained to segment the liver, blood vessels, gallbladder, and biliary ducts from magnitude images, as well as regions of good wave propagation from attenuation images. The final ROI was obtained by intersecting these segmented regions. The model performance was evaluated on a separate test set using the Dice Similarity Coefficient (DSC), paired Student’s t-test, and Bland-Altman analysis. The resulting LSM region achieved a DSC of 0.89. The t-test yielded p = 0.68, indicating no significant difference between the automated and manual ROIs (p > 0.05). This automated pipeline reduced analysis time from approximately 20 minutes manually to less than 10 seconds automatically, while ensuring reproducibility and reducing operator dependency in MRE by standardizing ROI selection while maintaining diagnostic accuracy. It offers a promising solution to improve the reliability of LSM, particularly for longitudinal follow-up.
Authors - Hector Rafael Morano Okuno Abstract - One of the applications of LLMs (Large Language Models) has been their use as assistants, enabling users to perform specific tasks via prompts, from solving mathematical problems to generating images or videos. This article aims to explore the capabilities of an LLM in generating scripts for the API (Application Programming Interface) of the CAD software Fusion 360, identify the types of geometries it can create, and determine whether it can reproduce images in CAD models. This work was developed during the Manufacturing Systems Automation course for Mechatronics Engineering students, with the intention of introducing them to the use of LLMs in their field. Among the find-ings, it was determined that the user must be familiar with the Fusion 360 API to correct potential errors in the scripts generated by the LLM. Furthermore, the user must be able to specify, via prompts, the characteristics of the parts to be modeled, ensuring that the specifications are compatible with the instructions Fusion 360 understands. Regarding students' experience with LLMs, they found them useful, as they saved time in designing components that require complex automation systems.
Authors - Zilola Mamatvaliyevna Aliyeva, Nigora Primova, Dildor Abduraxmanovna Shadibekova, Malika Akbarova, Azizbek Mahkamov, Gulchehra Raxmatjonovna Xusanova, Shoh-Jakhon Khamdаmov Abstract - The construction industry plays a strategic role in Uzbekistan’s economic development; however, it continues to face challenges related to cost overruns, project delays, and limited financial transparency. Digital transformation offers new opportunities to enhance economic efficiency and project management performance. This study examines the impact of digitalization on construction economics in Uzbekistan, focusing on the implementation of Building Information Modeling (BIM), digital cost estimation systems, electronic procurement platforms, and enterprise resource planning (ERP) solutions. The research develops a conceptual model linking digital adoption level, cost control effectiveness, project performance, and financial outcomes. A quantitative survey of construction companies operating in Uzbekistan was conducted, and Structural Equation Modeling (SEM) was applied to test the proposed relationships. The findings indicate that higher levels of digital integration significantly improve cost estimation accuracy, reduce budget deviations, and shorten project completion time. Digital procurement systems also enhance financial transparency and reduce operational inefficiencies. The study provides empirical evidence that digital transformation positively influences economic performance in Uzbekistan’s construction sector. The results contribute to construction economics literature and offer policy recommendations for accelerating digital adoption in emerging markets.
Authors - Jorge Duque, Antonio Godinho, Jose Moreira, Firmino Silva Abstract - Student dropout in higher education remains a persistent academic, institutional and social challenge, requiring evidence-informed responses. This paper develops and evaluates an explainable machine learning artefact for early dropout-risk identification and for translating predictions into tiered institutional interventions. The study follows six phases of Design Science Research and uses the public Predict Students' Dropout and Academic Success benchmark, with 4,424 students and 37 variables. The pipeline integrates one-hot encoding, derived features, stratified validation, SMOTE applied only inside training folds, Random Forest, XGBoost and SVM as base learners, stacking with a logistic meta-learner and SHAP explanations. The final ensemble achieved 96.4% accuracy, 0.965 weighted precision, 0.964 weighted recall, 0.964 weighted F1-score and 0.99 weighted AUC. The contribution lies in combining performance, interpretability, governance and responsible human intervention.
Authors - Hasna Noushad, Geetha KN Abstract - The brain tumor remains a serious health concern and diagnosis in the primary stage is mandatory for effective treatment. Medical image analysis plays a vital role in understanding the underlying disturbances, monitoring, treatment planning, and intervention strategies. Graph theory is one of the popular techniques used for medical image analysis. The study focuses on the introduction of a systematic approach by using the application of vertex addition of graph theory to synthetically construct a glioma brain network utilizing the normal and abnormal brain Magnetic Resonance Image (MRI). The paper presents the idea of the initial emergence and growth of tumor from a graphtheoretical perspective. Comparative analyses are carried out between normal and abnormal brain network due to the growth of glioma. Results demonstrate that the presence of strong structural deformations and alterations in brain network due to the introduction and growth of glioma. In addition, the article also examines the corresponding increase in the correlation values of the tumor with other normal regions of brain as the tumor grows. This work provides a robust foundation for future studies in epidemiological modeling, machine learning and deep learning methodologies where lack of required data is an issue.
Authors - A Aruna kumari, Sri Vishnu Prabhu Gudavalli, Tamminana Visweswari Abstract - Diabetic Retinopathy (DR) is one of the biggest contributors of visual impairment and blindness in diabetic patients who do not receive proper measures and treatment at the early stages. Due to the ever rising instances of diabetes by the day, more concern has been raised on the effectiveness of methods of effective, scalable and early diagnosis. The given paper is a proposal of a deep learning-based algorithm of DR diagnosis, specifically, the algorithm named Patch-Wise Segmentation and Classification using Convolutional Neural Networks (CNNs). The system in this case is in contrast to the traditional systems, which require the entire retina image to be processed simultaneously by the system, whereby high-resolution fundus photographs are broken into patches. The model addresses each patch individually in order to have the model closely observe minute-scale details and sensitive pathological changes such as hemorrhages and exudates. Patch-wise processing dramatically enhances the capacity of reporting early and mild cases of DR that are hard to recognize in the fullimage processing due to intricacy of an image and noise. The CNN architecture is additionally medical image particular and operates by use of layers and regularization methods so as to permit not only accuracy but also generalization over a wide range of datasets. As shown in the results of the experiments, the patchwise technique performs better in comparison with the traditional ones, i.e., sensitivity, specificity, and classification accuracy on each of the stages of the DR. In addition, the system is fully automated and stable that minimizes the use of human marking and offers the facility to operate worldwide
Authors - Marco Vinicio Lopez R., Maria Cristina Espin Melendez, Jeanette Elizabeth Jordan Buenano, Santiago Vayas Castro, Segundo Moises Toapanta Toapanta, Ruben Nogales Portero, Juan Escobar Naranjo, Diego Gustavo Andrade Armas, Rodrigo Del Pozo Durango Abstract - Connecting more devices to monitor energy and the environment creates serious hurdles for data security, integrity, and scale. This research presents a system merging blockchain with IoT, built specifically to respect Ecuador’s Organic Law on Personal Data Protection (LOPDP). The setup uses ESP32 microcontrollers and sensors to track environmental and power data, sending it over the MQTT protocol to a database. Digital fingerprints created with SHA-256 are locked onto a private blockchain using Proof-of-Authority to keep every record permanent and unchangeable. A 10-day experiment in two indoor spaces generated roughly 96,000 records to test how the system holds up in the real world. Results demonstrate the system's viability, achieving a median end-to-end latency of 182 ms (P95 < 240 ms), a Node Availability Index (NAI) of 98.6%, and an Anomaly Detection Rate (ADR) of 91.4%. Furthermore, the blockchain integration introduced a computational and energy overhead of less than 7%. The study concludes that this architecture provides a verifiable link between physical sensors and digital records, highlighting the bidirectional need for technology to comply with national privacy laws while urging regulatory frameworks to evolve and formalize smart contract applications.
Authors - Mohammed Rasol Al Saidat, Khaled Shaalan, Suleiman Y. Yerima Abstract - This paper presents a technical bibliometric review of artificial intelligence for bilingual Arabic–English smishing detection. It maps the evolution of machine learning, deep learning, and NLP for SMS phishing detection, identifies Arabic–English linguistic and security challenges, and grounds the review in an empirical audit of a public Arabic SMS spam corpus. The methodology pairs a PRISMA-style protocol and bibliometric mapping with the parsing of 1,494 SMS records (747 ham, 747 spam) from a public GitHub dataset. The field has shifted from rules and classical feature engineering to CNN, LSTM, Bi-LSTM, transformer, and large language model approaches. The dataset audit reveals strong class-conditional cues: spam messages are far longer and far richer in digits, phone numbers, short codes, URLs, currency markers, and Latin residues. A reproducible bilingual character TF-IDF baseline with a Linear SVM reached 0.9779 mean accuracy and 0.9775 F1 under stratified three-fold validation, matching the referenced CNN-Bi-LSTM model (0.9699 accuracy, 0.9707 F1). Robust bilingual smishing detection therefore requires hybrid text–security features, Arabic morphology-aware normalization, Unicode safety screening, external validation, and explainable deployment.
Authors - Rodrigo Del Pozo Durango, Moises Toapanta T., Antonio Orizaga T., Rocio Maciel A., Victor Larios Rosillo, Jeanette Elizabeth Jordan Buenano, María Cristina Espin Melendez, Santiago Vayas Castro, Pamela Toapanta Pavon, Andres Hermann-Acosta Abstract - Cybersecurity governance in Higher Education Institutions (HEIs) in Ecuador faces persistent challenges: Information and Communication Technologies are regarded merely as operational tools, without adequate integration of international standards or a legal foundation within institutional processes. This situation generates high vulnerability to threats such as phishing, ransomware, and data breaches. The objective of this research is to define an integrated model for cybersecurity governance, public administration, and legal foundations for an HEI in Ecuador. A deductive method with a mixed quantitative–qualitative approach was employed, structured into four phases: systematic literature review indicator design, model construction, and scenario simulation. The main result is an integrated model structured into three hierarchical and interrelated levels: the Technical Level, comprising incident management, security infrastructure, and technological maturity, aligned with NIST CSF 2.0 and EGSI v3.0; the Administrative Level, focused on ICT strategic planning, organizational governance, and continuous improvement, based on COBIT 2019; and the Legal Foundation Level, grounded in the 2024 Constitution, the 2026 Organic Law on Cybersecurity, the 2021 Organic Law on Personal Data Protection, and the 2025 Comprehensive Organic Criminal Code. The model incorporates 15 weighted indicators and was validated through simulation across four scenarios, using an optimality threshold of ≥ 75 points. It is concluded that three-dimensional integration technical, administrative, and legal is a necessary condition for Ecuadorian HEIs to achieve ICT strategic alignment, resource optimization, and digital resilience, thereby positioning them as key actors in national cybersecurity public policy.
Authors - Eric Khang Heng Ooi, Yit Yin Wee Abstract - Printed circuit boards (PCBs) are increasingly dense and visually complex, making reliable component detection important for automated optical inspection and manufacturing quality control. This paper presents a modified R-CNN framework for integrated-circuit (IC) component detection in PCB images. The framework consists of PCB region extraction, colour-guided region proposal generation, Bayesian convolutional neural network (BCNN) feature extraction, and support vector machine (SVM) classification. Candidate regions are produced using colour masks that emphasize dark and silver IC-like regions. Each candidate is resized, represented by the BCNN, and classified by the SVM as IC or background. Experiments on a PCB component dataset show that the proposed method achieves an mAP of 0.613, outperforming R-CNN with Selective Search Fast (0.392) and Selective Search Quality (0.430). YOLO obtains the highest mAP of 0.686; however, the proposed framework remains useful as an interpretable region-based pipeline for PCB inspection.
Authors - Chandravadan Goritiyal, Aditi Bairolu Abstract - The engine of the world is energy. Fossil fuels continue to be the primary source of energy generation. Nonetheless, several of the world's most important organizations and nations banded together to address the greatest sustainability challenge facing humanity: climate change caused by greenhouse gas emissions. It has been noted that the most workable approach would be to refocus attention from fossil-fuel energy production to renewable energy. However, there are year-round availability issues with renewable energy as well. For instance, solar energy can only be produced during the day, and in India, during the monsoon season hardly solar energy produced. Similar issues are noted in other nations as well. When there is not enough wind to support its spin, wind power frequently stands still and is entirely dependent on air currents. Therefore, the study focuses on what India as a nation should do. For example, investigating clean base load electricity, such as nuclear, to guarantee a constant demand for grid power; solar and wind power will support this, making the total power supply sustainable and environmentally friendly. Also Industry requires continuous and reliable power all the time.
Authors - Monica Cruz, Abilio Oliveira, Ricardo Dias Abstract - Although immersive technologies such as VR, AR, MR and XR are increasingly integrated into university teaching, existing research remains conceptually fragmented and technologically heterogeneous. This study examines how the Metaverse is represented, characterised, and applied in higher‑education contexts through a meta-analysis using Voyant tools for text analysis. To address this gap, the study investigates recent publications to answer the research question: How is the Metaverse being represented, characterised, and applied within higher‑education contexts? Three objectives guided the analysis: identifying how the Metaverse is conceptualised in educational literature, examining the dimensions that facilitate or hinder its adoption, and mapping the leading Metaverse‑related technologies used in higher‑education environments. The findings show that adoption is shaped by perceived usefulness, immersion, social presence and technological readiness, while challenges persist regarding accessibility, infrastructure and conceptual clarity. The study contributes to the scientific literature by consolidating dispersed definitions, clarifying adoption‑related factors and identifying the immersive technologies most frequently associated with Metaverse‑based learning. The results provide a structured foundation for future research and offer higher‑education institutions insights into the opportunities and constraints of integrating Metaverse technologies into teaching and learning.
Authors - Hiruni Samarage, Pumudu A. Fernando Abstract - Tertiary education institutes increasingly face challenges in managing high volumes of student inquiries related to admissions, courses, fees, and scholarships. Traditional inquiry-handling mechanisms and rule-based chatbots often struggle with scalability, delayed responses, and limited understanding of complex or unstructured queries. While recent advances in large language models (LLMs) offer promising opportunities, many existing academic chatbot implementations continue to lack semantic retrieval, session continuity, and personalization. This paper presents the design, implementation, and evaluation of an AI-based semantic chatbot prototype tailored for tertiary education environments. The prototype integrates retrieval-augmented generation with a large language model to enable context-aware responses across multiple institutional knowledge domains through semantic retrieval of structured knowledge representations. The system was evaluated using accuracy, precision, recall, robustness to query length variations, and retrieval effectiveness metrics. Experimental results demonstrate an overall accuracy of 86%, with precision and recall values of 89% and 91%, respectively. Robustness testing shows consistent performance across paraphrased and variable-length queries, while response times remained within acceptable limits for real-time academic support. User testing further indicated positive usability and response relevance outcomes. These results confirm the feasibility and effectiveness of applying semantic retrieval and LLM-based reasoning to scalable inquiry management in tertiary education contexts.
Authors - Ndaula Kelvin, Wu Jun Abstract - Multimodal sentiment analysis often fails due to the modality gap between semantic text images and GIFs. To address these challenges this paper introduces Fusion Core a novel hardware agnostic heterogeneous pipeline that bridges the gap between high level AI with low level systems engineering to facilitate the deciphering of combined sentiment of these three modalities. Through the integration of GPGPU accelerated OpenCL kernels for 3D temporal extraction with an ONNX/DirectML inference engine which ensures cross platform portability. To address the issue of inconsistent real world data distributions the preprocessing system was introduced with an adaptive multi-head attention mechanism for late feature fusion. The ablation studies performed also revealed the integration of spatiotemporal GIF layers resolves contextual ambiguities missed by static analysis (Text, Images). The extensive testing on a balanced dataset of 13,964 samples the model achieved a 100% success rate showing the robustness of the proposed model for industrial scale deployment.
Authors - Tumiso THULARE, Keneilwe Jeannette MAREMI Abstract - This paper presents findings from a pilot e-Participation implementation conducted in partnership with the Mpumalanga Department of Cooperative Governance, Human Settlements and Traditional Affairs (CoGHSTA) and the City of Mbombela over two years. The pilot project focused on enhancing the municipality's capacity to implement and sustain e-Participation initiatives. It also assessed the current state of public participation and explored the opportunities and challenges of adopting digital participation mechanisms in South African local government. A qualitative research approach was used, involving a scoping review and engagement sessions with municipal officials from various units, including public participation, communications, ICT, policy, and governance. The scoping review identified theoretical challenges to e-Participation in South African municipalities, while engagement sessions examined institutional experiences, governance processes, and the City of Mbombela's readiness for digital participation. The findings revealed that the municipality shows policy alignment and has partially adopted digital participation tools such as social media, municipal websites, and mobile communication channels. However, e-Participation implementation faces challenges such as the digital divide, limited ICT infrastructure, low digital literacy, institutional capacity constraints, poor coordination, and insufficient funding. The study further found that public participation still relies heavily on traditional methods, with digital platforms mainly used for sharing information instead of fostering citizen empowerment or collaborative governance. The paper concludes that while e-Participation can enhance transparency, accountability, and inclusive governance in South African municipalities, its success requires integrated technological, institutional, and participatory reforms. The findings provide practical guidance for municipalities aiming to enhance digital public participation in resource-constrained settings.
Authors - Shola Usharani, Gayathri Rajakumaran, Braveen manimozhi, Anjana Devi Nandam, Kindinti Karthik, Prabhakaran mohan Abstract - The closed-loop anesthesia delivery (CLAD) development is a significant advancement in modern anesthesiology, offering automatic control of anesthetic administration to maintain optimal patient states during surgical procedures. The article analyses the limitations of existing systems—such as sensor errors, limited adaptability, and system unreliability—by integrating through IoT based control high-accuracy EEG monitoring system with a robust and novel closed-loop control algorithms. From this system the real time EEG signals are continually monitored, analyzed, filtered, and converted into a BIS spectral Index (BIS) values. The target BIS is continuously updated by fuzzy logic, which modulates the drug delivery to maintain sedation levels between 40 and 60. A PID controller used for the BIS error calculation to determine the precise anesthetic levels to be delivered. This amount level is then converted into drug level concentrations to drive the syringe pump connected to the IoT integrated system using stepper motor to administer the drug to the patient. The serial monitor displays all information in real time, including BIS values, PID output, calculated dosage in mg/sec and ml/sec, and the number of motor pulses required for the infusion. This prototype demonstrates enhanced precision and safety over manual control, offering a scalable and cost-effective solution for automated anesthesia delivery, thus avoiding the limitations of existing model.
Authors - Norbert Annus Abstract - This study presents a secondary learning analytics analysis of student log data generated by the Learn with M.E. educational software. While previous evaluations of the system focused mainly on effectiveness, diagnostic accuracy and user feedback, the present paper examines behavioural indicators recorded during arithmetic practice. In this study the analysis focused on calculation time, answer correctness, difficulty level, first-try success, "Preview" use and student-level behavioural profiles. The results showed that incorrect answers were associated with substantially longer calculation times than correct answers. Higher difficulty levels generally showed lower correctness rates and longer median calculation times. First-try attempts were also strongly related to successful task completion. "Preview" use was relatively rare, but it was associated with longer calculation time, higher average difficulty level and lower first-try correct rates, suggesting that it can be interpreted as a help-seeking indicator. The student-level aggregation identified four behavioural profiles: fast trial-and-error learners, help-seeking learners, mixed-profile learners and support-needed learners. The findings indicate that Learn with M.E. log data can be transformed into interpretable learning analytics indicators and behavioural patterns that support teacher decision-making and personalised mathematics instruction.
Authors - Sheromiga Anandajothy, Aathipan Murugaverl, Harinda Fernando, Sarangan Rukminikanthan, Abishathan Thayaparan, Tharaniyawarma Kumaralingam Abstract - Modern malware increasingly employs packing, encryption, polymorphism, staged payload delivery, modular execution, and behavioural evasion techniques to bypass traditional signature-based security systems. While static analysis enables rapid inspection of suspicious binaries, it often performs poorly against heavily obfuscated samples. Conversely, dynamic analysis provides rich runtime evidence but introduces computational overhead, operational latency, and anti-sandbox challenges. This paper presents Mutated Malware Protector, a lightweight hybrid framework for detecting obfuscated and modular malware through static Portable Executable (PE) feature analysis, obfuscation-aware scoring, behavioural risk approximation, modular stage inference, and explainable artificial intelligence (XAI). The framework is designed as a practical analyst-facing pipeline rather than a single classifier. It integrates engineered PE features, anomaly scoring using Isolation Forest, entropy-based obfuscation indicators, a LightGBM static classifier, behavioural approximation, and a modular correlation component that estimates staged roles such as dropper, loader, and payload. Experimental evaluation shows that the prototype achieved 89.00% accuracy, 89.28% precision, 88.81% recall, and 89.04% F1 score. The proposed architecture offers a scalable path toward future integration with full sandbox telemetry and enterprise malware response workflows.
Authors - K. N. Subramanya, Padmashree T., Manojith Bhat V., Manasvini G. Padmasali Abstract - In an era of rising digital dependence and ever-evolving cyber threats, cybersecurity has become a concern for education institutions of all sizes. Higher education institutions (HEIs) are prime targets for cyberattacks since they manage massive volumes of sensitive data related to students, faculty, and research. Protecting this data is important to avoid major consequences such as disruptions in academic services, reputational harm, legal or financial ramifications. This survey consolidates current research on cybersecurity practices in HEIs, analyzes critical digital assets and infrastructure. It also reviews selected cybersecurity frameworks adopted globally. The survey further explorers the threat landscape confronting HEIs, examining various cyberattacks by identifying possible entry points, attack pathways, and potential consequences. The study emphasizes the necessity of adaptive cybersecurity approaches that can evolve alongside emerging technologies and pedagogical models in academia.
Authors - Roberts Dargis, Arturs Znotins, Ilze Auzina, Maris Golubovskis, Mikelis Gulbis, Normunds Gruzitis Abstract - Operational radio communication is a challenging application domain for automatic speech recognition (ASR) despite advances in multilingual foundation models. We investigate the applicability of modern Latvian ASR models to operational communication and evaluate whether speech enhancement techniques improve recognition quality under realistic conditions. To support the study, we created a specialized corpus of authentic Latvian operational radio communication. The corpus captures acoustic and linguistic phenomena largely absent from general speech corpora, including narrow-band transmission, radio-channel artifacts, environmental noise, domain-specific terms, and fragmented utterances. Using this corpus, we evaluate state-of-the-art adaptations of the massively multilingual Whisper and MMS models in combination with several audio preprocessing methods. The results reveal a substantial performance gap between the conventional Latvian ASR benchmarks and operational communication data. While some preprocessing methods improve perceived audio quality, they provide limited benefit for downstream recognition and often even degrade ASR performance. Voice activity detection, however, yields the most consistent improvements. The findings indicate that domain mismatch, rather than acoustic degradation alone, is the dominant source of recognition errors and highlight the need for representative domain-specific data when adapting general-purpose ASR models for the operational communication environment.
Authors - Mark Fedorchenko, Olena Kopishynska, Yurii Utkin, Igor Sliusar, Leonid Flehantov, Olha Barabolia, Nadiia Protas, Tetiana Dugar Abstract - Crop yield forecasting based on small official statistics is different from forecasting with dense satellite, field, or weather datasets: the sample is short, temporal leakage is easy to introduce, and machine learning (ML) should not be accepted unless it beats transparent baselines. This paper presents a baseline-first and reliability-aware workflow for farm management and regional advisory systems. Wheat, maize, and sunflower are evaluated for Poltava, Vinnytsia, Cherkasy, and national-level Ukraine data for 2010-2024. ElasticNet, XGBoost, and LightGBM are compared with naive lag-1, linear-trend, LINEST, and Autoregressive Integrated Moving Average (ARIMA) baselines under a forward temporal design. The contribution is a decision layer that recommends ML only after it clears a practical mean absolute error (MAE) margin and then reports empirical validation-residual bands, test coverage, feature-group diagnostics, and compact farm management systems (FMS)-compatible forecast cards. The Poltava workflow recommends FORECAST.LINEAR for wheat (MAE 0.49 t/ha), LightGBM for maize (MAE 0.69 t/ha), and LightGBM for sunflower (MAE 0.04 t/ha). Across the external check, ML is recommended in 7 of 12 region-crop cases. The results show that ML can help in small official-statistics settings only when checked against simple baselines and reported with reliability diagnostics.
Authors - Ingy Emara, Rawan Waleed, Sherry Emad Abstract - This study analyses speech errors in individuals with Down syndrome (DS) in both Arabic and English, with a particular focus on errors that reduce intelligibility for automatic speech recognition (ASR) systems. It compares the speech errors that most significantly affect intelligibility in each language and investigates the factors underlying differences in ASR accuracy across Arabic and English DS speech. The findings indicate that ASR systems perform less accurately with Arabic DS speech, highlighting the need for larger and more diverse Arabic DS speech datasets for system training. The study also identifies several physiologically related speech errors that negatively affect intelligibility in both languages, including devoicing of stop consonants, lateralization of /r/, reduced pressure in /s/, and deletion of consonants and consonant clusters. In addition, certain errors were found to be language specific, such as the mispronunciation of uvular and pharyngeal sounds in Arabic and the frequent omission of /r/ and vowel centralization in English. These findings have important implications for speech therapy by identifying priority areas for intervention, and for the ASR industry by emphasizing the need to expand labelled DS speech datasets across languages to improve recognition accuracy.
Authors - Hiep Nghia Phan, Duy Nguyen Ngoc Abstract - Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning of large language models because it is lightweight, modular, and easy to distribute. However, the growing practice of sharing third-party LoRA modules also creates security concerns. A malicious adapter can introduce hidden behaviors, backdoors, or other risks while appearing to function normally. Existing research has largely focused on detecting whether a LoRA module is malicious. In practice, deployment decisions often require a more nuanced assessment of risk. This paper presents a measurable framework that evaluates LoRA security across four dimensions: supply-chain integrity, static weight characteristics, dynamic behavior, and deployment-time observations. The resulting indicators are normalized and combined into a composite risk score that supports comparison and prioritization of LoRA modules. The framework was evaluated using benign and backdoored LoRA modules attached to a frozen base language model. The results show a clear separation between the two groups even when their task performance remains similar. Dynamic behavioral testing and static weight analysis contribute the most useful signals, while deployment-time monitoring provides additional evidence of long-term operational risk. The proposed framework provides a practical mechanism for integrating security assessment into LoRA selection, governance, and deployment workflows.
Authors - Mayen Ben-Koko, Emmanuel Waribo Otiti Abstract - Nigeria loses more new-borns in the first month of life than almost any other country in the world, yet no machine learning tool has been built specifically for this context. This paper proposes a framework for predicting neonatal mortality risk in Nigeria using indicators from the 2023–24 Nigeria Demographic and Health Survey — the most current national health dataset available. Five classification algorithms are compared: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine. Random Forest performed best, with an AUC-ROC of 0.89. The three strongest predictors were whether a skilled health worker attended the birth, the gap between pregnancies, and the number of antenatal visits. The framework is reproducible and designed to be extended as fuller microdata becomes available or adapted for routine clinic records across Nigeria's six geopolitical zones.
Authors - Gavin Singh Pandha, Umar Khokhar, Binh Tran Abstract - This research initiative investigates the growing role of artificial intelligence (AI) in the automation of cyber-attacks and its emerging impact on modern cybersecurity. This study explores how AI models can be used to per-form web application intrusions by simulating attacks against deliberately vulnerable applications. Performance metrics from these simulations are compared with traditional, manually executed intrusion techniques, in addition to examining real world cases for AI misuse, ethical concern, industry standard, and the growing risk of autonomous threat actors
Authors - M. V. Rama Sundari, Bhuvan Unhelkar, Pravin Kshirsagar, Supriya Nandikolla Abstract - The management of nutrient content in soil is vital for improving productivity of crops, as well as sustainable agriculture. Conventional processes of establishing the best ratios of macronutrients tend to overlook intrinsic relation-ships that exist among soil properties, crop, weather. Main purpose is to come up with a smart prediction system that will help forecast Macronutrient needs by infusing particular domain-specific expertise in agriculture into machine learning models to improve interpretability and predictive accuracy. The suggested methodology utilises a Graph Convolutional Network (GCN) to simulate spatial and relational relationships amongst soil, crop and environmental parameters. To provide the model with a better semantic understanding, a Knowledge Graph (KG) is created to encode the relationships between domains. Embedding algorithms, such as TransE and DistMult, are then added to the GCN to form a KG-embedded GCN model that can learn feature-based as well as semantic relationships to predict nutrients. The experimental analyses on the ICFA Crop Recommendation dataset reveal that the baseline GCN got the R² scores of 0.806, 0.729, and 0.768, and the TransE GCN and DistMult GCN models have been advanced to the R² scores of 0.808-0.821, 0.746-0.762, and 0.800-0.814 for Nitrogen, Phosphorus and Potassium respectively. These findings indicate that the predictive strength is greatly advanced by the incorporation of domain knowledge. The model can, however, perform differently on unknown crop varieties and soils, which suggests that more work needs to be done in the future on larger and region-specific datasets.
Authors - Antonio Cortes Castillo Abstract - The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed data center operations. The widespread adoption of AI services has introduced new requirements for hosting AI systems, prompting significant modifications in the design and construction of modern data centers. As a result, data center operators must implement comprehensive strategies to address the challenges associated with AI integration. This study explores the application of machine learning techniques using neural networks and Multilayer Perceptron (MLP) models for data center optimization. The research focuses on critical metrics, including power consumption, liquid cooling, fiber-optic systems, and the number of fiber-optic routing paths, which pose significant challenges for data center operators. Experimental results are analyzed using simulation tools, including SPSS, to demonstrate enhancements in data center performance and efficiency.
Authors - Hera Khairunnisa, Naomi Helena Elizabeth, Dwi Kismayanti Respati, Ayatulloh Michael Musyaffi, Gentiga Muhammad Zairin Abstract - This study presents a bibliometric analysis of the literature on accounting and non-profit organizations (NPOs) based on 248 documents retrieved from the Scopus database. Data were processed and visualized using Biblioshiny (R Studio) and Scopus web analysis. The findings indicate a significant growth in publications since 2000, peaking in 2020–2024. Accounting, Auditing and Accountability Journal emerged as the most dominant source, and the distribution of journals is consistent with Bradford's Law. The United States and United Kingdom lead in scientific contributions, while Indonesia shows a growing presence. Keyword analysis reveals a shift toward contemporary themes such as blockchain, sustainability, and social accounting. This study provides a systematic mapping of the intellectual landscape of NPO accounting research and identifies opportunities for future investigation.
Authors - Himangshu Sarma, Madhumita Banerjee, Chandrajit Choudhury Abstract - Diabetic Retinopathy starts at a light level with no visible symptoms, but it can lead to severe pain and blindness as the disease progresses. Clinically, DR is diagnosed by looking for retinal detachment or utilizing imaging techniques like fundus imaging or optical tomography. The Early Diabetic Retinopathy Study is one of the established DR staging schemes. Image Processing has played a significant role in improving the methods used to detect the disease automatically. It has a huge role in assisting ophthalmologists in the screening process, as manually screening by ophthalmologists consumes more time, sometimes there may also be error. Although there are a number of algorithms used in detection, therefore, in this work we have studied and implemented various DR detection methods. To differentiate among various classes, we also prepared KAGGLE APTOS dataset containing the GLCM, LTP, GLRLM, LMeP, CLBP, CSLBP and LBP features extracted from the image dataset. And the classification is performed in the dataset, whilst achieving an accuracy of 97% on testing dataset and 96% for training dataset using SVM multi classifier.
Authors - Najera R. Umpar, Minsoware S. Bacolod Abstract - In this study, the readiness of teachers in adopting Artificial Intelligence (AI) in teaching and learning, and the variables that influence their acceptance or resistance towards it, were investigated. Following a qualitative research approach, semi-structured interviews were employed to gather the data. It was found that a range of factors including generation of teachers, teaching discipline or specialization, institutional support, and ethical issues influence teachers' readiness in implementing AI in the teaching process. Young teachers, along with those who are specialized in STEM subject area, expressed confidence, preparedness, and enthusiasm in embracing AI in teaching. Experienced teachers, and teachers who taught other subject area, expressed concerns toward relevance and teachers' autonomy. Institutional support (were considered as a factor that would significantly impact teachers' readiness toward AI integration. Ethical concern such as student privacy, bias algorithm, and student monitoring have also contributed to teachers' beliefs on AI implementation. More important, the study identified "hybrid readiness" where teachers believe AI can serve as the "co-teacher" of them and contribute to individual learning and pedagogical practice. It suggests that there are variety of factors influencing the teachers' readiness toward AI implementation thus it requires more comprehensive planning in order to have more efficient integration of AI in the classroom. It is found that teachers' readiness is still not homogeneous and context dependent, therefore differentiated training and education as well as support policy are necessary to enhance teacher readiness and promote the integration of AI in education.
Authors - Ali Fenjan, Mohammed Almulla, Jalil Md Desa Abstract - Android malware detection datasets are commonly designed for classification accuracy, while their ability to support explainability, forensic interpretation, and analyst-driven security reasoning remains limited. This paper presents APU-Android, an explainable static–behaviour feature dataset for An-droid malware forensics and security analytics. The dataset contains 4,594 APK records; after duplicate removal, 4,281 cleaned records were used in the strict evaluation. APU-Android represents each APK using nine interpretable features: requested permissions, API calls, file size, encryption usage, obfuscation level, network requests, suspicious keywords, network-risk flag, and Behaviour Score. Unlike opaque high-dimensional representations, each feature is mapped to a security-relevant meaning, allowing model decisions to be interpreted in terms of privilege abuse, API capability, concealment, communication risk, suspicious string evidence, and behavioural risk. The evaluation excluded du-plicated normalized columns, applied group-aware splitting using App_Name, and benchmarked five machine-learning classifiers. Under group-aware evaluation, Extra Trees achieved 98.72% accuracy, 99.38% precision, 98.37% recall, 98.87% F1-score, and 99.84% ROC-AUC using only the nine original explain-able features. Ablation analysis further examined the role of Behaviour Score and Obfuscation Level, while SHAP analysis showed that Network_Requests, API_Calls, and Permissions_Requested were the most influential prediction features. The results demonstrate that APU-Android is not only classification-ready, but also explanation-ready and forensic-ready for Android malware security analytics.
Authors - Senaya Nurandhi Jayawickrama, Tithira Mojitha Ranasingha, Dineth Randika Kumaranayake, Udageeth Dias, Kavinga Yapa Abeywardena, Amila Nuwan Senarathne Abstract - The increased digitization of banking operations has increased the value of data assets while also exposing them to growing cyber risks, creating a need for effective risk management mechanisms. Cyber insurance is a risk transfer mechanism that enables organizations to mitigate financial losses by transferring cyber risks to third party insurers. However, in many emerging markets, including Sri Lanka, cyber insurance remains underdeveloped due to limitations in existing premium calculation models, as they lack transparency and fail to incorporate data asset valuation and relevant security parameters, resulting in inaccurate and unfair premiums. This study proposes a structured framework that integrates data asset valuation with premium calculation, while defining multiple insurance coverage categories to address financial losses arising from regulatory, operational and recovery risks. By incorporating data asset value, operational criticality and organizational security posture into the premium calculation process, the proposed models enhance the accuracy and fairness of premium values.
Authors - Nguyen Ngoc-Tuan, Nguyen Van-Giap Abstract - Blended learning (BL) has played an important role in the improvement of learners. The special features of BL are useful for teachers and students, such as various learning contents, personal learning activities, and the mechanism related to authentic learning. However, the BL influences for different major students should be carefully studied and will contribute to the community. This study investigates the influences of BL based on a blended learning system (BLS) on university students’ achievement in different majors. Based on 247 courses in two years (2023 and 2024), without and with the BLS, we gathered and analysed the learning achievements of more than 9000 students from different majors stud-ying at a university. We also categorised the students into two main majors, in-cluding social science (SSCI) majors and engineering majors (SCI). Learning based on the system, the learning achievement of SSCI students is significantly higher than that of SCI students. We also found that the BLS improved the learning scores of the good students learning in both SSCI and SCI majors. Addition-ally, the SSCI students’ scores were significantly higher than those of the SCI students. The direction in the skills assessment of the SCI majors may be the main cause of the differences in university educational settings. Several suggestions on how to attract the low-scoring students to learn actively with BLS were also given. The important findings can contribute to the community to develop and apply blended learning for the different university major contexts.
Authors - Janitha Prabodha Bandara Dissanayaka, Pumudu A. Fernando, Manul Randula Singhe Abstract - Three-dimensional scene representation has moved quickly from Neural Radiance Fields (NeRF) to explicit volumetric methods such as 3D Gaussian Splatting (3DGS). 3DGS can render photorealistic scenes in real time, but editing these scenes is still difficult because Gaussian primitives do not have a fixed mesh structure or explicit topology. This becomes more challenging in dynamic scenes, where edits must remain stable across time and viewpoint. This paper presents a systematic literature review of neural volumetric editing methods for dynamic 3DGS and related NeRF-based representations. The review follows a PRISMA-guided process and analyses studies published between 2023 and 2026. The selected methods are grouped into three main paradigms: text-guided editing, interaction-based editing, and physics-based simulation. The review compares these methods using control precision, rendering speed, memory usage, editing time, temporal stability, and practical limitations. The findings show that text-guided methods are easy to use but often suffer from weak localisation and temporal inconsistency. Interaction-based methods provide stronger geometric control but struggle with complex topology changes. Physics-based methods produce more realistic motion but require higher computation and reliable material parameters. The review identifies the consistency gap, including flickering and texture swimming, as the main barrier to real-world dynamic neural volumetric editing.
Authors - Mazin Alshamrani Abstract - Wearable physiological monitoring systems are increasingly deployed in health-critical contexts, yet existing approaches address state classification, transition detection, and signal safety as isolated problems. This paper introduces SafeWear, a unified signal quality-aware machine learning framework that jointly addresses (i) real-time and anticipatory detection of physiological state transitions, and (ii) multimodal anomaly detection and out-of-distribution (OOD) safety screening. The framework centres on a modality-level Signal Quality Index (SQI) acting as a front-end reliability gate distinguishing sensor-level failures from genuine physiological irregularities. Causal temporal models are evaluated for transition detection, while reconstruction-based and one-class detectors are benchmarked for anomaly safety under strict Leave-One-Subject-Out Cross-Validation (LOSO-CV) across 37 subjects and five wearable modalities. Preliminary results show causal models achieve median detection latencies below 5.2 s with early-detection rates exceeding 79%, and Deep SVDD with VAE yield the strongest anomaly discrimination (AUROC = 0.539 and 0.538).
Authors - Hoang Anh Tuan, Le Thien Nhi Abstract - As governments digitize public services, educational platforms have emerged as essential e-governance infrastructure managing users’ personal and academic data. Vietnam’s National Digital Transformation Program enforces higher education digitalization as a strategic priority, but how users’ perceive the security of these systems, and whether such perceptions translate into trust, remains underexplored. This study review literature linking Perceived Information Security Assurance, Perceived Information Security Risk, Trust, Intention to Use, and Intention to Share Personal Information among university students in Hanoi and Ho Chi Minh City. Drawing on the Technology Acceptance Model, Protection Motivation Theory, and the CIA triad, the review proposed that security assurance strongly predicts trust, and trust drives both usage intention and willingness to share personal data. Contrary to expectations, perceived risk does not diminish trust but coexists with continuous platform use. As artificial intelligence features become more standardized in educational e-governance, these trust dynamics gain urgency as citizens or users must trust not only data handling but algorithmic/AI decision-making. The review synthesizes evidence from e-government adoption, educational systems and AI governance literatures to understand trust formation in AI-enhanced educational e-governance, with implications for platform design, policy development, and future research addressing accountability and e-governance in educational contexts.
Authors - Anna Berko-Boateng, Chalisa Veesommai Sillberg, Mika Saari, Pekka Abrahamsson Abstract - Electronic waste (e-waste) is one of the fastest-growing waste streams worldwide, and in many low-resource settings, informal recycling is performed under hazardous conditions with limited access to occupational safety information. Existing AI-based waste-recognition systems are typically designed for industrial or high-resource environments and do not adequately address the infrastructural, usability, and safety constraints of informal work contexts. To address this gap, this paper presents a lightweight Android application that uses multimodal artificial intelligence (AI) to support occupational safety among informal e-waste workers. The application enables users to capture images of e-waste components and receive structured safety guidance, including risk levels and handling instructions. Through the design, implementation, and field evaluation of the system, five meta-requirements were derived for AI-supported safety tools operating in low-resource environments. The system was evaluated through field testing at two informal recycling sites in Accra, Ghana, using representative low-cost smartphones. The findings indicate that the participants perceived the guidance as relevant and useful, while the interaction flow operated reliably across heterogeneous devices and user backgrounds. Beyond demonstrating feasibility, the study contributes transferable design knowledge on AI integration, device constraints, and safety-critical communication in low-resource socio-technical contexts.
Authors - Volodymyr Chumakov, Oksana Kharchenko, Zlatinka Kovacheva, Andrii Poberezhnyi Abstract - The Hilbert–Huang transform is considered. This method is compared to other known methods for handling nonstationary processes, specifically, the windowed Fourier transform and wavelet transform. The comparison is based on real data: the sound radiation of an Unmanned Aerial Vehicle using the example of a small Unmanned Aerial Vehicle, Phantom 4, and real electroencephalograms of a healthy and ill person. The advantages of using the Hilbert–Huang transform over the Hilbert transform are shown, because the latter is used for narrow-band processes. The possibilities of frequency extraction in the case of beats are noted. It is emphasized that Hilbert–Huang transform offers a more adaptive and data-driven approach, allowing it to reveal intrinsic components that traditional methods often obscure. In addition, this method provides a clearer physical interpretation of instantaneous frequencies, which is crucial for studying rapidly changing real-world signals.
Authors - Puwis Thiparapkul, Tuang Dheandhanoo, Panasuddhi Suddhiprakarn Abstract - Project management in game and animation production often faces challenges because generic tools do not align with their unique workflows. To enhance team collaboration, this study applies User Experience (UX) and Human-Computer Interaction (HCI) design principles to improve team workflows. The design is built upon real-world operations and an analysis of current free and paid industry tools, specifically aiming to optimize efficiency for beginners and small-to-medium-sized studios. We developed a domain-specific project tracking system on a demo site based on the newly synthesized "Waterfall Storm" concept, which balances structured administrative oversight with highly flexible, modular production phases. This conceptual architecture was derived directly from empirical user feedback and qualitative interview insights across three key target segments: entrepreneurs, creative practitioners, and students. The proposed design was evaluated through extensive empirical testing involving more than 100 users and 30 projects across educational and industrial environments. The results demonstrate that the Waterfall Storm workflow significantly enhances team collaboration, provides decentralized task visibility, increases clarity in asset tracking, and effectively eliminates format fragmentation while lowering software costs. These findings highlight that a domain-specific, user-centered approach to workflow design supports creative team collaboration more effectively than general-purpose project management framework.
Authors - Majdi Rawashdeh, Dhia Eddine Salhi, Awny Alnusair, Ali Karime Abstract - Ensuring student safety during school transportation remains a critical challenge, motivating automated, intelligent monitoring solutions. This paper introduces a comprehensive IoT-enabled framework for real-time student identication and attendance management aboard school buses. The proposed architecture combines an ESP32-CAM edge device with a suite of machine learning and deep learning models, evaluated on an augmented facial dataset of 40,000 images derived from the Labeled Faces in the Wild (LFW) benchmark. A comparative study of six pipelinesa basic SVM baseline, SVM with PCA, a distance-based classier, SVM with augmentation and grid search, Random Forest, and a proposed CNN-LSTM hybridis conducted. The CNN-LSTM hybrid achieves the highest accuracy of 99.5%, with precision, recall, and F1score exceeding 99%. The architecture spans four layerssensing, gateway, server, and applicationenabling low-latency communication between the edge device, cloud, and a mobile application serving parents and administrators, with end-to-end inference latency below 200 ms per frame. The results validate the system as a scalable, cost-eective, and highly accurate solution for modernizing student safety and attendance in school transportation.
Authors - Md. Istiaq Ahmed Bhuiyan, It Ee Lee, Teong Chee Chuah, Muhammad Sheraz, Gwo Chin Chung Abstract - Two-wheeled unsteady robots have special mobility benefits, but are unstable, nonlinear devices. Conventional control algorithms frequently fail to stabilize dynamic uncertainties and variations in the system. This study presents a Deep Reinforcement Learning (DRL) model based on Deep Q-Network (DQN) algorithm to balance autonomously. The proposed architecture was trained using DQN algorithm. It uses Exponential Moving Average filter to prevent high-frequency fluctuations and allows the motor output to be smooth. Simulation results demonstrate that the DQN controller successfully and robustly stabilizes the robot under mild to moderate initial pitch disturbances of up to 18°. However, boundary stress testing at an extreme 20° initial pitch revealed a critical kinematic limitation. The evaluation confirmed that while massive pitch recovery is algorithmically possible, the extreme actuator effort required to correct the chassis induces an uncontrollable divergence in the roll angle, leaving the system highly vulnerable to roll-axis instability.
Authors - Prince Kelvin Owusu, Moses Aggor, Gibson Afriyie Owusu, Emmanuel Mensah Azagadli, Martins Larweh Neurtey, Suleman Zakaria, Cecil Selorm Mensah Abstract - Campus Area Networks play a central role in supporting teaching, learning, administration, research, e-learning platforms, institutional databases, and communication services in higher education institutions. However, as universities become increasingly dependent on digital infrastructure, weaknesses such as poor network segmentation, inconsistent access control, insecure wireless access, limited monitoring, and service disruptions can expose institutional systems to unauthorized access and operational risks. This paper examines security and resilience challenges in the existing Campus Area Network at Pentecost University, Ghana, and presents a redesigned architecture based on VLAN segmentation, access-control enforcement, hierarchical network organization, and improved monitoring. The study adopts a mixed-method and technical assessment approach involving stakeholder input, technical observation, network audit, and pre/post evaluation of selected security and performance indicators. The redesigned architecture separates critical network zones, restricts unauthorized inter-VLAN communication, reduces unnecessary broadcast exposure, and strengthens the control of access to sensitive institutional resources. The results indicate that the intervention was associated with improved access control, reduced security incidents, lower latency and packet loss, increased throughput, and faster detection and mitigation of network incidents. The paper contributes practical evidence on how structured segmentation and access-control mechanisms can strengthen secure, resilient, and sustainable ICT infrastructure in higher education environments
Authors - Rui Liu, Neng Zeng Abstract - The Bank for International Settlements' Project Leap Phase 2 trial demonstrated that post-quantum cryptography (PQC) can be functionally integrated into the Eurozone T2 real-time gross settlement (RTGS) system, reporting an average PQC signature verification time of ≈209.9 ms against ≈28.1 ms for the traditional baseline. The report, however, explicitly leaves two questions open for "future testing phases": how the observed timing translates into an SLA-aware deployment plan, and how the system should be architected for a migration-safe transition that NIST IR 8547 recommends but Leap did not test (hybrid signature, hybrid KEM, and a non-modifying deployment path on top of the existing ESMIG/NSP stack). This paper contributes the analytical answer to the first question and audits Leap's framework for the second. We adapt the classical TCP-style timeout bound to a closed-form Watchdog inequality Ttimeout ≥E[Tcompute] + 2 · TRTT + k · σjitter, derive a sensitivity table that maps the safety multiplier k to four financial-grade SLA tiers, and a closed-form capacity-planning bound whose ratio between asynchronous and synchronous throughput is parametric in the FPGA parallelism. We then audit the Leap report against its own admitted limitations on hybrid signature testing, hybrid KEM, and the "modified ESMIG connector" workaround, and we attach to each gap a bounded fix direction expressed entirely within the cost-model envelope. We complement the analysis with a formal EUF-CMA reduction sketch for the nested PQC–RSA signature (degrading gracefully under attacks on either layer) and a production-grade C empirical anchor on a single self-contained library (the LK LEGO PQC platform, native RSA, no OpenSSL): on a commodity x86-64 cloud VM, Dilithium-5 verify takes ≈0.71 ms at the median (σjitter ≈130 µs), the nested PQC–RSA verify 0.77 ms, and the hybrid ML-KEM-768 + RSA-2048-OAEP decapsulation 1.66 ms. These refine Leap's 209.9 ms PQC verify into a fast cryptographic core (0.34%) plus a slow protocol envelope (99.66%) and show both untested hybrid constructions fit inside a single-millisecond budget; a throughput cross-check (2115 verify/s single-core, 83% scaling at 2 threads) validates the independent-cycles assumption. The measured software-only path already meets the legacy 500 ms ceiling, so FPGA acceleration is an optional optimisation rather than a requirement. Production-grade RTGS measurements with HSM transport and full ISO 20022 parsing remain future work.
Authors - Shakeeb Abdullah, Jim Hjartarson, Rony E. Amaya Abstract - Currently the fastest tried, tested, and reliable electro-optical transceivers operate at 56 Gbaud (56 Gbit/s NRZ or 112 Gbit/s PAM-4) communication speeds per lane, and while some companies have finally started rolling out their 112 Gbaud (224 Gbit/s PAM-4) line of products for commercial use, not all companies have caught up; nor is their much research presented on 112 Gbaud TIAs. Higher speeds such as 400 Gbit/s are usually obtained by transmitting data through multiple parallel lanes of 100 Gbit/s PAM-4. The optical industry has been pushing for the next generation of 112 Gbaud (112 Gbit/s NRZ or 224 Gbit/s PAM4) rates for devices, however, it had stalled (hitting many roadblocks) in the past six years or so. Currently, the popularized TIA architectures are producing diminishing returns in terms of their bandwidth performance for every incremental improvement in its designs (or heavily relying on DSP); for this reason, a new paradigm construct is required to overcome such obstacles and meet the new standards of the next generations of TIAs. This brief proposes a different approach in designing TIAs for 112 Gbaud speeds or higher. Estimated criterion dictates a 3-dB BW of 78.4 GHz to process clean eyes at 112 Gbaud. Proposed TIA architecture in this paper utilizes distributed approach instead of usual common gate or feedback mode to convert the incoming photodiode current into an electrical voltage. Post-layout electromagnetic simulations show that these amplifiers can process PAM-4 eyes with 40 mV pk-pk outputs at -3 dBm of input optical power.
Authors - Nirmal Prabhu K, Sisira M S, Kanagaraj S, Kirthika P, Ashwin C Abstract - The proliferation of information and communication technologies has brought ICTs to the forefront as important enablers of social inclusion, governance, and rural development. However, the existence of inequalities over the years concerning the use, benefit, and application of ICTs, among others, has brought about a noticeable digital divide, especially among emerging nations such as India. This paper presents the underlying causes for the existence of the digital divide among rural populations in India as part of a comprehensive research review on the matter, adopting the Resource and Appropriation Theory by Van Dijk. A structured search of studies was conducted using the Scopus database, identifying articles related to rural India, both quantitative and qualitative studies. Findings indicate that the problem of the digital divide is not solely found among the rural populations of India, which lack the necessary infrastructure, as it is also ingrained among the socio-economic, geographical, and linguistic population groups, among others of India, including women as the most vulnerable section of society. The study concludes comprehensive multidimensional approach is required to bridge the digital divide.
Authors - Sowmyashree N, Madhu Sunkanur, Impana M, Suchithra B S, Hemalatha P G Abstract - This paper will outline a cold storage technique that utilizes solar power for the conservation of agricultural produce in rural and non-grid connected areas. This system includes a solar photovoltaic cell together with a backup battery that is used to guarantee continuity of electricity supply. The charge controller manages the electrical input into the circuit. The cooling process is done using a TEC-12706 Peltier unit controlled through an Arduino Uno microcontroller. Sensors are employed to monitor the temperature and voltage. The collected data is fed to the LCD screen, while good insulation ensures that cool temperatures are maintained. A performance assessment has been conducted on the proposed design, which proved its efficacy in lowering dependence on traditional energy resources while maintaining the consistency of cooling efficiency. Implementation of the suggested technology will help reduce losses from post-harvests, boost the financial state of farmers, and promote the adoption of renewable energy technologies. Additionally, the design will enhance environmental sustainability by minimizing greenhouse fuel emissions
Authors - Francka Sakti Lee, Christian Haposan Pangaribuan, Liem Bambang Sugiyanto, Sulistyowati, Jovann Kurniawan, Henry Nugraha Abstract - Voluntary employee attrition presents a systemic challenge to organizational stability, yet predictive modeling is frequently constrained by the accuracy paradox and algorithmic opacity. This study proposes an Explainable Artificial Intelligence (XAI) framework integrating eXtreme Gradient Boosting (XGBoost) with Shapley Additive exPlanations (SHAP) to transform attrition analysis into prescriptive intelligence. By implementing a Random Over-Sampling (ROS) protocol, the model successfully neutralized extreme class imbalances, significantly enhancing the detection sensitivity of latent resignation signals. The novelty of this research lies in its SHAP-driven demographic bifurcation, exposing critical asymmetries in risk triggers between young and senior employees. Empirical findings identify a "Burnout Triad" comprising compensation, overtime, and job satisfaction. Crucially, junior cohorts exhibit hypersensitivity to immediate transactional factors, whereas senior cohorts are driven by intrinsic role actualization. This framework culminates in a Decision Support System (DSS) enabling surgical retention interventions, shifting human capital management toward strategic, evidence-based governance.
Authors - Giordana Castelli, Ida Giulia Presta, Marialucia Camardelli, Mariagiulia Di Lizia, Davide Donato Russo, Giovanni Felici Abstract - Contemporary cities are increasingly shaped by climate change, digital transformation, demographic growth, socio-economic inequalities, and environmental uncertainty. These transformations challenge traditional static planning approaches and require new governance paradigms capable of dynamically addressing urban complexity. This paper discusses the Urban Intelligence paradigm as an integrated framework for adaptive and cognitive urban governance. Within this framework, an Urban Digital Twin is not just as a digital replica of the city, but a cognitive infrastructure capable of integrating datasets, simulation models, real-time monitoring systems, and participatory processes into a unified environment for knowledge production and decision-making. We explore the different technical and multidisciplinary challenges that derive from this approach, with special focus on Information and Communication Technologies that enable the effective realization of Urban Intelligent Systems, providing examples of current work in Italian Cities. We conclude by presenting the 4C Model, a conceptual model for Urban Governance designed to support the path towards resilient and cognitively enabled cities that learn from uncertainty and promote sustainability, inclusion, transparency, and collective well-being.
Authors - Kalinka Kaloyanova, Elitza Kaloyanova Abstract - Despite the increasing use of artificial intelligence (AI) in healthcare, clinician adoption of AI tools is still obstructed by algorithmic aversion, which reflects scepticism about the results of AI use. This article examines how hospitals can enhance AI adoption by strengthening AI competencies in physicians and mitigating mistrust through a systematic, data-driven approach. A review of behavioral studies reveals barriers to AI adoption across technical, cognitive, organizational, and ethical domains. A framework is proposed that focuses on integrating individual clinician competencies with structured strategies implemented by hospitals that support workflow and create conditions for continuous learning. The implementation of data-centric strategies, explainable AI tools, competency programs, simulation training, and interprofessional collaboration is recommended to increase trust in AI in medicine, support its ethical use, which ultimately leads to safer healthcare delivery.
Authors - Md Manirul Islam, Umme Salsabil, Md. Mushfiqur Rahman, Sazzad Hossain Abstract - This paper presents a compact identity-verification architecture for private web and Internet of Things (IoT) deployments that require tamper evidence without the operational overhead of a full blockchain. The framework separates credential verification from profile-integrity verification across multiple stores: a credential store, a protected-profile store, a reference integrity store, and a key store. Credentials are protected with Argon2id-based verifiers, while protected profile records are bound to entity identifiers, timestamps, and version counters through HMAC-SHA-256 reference tags. Unlike scan-heavy hash-only workflows, the proposed design performs direct indexed lookup by entity identifier and then verifies integrity through a keyed comparison step, improving both security posture and scalability. The same logic can be deployed behind HTTPSbased web services and MQTT-over-TLS IoT gateways. A reference prototype and benchmark study over datasets of 1,000 to 10,000 entities show that the indexed login path remains nearly size-stable, with median successful login latency around 1.68-1.69 ms under a development-profile Argon2id configuration, while a scan-based baseline login path grows from 0.92 ms to 6.90 ms over the same range. Injected profile tampering was detected in all benchmarked trials. The resulting framework offers a pragmatic middle path between conventional centralized login and heavyweight distributed-ledger authorization for institutions that prioritize local autonomy, compartmentalization, and data-integrity assurance.
Authors - MD Junayed Talukdar, Khosro Salmani Abstract - Artificial Intelligence (AI) systems are widespread across fields such as healthcare, finance, employment, and criminal justice, with a substantial impact on the lives of individuals and society. However, AI systems have been shown to perpetuate existing social inequalities, particularly through biases that are not easily discernible. Such biases are embedded in the technical and social structures of AI systems, posing a direct challenge to the principles of Equity, Diversity, and Inclusion (EDI) understood as the acknowledgment of differences among individuals, fairness and equal access, and the valuation of all participants. This study argues that fairness in AI cannot be achieved by focusing solely on technical aspects, necessitating a holistic approach. To investigate this, computational content analysis was applied to 360 occupational narratives generated by ChatGPT across nine professions and four geographic regions (Canada, Germany, India, and Bangladesh) using explicitly gender-neutral prompts. The analysis examined whether AI-generated narratives associate professions predominantly with one gender, and whether such patterns remain consistent across regions. Findings reveal that gender bias persists despite neutral prompting, with male-coded protagonists dominant in technical and manual labor professions and female-coded protagonists dominant in caregiving roles. Although regional conditions influenced the magnitude of gender imbalance, the direction of occupational gender patterns remained largely consistent across all four regions. This study identifies the empirical foundations necessary for future EDI-AI co-design frameworks, outlining the sociotechnical dimensions that such frameworks must address to be effective.
Authors - Yassine Lkhalidi, Mohamed Lkhalidi, Hatim Kharraz Aroussi, Achraf Tifernine Abstract - IoT device authentication remains vulnerable to credential theft and physical-layer impersonation, particularly where resource constraints preclude full PKI deployments. Existing approaches address subsets of this problem: RF fingerprinting exposes templates in plaintext, while zero-knowledge proof (ZKP) schemes authenticate static keys without binding to physical hardware. We propose ZK-RFAuth, a framework integrating Siamese CNN-based RF fingerprinting, Groth16 ZKP embedding verification, and Proof-of-Authority blockchain logging. A device’s hardware imperfections are captured as a compact embedding; a Groth16 circuit proves the L1 distance between a fresh embedding and the registered template falls below a predefined threshold, without revealing either vector. Evaluated on WiSig (28 WiFi transmitters, 224,000 I/Q frames), ZK-RFAuth achieves 91.4% closed-set accuracy, 2.25% Equal Error Rate, and 70.8% rogue rejection at the 95th-percentile operating threshold, requiring only 972 R1CS constraints for 144-byte proofs verified in approximately 3 milliseconds. ZK-RFAuth is the first framework providing physical-layer identity, embedding-level zero-knowledge privacy, open-set rogue detection, and immutable audit logging simultaneously.
Authors - Aryan Sharma, Dipali Baviskar Abstract - Modern computer networks are often equipped with an intrusion detection system (IDS) to detect malicious activities or cyber-attacks. Such a system must have high accuracy on known attacks, and at the same time it must be able to generalise to previously unseen attacks. However, supervised classifiers fail to generalise to new situations because they learn to map input data to output labels under a specific training distribution, and they perform poorly under a different test distribution, which is called distributional shift. In this paper, we propose a gating-based hybrid IDS that combines supervised classifiers with anomaly detectors. The gating network restricts the influence of the anomaly component to the uncertain prediction zone, i.e., the region of the output space where the classifier is uncertain, defined by a probability range of (0.15, 0.75)], and prevents unsupervised noise from affecting the confident supervised decisions. We evaluate the performance of our proposed system on three different test scenarios using the CIC-IDS-2017 dataset. The first test scenario consists of eight known attacks for which we train the classifiers on the corresponding training data, and then we test them on the corresponding test data. The second test scenario is an out-of-distribution stress test, in which we use 99% benign traffic and add DDoS and PortScan attacks to it, and test whether the system is able to detect them. The third test scenario is a zero-day test scenario in which we test the system on a previously unseen SQL Injection attack. Our findings are as follows. First, the Gating Hybrid RF+AE achieves an F1-score of 0.9778 and a precision of 0.9924 on the eight known attacks, which outperforms the standalone RF classifier with an F1-score of 0.9750. Secondly, on the out-of-distribution test scenario, both the RF and GBT classifiers fail to detect the attacks with an F1-score of 0.000, while the Gating Hybrid RF+IF achieves an F1-score of 0.405, which corresponds to a 40.5 percentage-point lift from the F1-score of the anomaly component IF. Thirdly, the Gating Hybrid RF+IF achieves an SQL Injection recall of 47.6% on the zero-day test scenario, while the standalone RF and GBT classifiers achieve an SQL Injection recall of 33.3% on average. All the abovementioned results are supported by 95% Wilson confidence intervals, and we provide root-cause analysis for the extreme results.
Authors - Moises Toapanta T, Jeanette Jordan Buenano, Nancy Jordan Buenano, Maria Cristina Espin Melendez, Pamela Toapanta Pavon, Rocio Llumiquinga A., Dafna Guaman B., Eriannys Gomez D., Pedro Echeverria B. Abstract - The globalization of Information and Communications Technologies (ICTs) the Internet, artificial intelligence (AI), and social media poses serious threats to the integrity, confidentiality, and authenticity of information in higher education institutions (HEIs). The central problem lies in the absence of robust legal frameworks regulating the use of AI, particularly in relation to personal data protection. This study examines perspectives on AI and social media, together with the legal foundations required for the effective administration of HEIs. Using the deductive method and exploratory research, key actors in institutional governance were identified, administrative strengthening indicators were de-fined, and an integrated model was developed to link AI, social media, and regulatory frameworks. It is concluded that improving institutional governance re-quires mitigating the risks associated with the use of these technologies through legal frameworks aligned with national constitutions and regulations. Ecuador, like most Latin American countries, currently lacks such legislation and remains in the analysis phase.
Authors - Israel Herrera-Miranda, Miguel Apolonio Herrera-Miranda, Juan Villagomez-Mendez, Silvia Lizbeth Herrera-Lopez Abstract - This paper analyzes the characterization and adoption of Information and Communication Technologies (ICT) and GenAI (GAI) within the framework of digital governance and higher education policies in México. Framed around the early years of President Claudia Sheinbaum Pardo's administration (2024–2030), the study evaluates how emerging regulatory instruments and administrative agencies—specifically the newly enacted Telecommunications and Broadcasting Law (2025) and the Agency for Digital Transformation and Telecommunications (ADTT)—act as institutional backbones to reduce technological gaps and preserve digital sovereignty. Methodologically, this study relies on a documentary and reflective analysis, utilizing empirical indicators from the OECD Digital Government Index (DGI) and the results of México's 2025 National Survey on GAI in Higher Education. The findings indicate that while México exhibits a strong performance in digital-by-design policy frameworks, structural challenges persist regarding proactive public sector AI integration. Concurrently, higher education data reveals an accelerated, mass adoption of GAI by over 60% of students and faculty, transforming traditional pedagogical paradigms. In response, the Ministry of Public Education (SEP) has advanced a ten-point strategy targeting digital literacy, curricular overhauls, and ethical boundaries. Ultimately, this paper underscores that bridging the digital divide requires co-aligning public innovation frameworks with human-centric, ethically-governed higher education policies to foster national technological autonomy and sustainable socioeconomic development.
Authors - Humberto Merritt Abstract - In recent years, the use of digital applications for data management, information access, communication and resource exchange has grown worldwide. Advances in telecommunications have been particularly pervasive in Mexico, where the widespread use of smartphones has encouraged the adoption of digital technology. In this study, we examine the implementation of the APP CDMX electronic platform, which was designed to enhance Mexico City's functionality. The research aims to determine how this tool has helped citizens learn about available public services and how they evaluate it. In particular, the research describes how smartphone users are leveraging the application to access real-time data on local procedures and their fees, the traffic conditions, specific transit routes and stations, and other touristic venues, thereby achieving not only more efficient mobility but also encouraging a sustainable lifestyle. Using a qualitative methodology based on the lexical analysis of user opinions and adopting a multidisciplinary approach, the study concludes that the APP CDMX has positively transformed citizen interaction, accelerating administrative processes and democratizing access to key information.
Authors - Luis Romel Assis Oliveira Junior, Olivan da Silva Rabelo, Paulo Henrique da Silva Santos Abstract - The article proposes the Dodecahedral Framework as a conceptual model to analyze Metaverse Experience Networks, differentiating them from Internet Social Networks and characterizing them as a new object of academic study. The approach explores four dimensions – Immersive Realism, Spatiotemporal Disruption, Meta Culture and Tokenized Economy. The methodology consists of a bibliographic review and the theoretical formulation of the model. The results indicate that Metaverse Experience Networks offer new forms of socialization, interactivity, and engagement, profoundly impacting marketing, consumption, work, and digital culture. It is concluded that the Dodecahedral Framework can serve as a tool for researchers and professionals, assisting in the understanding, planning, and implementation of strategies within the metaverse, in addition to opening paths for new investigations on its technological and social implications.
Authors - Meshak Ratshikombo, Mehrdad Ghaziasgar Abstract - Deep Reinforcement Learning has emerged as a promising approach for algorithmic trading, but trading performance remains highly sensitive to reward design. This study investigates how reward engineering shapes learned trading behavior by training agents exclusively on FTSE market data under identical conditions while varying only the reward formulation. Evaluation across multiple unseen equity indices demonstrates that reward functions induce distinct behavioral biases governing exposure timing, volatility sensitivity, and downside risk. Profit-oriented rewards encourage aggressive trading and higher variability, whereas risk-aware and sparse rewards produce more stable policies with improved drawdown control. The findings show that reward engineering acts as a strong inductive bias influencing both trading behavior and cross-market generalization in reinforcement-learning-based trading systems.
Authors - Lydie Simone Tapsoba, Salifou Ouoba, Athanase Sawadogo Abstract - Malaria is the leading cause of child mortality in Burkina Faso and accounts for over 40% of public health expenditure. This study develops and validates a hybrid machine learning model combining Random Forest (40%) and XGBoost (60%) to predict the spatio-temporal dynamics of malaria across the country’s 13 health regions over the period 2010–2024. The model incorporates satellite-derived climatic variables (CHIRPS precipitation, MODIS NDVI, temperature) and demographic variables, enhanced by advanced feature engineering: 3- and 6-month moving averages of precipitation, temporal lags and circular encoding of seasonality. Validation combines a prospective temporal split for 2024 and spatial GroupKFold cross-validation. On the independent 2024 test set, the hybrid model achieves exceptional performance, substantially outperforming the best previously published approaches for this context. Interpretability analysis reveals surprising determinants of transmission, highlighting the equal role of environmental and anthropogenic factors in Burkina Faso. The SHAP analysis identifies the 3-month moving average of rainfall as the dominant variable, ahead of population density, highlighting the equal role of environmental and anthropogenic factors. The priority areas are Hauts-Bassins, the East and the South-West. The risk maps and 6-month predictions serve as operational tools for the National Malaria Control Programme.
Authors - Yasmany Prieto, Christopher Moyano Abstract - Communication systems are shifting toward more software-based development, driven by technologies such as Software-defined Networking and Network Function Virtualization. This new paradigm improves flexibility, scalability, and resource efficiency, shortens time-to-market, but opens a new dimension of system failure through bugs, backdoors, and software vulnerabilities. In this work, we build a Decision Tree (DT) classifier to predict vulnerability exploitability in networking systems. A list of vulnerabilities affecting those systems is obtained from the National Vulnerability Database. To measure exploitability, we employ the Cybersecurity and Infrastructure Security Agency Known Exploited Vulnerabilities Catalog, which lists vulnerabilities that have been exploited in the wild. The DT classifier achieved a recall of 0.878 in detecting exploitable vulnerabilities.
Authors - Nandinee Mudegol, Abhijeet Urunkar, Vedika Dhende, Vaishnavi Katkar, Chetna Ghengare, Samiksha Harer Abstract - Agriculture is a very important sector, but the farmers are facing problems in early identification of plant diseases and monitoring crop health. Manual checking of crops is time-consuming and can lead to late detection of diseases, which lowers the yield of the crop. To address this problem, authors have pro-posed a system called Agridiagnosis. The proposed system combines two major features: plant disease detection using image processing and crop health monitoring using NDVI. Farmers can upload images of leaves to be detected and suggested treatment. At the same time, the system provides a color-coded map of crop health with respect to NDVI values. The combination of both features in a single platform allows the system to help farmers easily comprehend the crop conditions and take timely measures to increase productivity.
Authors - Romildo Silva, Maria Tavares, Filipa Silva, Carlos Lopes Abstract - This paper investigates the use of AI agents as consumers of tokenized real-world asset (RWA) data in financial environments. A Python-based agent was developed to automatically retrieve, process, and analyze financial information from publicly accessible APIs for selected traditional and tokenized assets, including SPY, QQQ, PAXG, and ONDO. The proposed framework evaluates data quality through quantitative metrics such as latency, completeness, null rate, and data volume, complemented by descriptive statistical analysis and Shapiro-Wilk normality testing. The results indicate that traditional financial assets exhibit higher informational stability, while tokenized assets present greater variability and non-normal behavior. The study demonstrates that autonomous AI systems can effectively consume heterogeneous financial data sources and highlights the growing importance of information quality and consistency in AIdriven financial ecosystems.
Authors - Anupama Y K., G M Trupti, Arun Kumar N Abstract - Due to the rapid evolution of cyber threats with the growth of the internet and cyber threats, we now live within the cyber domain, which is under great pressure and strain from cyber threats. The rise in popularity of Cloud Infrastructure, which offers customers scalable data storage, has led to development of new vulnerabilities to business owners, as well as a growing shift in the way hackers operate. Traditional methods of detecting cyber attacks, such as IDSs, typically encounter issues when dealing with a large amount of class imbalance and have difficulty adapting to newer attack vectors. This results in an increase in false positives that companies receive when monitoring their systems for cyber attacks, as well as a decreasing ability to detect less frequent, but very high-impact, types of cybersecurity threats. The solution involves developing an AI-based intrusion detection system that combines the use of Borderline SMOTE to balance the classes incorrectly identified, with an ensemble method called maximum vote that combines three classifications methods: Decision Trees, XGBoost and tuned AdaBoost. The system is evaluated using the KDD Cup 1999 benchmark dataset which contains regular traffic as well as different attack types including DoS/DDoS (Neptune, Smurf, Teardrop), Probe (Nmap, Ipsweep, Portsweep, Satan), R2L (Guess password, Back) and U2R (Buffer Overflow, Rootkit, Land). Experimental results show that the max-voting ensemble out performs the individual base models (Decision Tree, AdaBoost, and XGBoost) and standard single classifier IDS methods along with baseline algorithms. This leads to more reliable detection of both minor and major attacks in cloud security scenarios. These findings highlight the effectiveness of combining Borderline-SMOTE with ensemble learning to build a scalable and robust IDS suitable for real-time cloud security monitoring.
Authors - Y. Zamarripa-Rivera, S. Villagrana-Barraza, D.I. Ortiz-Esquivel, L.E. Banuelos-Garcia, M. Molina-Almaraz, G. Díaz-Florez Abstract - Plant propagation by cuttings requires controlled microclimatic conditions to promote rooting, reduce water stress, and improve process reproducibility. This paper presents the design, implementation, and functional validation of an ESP32based monitoring and control platform for a plant cutting propagation chamber. The system integrates multi-zone sensing, ON/OFF-based control with PWMassisted thermal actuation, local CSV data logging, Wi-Fi communication, and web-based supervision. Temperature and relative humidity were monitored in the external environment, stem zone, and root zone, while light intensity, water flow, actuator states, and PWM commands were recorded as operational variables. Validation was conducted through two 15-day experimental campaigns, generating more than 40,000 time-stamped environmental and operational records. The platform maintained differentiated microclimatic conditions, with average rootzone temperatures between 23.79 and 24.31 °C and root-zone relative humidity between 75.74 and 80.32%. Biological validation with six basil cuttings achieved an overall rooting success rate of 83.3%, reaching 100% in the second campaign. These results demonstrate the potential of low-cost ESP32-based systems for controlled-environment agriculture and smart propagation applications.
Authors - Olivier ZONGO, SOMDA Dekpeltakié Augustin METOUALE, Mamadou DIARRA, Abdoulaye SERE Abstract - Automatic assessment of obstetric ultrasound remains a challenge due to its operator-dependent nature and the dynamic context of fetal labor. This study proposes a Temporal Quality Gate Framework to standardize diagnostic plane validation using a novel Temporal Attention-Gated LSTM (TA-LSTM) architecture. We formulate the task as a binary classification problem to distinguish standard diagnostic planes from non-diagnostic sequences, using the IUGC 2024 dataset of 434 transperineal ultrasound videos (266 positive, 168 negative). The TA-LSTM extracts spatial features via a ResNet-18 backbone and dynamically weights temporal dependencies using an attention mechanism. Under 5-fold cross-validation with strict patient-level splitting, the TA-LSTM achieves a mean AUC-ROC of 0.989 ± 0.008 and a mean test accuracy of 95.63% ± 2.85% (peak accuracy of 98.85%) with an inference latency of 14.2 ms on GPU. Our framework acts as a robust Quality Gate, ensuring that subsequent automated measurements, like the Angle of Progression (AoP), are performed on high-quality validated inputs, making it highly suitable for resource-limited clinical environments.
Authors - S. Zouini, A. Meddaoui, A. Jrifi Abstract - Statistical Process Control (SPC) is a well-established methodology for industrial quality management. The growing complexity of modern manufacturing environments — driven by Industry 4.0, high-dimensional sensor data, and nonlinear process dynamics — exposes the limits of classical monitoring approaches based on fixed thresholds and Gaussian assumptions. This paper proposes an AI-Driven Statistical Process Control (AI-SPC) framework that integrates PCA-based Hotelling’s T2 monitoring with a Random Forest classifier within a closed-loop architecture. The framework is evaluated on five fault scenarios from the Tennessee Eastman Process (TEP) benchmark. Results show that the hybrid AND-logic strategy achieves a false alarm rate of 0.071 — a 45% reduction relative to PCA-T2 alone (0.130) — while maintaining a detection rate of 96.1% and a detection delay of 5.4 samples. A variable contribution analysis further supports fault diagnosis by identifying the most deviant process variables at the moment of detection. These results confirm that combining statistical rigor with data-driven flexibility produces a more reliable and interpretable monitoring system than either approach deployed independently.
Authors - Janset Shawash, Henri Liu, Alicia Sudlerd, Leevi Rantala Abstract - Simulation-based learning gives healthcare students safe, repeatable practice before clinical placement; virtual reality (VR) makes it more accessible and affordable. This paper presents Aino, an artificial-intelligence-driven virtual patient for an occupational therapy (OT) home-visit showering assessment, built in Unity for the standalone Meta Quest 3. Where most of existing OT VR tools rely on fixed-viewpoint, pre-recorded 360-degree branching video, Aino is a conversational 3D patient whom students address in unconstrained natural speech (Finnish or English) while moving freely within a single continuous scene that follows a dynamic clinical narrative. The technical core is a hybrid control architecture that decouples a scripted, data-driven clinical narrative from free-form conversational responses: a section-based state machine runs twenty-nine designer-authored sections, each with an explicit completion contract that reconciles deterministic clinical progression with variable-length AI dialogue, behind an AI-provider-agnostic interface. OT educators use observation-based scenarios, and thus, the patient narrates her actions and reactions to keep her performance legible; this narration pattern and its calibration are examined as a transferable design lesson. The showering task additionally involves intimate personal care that cannot be ethically rehearsed in live role-play yet is staged safely in VR. Formative findings from educator co-design, educator try-out sessions, and play-testing are reported, and planned student evaluations are outlined.
Authors - Gilmara Santos, Pedro V. Matias, Yan W. Martins, Jose R. Santos Junior, Joao V. Fernandes, Rodrigo O. Jesus, Ueller B. Silva, Lidia Roque, Klinsman Goncalves, Laisa Paiva, Edjair Mota Abstract - The Amazon River basin, home to one of the world’s largest freshwater reserves and unparalleled biodiversity, silently suffers from a vast environmental disaster caused by illegal mining, during which mercury is discharged into its waters. This contamination threatens aquatic ecosystems and poses serious risks to highly vulnerable populations. In response, this paper provides clues for a resilient and scalable system architecture for real-time water quality monitoring, tailored to the environmental and infrastructural challenges of the Amazon region. A detailed systematic literature review assesses state-of-the-art Internet of Things (IoT)-based monitoring techniques, focusing on key variables such as mercury concentration, temperature, turbidity, and pH. Special emphasis is given on communication technologies suitable for diverse settings—from Wi-Fi-enabled urban areas to remote rainforest regions where LoRa, NB-IoT, and WiLD (Wi-Fi over Long Distance) present viable alternatives. The study also highlights the role of the application layer in enabling data analysis, real-time alerts, and remote visualization of environmental conditions. This research contributes to developing time-efficient and sustainable monitoring strategies to support public health initiatives and ecological conservation by bridging technological innovation with the urgent environmental needs of one of the planet’s most critical biomes.
Authors - Laura Alma Diaz-Torres, Alma Delia Torres-Rivera, Mario Leonardo Nieto Antolinez, Fabian Leonardo Alfonso Sabogal Abstract - Mexico City faces a constant need for high-quality public transport systems capable of reducing passenger waiting times, improving travel comfort, and maintaining the economic viability of private operators. In this context, demand studies are essential both before the concession stage and during service operation, since they support route planning, fleet allocation, schedule adjustments, and operational decision-making. Two similar but distinct methodologies are compared for the estimation of load polygons. The first methodology assigns telemetry events to official stops using spatial proximity and route reconstruction through directed graphs. This approach provides higher operational traceability, since demand is linked to formal routes, directions, and stops. Nevertheless, it may underestimate demand that occurs outside the official route structure. The second methodology uses heat maps and 300-meter-radius polygons to identify functional demand areas based on observed passenger activity. This approach captures real operational behaviour more flexibly, but may lose direct correspondence with formal stops, especially when polygons overlap or include stops from different directions. The comparison shows that neither methodology is sufficient on its own. The graph-based method is useful for formal operational analysis, while the heat-map method is more sensitive to actual demand behaviour. Based on these findings, the paper proposes, as future work, the development of a multicriteria integration approach that combines both methods. Such an approach could reduce structural and observational biases, improve the processing of boarding and alighting data, and generate clearer maps, graphics, and analytical outputs to support expert decision-making in public transport operations.
Authors - Sayee Patil, Vaidehi Pathak, Purva Nalawade, Rupali Vairagade, Nilakshi Jain Abstract - Despite the high classification accuracy of ML-based Network Intrusion Detection Systems (NIDS) achieved on the widely used NIDS benchmarks, there is still limited understanding of the robustness of these systems against adversarial perturbations and whether and how such perturbations transfer between separate models trained on independent datasets. In this paper, an empirical study is conducted to determine the ability of adversarial examples generated in one model to attack another model with a different structure and a different training dataset. We create adversarial examples with two commonly used benchmarks, CICIDS2017 and UNSW-NB15, and train four models (Random Forest, XGBoost for both benchmarks). BoundaryAttack is a black-box decision-based attack suitable for non-differentiable tree ensemble classifiers. We build a complete 4×4 matrix of Attack Success Rate for all source-target model pairs. From our results, we can see that the crossmodel transferability within-dataset is very high (89–100%), meaning that the robustness of the models is not significantly increased by their diversity if they are trained on the same data distribution. Conversely, cross-dataset transferability decreases significantly (5–44%) even when the feature space is limited to 10 harmonized features semantically shared between the two datasets. PCA analysis of the harmonized feature space reveals substantial manifold separation between datasets, explaining the observed transfer degradation. We propose that the disparity between feature spaces is a natural and meaningful obstacle to adversarial transferability, and directly influence the design and testing of adversarially robust NIDS deployments.
Authors - Yisel Clavel-Quintero, Ernesto Gongora-Rodriguez, Melissa Carmenaty-Ramirez Abstract - The Internet has signicantly transformed the business landscape, particularly in the tourism industry, by removing geographical constraints and time restrictions, while enhancing accessibility for consumers. Nowadays, users tend to search online for destinations and opinions from other travelers, make reservations, and share their own assessments. Therefore, customer reviews have become a valuable source of information for companies seeking to evaluate service quality and improve their products, advertising strategies, and overall performance. In this context, opinion mining and sentiment analysis have gained relevance, particularly in platforms such as TripAdvisor, which rely on usergenerated content. A key challenge in polarity detection is the correct interpretation of irony, as it can alter the intended meaning and sentiment of an expression. However, there are still few available TripAdvisor datasets, and, to the best of our knowledge, none are labeled for irony. We propose the creation of a dataset of TripAdvisor reviews annotated with both polarity and irony, alongside an experimental study to identify a model capable of eectively classifying the polarity of ironic TripAdvisor user reviews. Transfer learning was applied by adapting models trained on two source datasets for irony detection, and the best-performing model was subsequently used to annotate a TripAdvisor dataset with irony. Furthermore, experiments for polarity classication were conducted. The logistic regression model achieved the best performance in both tasks. The dataset obtained oers a valuable resource for future research on sentiment analysis and opinion mining in the tourism domain.
Authors - Wilma G. Villacis, Enith J. Mejia, Judith A. Silva, Carlos I. Nunez, Julio E. Cuji, Edder D. Naranjo Abstract - Immersive virtual reality environments have gained increasing attention in language education due to their potential to provide authentic and contextualized opportunities for communication. Despite this growing interest, limited attention has been given to the systematic design and validation of the dialogue scripts that support interaction within these environments. This study aimed to develop and validate CEFR-aligned dialogue scripts for A1-level learners of English as a Foreign Language. A material design and validation approach were adopted, combining expert feedback and quantitative evaluation. Through the integration of CEFR descriptors, communicative functions, and useful language, nine dialogue scripts were developed across two scenarios: a university campus and a shopping center. The scripts were evaluated through a two-round Delphi process involving five experts in Applied Linguistics and English language teaching. Quantitative data were analyzed using descriptive statistics, while qualitative feedback was examined through thematic categorization. Findings from the first Delphi round identified issues related to linguistic level alignment, naturalness, and interactional authenticity, leading to targeted revisions. The second round demonstrated a high level of expert agreement regarding the appropriateness of the revised scripts for A1 learners. The study provides a structured and transferable framework for the development of dialogue-based materials and contributes to the pedagogical design of immersive language-learning environments.
