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 - 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 - 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 - 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 - Polinati Vinod Babu , M.V.P Chandra Sekhara Rao , Kolagotla Venkateswara Reddy , Manukonda Ravi Chandra , C P Pavan Kumar Hota , Kurumalla Suresh Abstract - The web services today are flooded with automated bots, distributed request floods and IP based attacks. Traditional firewalls are based on either a static signature rule or an opaque machine learning model that is not very transparent. We introduce ShieldNet, a lightweight Web Application Firewall (WAF), which is developed in FastAPI and Redis as a state storage. The explainable real-time request filtering performed by ShieldNet is on the basis of a deterministic decision pipeline, a combination of IP reputation scoring (Redis backed, counts on infractions), SlowAPI based rate limiting, Detection of bot user agent, Up-to-date use of open access threat feeds (spamhaus, abuse.ch, etc.). Request identities can be provided as either permitted (HTTP 200), rate limited (429) or blocked (403); all of which can be traced through the middleware layers. Synthetic load testing and feed validation reveal low latency, high throughput and high detection fidelity. The system has good performance, scalability and interpretability balance thus it can be used in real-time web security implementation.
Authors - Apolinar P. Datu, Pamela V. Zuniga, Chona S. Lajom, Jobert D. Bravo, Renen Paul M. Viado, Ma. Yvonne Czarina C. Angcaya, Maricris Punzalan Abstract - Higher education institutions play a critical role in advancing sustainable development, particularly through effective and inclusive communication practices supported by digital technologies. This study examined the role of Information and Communication Technology (ICT) standards in shaping effective communication strategies for sustainable development in higher education. Using a quantitative descriptive research design, data were collected through a structured questionnaire administered to thirty (30) higher education personnel, including faculty members, administrators, and ICT staff aged 22 years and above. A 4-point Likert scale was utilized to measure levels of ICT standards implementation, communication effectiveness, accessibility and inclusivity, system interoperability, and stakeholder engagement. The findings revealed a high level of ICT standards implementation across participating institutions. Results further showed that ICT standards strongly influence the clarity, reliability, timeliness, and organization of communication strategies. Standardized ICT practices were also found to enhance accessibility and inclusivity in digital communication, support system interoperability, and improve stakeholder engagement in sustainability-related initiatives. Overall, the study highlights that ICT standards serve as essential enablers of coherent, inclusive, and sustainable communication in higher education. The findings underscore the importance of strengthening ICT standards to support institutional sustainability goals and foster meaningful stakeholder participation.
Authors - A Aruna kumari, Sri Vishnu Prabhu Gudavalli, Tamminana Visweswari Abstract - This project presents a hybrid architecture, which combines sequential and non-sequential models, to detect diabetic retinopathy (DR) in retinal fundus image data using deep learning. The model has a non-sequential backbone feature extraction layer, a pre-trained ResNet50, built using the Functional API of Keras to allow flexibility and skip connections. On top of that, a functional custom sequential classifier head is added, comprising of, e.g., GlobalAveragePooling2D, Dense, and Dropout layers, also constructed in a functional manner to ensure the architecture is coherent. The classifier is used to make a binary prediction: DR or not. The model was trained using the EyePACS data using different techniques of image preprocessing, including resizing, normalization, and augmentation. The resulting architecture is completely accurate and exhibits good generalization on unseen data, which indicates the effectiveness of transfer learning in combination with deep sequential classifiers. This bifurcated architecture is understandable and performance-wise attractive to be used in clinical decision support in diabetic retinopathy screening.
Authors - Masaki Murakami, Atsuhiro Goto Abstract - Post-quantum cryptography (PQC) migration in the financial sector is a resource allocation problem shaped by network interdependence. This study develops a prototype dynamic simulation model that incrementally introduces cyber-risk internalization: starting from a baseline without cyber-risk channels, adding firm-level direct cyber loss, and finally incorporating systemic cyber loss propagated through the financial network. The paper quantifies how the scope of cyber-risk internalization determines migration outcomes under a shared investmentallocation framework. Results show that firm-level internalization accelerates early migration but remains insufficient for full sector-wide coverage, whereas systemic-risk internalization can close the late-stage adoption gap under sufficiently supportive policy conditions. This distinction shows that policy support for PQC migration should not be understood only as cost reduction, but also as a mechanism for internalizing network externalities. Through nested comparisons of these model variants, this study demonstrates how each risk channel distinctly shifts migration outcomes.
Authors - Akbar Doniyorovich, Doniyor Gulomov Zaynobidin o'g'li, Dilshoda Akramova, Xushmamatova Aminakhon Rustam qizi, Tukhtaeva Shakhnoza Gaybulla kizi, Abdimurodova Shakhnoza Anvar qizi Abstract - This article discusses the theoretical foundations and рractical directions of innova tive didactic methods aimed at develoрing the comрetence of using artificial intelligence (AI) in рedagogical activities. The study analyzes the рroblem of forming teachers' digital literacy, technological readiness, and ability to integrate intellectual technologies into the educational рrocess. The article рroрoses methods aimed at increasing the innovative comрetence of teach ers based on a didactic aррroach and a steр-by-steр learning model develoрed to achieve effec tive results in the use of AI tools in education. These methods include aррroaches such as рroblem-based learning, рroject work, reflexive analysis, and collaborative learning with AI as sistants. According to the results of the study, the systematic introduction of artificial intelli gence technologies into the educational рrocess develoрs the information-analytical, methodo logical, creative, and reflexive comрetence of teachers. The use of innovative didactic methods increases the effectiveness of teacher activity, individualizes the educational рrocess, enhances analytical thinking, and a creative aррroach. The scientific results рresented in the article serve as a scientific and methodological basis for the рrocess of modernizing the рedagogical education system, imрlementing the conceрt of digital рedagogy into рractice, and рreрaring future teachers to work with artificial intelligence technologies.
Authors - Apolinar P. Datu, Jobert D. Bravo, Reine Joshua L. Cruz, Helga Marie B. Cabarle, Najera R. Umpar, Minsoware S. Bacolod Abstract - In today’s rapidly evolving Information and Communication Technology (ICT) landscape, digital tools have become essential in shaping how research is presented, shared, and collaboratively developed. This study aimed to quantitatively examine the collaborative benefits of digital tools and their impact on user engagement during research presentations. Using a quantitative descriptive–comparative research design, data were collected from thirty (30) respondents consisting of students, educators, ICT professionals, and administrative staff who regularly engage in ICT-based collaborative activities. A structured survey questionnaire was used to gather numerical data on the frequency of tool usage, levels of collaboration, and user engagement. Descriptive statistics such as frequency, percentage, and weighted mean were employed to analyze the data. The findings revealed that digital collaboration tools are frequently used in ICT-related activities and are strongly associated with improved coordination, communication, and teamwork. Respondents reported high levels of collaboration and engagement, particularly when using interactive features such as real-time communication, shared document editing, and feedback mechanisms. These features were found to enhance motivation, focus, and willingness to participate actively in research-related tasks. Overall, the study highlights the importance of intentional and effective integration of digital tools in research presentations to foster meaningful collaboration and sustained engagement in ICT-based academic and professional environments.
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - Inadequate Resource Issues (IRI) are one of the challenges in Strategic Management (SM). This study concentrated on applying System Dynamics (SD) modeling to solve IRI. Strategic standard tools like SWOT analysis, PESTEL analysis, and the Resource-Based View have proven effective in addressing IRI; unfortunately, developments like digital transformation, long-term sustainability, and the rise of emerging market multinational corporations are poised to shape the future of SM in these regions. These traditional methods are no longer coping with new technology; hence, the authors implemented a new SD model to tackle the IRI in SM. Additionally, most strategic managers are incapable of developing an SD model due to mathematical and scientific complexity. Although SD is a reliable technique for handling complex issues in management, most managers reject SD because of the implementation’s need for scientific and mathematical requirements. To solve IRI mathematically and make scientific predictions about what would happen if variables were altered in the upcoming five years (2025–2035) and the impact on customers, the study created an SD model.
Authors - Arosha de Silva Abstract - The COVID-19 pandemic disrupted educational systems worldwide and required schools to adopt online learning within a short period. In Sri Lanka, secondary school teachers encountered numerous difficulties while adapting to virtual teaching environments. This study examines the challenges experienced by teachers when conducting online instruction during the pandemic. A mixed-methods approach was adopted, combining qualitative interviews with quantitative survey data collected from secondary school teachers and educational professionals. The findings revealed that teacher motivation, technological infrastructure, and increased workload significantly influenced the effectiveness of online teaching. Difficulties related to internet access, digital resources, and professional demands affected teachers’ ability to deliver lessons efficiently. The study highlights the importance of institutional support, professional training, and improved access to technology in strengthening online education. The findings may assist policymakers and educational institutions in developing effective strategies to support online and blended learning initiatives in the future.
Authors - Bharg Trivedi, Chaitaili Chandankhede Abstract - There is a continuous change in Android malware because it is obfuscated, polymorphic, and structural. Such changing methods diminish the performance of conventional signature-based detection methods. In an effort to defeat this challenge, the present paper provides a model that uses CNN and GNN models. It is an integration of spatial byteplot representations and structural call graph representations to successfully identify Android malware. Our study was based on a dataset of 1,159 real Android applications, and the used extraction technique was based on the static features. The CNN element of the structure Recognized robust spatial attributes of the grayscale images of the byteplot data with a ResNet-50 network. Meanwhile, the GNN component of the structure used a GraphSAGE network to derive structural representations of automatically generated function call graphs. The fused representations are combined into a 2304 dimensional feature vector. It is also optimized by making use of different methods such as Mutual Information. In this study, an Extreme Gradient Boosting Classifier on the fused representations to achieve successful Android malware detection. The assessment indicates that the framework attains a classification accuracy of more than 99% with businesses across the cross-validation holding the same accuracy.
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - Knowledge management (KM) is an essential company training process that incorporates four sequential factors: non-knowledgeable professionals, training to become knowledgeable professionals, new knowledgeable professionals, and knowledgeable and experienced professionals. Because the aforementioned variables are interconnected and make it extremely difficult to produce a measured solution, they must be thoroughly analyzed using mathematical formulas and reliable techniques. These issues impact businesses of all sizes, necessitating a versatile instrument for flexible KM analysis. Additionally, most KM managers dislike SD modeling due to its complexity, especially those without scientific training. This study recommended using system dynamic (SD) modeling rather than conventional tools to address the aforementioned issues. The use of SD modeling stems from three factors: (a) the examination of complicated dependencies; (b) the requirement for mathematical formulas; and (c) the graphical results in contrast to traditional methods. The study’s SD model included the four sequential factors and their relationships. KM managers should focus especially on the graph’s data when necessary modifications are needed.
Authors - A Aruna kumari, Tamminana Visweswari Abstract - Lung cancer is a fatal illness that causes several deaths worldwide and detection of lung cancer remains a challenge for medical professionals. Detection of cancer in early stages is difficult as the size of the tumor is very small making it difficult for medical professionals to detect. Cancer detected in the early stages can be treated with proper techniques which can save the lives of the patients. Due to excessive information in the CT scans, MRIs, X-rays, and PET scans the manual detection of lung tumor becomes extremely difficult. The methodology helps in detecting the presence of cancerous tissues in the lungs and predicting which stage of lung cancer is present. The methodology mainly includes image preprocessing, training the model, extracting features using deep learning algorithms and classifying the stage of cancer present as Normal, Benign, Malignant Stage 1, Malignant Stage 2, and Malignant Stage 3. Proper image processing techniques like image augmentation, image normalization and image resizing are applied on the IQ-OTH/NCCD dataset for extracting the necessary features which will be used while training the model. A hybrid model is created by combining two deep learning models, the Xception and MobileNetV2 architectures which can accurately distinguish between the different lung cancer stages and predict the stage of cancer. The performance metrices which include accuracy, precision, recall, f1-score and confusion matrix were also calculated to determine the accuracy of the proposed hybrid model. The proposed model helps in accurate and reliable diagnosis of lung cancer at early stages.
Authors - Erika Haydee Rubio-Camara, Oscar May Tzuc, Elsy Maria Rosales-Uc, Fran-cisco Gilberto Herrera-Chale, Roman A. Canul-Turriza, M. Jimenez Torres Abstract - Mechanical vibration energy harvesting has emerged as a promising strategy for supporting sustainable energy generation in industrial environments, where machinery and transportation systems continuously produce recoverable vibrational energy. This study presents the development and evaluation of predictive models based on deep multilayer perceptrons (DMLP) and Convolutional Neural Networks (CNNs) for estimating the energy potential associated with mechanical vibrations under industrial operating conditions. A simulation frame-work was implemented using experimentally reported operational ranges, including vibration frequencies between 10 and 50 Hz, amplitudes from 0.01 to 0.03 m, and temperatures between 25 and 45 °C. The analysis considered piezoelectric, electromagnetic, and triboelectric harvesting mechanisms to evaluate model adaptability under different scenarios. The predictive framework was implemented using TensorFlow and validated through a 10-fold cross-validation strategy combined with hyperparameter optimization. Results indicate that both architectures achieve high predictive capability for estimating harvested energy; however, CNN models consistently outperformed Deep MLP models, obtaining lower prediction errors and higher stability across validation folds. The superior performance of CNNs is associated with their ability to capture localized patterns and structured relationships within vibration-related data. The proposed method-ology demonstrates the feasibility of integrating artificial intelligence techniques into vibration-based energy harvesting systems for industrial applications. Furthermore, the study provides a computational framework for evaluating operational conditions, optimizing harvesting performance, and supporting the design of sustainable self-powered monitoring systems.
Authors - Khumbelo Difference Muthavhine, Mbuyu Sumbwanyambe Abstract - The financial management involves outstanding debts, trade receivables, deduction charges, economic value, generating capacity, and stockpiling accumulation. These six variables mentioned above must be carefully examined using mathematical formulas and trustworthy tools because they are interrelated, which makes it very difficult to establish a solution. These challenges affect companies of all sizes, requiring a flexible tool for adaptable financial management analysis. To mitigate the aforementioned problems, this study suggested system dynamic (SD) modeling to handle the problem instead of using traditional tools. The SD modeling is used because of (a) complex dependencies analysis, (b) the need for mathematical formulas, and (c) the graphical outputs compared to traditional tools. The study constructed an SD model with six variables and their interconnections. When adjustments are required, financial managers should pay particular attention to the out-of-graph data.
Authors - Mariusz Szynkiewicz Abstract - The protection of information resources is one of the central issues in contemporary information science, and one also significant from an IT perspective – particularly in the context of cybersecurity. In this article, I propose one possible approach to addressing this challenge. The proposal concerns the protection of information resources in a broad sense: from the stage of information acquisition and creation through to its distribution. In the following sections, I outline the main assumptions of a comprehensive model for the protection of information resources, discussed in the context of building the information resilience of participants in digitised information exchange processes, and in relation to issues associated with the concept of cyber hygiene. The central thesis of the article rests on the assumption that effective protection of information resources is possible only on the basis of an integrated model addressing the following procedures: (a) validation – the assessment of the level and value of a given resource; (b) threat identification – the logical level, scale, and types of vulnerability; (c) detailed analysis of the type of abuse – qualitative diagnosis; (d) selection of techniques and methods for counteracting a given class of attack – the methodological level; and (e) selection of possible corrective and preventive measures – the elements of cyber hygiene.
Authors - S. Jimenez-Garcia, V. Zorrilla-Munoz, G. Martinez-Navarrete, N. Garcia-Aracil, A.M. Peiro-Peiro Abstract - This paper presents an integrated framework that combines digital screening, ergonomic assessment, humanoid robot benchmarking and immersive virtual reality (VR) training to support the prevention of musculoskeletal disorders and burnout in nursing professionals. The framework is grounded in occupational health data from Spanish nursing professionals and incorporates sex/gender and anthropometric differences as design variables. A descriptive cross-sectional analysis was performed on 316 professionals from the European Health Survey in Spain, filtered by occupation, and complemented with the PROBEREN project approach. Two high-demand activities in Internal Medicine and Infectious Diseases units were selected: hygiene, comfort care, pressure ulcer prevention and postural changes in bedridden patients; and intrahospital transfers under isolation or clinical support. The sample showed a strong proportion of women (86.4%), mean age of 45.97 years, chronic health problems in 54.7%, prescribed medication use in 51.9%, recent physical pain in 46.8% and pain interfering with daily activities in 31.6%. These findings support a transition from descriptive profiling to proactive prevention. The proposed ecosystem links early screening, capture of expert movements, biomechanical comparison with a humanoid robot, personalized VR training and longitudinal reassessment. Future pilot validation should evaluate usability, VR-related fatigue, ergonomic risk reduction, burnout, pain and implementation barriers in real clinical settings.
Authors - Aleksander Karastoyanov Abstract - Reinforcement learning (RL) has been widely proposed for adaptive virtual machine (VM) right-sizing in cloud environments, yet most published work reports results from a single random seed with a fixed reward formulation, conditions that may not reflect genuine generalization. This paper addresses both limitations through a systematic multi-seed, multi-reward evaluation of a Proximal Policy Optimization (PPO) agent applied to VM right-sizing on the Alibaba Cluster Trace 2018. Fifteen independent training runs (three reward configurations × five seeds) are conducted on a ten-VM simulation environment. The key finding is that reward formulation, not the RL algorithm per se, is the dominant determinant of SLA compliance: the unified reward variant (Config A) achieves a mean SLA violation rate of 1.1% (±2.11) across five seeds, statistically comparable to a Threshold baseline (2.43%), while dimension-aware variants expose a critical instability in memory-saturated environments. A one-sample t-test yields t = −1.40, p = 0.23, confirming that neither superiority nor inferiority relative to Threshold can be claimed, and motivating the need for larger seed sets and alternative reward designs in future work. Three empirically grounded reward design principles are derived for practitioners deploying RL-based resource managers in high-memory-pressure infrastructure.
Authors - Soham Paithankar, Supriya Narad Abstract - Agriculture is a major contributor to India economy and supports the livelihood of a large population, however, traditional farming practices largely depend on manual observation, weather information, which often results in resource utilization and reduced crop productivity. The increasing variability of climate condition further challenges. The integration of internet of things (IoT) and software technologies provides an effective approach and data-driven decision by enabling real-time monitoring and data-driven decision making. This paper presents such as temperature, humidity, and soil moisture using field sensor data. The collected information is processed and stored using Python based software and compared with real-time weather information obtained through a weather API. A dashboard interface developed using Flask and streamlit visually presents sensor data, weather data, and comparative result to support informed decision-making system. The system demonstrates how low-cost IoT devices combine with software platform can improve agriculture monitoring, optimize resource usage, and support sustainable farming practice, this study highlights the potential of IoT and software integration in transforming the potential of IoT. agriculture into an intelligent data-driven system suitable for developing region such as India.
Authors - Emmanuel Opoku Debrah, Sunet Eybers, Corne J. van Staden Abstract - Student Information and Management Systems (SIMS) are increasingly being implemented in higher education institutions (HEIs) in developing countries. However, with these mandatory systems, there is limited evidence of user satisfaction. This systematic literature review investigates the influence of technological, organizational, and environmental (TOE) factors on user satisfaction with the use of mandatory SIMS in HEIs in developing countries, with a focus on Ghana. A search was conducted across six databases and Google Scholar, focusing on the past decade (2015-2025). Two reviewers independently evaluated the studies' quality using the Mixed Methods Appraisal Tool (MMAT 2018). The average consensus MMAT score was 4.08/5 (7 High, 14 Moderate, 5 Lower quality). A total of 1,382 records were screened and considered for duplicates and applicability. The result was 27 empirical studies. A narrative synthesis supported by thematic mapping identified the most frequently reported determinants: system quality, reliability, and performance (n=18); organizational support, IT capacity, and management readiness (n=12); perceived usefulness, ease of use, and user experience (n=10); ICT infrastructure and connectivity (n=9); and information quality (n=6). The synthesis suggests that TOE factors are not independent: technological advantages can be offset by organizational weaknesses, and environmental factors set an upper limit to satisfaction. This suggests that Ghanaian HEIs focus on integrated investments in user training, technical support, and infrastructure rather than just system upgrades.
Authors - Md Manirul Islam, Md. Mushfiqur Rahman , Sazzad Hossain Abstract - Moving IoT-edge systems to post-quantum security is not as simple as replacing one algorithm with another. Different parts of the system have different security needs. Short-lived telemetry, control messages, firmware updates, and trust records should not all be protected in the same way. Device limits are also very different: a tiny leaf sensor, a capable actuator, a gateway, and a cloud service do not have the same memory, energy, or bandwidth budget. This paper presents an adaptive hybrid classical-post-quantum cryptographic framework for such heterogeneous environments. We make four main contributions. First, we define a four-tier system and threat model for resource-constrained IoT-edge deployments. Second, we describe profile selection as a practical decision problem that balances security, latency, energy, memory, and bandwidth. Third, we propose a small profile catalog, P0 to P4, that maps classical, hybrid, and PQ-first choices to real deployment roles. Fourth, we evaluate the framework through a benchmark-grounded experimental emulation using current standards and published device measurements. The main idea is simple: apply the strongest protection where compromise would hurt most, while keeping weak devices usable in the real world.
Authors - Imane Bari, Mounir Oubenyahya, Abdellatif Aziki, Fouad Achemchem Abstract - Climatic conditions and water scarcity in the Souss Massa region of Morocco are causing a significant decline in fodder supply and threatening the continuity of dairy farming. In response to these structural constraints, dairy cooperatives have adopted hydroponic green fodder (HGF) cultivation, an approach that reduces water consumption and frees agricultural land for higher-value crops. This study analyses the financial performance and structural effects of HGF adoption in two dairy cooperatives selected as pilot cases. Using a descriptive and explanatory methodology based on accounting and financial records collected over 4 to 6 years, combined with a non-parametric econometric model, the study assesses production costs, financial viability indicators, and the effect of innovation on key financial ratios. Results confirm the financial viability of the HGF investment and document its temporary effects on financial independence and self-financing capacity, while highlighting the social dimension of this innovation in preserving livestock farmers’ livelihoods.
Authors - Swetha P. , Maniratnam, Prasad B Honnavalli Abstract - The dark web has been identified as a major source of organizational risk, facilitating underground markets for stolen credentials, proprietary data, and pre-attack threat intelligence. An organization lacking visibility in these underground marketplaces faces attacks entirely beyond conventional monitoring solutions. This paper introduces a realtime dark web monitoring system integrating anonymous browsing via Tor, Selenium WebDriver, and open-source Wazuh Security Information and Event Management (SIEM) to create a unified threat intelligence solution. The system performs continuous keyword searching for organizational name references and autonomously browses authenticated dark web marketplaces for content extraction. Identified events are serialized as structured JSON messages and ingested into Wazuh via custom decoder and rule definitions, providing actionable alerts on the analyst dashboard within seconds. The system has been evaluated over 24-hour continuous sessions on two live dark web markets, achieving 95% keyword detection accuracy, 1.4 seconds average alert latency to dashboard, and stable long-duration browser operation without application crashes. The system requires no commercial licensing, is fully configurable to organizational targets, and features native SIEM integration, making it a viable and accessible solution compared to proprietary dark web intelligence services.
Authors - Pam Cole Abstract - As artificial intelligence transitions from passive tools to autonomous agents, governance frameworks designed for slower systems are being asked to contain behavior they were not engineered to address. Two converging forces widen the resulting gap. Synthetic Trust describes the industrialization of unearned credibility, where AI systems simulate socio-emotional cues to suppress verification. Ungoverned Acceleration describes autonomous execution at a pace that outruns institutional oversight. Drawing on cross-industry research covering sixteen major AI systems, peer-reviewed findings on emotional manipulation, and documented incidents across sectors, this paper proposes the A.W.A.R.E. Governance Flywheel, organized around Identity, Trust, and Control spokes with Visibility, Attribution, and Containment as operational mechanisms, bound by Accountability. A comparative analysis of six frameworks across six continents shows no widely adopted framework currently operationalizes agentic governance at this specificity. The paper extends the framework with operational metrics, industry applications, adoption barriers, and three testable propositions.
Authors - Yasmine AGOUN, Cheikh SALMI, Nour El-Houda SENOUSSI Abstract - The rapid growth of the Internet of Things (IoT) has made cybersecurity prone to several vulnerabilities, highlighting the need for a semantic and well-organized structure for cybersecurity knowledge to ensure reliable threat detection and mitigation. In this paper, we propose IoTSecOnto, a largely automated pipeline for building a security-centric Internet of Things (IoT) ontology. The pipeline combines automated security literature mining, text analytics, natural language processing, Large Language Models (LLMs), and formal concept analysis. This approach reveals domain-specific concepts and relations and organizes them into a coherent hierarchy. A human-assisted review phase is needed to ensure the reliability and the accuracy of the derived security knowledge. In addition, the ontology is designed to be flexible and can be improved over time. IoTSecOnto is implemented using OWL 2, RDFLib, SPARQL, and SHACL constraints. We used a Mirai botnet and ontology quality metrics to demonstrate its effectiveness. The obtained results confirm the ability of IoTSecOnto to support knowledge sharing, automated reasoning, and improved threat analysis across diverse IoT settings.
Authors: Nellylyn Moyo, Naume Sonhera Abstract: The adoption of generative artificial intelligence (GenAI) has quickly evolved from its experimental stage to a question of institutional governance in higher education. Although initial approaches to the integration of GenAI in universities have been characterized by ad hoc trial-and-error methods and inconsistent policies, higher education organizations are now taking a more systematic approach to the regulation and implementation of GenAI for educational, research, and administrative purposes. This study provides a conceptual critical literature review of the approaches being adopted by universities toward GenAI, emphasizing trends in the global context and in Africa and South African higher education. The study demonstrates that GenAI readiness should not be viewed solely in terms of willingness to use AI technologies but rather as a multifaceted construct. Institutional governance, on the other hand, has evolved to incorporate responses related to issues of academic integrity, redesigning assessments, AI literacy, requirements for disclosure, development of staff, protection of data, and responsible-use guidelines. Nevertheless, such an evolution is patchy, reactive, and very much dependent on institutional capacities, disciplinary differences, and available resources. In the case of African higher education, the possibilities generated by the advent of GenAI have been heavily determined by structural limitations, such as digital inequality, uneven infrastructures, immature policies, and lack of capacity building measures. South Africa emerges as a particularly interesting example because of the emergence of governance responses in a space characterised by inequalities related to access, digital literacy, and institutional capacity. The study argues for an approach to GenAI adoption readiness that would consider the issue at three interdependent levels: individual readiness, organisational readiness, and structural readiness. Such an approach would enable a more contextually sensitive perspective on responsible GenAI adoption.
Authors: Nellylyn Moyo, Sello Prince Sekwatlakwatla, Tranos Zuva Abstract: GenAI has created considerable opportunities and challenges within institutions of higher education. While GenAI tools could help with teaching, learning, assessment, feedback, academic writing, research, and administrative functions, the uptake of these technologies cannot merely be viewed from a perspective of accessibility. In this study, an approach towards institutional readiness for adopting GenAI within higher education institutions is provided by examining infrastructure, governance, training, readiness for ethical adoption, reassessment, and fit-to-use practices. Peer-reviewed literature written between 2020 and 2026 is reviewed here to show that institutional readiness for GenAI adoption must be viewed as an organisational capability rather than as a static technology readiness state. The conclusion can be drawn that while reliable digital infrastructure is an indispensable requirement, institutional readiness also requires governance maturity, clarity of policy, integrity, protection of data, professional development of faculty members, AI literacy of students, as well as rethinking of assessments. The readiness concept is additionally influenced by factors such as digital inequality, varying capacities of institutions, limited resources, and policies in the higher education systems in Africa and South Africa. Fit to use alignment and practice-policy alignment are two concepts that may be useful in determining how well integrated the use of GenAI technology is into educational processes. GenAI can only be used responsibly if there is proper alignment among various aspects, including technology, policy, ethics, and context.
