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 - 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 - 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 - 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 - Wonseong Kim Abstract - Geopolitical risk now tends to surface in information environments well before it shows up as physical disruption or moves in market prices, and often before any policy response. Current geopolitical risk indices track the salience of news, and a separate body of work on disinformation detection looks for signals of manipulation. What neither line of work does is connect manipulated discourse to the channels through which food, energy, supply chain, sanctions, and macro-financial risks are actually transmitted. To address this gap, the paper develops a DISARM-informed large language model framework for narrative-based monitoring of disinformation-driven geopolitical risks. The framework is organised as a four-layer architecture that brings together observable manipulation signals, the classification of narrative function, mapping onto risk domains, and the construction of indicators. From these layers it derives interpretable indicators that capture manipulated risk discourse, gaps in framing, concentration of narratives, and transmission across domains. Our central claim is a methodological one: once observable manipulation, narrative function, and risk-domain mapping are represented together, unstructured multilingual media can be turned into auditable early-warning signals. We intend the result as a decision-support instrument for sustainable security monitoring, not as a means of attribution or causal estimation.
Authors - Nassour Annour Saad, Mahamat Issa Hassan, Marayi Choroma, Mahamat Atteib Ibrahim Doutoum, Djaury Dadjia Abstract - Pediatric type 1 diabetes (T1D) remains a major public health priority, especially in low-resource settings where presenting diabetic ketoacidosis is still common. This systematic review (2020–2025), conducted under PRISMA 2020 and complemented by TRIPOD/TRIPOD+AIinspired criteria for predictive models, synthesizes AI/ML work on early screening and risk stratification in children. Islet autoantibodies and genetic risk scores improve discrimination, but the literature shows substantial AUC variability depending on sample size, calibration, and validation design (single split versus repeated or family-level validation). Ensemble models often outperform classical approaches with multimodal data. We emphasize external validation, class-imbalance handling, and reproducible pipelines. The main gap remains the absence of models simultaneously integrating autoantibodies, HLA/GRS, and C-peptide.
Authors - Imane Bari, Abdellatif Aziki, Zineb Alaoui Abstract - This study analyses the relationship between digital operational risk management and the financial performance of industrial firms in the Agadir region of Morocco and investigates the role of artificial intelligence in risk governance. Using a quantitative survey of 50 industrial firms and linear regression with principal component analysis, the results show that a structured digital risk management framework, covering identification, assessment, and mitigation of threats, is positively and significantly associated with financial performance (R = 58.7%, F = 3.518, p < 0.05). Several constraints are identified, including skill shortages, limited technological resources, and insufficient digital governance culture. The study further shows that AI-based tools, through automated anomaly detection and predictive analysis, strengthen the effectiveness of risk management frameworks. These findings support the integration of AI as a technical component of operational risk governance in industrial settings.
Authors - Libero Nigro, Franco Cicirelli Abstract - This paper builds on the Hartigan-Wong (HW) algorithm for unsupervised clustering. Although basic HW comes with an intrinsic high computational cost, it is known to be a better solution than K-Means, because it is less likely to get stuck around a sub-optimal solution of the data space. The paper, in particular, proposes a variant of HW, named Evolutionary HW (E-HW), which embodies genetic concepts and favors the achievement of more accurate clustering. E-HW depends on the use of a population of candidate solutions (centroid configurations), preliminarily created. E-HW is fed by a solution extracted from the population, which gets refined (crossed) and possibly replaced (mutated) following the basic HW operations. New generations of the population then come into existence. E-HW can be repeated a certain number of times, after that, experimental results highlight that the population favors the emergence of a solution close to the optimal one. To smooth out the computational burden, many operations of E-HW are implemented in parallel Java, so as to exploit the computing benefits of modern multi-core machines. The paper demonstrates the effectiveness of E-HW by using a collection of benchmark datasets, and the clustering results are compared with those achieved by competitor algorithms.
Authors - Ilgar G. Aliyev, Konul Gafarbayli, Firangiz Mammadrzayeva Abstract - Modern parallel gas pipeline systems require intelligent and operationally reliable emergency-management mechanisms capable of distinguishing real leakage events from normal technological transients under real-time operating conditions. Although IoT- and SCADA-based monitoring technologies are widely used in modern gas transmission infrastructures, most existing systems primarily rely on threshold-based supervision or empirical data-driven methods, which often lack physical interpretability and analytical decision-making capability. This paper proposes an intelligent IoT-driven emergency-management and analytical decision-making framework for parallel gas pipelines based on the integration of digital monitoring technologies with analytical gas-dynamic modeling. The proposed cyber-physical architecture combines wireless pressure sensors, SCADA-assisted supervisory control, synchronized shut-off valves, and analytical decision algorithms to ensure real-time identification, localization, and mitigation of emergency operating modes. Analytical criteria are developed for distinguishing emergency and technological pressure variations, estimating emergency detection time, localizing the leakage coordinate, and determining the optimal activation time of interconnecting pipeline valves. The proposed framework enables rapid isolation of damaged pipeline sections while ensuring adaptive gas redistribution through intact parallel lines. Unlike conventional monitoring-based approaches, the developed methodology transforms emergency control into an analytically justified intelligent supervision mechanism capable of minimizing gas losses, preventing cascade disturbances, and improving operational sustainability. The integration of IoT-based sensing with analytical decision-making additionally improves compatibility with Industry 4.0 and digital twin concepts for future smart gas transmission infrastructures.
Authors - Kirill Kalichkin, Tatiana Gritskevich Abstract - The study is devoted to the analysis of vibration agnostics problems as a method of preventive control in the design of wheel sets of railway locomotives. The study examines vibration agnostics as a preventative control method for designing individual mechanisms and components of railway locomotive wheel sets designed for long-term, safe operation. Currently, the main issue with the mechanical drives of wheel-motor unit assemblies and motor-anchor bearing assemblies in railway locomotive wheel sets remains increased vibration during highfrequency operation. The authors analyze the prediction of potential defects using digital twins, the goal of which is to enable engineers to accurately predict solutions when similar defect signs are detected during operation of different digital twin scenarios. This enables the development of preventative measures to prevent accidents at the early stages of wheel set defect development.
Authors - Mariia Nazarkevych, Vasyl Lytvyn, Oleg Stechkevych, Hanna Nazarkevych, Roman Cholkan, Danyil Korotych Abstract - An information technology for adaptive enterprise management using weak signals has been developed, which is based on the collected information about the environment, the assessment of factors affecting the enterprise, the calculation of the indicator of the impact on the enterprise based on integral dependence, the method of detecting weak signals and predicting the state of the enterprise, which provides high sensitivity taking into account changes in the environment and increases the efficiency of enterprise management. A method of recognizing weak signals is shown, which, by comparing the permissible value with the difference between the found and predicted values of the indicator of the impact on the smart enterprise based on integral dependence, provides early detection of threats or opportunities for the smart enterprise. It is proposed to develop a smart enterprise management system using weak signals based on an integrated approach and in accordance with the following principles: systematicity; integration of computer, communication and software components; modularity; openness; compatibility; variable equipment composition.
Authors - Marlon Kulatunga, Kaavya Raigambandarage, Senali Guruge, Themiya Alwis, Amila Nuwan Senarathne, Kavinga Yapa Abeywardena Abstract - Contemporary Kubernetes deployments suffer from two fundamental shortcomings: admission control mechanisms apply static rule sets without accounting for namespace operational context, and content inspection services governed by RFC 3507 remain disconnected from the orchestration layer. This work presents an integrated four-module security framework that jointly addresses both deficiencies. A probabilistic namespace characterisation algorithm employing seven weighted indicators achieves 96.7% accuracy in determining deployment tiers, even when metadata labels are absent or deliberately misleading. A compliance-driven policy orchestrator aligned with CIS Kubernetes Benchmark controls and PCI-DSS v4.0 requirements translates a unified constraint representation into artefacts for both OPA Gatekeeper and Kyverno, attaining 99.2% cross-engine decision parity. An environment-responsive traffic manager generates tier-specific Istio routing configurations, while a custom Kubernetes operator governs content scanning pod lifecycles through a multi-dimensional wellness metric that captures security-relevant signals invisible to conventional autoscalers. Evaluation on a five-node K3s cluster demonstrates full compliance coverage across 93 benchmark controls and 28 regulatory mandates, sub-five-second failover under all disruption scenarios, and correct detection of degraded scanning capability that CPU and memory metrics alone would overlook.
Authors - Ronewa Gilbert NTHATHENI, Tumiso THULARE Abstract - The rapid advancement of digital technologies has transformed the relationship between governments and citizens, creating new opportunities for participatory governance through e-government initiatives. This study evaluates the effectiveness of online engagement platforms in promoting democratic governance in South Africa. Using a scoping review methodology, the research examines the benefits, challenges, and contextual dynamics shaping citizen participation through digital platforms. Findings suggest that while online engagement tools enhance transparency, accountability, and access to information, their effectiveness is constrained by structural barriers such as the digital divide, limited institutional capacity, and low digital literacy. The study concludes that the success of e-participation initiatives depends on inclusive design, infrastructure investment, and meaningful government responsiveness. Recommendations are provided to strengthen digital governance and improve citizen engagement outcomes.
Authors - Supriya Narad Abstract - Agricultural economies are predominantly relevant in developing countries, wherein the farmers have to struggle operating under the impact of several constraints posed by crop diseases. Among food crops, the potato is a major one with vulnerable destructive diseases like Early Blight and Late Blight, capable of destroying the yield if detected late. Old methods of visual inspection are time-consuming and sometimes erroneous because of laxity, human error, and lack of expertise. With this research, an automated intelligent disease detection system is devised, making use of image processing and deep learning, Arduino, specifically Convolutional Neural Networks (CNNs). The model was trained using potato leaf images from the Plant Village dataset, which are improved using various preprocessing techniques, including color space conversion, image augmentation, and image resizing. The proposed CNN architecture achieved a high rate of classification accuracy of 97.2% in distinguishing healthy leaves vs. Early blight and Late blight infected leaves. Lightweight, reliable, and fast, it supports implementation on mobile or handheld devices in low-resource environments, thus giving farmers the ability to use them for timely diagnostics. The system has good prospects for scaling up for other crops and disease types in future versions.
Authors - Khadidje OUSMANE KOSSI, Mandicou BA, Bachar Haggar SALIM, Simon Antoine SARR, Maboury DIAO, Alassane BAH Abstract - Heart disease in athletes remains a significant challenge in sports cardiology and an important public health concern, particularly among young competitive individuals at risk of sudden cardiac events. Although pre-participation screening programs are widely implemented, diagnostic uncertainty persists, especially in distinguishing physiological cardiac remodeling from pathological cardiomyopathy. This complexity results from the interaction of genetic predisposition, structural adaptation, electrophysiological variability, and cumulative training exposure. Using the PRISMA framework, this study presents a systematic review of research published between 2015 and 2025 to evaluate the application of artificial intelligence (AI) in the diagnosis and monitoring of cardiovascular diseases in athletes. The analysis reveals that most studies rely on unimodal, monocentric, and retrospective designs, often based on limited datasets and lacking external validation. Despite high reported performance metrics, performance degradation of 5–10% in external cohorts is frequently observed. Furthermore, explainability techniques are inconsistently applied, and real-world clinical integration remains limited. Only a small number of studies adopt multimodal approaches integrating electrophysiological, imaging, biological, and training-related data. These limitations restrict the clinical translation of AI models. Future research should prioritize multicenter, diverse, and explainable multimodal frameworks to support reliable cardiovascular risk stratification and return-to-play decision making.
Authors - Oluwaranti A. Omowami Abstract - Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational injuries in construction, driven by heavy lifting, awkward postures, repetitive motion, and whole-body vibration. Traditional ergonomic risk assessment methods are retrospective and unable to capture the dynamic conditions of construction sites. Wearable sensor technologies offer a real-time, objective alternative. This structured narrative review examines the implementation, accuracy, and occupational health outcomes of wearable sensor systems applied to ergonomic risk monitoring among construction workers. A structured review of peer-reviewed literature from 2017 to 2024 identified six sensor categories: inertial measurement units (IMUs), wearable insole pressure systems, surface electromyography (sEMG), electrodermal activity (EDA) sensors, heart rate monitors, and smartphone embedded sensors. Reported posture classification accuracy reached up to 99.01% under controlled conditions using deep learning classifiers. Key implementation barriers include sensor discomfort, motion artifacts, worker acceptance, data privacy and cybersecurity concerns, and the multi-employer structure of construction. A consistent gap exists between laboratory validation accuracy and real-world field performance. Occupational health outcome studies remain limited. Future priorities include longitudinal field validation and integration with behavior-based safety frameworks.
Authors - Lucian Julio Felix da Costa, Claudio de Souza Baptista, Andre Luiz Firmino Alves Abstract - Auditing public procurement processes is essential to ensure transparency, accountability, and efficiency in the management of public funds. However, the increasing complexity of procurement procedures poses significant challenges for auditors, particularly regarding the timely detection of pricing irregularities. This paper presents a software tool for price comparison designed to support the identification of overpricing and underpricing in public works procurement. The proposed solution leverages semantic retrieval and historical price comparison techniques to analyze procurement data and integrate up-to-date market information. Additionally, the application provides interactive visualizations and semantic retrieval mechanisms to support auditors during procurement price analysis activities. The expected contribution of this study lies in improving the effectiveness and accuracy of procurement oversight, strengthening financial analysis processes, and contributing to the prevention and deterrence of fraudulent practices among bidders.
Authors - Vanderson dos Santos Araujo, Eliane Tamara Lima Oliveira, Pedro Manoel Hermínio Alves, Andre Luiz Firmino Alves, Claudio de Souza Baptista Abstract - Auditing public tenders requires analyzing lengthy documents to verify compliance with tender notices, a time-consuming task prone to human error. This article empirically evaluates the use of Large Language Models (LLMs) to assist with auditing tender notices. A total of 50 official tender notices and 736 audit instances were analyzed, comparing three context-provisioning strategies: expanded context windows, integrated file retrieval, and a custom Retrieval-Augmented Generation (RAG) pipeline. The results show that no single approach is superior across all scenarios. Models with long windows performed better at confirming explicit conformities, whereas retrieval-based strategies demonstrated greater sensitivity to potential non-conformities due to omissions. The analysis also indicates that the type of question strongly influences performance, especially for interpretive questions or those that rely on the absence of documentary evidence. As a key contribution, the study demonstrates that the effectiveness of AI-assisted auditing depends on the combination of the contextualization strategy, the quality of the retrieved context, and the formulation of the questions, reinforcing the role of LLMs as tools to support the auditor.
Authors - Ahmed Belgaid Abstract - Digital transformation has profoundly altered the organization of work and reinforced the importance of mastering digital tools for employability and productivity. In this context, this study highlights the challenges of digital accessibility for employees with visual impairments and its application through an analysis of the new WCAG standards. The aim of our analysis is to demonstrate that digital accessibility consists of guaranteeing an inclusive digital transformation; it is not limited to simply ac-quiring digital solutions or adapting existing ones. It is a comprehensive preparation process and an integrated approach involving various stakeholders within the company.
Authors - BENABDELLAH Nouhaila, CHRAIBI Abdeslam, BENRREZZOUQ Rhizlane Abstract - The importance of smart logistics dashboards as key tools for digital transformation in manufacturing companies is becoming more recognized. They enable real-time visibility, in-tegrate various data sources, and allow for analysis. However, there is limited research on how these dashboards affect decision quality and operational performance in emerging countries. This paper explores the impact of smart logistics dashboards on data-driven decision making (DDDM) and operational performance in Moroccan manufacturing firms. We conducted a quantitative survey among logistics and operations managers and examined a proposed concep-tual framework using PLS-SEM. The findings showed that the capabilities provided by smart dashboards significantly improve decision quality through better data integration and real-time analytics. Additionally, DDDM plays a key role in the relationship between smart dashboards and operational performance. These results are important for the field of smart logistics and digital transformation and have valuable practical implications for management in manufactur-ing firms in emerging countries.
Authors - Francisco Castro Murillo, Teresita de Jesus Alvarez Robles, Andres Sandoval Bringas, Monica Carreno Leon, Francisco Javier Alvarez Rodriguez Abstract - Web accessibility for visually impaired users remains a critical challenge in HCI. While technical compliance with WCAG is well-established, knowledge of User Experience (UX) evaluation methods tailored to non-visual interaction is fragmented. This paper presents a systematic mapping of the literature (2016–2026), analyzing 18 high-quality studies selected from 134 records retrieved from IEEE Xplore, ACM DL, SpringerLink, and ScienceDirect. Results show that user testing is the predominant method, often combined with standardized questionnaires like SUS and NASA-TLX. However, critical gaps persist: small sample sizes, inconsistent participant reporting, and a lack of metrics designed for non-visual interaction. This study contributes a taxonomy of visually impaired user profiles and identifies the technical requirements for building an inclusive web usability laboratory. By bridging the gap between technical auditing and real-world user satisfaction, this work provides a roadmap for the development of the MEUX LAB, ensuring more equitable digital evaluation environments.
Authors - Vikas Pandey, Sanasam Chanu Inunganbi Abstract - Plagiarism has become a serious problem in universities, research organizations, and professional workplaces, affecting academic integrity and the originality of work. While cloud-based detection tools are widely used, they share a fundamental problem that rarely gets discussed openly: every document is required to be submitted and handed over to a third-party server. For unpublished research, legal drafts, or any sensitive material, this trade-off is not acceptable. The proposed method, named ZERO, takes a different approach and runs entirely on the local machine, watching the clipboard quietly in the background and scoring text against a local TF-IDF corpus in under 200 milliseconds, with no uploads, no accounts, and no data leaving the device. An optional web scanning module is available when broader source coverage is needed. On top of the similarity score, ZERO provides a word-level risk heatmap, a sentence-by-sentence originality breakdown, a stylometric module called Writing DNA, and a scan history timeline. Testing on 60 hand-labelled samples showed that a recalibrated scoring curve brings the average score on original technical writing down from 34.7% to 9.8%, while keeping verbatim-copy detection at 95%. API credentials are stored in the OS keychain, and inter-process communication is locked to a strict channel whitelist, making the application well-suited for confidential and pre-publication work.
Authors - Sanae.Chakir, Adil.Bekraoui, El moukhtar Zemmouri, Hassan.Majdoubi, Mhamed. Mouqallid Abstract - For cuttings to successfully root indoor environmental conditions in a greenhouse are essential. This article examines the efficacy of two predictive models, linear regression and artificial neural networks, in predicting the parameters associated with rooting plant cuttings. For evaluation the analysis uses the RMSE MAPE and R² indices. According to the results artificial neural networks perform better than linear regression in terms of prediction accuracy. By utilizing these insights, farmers can use artificial neural network models to implement optimal control strategies which will al-low them to accurately predict indoor variables and ultimately increase crop productivity.
Authors - Miroslav Stefanov, Stoyan Denchev, Kristiyan Stefanov Abstract - Large Language Models (LLMs) are increasingly used in educational settings, but they are not inherently constrained to the boundaries of specific instructional materials. This can lead to unsupported claims, external knowledge leakage, and reduced instructional precision. This study proposes and evaluates a lesson-bounded LLM architecture for reliable educational AI systems. The architecture combines retrieval-augmented generation, context restriction, structured response control, explicit refusal behavior, and post-hoc confidence calibration. Using a multi-domain instructional dataset and a benchmark of inscope and out-of-scope questions, the proposed system is compared against an unconstrained baseline LLM. Results show strong retrieval discrimination and boundary control, with high Area Under the Receiver Operating Characteristic Curve, high Average Precision, strong refusal recall, low out-of-scope answer rate, reduced verbosity, and improved support-based instructional density. Calibration analysis further shows that raw retrieval scores are not reliable probability estimates, but Platt scaling substantially improves confidence reliability. These findings suggest that lesson-bounded architectural constraints can improve the controllability, auditability, and reliability of intelligent educational systems while highlighting the need for stronger factuality evaluation and confidence interpretation.
Authors - Hatem Yousif Alkhonini, Fethi Fkih Abstract - This study evaluates five post hoc explanation methods using XAI-Bench under controlled settings with ground-truth explanations. Results show significant differences in robustness, with MAPLE outperforming Shapley-based methods under high correlation. Feature correlation impacts explanation quality more than the choice of method, and performance degrades with increasing dimensionality-especially for LIME. Exact methods become infeasible beyond d=10, and robust evaluation requires multi-seed replication.
Authors - Maria Jose Cantos Cedeno, Kevin Michael Mero Ramirez Abstract - The spread of disinformation through social media, messaging apps, and other digital channels is a growing problem in Ecuador, due to the limitations of manual fact-checking processes in the face of the high volume of information. In this context, Transformer-based models are presented as high-potential solutions for detecting fake news across various domains and languages. The objective is to comparatively evaluate Transformer architectures pre-trained using fine-tuning techniques for the automatic classification of fake and real news in the Ecuadorian context. The CRISP-DM methodological framework was applied to guide the development of deep learning models. A balanced dataset of 5,000 news items in Ecuadorian Spanish was constructed, equally distributed between real and fake news. Data processing was carried out through a 12-stage sequential pipeline to reduce noise, prevent data leakage, and preserve relevant semantic features of Spanish. Furthermore, the models were trained using homogeneous hyperparameters and evaluated using various metrics employed in the scientific field. As a result, all models exceeded 89% accuracy; BETO achieved the highest precision, mBERT the best recall, and DistilBERT the highest computational efficiency. It is concluded that Transformer architectures proved to be scalable, effective, and viable solutions for the automatic detection of fake news in Ecuador.
Authors - Arsalan Vahi Abstract - The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation, there is a significant gap in addressing different security aspects, such as design principles, specific to the IoT environment. This study aims to bridge this gap. This research presents an examination with focusing on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a concise overview of lightweight ciphers with emphasizing on their security challenges which is an essential consideration for real-time and resource-constrained applications.
Authors - Amine El Ameri, Ahmed Drissi Abstract - Quantum computing threatens TLS through the Harvest Now, Decrypt Later (HNDL) attack: adversaries record encrypted traffic today and decrypt it once quantum capabilities mature. Existing approaches integrate post-quantum key encapsulation mechanisms directly into the TLS handshake; while cryptographically sound, they inflate the first handshake message and cause IP fragmentation of the ClientHello record that some middleboxes reject, leaving these solutions undeployable on today’s Internet. This paper proposes PH-PQ-TLS, a post-quantum key establishment framework for TLS 1.3 that adds post-quantum confidentiality while preserving the standard handshake, thereby avoiding fragmentation and middlebox incompatibilities. The framework requires no redesign of TLS, uses only standardized TLS mechanics, and is crypto-agile by design. We provide a full Go implementation and evaluate its overhead against a standard TLS 1.3 baseline. The ClientHello record measures 289 bytes, well below the minimum IPv6 Maximum Transmission Unit of 1280 bytes, whereas a hybrid ML-KEM-768 ClientHello reaches roughly 1473 bytes and exceeds that threshold. The post-handshake upgrade adds 1.18 ms of latency per full connection, a one-time cost amortized over the session lifetime and avoided on Pre-Shared Key resumed connections. These results show that HNDL protection does not require redesigning TLS, replacing the Web PKI, or accepting deployment failures, offering a practical, incremental path toward quantum-resilient TLS on today’s infrastructure.
Authors - Sayuni Dewapriya, Pehan Gunasekara, Shenal Peiris, Charith Herath, Kavinga Yapa Abeywardena, Ayesha Wijesooriya Abstract - DNS is often trusted within modern network environments, making it a common channel for covert communication, malware activity, and infrastructure abuse. This paper presents a near-real-time AI/ML-based DNS threat detection framework designed to identify suspicious DNS behaviour and transform raw network activity into actionable security events. The proposed approach combines machine learning, behavioural analysis, flow-based detection, event aggregation, risk scoring, and contextual threat intelligence to improve visibility across plaintext DNS and DNS-over-HTTPS traffic patterns. The framework supports practical security operations by reducing raw alert noise and producing structured outputs suitable for dashboard monitoring and SIEM-based investigation. Evaluation using public datasets, generated attack traffic, and live DNS traffic demonstrates that the framework can support effective DNS threat monitoring, alert prioritisation, and SOC-level analysis.
