Systems and Computing (e-ISSN: 3088-7941) Systems and Computing (SyCom) is an international, peer-reviewed, Diamond Open Access journal (no fees for authors or readers), established in 2025. Systems and Computing (SyCom) adopts a broad and interdisciplinary perspective, publishing rigorously reviewed theoretical and applied research that addresses contemporary challenges and promotes innovative, efficient, and scalable solutions. The journal is published online and issued biannually (January and July) by Echahid Cheikh Larbi Tebessi University (Tebessa, Algeria) through the Laboratory of Mathematics, Informatics, and Systems (LAMIS), ensuring strong institutional support and long-term academic continuity. In addition to regular issues, the journal publishes special issues dedicated to selected conferences, emerging research topics, and invited contributions from leading researchers. Systems and Computing is actively pursuing inclusion in several recognized international indexing databases to enhance the visibility, accessibility, and global dissemination of published research. SyCom adheres to internationally recognized standards of peer review, publication ethics, and academic integrity, ensuring the quality and reliability of all published content. For Authors Authors are encouraged to review the following sections carefully before submitting their manuscripts: Aims and Scope Author Guidelines Peer Review Policy Publication Ethics Manuscripts that do not comply with the journal’s guidelines may be returned to the authors for revision prior to peer review. Learn More about the journal in the Journal Information section.
Laboratory of mathematics, informatics and systems (LAMIS), Echahid Cheikh Larbi Tebessi University- Tebessa, Algeria
Articles published in SyCom are open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
InSAR Phase Unwrapping: A Comprehensive Review of Algorithms, Challenges, and Future Directions
Context: Phase unwrapping is a fundamental step in interferometric Synthetic Aperture Radar (InSAR), requiring the reconstruction of a continuous phase field from observations available only modulo-2π. Objective: This review critically analyzes the main methodological approaches for InSAR phase unwrapping and highlights their strengths, limitations, and research trends. Method: Existing methods are categorized into four families: classical single-interferogram, optimization-based, multi-baseline and network-based, and learning-based approaches. Their principles and performance are compared under challenging conditions such as noise, decorrelation, and phase discontinuities. Results: Classical methods are computationally efficient but sensitive to local errors. Optimization-based approaches improve global consistency at higher computational cost. Multi-baseline and network-based methods enhance robustness through redundancy, whereas learning-based methods provide flexibility but remain limited by generalization and consistency. Conclusion: The review identifies key trade-offs among existing methods and emphasizes the need for hybrid and adaptive frameworks to achieve robust, scalable, and reliable InSAR phase unwrapping.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Revisiting Classical and Deep Learning Models for Multi-Class Brain MRI Classification under Severe Data Constraints
Context: Deep learning has great potential in neuroimaging but its efficacy is often constrained by the availability of large, standardized datasets. Data scarcity and heterogeneity are still major issues in many biomedical research settings. Objective: In this work, we analyze the performance of traditional machine learning models and deep neural networks for five-class brain MRI classification (Alzheimer’s illness, ischemic stroke, meningitis, brain tumor, and normal brain) using a small heterogeneous dataset of 2D slices. Method: We tested Local Binary Patterns (LBP) with K-Nearest Neighbors (KNN) and Gray-Level Co-occurrence Matrix (GLCM) with KNN, Support Vector Machine (SVM), tuned multilayer perceptron (MLP), lightweight convolutional neural network (CNN) and transfer learning model based on ResNet-50. The study focuses on model behavior, generalization, and limitations in constrained data scenarios, rather than just correctness. Results: The studies indicate that CNNs exploit spatial characteristics but generalize poorly, whereas SVM is quite accurate due to dataset bias. ResNet-50 demonstrates early saturation, indicating the disparity between model size and dataset scale. Conclusions: This work highlights the need for careful interpretation of numerical accuracy in tiny medical imaging datasets, and provides practical insights into how different model families respond to real-world data restrictions. These results provide insights for biomedical imaging studies, informing model choice in resource-limited settings, and providing opportunities for future clinical applications in settings with limited data availability.
Blind Content-Adaptive Multi-Image Steganography Using Canonical Sparse Multi-Transform Residual Maps and AES-GCM
Context: Multi-image LSB steganography can reduce payload per cover image, but conventional content-agnostic allocation and non-blind extraction limit practical use and do not adequately address modern steganalysis. Objective: This work develops a blind, lossless-delivery multi-image steganography method that uses canonical sparse multi-transform residual maps to determine payload allocation and embedding locations. Method: Each cover is converted to an LSB-normalized canonical image, (C_i=I_i\ \mathrm{AND}\ 254), which remains invariant after one-LSB substitution and enables the receiver to reconstruct the DWT/DCT residual maps, residual-energy allocation, and keyed embedding schedule from the received stego images alone. AES-256-GCM provides authenticated encryption, while a collision-free keyed bootstrap header conveys the required protocol information and a fresh 96-bit nonce. Experiments used a publicly available 9,000-image, (256\times256) grayscale BOSS-derived collection, arranged as 4,500 two-image transmissions and split at group level into 80% training, 10% validation, and 10% test groups. Results: At requested rates of 0.05, 0.10, 0.20, and 0.30 bpp, all 36,000 group-method-rate executions achieved canonical equality, successful AES-GCM authentication, exact plaintext recovery, and zero observed sealed-payload BER. At 0.05 bpp, CSR-LSB achieved (64.49\pm1.87) dB PSNR and (0.999888\pm0.000105) SSIM on 900 held-out test images. It reduced compact residual-feature detector AUC from 0.9978 to 0.8514 and deep residual CNN AUC from 0.9957 to 0.9366 relative to uniform keyed LSB. Conclusions: CSR-LSB provides blind authenticated recovery and lower detectability than uniform keyed LSB, although modern detectors still identify its stego images reliably; future work should incorporate distortion-minimizing embedding to improve resistance to advanced steganalysis.
Smart Plant Disease Diagnosis via MERN Stack Interface and PyTorch Deep Learning Models
Context: Early identification of plant diseases plays a crucial role in enhancing crop productivity and promoting sustainable agricultural practices. Advances in artificial intelligence and web-based technologies have paved the way for smart systems capable of automatically diagnosing diseases in crops like rice and sugarcane. Objective: This research focuses on developing a smart plant disease diagnosis system that integrates deep learning techniques with a MERN (MongoDB, Express.js, React.js, Node.js) stack to provide accurate, real-time classification of rice and sugarcane leaves diseases through a user-friendly web interface. Method: The proposed framework employs a Convolutional Neural Network (CNN) built with PyTorch and trained using a carefully curated dataset of diseased rice and sugarcane leaf images. The developed model was incorporated into a web application built using the MERN stack to enable seamless frontend-backend communication and real-time disease prediction. The model’s effectiveness was assessed using evaluation metrics such as precision, recall, F1-score, and confusion matrix analysis. Results: The CNN model achieved high classification performance, with an average class accuracy of 95.92%, overall classification accuracy of 91.83%, average precision of 91.85%, average recall of 92.05%, and average F1-score of 91.86%. Confusion matrix analysis further validated the model’s efficiency in accurately recognizing rice and sugarcane leaves diseases. The integrated web platform demonstrated efficient and user-friendly real-time disease prediction capabilities. Conclusions: The developed AI-based plant disease detection system highlights the effectiveness of integrating deep learning techniques with modern web technologies to support scalable agricultural solutions. The system provides a practical solution for farmers and agronomists seeking early and accurate crop disease detection. Future enhancements may include multilingual support, mobile application integration, and agronomic advisory modules to further advance precision agriculture initiatives.
A Blockchain Adoption Framework for Secure Digital Credentials and Academic Record Verification in Higher Education
Context: Higher education institutions increasingly rely on digital systems to manage academic records and certification processes; however, traditional centralized architectures remain vulnerable to data breaches, record tampering, and credential forgery, which undermine trust in academic qualifications. Objective: This study develops a blockchain adoption framework to secure digital credentials and improve academic record verification in higher education institutions. Method: A qualitative conceptual approach grounded in the interpretivist paradigm was employed. Secondary data were obtained from peer-reviewed literature, institutional reports, and documented blockchain-based education systems, and qualitative content analysis was used to identify adoption drivers, benefits, challenges, and implementation requirements. Results: The analysis identified technological, organizational, and environmental adoption dimensions and informed a TOE-based framework comprising digital certification, secure academic record storage, automated credential verification, inter-institutional data sharing, and continuous improvement. The findings indicate that blockchain can strengthen credential security, improve verification efficiency, and preserve record integrity, although adoption remains constrained by technical capacity, cost, integration, interoperability, and regulatory challenges. Conclusions: The proposed framework provides higher education institutions with a structured basis for readiness assessment and implementation planning. It also supports future empirical validation and policy development.
A Metaheuristic-Optimized Ensemble Learning Framework for Sustainable Water Quality Forecasting in Multi-Sensor Environmental Monitoring Systems
Context: For long-term environmental management, it is important to accurately predict water quality, especially as modern multi-sensor monitoring systems generate large volumes of high-dimensional physicochemical data. Conventional statistical and machine learning methodologies frequently fail to capture nonlinear environmental dynamics or rely on manually adjusted model configurations, thereby limiting predictive accuracy and scalability. Objective: To tackle these issues, this research introduces a metaheuristic-optimized ensemble learning framework for long-term water quality prediction in multi-sensor environmental monitoring systems. Method: The proposed framework combines Particle Swarm Optimization (PSO) with Random Forest ensemble learning to enable automatic hyperparameter optimization within a repeatable end-to-end modeling pipeline. Comprehensive data preprocessing and domain-informed feature engineering were applied to better represent physicochemical interactions and temporal environmental patterns. Results: The experiment was conducted using a large-scale water quality dataset comprising 24,265 validated samples. Conclusion: The framework provides a metaheuristic-optimized ensemble learning approach for long-term water quality prediction in multi-sensor environmental monitoring systems.
Academic Integrity in the Era of Generative AI: Students’ Perceptions, Ethical Boundaries, and Risk-Taking Behavior in Nigerian Universities
Context: This study examined the intersection of academic integrity and Generative Artificial Intelligence (GenAI) adoption among students in Nigerian universities, addressing a critical gap in empirical, student-centered research from the Global South. Objective: To investigate students' knowledge, usage patterns, and perceptions of GenAI, as well as their awareness of academic integrity and the behavioral factors that shape ethical decision-making in AI use. Methods: A quantitative cross-sectional survey was conducted with 262 undergraduate and postgraduate students from nine Nigerian higher education institutions. The study was informed by relevant literature from major academic databases. Data were collected via a structured questionnaire and analyzed using descriptive and inferential statistics, with Prospect Theory applied as the theoretical framework. Results: Findings revealed high AI literacy, with 84.7% of participants already integrating AI tools into academic work. However, a significant knowledge–behavior gap emerged: while over 90% acknowledged the importance of academic honesty, only 36% believed AI use required disclosure. This ethical ambiguity was compounded by weak institutional guidance: 74.8% of students reported being unaware of their university AI policies. Inferential analysis indicated that students engage in risk–reward evaluations, where low perceived detection risks and academic pressures frequently outweigh potential sanctions. Conclusion: This study concludes that AI-related academic misconduct is often a rational behavioral choice driven by perceived institutional unpreparedness rather than ignorance. It calls for a transition toward adaptive academic integrity frameworks that prioritize ethical awareness and transparent academic policy communications to students.
A Catastrophe-Theoretic Framework for Regime-Switch Losses: Diagnostic Indices Are Not Welfare Criteria
Context: Crisis diagnostics quantify geometric proximity to regime change, but their normative status is often overstated. Objective: To determine when catastrophe-based diagnostic indices can legitimately acquire normative content in welfare analysis. Method: The paper develops a dynamic governance model with deformable triple-well potential geometry, catastrophe-theoretic discriminants, and planner objectives under baseline and reform channels. Results: Diagnostic minimization and welfare maximization are shown to be non-implicative in general; under an explicit reform channel and an internalized switching-loss term, local reductions in HCFI become directionally aligned with welfare improvement. A stylized Soviet-Russia path and a reproducible computational protocol illustrate the mechanism. Conclusions: Catastrophe-based diagnostics are not welfare criteria per se, but they can acquire normative relevance when structural deformation and switching exposure are explicitly incorporated into the policy objective.
Evaluation of SE, ECA, and CA Modules in ResNet-50 for the Classification of Grape Leaf Diseases
Context: Plant diseases pose a major threat to agricultural production, as they hinder crop growth and significantly reduce yields. In this context, the automatic detection of diseases using artificial intelligence techniques represents an effective solution. Objective: This work presents a comparative study on the integration of attention mechanisms into the ResNet-50 architecture, applied to the classification of grape leaf diseases. Method: Three attention modules were considered: SE (Squeeze-and-Excitation), ECA (Efficient Channel Attention), and CA (Coordinate Attention), each integrated separately into the model to evaluate its impact. Results: The performance of the different configurations was assessed by considering the quality of the results, the model complexity, and the processing speed. Experimental results show that the ECA module achieved the best performance with a reduced number of parameters, followed by SE and then CA. Conclusion: This study highlights the contribution of attention mechanisms in improving the effectiveness of CNN architectures for plant disease detection.
Benchmarking New Adaptive Equalizers Leveraging a Different Robust Adaptive Algorithms: NLMS, APA, PAP, and MRNQ
Context: In the literature, numerous adaptive equalization techniques have been developed to effictively reduce inter-symbol interference in digital communication systems. Objective: This study conducts a comparative analysis of four such adaptive algorithms: NLMS, APA, PAP, and MRNQ, implemented within a decision feed-forward equalizer (DFE) stucture. The purpose of this evaluation is to identify the strengths and limitations of each algorithm under the same transmission conditions. The insights gained from this comparaison can guide the choice of equalization strategy for practical implementations in modern communication systems. Method: The performance of each equalizer is assessed using multiple evaluation criterion, including computational complexity, constellation diagram analysis, the Nyquist criterion, and mean squared error (MSE) performance. Results: All four algorithms effectively reduce inter-symbol interference and ensure proper signal equalization. However, DFE-PAP and APA-DFE show superior speed and accuracy, MRNQ-DFE offers a good trade-off, and NLMS-DFE performs the weakest. Conclusions: In summary, the study highlights DFE-PAP as the most efficient and pratical solution, combining strong ISI mitigation with low computationnal cost. While all algorithms perform well, DFE-PAP and APA-DFE demonstrate clear advantages in convergence speed and accuracy.
Impact of Divergence Angle on FSO System Performance: A Study of SNR, BER, and Q Factor
Context: Free Space Optics (FSO) transmission is a promising alternative to conventional wired networks, offering high bandwidth, low latency, and enhanced security. However, FSO performance is highly sensitive to physical factors, particularly the divergence angle of the optical beam. Objective: This study aims to evaluate the impact of beam divergence on key FSO system performance metrics to guide optimal system design. Method: MATLAB-based simulations were conducted to model Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), and Q factor under varying divergence angles. The sensitivity of system performance to beam alignment and signal strength was analyzed across different divergence configurations. Results: Results indicate that smaller divergence angles provide higher SNR and Q factor values and lower BER, ensuring superior communication quality but requiring precise alignment. Conversely, larger divergence angles tolerate misalignment more effectively but at the cost of reduced signal quality. Conclusion: These findings emphasize the critical role of optimizing beam divergence to balance alignment tolerance and signal integrity, thereby enhancing the efficiency and reliability of optical wireless communication systems.
Improving Arabic Dialect Processing in IoT Systems: A Comparative Study of Baseline and Dialect-Aware AI Models
Context: Bringing voice-controlled interfaces into Internet of Things (IoT) systems has created fresh opportunities for smart environments. However, existing voice assistants often struggle with non-standardized languages, especially Arabic dialects. Objective: This research paper explores the challenges and potential of integrating five Arabic dialect variants, namely Modern Standard Arabic (MSA), known as Fusha (الفصحى), Egyptian, Levantine, Gulf, and Algerian dialects, into AI-driven IoT systems. Methods: For each dialect, a comparative simulation was performed using two AI models: a baseline model and a dialect-aware model. Key simulated metrics included automatic speech recognition (ASR) accuracy, intention recognition, task success rate, and system response time. Results: The results consistently show that the dialect-aware model outperforms the baseline model in all metrics. It provides higher ASR and intention recognition accuracy, improved task success rates, and faster response times, especially for regional dialects. The Algerian dialect, while still challenging, benefited significantly from the dialect-aware adaptations of the improved model. These results highlight the potential of dialect-aware AI to close the performance gap caused by linguistic variation and code-switching. Conclusion: This study highlights the importance of considering linguistic diversity when developing accessible, culturally appropriate IoT interfaces that ensure a more inclusive and natural user interaction.
Recognition of GUI Web Elements using the Deep Learning Approach and Yolo Architecture
Context: The growing complexity of modern web and mobile applications, shaped by rapid UI/UX advancements and agile software development practices, demands efficient and automated recognition of graphical user interface (GUI) elements, as manual testing remains costly and time-consuming. Objective: This study aims to evaluate the performance of the YOLOv8 deep learning architecture for accurate recognition of diverse GUI components, such as buttons, fields, headings, links, and images, from real-world application screenshots. Method: A small YOLOv8 model was trained on the Roboflow GUI element detection dataset containing over 1,000 annotated website screenshots, using 35 training epochs, a batch size of 8, and an image resolution of 640×640. Results: The model achieved measurable improvements, with precision rising from 0.368 to 0.454, recall increasing from 0.296 to 0.425, and mAP50–95 improving from 0.101 to 0.232. Strongest detection was observed for buttons and input fields, while weaker performance was noted for iframes and labels due to their inherent ambiguity. Conclusions: The results demonstrate that YOLOv8 has significant potential in automating GUI recognition, reducing reliance on manual testing in agile workflows, and improving interface validation. Further optimization with larger datasets and advanced augmentation methods is recommended to enhance robustness and generalization
Developing an Integrated HMSAM-FoMO Model and Its Instrument Testing for Social Live Streaming
Context: The rapid growth of social live streaming platforms has transformed how individuals interact and experience digital entertainment. Despite this expansion, prior studies have largely focused on commercial or gaming-related live streaming, leaving the social and hedonic aspects underexplored. Furthermore, psychological mechanisms such as the Fear of Missing Out (FoMO) have received limited attention in explaining behavioral intention in live streaming environments. Objective: This study aims to develop and validate an integrated model combining the Hedonic-Motivation System Adoption Model (HMSAM) with FoMO to explain users’ behavioral intention in social live streaming contexts. Method: A quantitative approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from 57 Indonesian social media users. The research process consisted of three stages: conceptual integration, instrument operationalization, and empirical validation, supported by expert qualitative evaluation to enhance construct clarity and theoretical consistency. Results: The findings show that hedonic motivations such as joy, curiosity, and immersion strongly influence behavioral intention, while FoMO acts both as a direct determinant and as a mediator linking hedonic motivations to user engagement. Two indicators (CTR2 and CTR4) were removed for weak conceptual fit, and constructs with AVE values slightly below 0.50 (Control and Perceived Usefulness) were retained for theoretical reasons. The integrated HMSAM–FoMO model explained 64.4% of the variance in behavioral intention, demonstrating strong explanatory power. Conclusions: The study contributes theoretically by extending HMSAM with FoMO to capture both emotional enjoyment and psychological tension in digital engagement. It also provides practical guidance for designing psychologically balanced live streaming experiences that enhance enjoyment while minimizing FoMO-induced anxiety. These findings offer a foundation for future research and policy development toward healthier digital participation.
Avoiding Local Minima in Overlap-Based Clustering by Random Swap
Context: Cluster overlap has been recently introduced as an alternative objective function to the traditional sum-of-squared error (SSE) function used in k-means. It estimates the overlap by counting how many points are shared between the neighbor clusters. The overlap k-means was shown to provide more stable centroid locations than SSE, but at the cost of losing the dynamics of the k-means algorithm. Objective: In this paper, we address this drawback by adding a random swap step to the algorithm and introducing a new Overlap random swap. Method: It consists of three steps: (1) selective replacement of the centroids, (2) standard cluster assignment step, (3) weighted centroid calculation giving less weight to the overlapping points. Results: Experimental results confirm that ORS achieves competitive clustering accuracy, stable centroid locations, and improved boundary separation. The clustering accuracy (ACC) is about 95% and the centroid index (CI) is about 0.1. The algorithm converges within 3000 swap iterations. Conclusions: These results indicate that ORS helps avoid local minima and maintains the robustness of overlap-based clustering.
Systems Thinking and Systems Dynamics Approach to Demand Side Management
Context: Demand Side Management (DSM) provides utilities with tools to modify end users’ consumption patterns, helping reduce peak demand, alleviate stress on the power grid, and even support with the integration of renewables. Nonetheless, DSM is an inherently complex and interdependent system stretching across numerous factors such as economic, behavioural, and technological with different feedback dynamics and competing incentives. To address this complexity a Systems Thinking and Systems Dynamics approach is applied. Objective: The aim of this paper is to develop a quantitative Systems Dynamics model in the Vensim simulation environment, capturing the dynamic transition between short run and long run price elasticities. Methods: The model defines variables such as baseline demand, electricity price, and both short-run and long-run elasticities. A stock variable, Effective Price Elasticity, is used to dynamically represent the shift from short-run to long-run responses. Simulation parameters were gathered from literature. Results: Simulations demonstrate that the model confirms expected behaviour, showing an initial less elastic response to the price changes followed by a more notable gradual adjustment over time as the effective elasticity converges to its long-run value. Conclusion: This model can provide a framework for simulating the dynamic interplay between electricity price and demand. Its ability to simulate short-run and long-run elasticity effects offers valuable insights for energy planners and policy makers.
Customer Churn Prediction in Neobanking System Using Predictive Analytics and Feature Selection
Context: Customer behavior, including loyalty and satisfaction, is increasingly volatile due to rapid technological and market changes. Neobank startups, in particular, face significant challenges related to customer churn, which can severely impact profitability and reputation. Objective: To develop a predictive model that identifies customers at risk of churning in the neobanking sector, enabling proactive retention strategies. Method: This study employed data mining techniques using three classification algorithms, Logistic Regression, Naïve Bayes, and Decision Tree, implemented in the WEKA platform. Feature selection methods based on accuracy, precision, and correlation were applied to identify key churn indicators. Results: The Decision Tree algorithm outperformed the others, achieving an accuracy of 80.5%. It demonstrated superior performance, particularly when all features were included in the model. Key predictive features were successfully identified through feature selection techniques. Conclusions: The findings confirm that Decision Trees are effective in predicting customer churn in neobanks. Understanding and targeting the key factors influencing churn can help neobanks retain customers and maintain a competitive edge.
Classification, Customer Churn, Churn prediction, Data Mining
Copyright (c) 2025 Abdulrauph Olanrewaju BABATUNDE, Sheriffdeen Ade YINUSA, Idowu Dauda OLADIPO, Ayisat Wuraola ASAJU-GBOLAGADE (Author) https://creativecommons.org/licenses/by/4.0
Unveiling Identity Through Anatomy: Person Verification Using Vision Transformers on Chest X-Rays Radiographs
Context: The prospective utilization of medical imaging data for reliable individual identification and authentication has garnered significant interest in both security and healthcare sectors. This importance is particularly amplified during disaster scenarios, where conventional means of human verification become ineffective. In these challenging conditions, Chest X-rays serve as an essential resource by capturing unique anatomical details of the rib cage, lungs, and heart features that persist as reliable verification even when the body is compromised. Objective: We propose the creation of an innovative verification system for image retrieval, specifically designed to enhance person verification using chest X-ray images. Method: The system integrates a deep learning paradigm, leveraging Triplet network architecture; while uniquely use cosine similarity as a metric to assess similarity and dissimilarity between image features. Such an approach enables a more nuanced and robust feature comparison, leading to improved retrieval accuracy and verification performance. Building upon this premise, we propose the creation of an innovative verification system for image retrieval. Notably, our framework employs a state-of-the-art Vision Transformer (ViT) as the backbone for the Triplet network. Results: The ViT backbone offers robust capabilities in extracting and contextualizing features, thereby enhancing the discriminative power of the triplet architecture and ensuring improved retrieval accuracy. This novel integration not only expands the existing toolkit for medical image analysis but also reinforces the reliability of identity verification systems. Conclusions: The dual use of geometric and angular similarity measures, coupled with the advanced feature extraction of the ViT, offers a precise and dependable solution, particularly in high-stakes scenarios such as emergencies and security-critical applications.
Chest X-rays, Cosine similarity, Person verification, Triplet Neural Network, Vision transformers (Vit).
Copyright (c) 2025 Hazem Farah, Akram Bennour, Syeda Sadia Afrin, Hama Soltani, Ali Adjal (Author) https://creativecommons.org/licenses/by/4.0
Empowering Biometric Security: A Paradigm Shift in Remote Identification
Context: In an increasingly digital world, the need for secure and reliable identity verification is more critical than ever. Biometric authentication stands out due to its inherent uniqueness and resistance to forgery, yet its performance is still affected by real-world challenges such as image noise and lighting inconsistencies. Objective: This paper aims to enhance the robustness and accuracy of biometric systems, specifically for remote authentication scenarios, by addressing the limitations posed by environmental variations. Method: The proposed solution is a novel FKP-based biometric authentication system that employs a Triple Texture Feature Extraction (TTFE) technique to capture detailed information from both spatial and frequency domains. To further improve recognition performance, Projective Dictionary Learning is used to refine the feature representation. For secure data handling, a fuzzy vault scheme is integrated to encrypt biometric templates using secret keys, allowing secure authentication over potentially untrusted networks. Results: Experiments conducted on standard benchmark datasets demonstrate significant improvements in both accuracy and resilience to challenging conditions such as noise and lighting variations. Conclusions: This integrated approach successfully enhances the reliability and security of biometric authentication systems, paving the way for more practical and scalable real-world deployments.
Biometric authentication, Dictionary learning, FKP, Fuzzy vault, Identity protection, Secure remote identification.
Copyright (c) 2025 Yacine BELHOCINE, Abdallah Meraoumia, Hakim Bendjenna, Mohammed Saigaa (Author) https://creativecommons.org/licenses/by/4.0
Attention-Gateway U-Net for Mammographic Mass Segmentation: An Empirical Study
Context: Accurate segmentation of breast masses in mammographic images is a critical step for early breast cancer detection and effective clinical decision-making. However, this task remains challenging due to variability in mass appearance, low contrast with surrounding tissues, and complex anatomical structures. Objective: This study proposes a novel deep learning framework based on an attention-guided U-Net architecture, specifically designed for the segmentation of suspicious masses in mammograms. The primary objective is to investigate the impact of integrating attention gates into the U-Net structure and to evaluate how these mechanisms influence the accuracy and robustness of mass segmentation. Method: Attention gates are embedded within the skip connections and function by dynamically highlighting relevant features from the encoder while suppressing less informative regions. This selective focus enables the model to more accurately delineate the often subtle boundaries of breast masses. Results: The proposed model is evaluated on the publicly available INbreast mammography dataset, and its performance is compared against the baseline U-Net and other state-of-the-art segmentation methods. Quantitative results demonstrate that the attention-enhanced U-Net significantly outperforms its counterparts in segmentation accuracy, particularly in challenging cases involving dense breast tissue or ill-defined masses. Conclusions: This study highlights the effectiveness of attention mechanisms in enhancing mammographic mass segmentation and represents a valuable step toward more intelligent and reliable Computer-Aided Diagnosis (CAD) systems for breast cancer.
Attention Gateway U-net, Breast Cancer, Mammography, Segmentation.
Copyright (c) 2025 Hama Soltani, Yousuf Islam, Hazem Farah, Issam Bendib, Mohamed-Yassine Haouam, Mohamed Amroune (Author) https://creativecommons.org/licenses/by/4.0
Towards Accurate Detection and Classification of Skin Cancer Using AI-Powered Image Analysis
Context: Skin cancer is one of the most common and lethal forms of cancer if not diagnosed early. Detection at an early stage can mean a world of difference in survival. Despite advances in technology, most areas continue to face a lack of available dermatology specialists, making timely diagnosis a challenge. This gap between the need for early detection and the limited availability of experts highlights the urgency for automated, reliable diagnostic tools. Objective: In response to this need, the purpose of this work is to create a fast and accurate system for skin cancer detection that distinguishes between malignant and benign lesions. Method: This study, based on a Convolutional Neural Network (CNN), addresses some challenges, including limited data, image quality issues, and classification. Results: A balanced training set was used to train the model, which achieved a test accuracy of 88.60% and a macro-averaged Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.95. Other significant metrics, such as precision, recall, and F1-score also validated the performance of the model. We developed a web-based interface to enable practical deployment and usability for real-world applications. Conclusions: The findings demonstrate the potential of deep learning techniques to enhance skin cancer detection and serve as a point of reference for future research on multi-class classification and real-time diagnostic platforms.
Convolutional Neural Networks (CNNs), Deep learning, Image classification, Medical imaging, Skin cancer detection.
Copyright (c) 2025 Houda Benaliouche, Ikram Guelib, Yahia Slimani, Dalel Salhi, Younes Guerfi (Author) https://creativecommons.org/licenses/by/4.0