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IJACSA Vol. 17 Issue 5 (2026)

Open Access | | 103 papers

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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1

A Lifecycle-Oriented Taxonomy of Open-Source Tools for Machine Learning

Author 1: Hamza Mallam Musa Mohammad Author 2: Isa Hussain Adam Mohammed Author 3: Abdullah Bajaber Author 4: Syed Abdur Rahman Author 5: Anas Sani Author 6: Atif Naseer

Machine learning (ML) offers various tools, frame-works and platforms for resolving complex problems in compu-tational science and engineering. Machine learning frameworks have emerged as the cornerstone of modern research and in-novation. It redefines how knowledge is produced, validated, and disseminated. Open-source machine learning frameworks are emerging as a promising way… Read full abstract & cite →

Open-source lifecycle taxonomy machine learning challenges
Abstract doi.org/10.14569/IJACSA.2026.0170501 PDF
2

Agentic Accountability: “The Buck Stops Where?” Ethical Frameworks for Human Oversight of Autonomous AI Systems

Author 1: Ornella Bahidika

The rapid evolution from generative Artificial Intel-ligence (AI) toward agentic AI, systems capable of autonomously planning and executing multi-step actions, has introduced an unprecedented accountability gap in modern computing. Unlike traditional AI tools that respond to discrete prompts, agentic sys-tems pursue goals across extended time horizons, invoke external services, and… Read full abstract & cite →

Agentic AI AI accountability human-in-the-loop AI ethics autonomous systems AI governance responsible AI oversight thresholds
Abstract doi.org/10.14569/IJACSA.2026.0170502 PDF
3

ExtRA++: A Conceptual Architecture for a Deep Learning System for Aspect-Based Sentiment Analysis in User Reviews

Author 1: G. Kanev Author 2: I. Valova

Aspect-Based Sentiment Analysis (ABSA) aims to identify opinion targets within textual reviews and determine the sentiment polarity associated with each target. Although transformer-based models have significantly improved contextual understanding in sentiment analysis, they remain limited in explicitly modeling structured knowledge and token-level dependencies. This study presents ExtRA++ (Enhanced Extractive Review… Read full abstract & cite →

Aspect-based sentiment analysis aspect extraction deep learning graph attention networks knowledge graph integration BERT conditional random fields natural language processing opinion mining transfer learning
Abstract doi.org/10.14569/IJACSA.2026.0170503 PDF
4

Learning Analytics for Student Performance and Early Detection of At-Risk

Author 1: Rhowel M. Dellosa

The rapid growth of online learning platforms has generated large volumes of student interaction data that may support learning analytics and early academic intervention. This study proposed an intelligent learning analytics system for predicting student performance and identifying at-risk students using online learning behavior data. The Online Learning Behavior Dataset… Read full abstract & cite →

At-Risk student detection learning analytics machine learning online learning behavior student performance prediction
Abstract doi.org/10.14569/IJACSA.2026.0170504 PDF
5

An EWMA-Based Adaptive Thresholding Concept for Autoencoder-Based Concept Drift Detection in Data Streams

Author 1: Siti Nurulain Mohd Rum Author 2: Qiao Song

The static thresholds derived from the primary validation timeframe (window) are a common method for the detection of reconstruction-based concept drift. In prolonged periods of data streams, progressive changes in reconstruction accuracy frequently lead to misalignment, giving rise to repeated false alarms and inconsistency in detection behavior. This study introduces… Read full abstract & cite →

Concept drift detection streaming data autoencoder adaptive threshold EWMA
Abstract doi.org/10.14569/IJACSA.2026.0170505 PDF
6

Hybrid CNN for Breast Cancer Detection in Multi-Modality Imaging

Author 1: Emmanuel Ofotsu Kwesi Bannor Author 2: S. Sarah Maidin Author 3: Vinayakumar Ravi Author 4: Nguyen Thi Thu Thuy Author 5: Nghiem Thi-Lich

Breast cancer (BC) remains the leading cause of cancer-related death all over the world. Early accurate detection is key to the improvement of patient prognosis. The ability of advanced Artificial Intelligence (AI) methods, with a focus on Convolutional Neural Networks (CNNs), to classify breast lesions obtained from mammography and ultrasonography… Read full abstract & cite →

Breast Cancer (BC) Artificial Intelligence (AI) Convolutional Neural Networks (CNNs) Explainable AI (XAI) hybrid model (or hybrid CNN)
Abstract doi.org/10.14569/IJACSA.2026.0170506 PDF
7

A Strategic Cloud Adoption Readiness Index (SCARI) Framework: Empirical Development and Validation in the Ghanaian Context

Author 1: Adade Sedom Percy Author 2: Emmanuel Ofotsu Kwesi Bannor Author 3: S. Sarah Maidin Author 4: Vinayakumar Ravi Author 5: Nguyen Thi Thu Thuy Author 6: Nghiem Thi-Lich

Cloud computing is known to be a major catalyst in facilitating a digital transformation in emerging economies. Currently, the process of transforming into a cloud computing indigenous nation, such as Ghana, faces a series of hitherto confronting challenges, including infrastructure challenges and resistance. This study, therefore, proposes a newly developed… Read full abstract & cite →

Cloud computing SCARI framework TOE-HOT-fit integration digital transformation process innovation
Abstract doi.org/10.14569/IJACSA.2026.0170507 PDF
8

Perceived Usefulness Without Ease of Use: A TAM-Based Evaluation of Enhanced Isabela State University Echague Online Resource App for a New Guild of Education (ISUE ORANGE) Functionalities

Author 1: Ricardo Q. Camungao

This study introduced a target enhancement for ISUE ORANGE, a customized learning management system (LMS) at Isabela State University (ISU)-Echague Campus, developed to address the post-2022 implementation challenges, specifically in progress tracking and exam integrity, through features such as question analytics, monitoring student reading progress, and online proctoring/examination. Modules were… Read full abstract & cite →

ISUE ORANGE learning management system Technology Acceptance Model perceived ease of use perceived usefulness structural equation modeling Cronbach's Alpha question analytics reading progress monitoring exam proctoring SUCs faculty-led innovation
Abstract doi.org/10.14569/IJACSA.2026.0170508 PDF
9

A Multilayer Secure Image Steganography Framework Using Edge-Adaptive Embedding and Pre-Encryption

Author 1: A F M Zainul Abadin Author 2: Rossilawati Sulaiman

High-capacity image steganography aims to conceal large volumes of data while preserving imperceptibility and resistance to statistical and visual detection. This study proposes a multilayer secure image steganography framework using edge-adaptive embedding and pre-encryption. The method utilizes multiple edge detectors, namely Canny, Laplacian of Gaussian (LoG), and Prewitt, to accurately… Read full abstract & cite →

Image steganography AES-CTR encryption hybrid edge detection edge-adaptive embedding BCFEO nature-inspired optimization statistical steganalysis
Abstract doi.org/10.14569/IJACSA.2026.0170509 PDF
10

Multimodal Machine Learning for Cybersecurity in Internet of Things Environments: A Literature Review

Author 1: Abdelaaziz NASSIRI Author 2: Azeddine Khyat Author 3: Kama El Guemmat Author 4: Mohamed Aazi

This research provides a comprehensive synthesis of Multimodal Machine Learning (MML) as a transformative paradigm for IoT defense. By integrating heterogeneous data streams, including network flow statistics, device-level telemetry, and behavioral biometrics, MML architectures facilitate a holistic understanding of system states. The algorithmic advancements that were analyzed are classified into… Read full abstract & cite →

Internet of Things (IoT) Multimodal Machine Learning deep learning cyber-physical security data fusion Intrusion Detection Systems (IDS) Edge AI Explainable AI (XAI)
Abstract doi.org/10.14569/IJACSA.2026.0170510 PDF
11

Optimizing the Multi-Omics Data Types and Variant Combinations for Accurate Breast Cancer Molecular Subtypes Classification

Author 1: Sajid Shah Author 2: Azurah A Samah Author 3: Syed Hamid Hussain Madni Author 4: Sarina Sulaiman Author 5: Zuraini Ali Shah Author 6: Wong Yee Leng Author 7: Aryati Bakri

Breast cancer is a highly heterogeneous disease with Luminal-A, Luminal-B, HER2-Enriched, Basal-Like, and Normal-Like molecular subtypes. Accurate classification of breast cancer molecular subtypes is essential for effective diagnosis, treatment, and planning. In recent years, multi-omics data has been widely used to improve classification performance. However, most of the existing studies… Read full abstract & cite →

Breast cancer molecular subtypes multi-omics data integration data types and variants datasets
Abstract doi.org/10.14569/IJACSA.2026.0170511 PDF
12

An AI-Driven Framework for Software Effort Estimation Based on Developer Performance Metrics

Author 1: Shaheer Ahmed Author 2: Nosheen Qamar Author 3: Faria Nazir Author 4: Nosheen Sabahat Author 5: Atif Ikram Author 6: Najla Abdulaziz Almousa Author 7: Hebah Abdullah Abubakr Author 8: Mohammed Abual-Rub Author 9: Abdulrahman Alojail Author 10: Marwan Abu-Zanona

Effort estimations, including budgets, hiring people, and project timelines, in the Agile methodology, are determined by tools like COCOMO and Function-Point analysis. This study presents a framework driven by artificial intelligence (AI) that uses almost real-time signals from Git platforms that track issues, and tools to determine code quality, convert… Read full abstract & cite →

Software effort estimation machine learning agile development artificial intelligence software analytics developer performance
Abstract doi.org/10.14569/IJACSA.2026.0170512 PDF
13

Cloud-Based Intelligent Surveillance for Digital Forensics: AI-Enhanced Criminal Investigations

Author 1: Aseel Abdullah Aljuhani Author 2: Fatima Hamed Aljuhani

Modern smart surveillance systems have become a core element of digital forensics workflows, offering real-time detection of weapons, fire, smoke, blood, cars, individuals, and other related objects. These systems improve evidence collection, accelerate threat identification, and enhance investigative efficiency. However, most traditional surveillance architectures operate without sufficient digital forensic awareness… Read full abstract & cite →

VigilEye digital forensics YOLOv8 real-time object detection evidence integrity
Abstract doi.org/10.14569/IJACSA.2026.0170513 PDF
14

A Fast and Efficient Residual Learning Framework Driven by Approximate Nearest Neighbor Search for Large-Scale Fingerprint-Based Visible Light Positioning

Author 1: Huy Q. Tran Author 2: Huu Lam Phan Author 3: Tan Nguyen Van

Localization based on received signal strength using the k-Nearest Neighbor method is quite common in indoor localization systems. However, as the fingerprint dataset grows, finding the nearest neighbors becomes time-consuming. In this study, we use Approximate Nearest Neighbor (ApNN) methods to accelerate nearest-neighbor search in RSS-based localization. We further propose… Read full abstract & cite →

Localization LED approximate nearest neighbor residual learning
Abstract doi.org/10.14569/IJACSA.2026.0170514 PDF
15

Insight Enhancement of Distortion Effects on the Quality in Imagery Environments

Author 1: Hanan Hassan Ali Adlan Author 2: Mona Alanazi Author 3: Kolood Alenezi Author 4: Muneera Aldhabbah Author 5: Muneera Alsubai Author 6: Rafa Bahobail Author 7: Reem Alharbi

Despite advances in technology and processing, image enhancement remains an open research area. The quality of the images has a great impact on the processing, analysis, and recognition. Images are a kind of multimedia data that demand huge resources. The image quality affects further processing, and if carefully utilized, will… Read full abstract & cite →

Digital image enhancement distortion image compression filter peak signal to noise ratio mean square error structural similarity index
Abstract doi.org/10.14569/IJACSA.2026.0170515 PDF
16

A Hybrid Neuro-Fuzzy and Machine Learning Model for Student Performance Prediction in Online Mathematics Education

Author 1: Beyza Esin Özseven Author 2: Turgut Özseven

Accurate and early prediction of student success in online mathematics education is critical for improving learning processes and developing personalized instruction strategies. However, students' problem-solving behaviors, interaction levels, and learning speeds in online learning environments inherently contain uncertainty, limiting the effectiveness of traditional assessment and singular machine learning approaches. This… Read full abstract & cite →

ANFIS ensemble learning fuzzy logic hybrid models learning analytics
Abstract doi.org/10.14569/IJACSA.2026.0170516 PDF
17

An Empirical Analysis of Google Play Data Safety Disclosures: A Consistency Study of Privacy Indicators in Mobile Gaming Apps

Author 1: Bakheet Aljedaani

The Google Play marketplace has introduced the Data Safety section to improve transparency regarding how mobile applications (apps) collect, share, and protect user data. This mechanism requires developers to disclose privacy and security-related practices, including data collection, data sharing, and data protection measures. However, the reliability of these disclosures depends… Read full abstract & cite →

Android security data safety mobile gaming apps privacy analysis static analysis
Abstract doi.org/10.14569/IJACSA.2026.0170517 PDF
18

Improving Emotion Recognition Accuracy Using a Multimodal Model (Face and Voice Video) Based on a Convolutional Neural Network (CNN)

Author 1: Karnadi Author 2: Ermatita Author 3: Abdiansah

Advancements in Artificial Intelligence (AI) technology have enabled the recognition of human emotions. Along with the development of deep learning and multimodal processing methods, emotion analysis can now be performed by utilizing multiple data sources simultaneously, such as facial expressions and speech signals. However, existing emotion recognition systems still face… Read full abstract & cite →

Emotion recognition CNN multimodal learning CREMA-D face voice accuracy deep learning
Abstract doi.org/10.14569/IJACSA.2026.0170518 PDF
19

Interlayer Coupling Strategy for Two-Layer Interdependent Command-and-Control Networks: Balancing Node Walk Betweenness and Operational Attributes

Author 1: Bo Chen Author 2: Yangkun Wang Author 3: Yufeng Chen Author 4: Yuqi Yang

In system-of-systems operations, command-and-control networks (C2Ns) increasingly exhibit hierarchical interdependence and cross-network coupling. Designing effective interlayer coupling strategies is therefore essential for improving the survivability of interdependent C2Ns under attacks. However, most existing coupling strategies are predominantly topology-driven and fail to jointly capture multipath information transmission characteristics and operational compatibility… Read full abstract & cite →

Interdependent command and control networks interlayer coupling strategy walk betweenness operational attributes
Abstract doi.org/10.14569/IJACSA.2026.0170519 PDF
20

Utilizing Structured Equation Modeling and Machine Learning in Investigating Digital Competency in Public School Teachers in Bukidnon, Philippines

Author 1: Nathalie Joy G. Casildo

The rapid transition to digital education in the Philippines, accelerated by the COVID-19 pandemic, has highlighted significant integration challenges for public school teachers in rural provinces like Bukidnon. While digital proficiency is essential, existing studies often rely on either purely descriptive analytics or standalone machine learning models, which frequently fail… Read full abstract & cite →

Structural Equation Modeling (SEM) Machine Learning (ML) digital competence DigCompEdu teacher proficiency SMOTE-ENN predictive analytics educational data mining
Abstract doi.org/10.14569/IJACSA.2026.0170520 PDF
21

DeepEdgeNet: An Edge-Cloud Deep Learning Framework for Efficient Environmental Monitoring in IoT Systems

Author 1: Qamar H. Naith

Deep learning (DL) is currently considered one of the most powerful tools for environmental monitoring. Many environmental variables, such as air quality, climate, water, and energy, are monitored using Internet of Things (IoT) technologies. However, the DL-based environmental monitoring systems heavily depend on the cloud, and hence they suffer from… Read full abstract & cite →

Internet of Things (IoT) edge computing federated learning machine learning
Abstract doi.org/10.14569/IJACSA.2026.0170521 PDF
22

Machine Learning Evaluation of the HiTar-2024 Dataset for Intrusion Detection in Smart Manufacturing Environments

Author 1: Adeeb Alhomoud

This study presents an investigation of the HiTar-2024 dataset performed in terms of the distribution of label attack types and the distribution of attacks by protocol, normal, and Denial of Service (DoS) connections over time. The investigation carried out a performance evaluation of the HiTar-2024 dataset using a machine learning… Read full abstract & cite →

Supervised learning intrusion detection system security smart manufacturing
Abstract doi.org/10.14569/IJACSA.2026.0170522 PDF
23

ZK-FedMed: Privacy-Preserving Federated Learning for Cardiovascular and Renal Disease Prediction

Author 1: Haewon Byeon

Protecting patient data confidentiality while enabling collaborative machine learning across distributed healthcare institutions remains a major challenge. This study presents ZK-FedMed, a privacy-preserving federated learning framework that combines CKKS partially homomorphic encryption for gradient protection, Rényi differential privacy with moments-accountant tracking, zk-SNARK-based integrity verification, TabTransformer feature extraction, SCAFFOLD aggregation, and… Read full abstract & cite →

Federated learning homomorphic encryption zero-knowledge proofs differential privacy transformer cardiovascular disease chronic kidney disease scaffold federated unlearning
Abstract doi.org/10.14569/IJACSA.2026.0170523 PDF
24

An Interpretable Machine Learning Approach for Inflation Forecasting in Indonesia Using Domestic Macroeconomic Indicators

Author 1: Ahmad Maimunif Author 2: Evaristus Didik Madyatmadja

Accurate inflation forecasting is essential for supporting forward-looking monetary policy, maintaining price stability, and preserving economic welfare. This study proposes an interpretable machine learning framework for inflation forecasting in Indonesia by integrating domestic macroeconomic indicators and seasonal variables. The study employs Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural… Read full abstract & cite →

Inflation forecasting machine learning explainable AI macroeconomic indicators time-series analysis monetary policy
Abstract doi.org/10.14569/IJACSA.2026.0170524 PDF
25

A Conceptual Model for Detecting Contactless Drug Distribution Based on Behavioural Analysis and Geospatial Visualisation

Author 1: Medeu Kurmangali Author 2: Talgat Akimzhanov Author 3: Kanat Kazhibaev Author 4: Kulambayev Bakhytzhan Author 5: Moshkalov Altynbek

This study proposes a multi-level system for detecting contactless drug distribution transactions. This system integrates behavioural pattern recognition as the primary detection channel, detection of night-time activity spikes as an enhancing module, and facial matching as an additional probabilistic evaluation layer. The system identifies a two-phase structure of covert transactions… Read full abstract & cite →

Anomaly detection flash detection pose estimation SNR crime mapping
Abstract doi.org/10.14569/IJACSA.2026.0170525 PDF
26

Revisiting Support Vector Machines: A Distance-Based Alternative to Deep Learning for Efficient Text Classification

Author 1: Siew Teng Koh Author 2: Anbuselvan Sangodiah Author 3: Norazira Binti A Jalil Author 4: Jafhate Edward Author 5: Nur Fatin Liyana Binti Mohd Rosely

Support Vector Machines (SVMs) remain competitive in text classification, sometimes achieving comparable performance with the deep learning approach, due to their strong generalization ability and robustness to overfitting. However, their strong classification performance relies heavily on the selection of kernel functions and parameters, resulting in substantial hyperparameter tuning. A previous… Read full abstract & cite →

SVM text classification Euclidean-SVM Cosine distance Manhattan distance Chebyshev distance Minkowski distance LSTM
Abstract doi.org/10.14569/IJACSA.2026.0170526 PDF
27

Pareto-Optimized Model Predictive Control for Dynamically Feasible Three-Dimensional Trajectory Generation in Robotic Manipulators

Author 1: Zeinel Momynkulov Author 2: Sayat Ibrayev Author 3: Azizah Suliman Author 4: Yegenberdi Tenizbayev Author 5: Batyrkhan Omarov

This study presents a Pareto-optimized Model Predictive Control (MPC) framework for dynamically feasible three-dimensional trajectory generation in robotic manipulators operating under physical constraints. Unlike conventional interpolation-based methods that emphasize geometric smoothness while neglecting system dynamics, the proposed approach integrates a second-order discrete-time model with explicit constraints on position, velocity, and… Read full abstract & cite →

Model Predictive Control trajectory planning robotic manipulators Pareto optimization multi-objective optimization dynamic constraints real-time control 3d motion planning
Abstract doi.org/10.14569/IJACSA.2026.0170527 PDF
28

Enhanced IoT-Driven Load Forecasting with Metaheuristic-Optimized Deep Learning for Logistics Planning

Author 1: Ramadan Babers Author 2: Walid Atwa Author 3: Mohamed Meselhy Eltoukhy Author 4: A. M. M. Madbouly

The integration of IoT technologies with smart logistics operations has opened unprecedented avenues for optimizing energy consumption in warehouse facilities. Accurate forecasting of electricity load is a key factor in cost reduction, operational efficiency, and sustainable energy management. This study presents an Enhanced Integrated Load Forecasting System (E-ILFS) that synergizes… Read full abstract & cite →

Electricity load forecasting IoT logistics planning metaheuristic optimization deep learning Harris Hawks optimization ResNet self-supervised learning
Abstract doi.org/10.14569/IJACSA.2026.0170528 PDF
29

A Smart IoT-Based Communication and Optimization System for Hajj and Umrah Services

Author 1: T. H. F. Harumy Author 2: M. Gahara Author 3: M. F. Sipahutar Author 4: A. Zakiyah Author 5: N. A. Hasna Author 6: E. Ikhsan Author 7: Yusuf N. Buwono Author 8: Haposan Delon Pasaribu

The management of Hajj and Umrah services requires reliable communication, continuous location awareness, and rapid emergency response because pilgrims move in dense and dynamic environments. This study presents a smart Internet of Things (IoT) based communication and optimization system for Hajj and Umrah services. The system integrates GPS-enabled wearable devices… Read full abstract & cite →

Internet of Things wearable communication Hajj and Umrah crowd monitoring deep neural network network performance congestion prediction
Abstract doi.org/10.14569/IJACSA.2026.0170529 PDF
30

Blockchain Consensus Mechanisms Contributing to Improved Trust in Knowledge Sharing: A Systematic Review

Author 1: Mohammad Fairus Bin Zulkifli Author 2: Rabiah Abdul Kadir Author 3: Mohamad Nazir Ahmad

Growing reliance on digital knowledge sharing across academic, corporate, and public sectors has raised serious concerns about data integrity, trust, and security. Blockchain consensus mecha-nisms offer a promising path forward through decentralized, transparent, and tamper-proof frameworks. This systematic review examines how these mechanisms enhance trust in knowledge sharing platforms, focusing… Read full abstract & cite →

Blockchain technology consensus mechanisms knowledge sharing distributed ledger technology trust systems
Abstract doi.org/10.14569/IJACSA.2026.0170530 PDF
31

Generative AI in Higher Education: A Systematic Review with Emphasis on Programming and Computer Science Education

Author 1: Abdulaziz Saidu Yalwa Author 2: Mohd Shahizan Othman Author 3: Lizawati Mi Yusuf Author 4: Muteb Sinhat Almarshadi

Generative Artificial Intelligence (GenAI) is rapidly reshaping teaching and learning in higher education, particularly in pro-gramming and computer science education, where tools such as ChatGPT and GitHub Copilot are increasingly used for code generation, debugging, conceptual explanation, and personalised learning support. Despite this growing use, the literature re-mains fragmented, with… Read full abstract & cite →

Generative artificial intelligence higher education programming education computer science education continuance intention technology acceptance model expectation–confirmation model PRISMA systematic literature review
Abstract doi.org/10.14569/IJACSA.2026.0170531 PDF
32

Translating Job Advertisements into Competency Taxonomy: An Interpretable Approach for Robotics Recruitment Analysis

Author 1: Zhiyan Xue Author 2: Yang Zhou

To translate robotics recruitment texts into an interpretable competency taxonomy, this study explored how latent topics can be induced from large-scale job advertisements and used to construct a structured competency taxonomy. A domain-specific preprocessing pipeline was applied to a corpus of robotics job advertisements, and latent topics were subsequently induced… Read full abstract & cite →

Competency taxonomy robotics industry Latent Dirichlet Allocation (LDA) Vocational Education and Training (VET)
Abstract doi.org/10.14569/IJACSA.2026.0170532 PDF
33

Enhancing User Experience in Virtual Reality for Children with Autism: A Significant Review

Author 1: Nur Aleesya Mohd Asri Author 2: Normala Rahim Author 3: Norsuhaily Abu Bakar Author 4: Wan Rizhan Author 5: Ismahafezi Ismail Author 6: Nur Saadah Mohd Shapri Author 7: Sarah Farhana Juhari

Virtual reality (VR) has become a promising technology to assist children with autism spectrum disorder (ASD especially in the areas of improving user experience with immersive, adaptive, and interactive environments. Nevertheless, current research is still disjointed in areas with very little synthesis on the effectiveness of VR in enhancing user… Read full abstract & cite →

Virtual Reality (VR) Autism Spectrum Disorder (ASD) User Experience (UX) multimodal analytics inclusive design
Abstract doi.org/10.14569/IJACSA.2026.0170533 PDF
34

Credit Card Fraud Anomaly Detection in Mobile Cloud Service Security Using Extended Isolation Forest with Hyperparameter Optimization

Author 1: Nur Izura Udzir Author 2: Nur Farihin Bidin Author 3: Aliyu Usman Shehu Author 4: Madihah Mohd Saudi Author 5: Azuan Ahmad Author 6: Muhammad Harith Noor Azam Author 7: Shazrin Azlin Ruslan Author 8: Nor Azlinda Abdul Halim

The surge in e-commerce has seen an increase in mobile-based credit card transactions, resulting in a sharp escalation of fraud that inflicts substantial financial losses on both consumers and corporations. Because these transactions increasingly rely on mobile cloud computing (MCC), this expansion has introduced critical security challenges, particularly in detecting… Read full abstract & cite →

Anomaly detection mobile cloud computing extended isolation forest credit card fraud detection multidimensional time series unsupervised machine learning
Abstract doi.org/10.14569/IJACSA.2026.0170534 PDF
35

Fine-Grained Image Classification Using Vision Transformer Model

Author 1: Zunaira Saleem Author 2: Uzma Jamil Author 3: Saman Iftikhar

Fine-Grained Image Classification focuses on unique features between visually similar subclasses within a wider category, which remains a challenging task due to low inter-class variations and high intra-class similarity. Conventional Convolutional Neural Network-based methods often struggle to accurately capture these minor differences. Utilizing self-attention techniques to represent global relationships within… Read full abstract & cite →

Data augmentation fine-grained image classification CIFAR-100 vision transformer model deep learning
Abstract doi.org/10.14569/IJACSA.2026.0170535 PDF
36

MRAE: Multi-Resolution Attention Ensemble with Hybrid CNN–Transformer Fusion for Breast Ultrasound Classification

Author 1: Hemin Kareem Azeez Alshateri Author 2: Ahmed Harbaoui

Breast ultrasound images can be classified as benign, malignant, and normal. Due to the imbalanced distribution of classes in breast ultrasound images, intra-class heterogeneity of lesions, and ultrasound artifacts like speckle noise, classification of breast ultrasound images remains a challenging problem. In this paper, we present MRAE, a hybrid architecture… Read full abstract & cite →

Breast ultrasound classification multi-resolution learning CNN–Transformer fusion cross-attention ensemble Vision Transformer (ViT)
Abstract doi.org/10.14569/IJACSA.2026.0170536 PDF
37

COAT: A Cross-Omics Attention Transformer for PAM50 Breast Cancer Subtype Classification

Author 1: Soufiane El Atfa Author 2: Abdelmajid Hajami Author 3: Hamid Machhour Author 4: Hakim Allali

Breast cancer is the most frequently diagnosed cancer in women worldwide, with approximately 2.3 million new cases annually. Accurate molecular subtyping is essential for guiding treatment decisions; however, existing PAM50 classifiers rely solely on mRNA expression and remain susceptible to normalization artifacts and platform-specific biases. To overcome these limitations, we… Read full abstract & cite →

Breast cancer subtyping PAM50 multi-omics integration cross-attention transformer TCGA-BRCA deep learning GradientSHAP
Abstract doi.org/10.14569/IJACSA.2026.0170537 PDF
38

Efficient Vulnerability Classification in IoT Networks: An Approach Using Convolutional Neural Networks and Tabu Search Optimization

Author 1: Feras Fares AL-Mashagba Author 2: Mohammad Othman Nassar Author 3: Essam Said Hanandeh

In this study, researchers propose a novel solution for efficient enhancement of vulnerability detection in several IoT environments. Efficient Vulnerability Classification has been introduced as the presented technique in IoT Networks (EVCIN). The method, EVCIN, which is a proposed approach, utilizes CNNs with Tabu Search Optimization. Customized CNN models have… Read full abstract & cite →

Vulnerability categorization IoT networks convolutional neural networks tabu search optimization cyber security I
Abstract doi.org/10.14569/IJACSA.2026.0170538 PDF
39

Scoping Review on Global Digital Policies and Transformation Strategies for Ageing Societies: Implications for Malaysia

Author 1: Ahmad Wiraputra Selamat Author 2: Shamsul Arrieya Ariffin

The rapid growth of ageing populations presents major social, healthcare, and economic challenges worldwide. Governments are increasingly adopting digital transformation strategies to support ageing societies through artificial intelligence (AI), the Internet of Things (IoT), telehealth, smart environments, and interoperable data systems. However, existing studies often focus on individual technologies, single-country… Read full abstract & cite →

Ageing nation digital framework ageing society digital transformation scoping review
Abstract doi.org/10.14569/IJACSA.2026.0170539 PDF
40

A Mid-Level Feature Fusion Framework Integrating PHLF and VGG16 for Robust Batik Pattern Detection

Author 1: Jani Kusanti Author 2: Edi Noersasongko Author 3: Purwanto Author 4: Moch Arief Soeleman

Javanese batik is characterized by distinctive motif patterns. The rapid evolution of designs through motif combination has introduced increased complexity, posing challenges for the community. People are increasingly less familiar with traditional Javanese original batik patterns. Many studies using CNN have been conducted to recognize batik patterns. However, it is… Read full abstract & cite →

A Mid-level feature detection fusion framework phlf traditional javanese batik
Abstract doi.org/10.14569/IJACSA.2026.0170540 PDF
41

YOLO-CBAM: A Lightweight Attention-Guided Deep Learning Framework for Real-Time Road Damage Detection

Author 1: Olzhas Olzhayev Author 2: Bakhytzhan Kulambayev Author 3: Azizah Suliman Author 4: Assel Rustem Author 5: Almira Madiyarova Author 6: Batyrkhan Omarov

Accurate and real-time assessment of road infrastructure is critical for smart city maintenance and transportation safety. However, conventional object detection models often struggle with complex environmental factors, such as varying illumination, shadows, and background noise, leading to false detections and missed fine-grained defects. In this study, we propose YOLO-CBAM, a… Read full abstract & cite →

Computer vision deep learning object detection YOLO architecture CBAM attention mechanism road monitoring transfer learning
Abstract doi.org/10.14569/IJACSA.2026.0170541 PDF
42

Deep Learning-Based Recommender System for Arabic Content with Integrated Sentiment Analysis of User Reviews

Author 1: Amani Al-Ajlan Author 2: Nada Alshareef

Recommender systems are widely used as an information filtering technology to automatically predict and identify a set of interesting items for users based on their needs and preferences. They are widely applied in many domains, including e-commerce, social media, education, and healthcare. Recommender systems employ various filtering approaches, such as… Read full abstract & cite →

Recommender system deep learning sentiment analysis LABR dataset Model-based collaborative filtering pre-trained model Arabic BERT pre-trained model AraBERT
Abstract doi.org/10.14569/IJACSA.2026.0170542 PDF
43

A Knowledge-Enhanced Cross-Modal Transformer Network for Sentiment Analysis in Intelligent Interaction

Author 1: Chunyan Huang Author 2: Xinlu Sun

With the rapid advancement of multimodal emotion recognition technology, sentiment analysis models that integrate heterogeneous information—such as facial expressions and vocal intonation—are driving human–computer interaction and affective computing toward multidimensional, objective, and highly accurate approaches. Conventional emotion recognition methods typically rely on a single-modal input and therefore struggle to capture… Read full abstract & cite →

Multimodal sentiment analysis transformer network emotion recognition psychological assessment intelligent interaction
Abstract doi.org/10.14569/IJACSA.2026.0170543 PDF
44

Enhancing Traffic Congestion Forecasting with Explainable Deep Learning: A Framework Using LIME for Transparent Intelligent Transportation Systems

Author 1: Ouhmidou Hajar Author 2: Nabou Abdellah Author 3: Elikram Moulay Ahmed

Intelligent transportation systems aim to improve traffic management and road safety, manage traffic effectively, and reduce roadway system congestion. This optimally requires estimating future traffic congestion. Unfortunately, the most popular machine learning and deep learning techniques can be unsuitable for model development in this task due to interpretability challenges. This… Read full abstract & cite →

Explainable artificial intelligence lime traffic congestion forecasting intelligent transportation systems random forest
Abstract doi.org/10.14569/IJACSA.2026.0170544 PDF
45

Mammographic Image Classification Based on KAN-CBAM

Author 1: Yuanyuan Wang Author 2: Vladimir Mariano

With the fast enhancement of deep learning, research on automatic detection of breast tumors is becoming increasingly in-depth. However, traditional CNNs’ linear kernel has difficulty not only in capturing the nonlinear combination of low-frequency structures and high-frequency details but also in fully exploring the nonlinear discriminative features in medical images… Read full abstract & cite →

KAN CBAM deep learning mammographic classification
Abstract doi.org/10.14569/IJACSA.2026.0170545 PDF
46

Enhancing Virtual Team Performance Through a Quantitative-Based Management Model (VTPM)

Author 1: Sathya A/L Nantha Kumar Author 2: Zulkefli Mansor

The rapid growth of globalization, digital transformation, and remote working practices has significantly increased the reliance on virtual teams within modern organizations. Despite their advantages, virtual teams continue to face challenges such as communication barriers, inadequate technology management, unclear role allocation, ineffective strategic planning, and excessive documentation practices, all of… Read full abstract & cite →

Virtual teams team performance remote work VTPM model communication quantitative study
Abstract doi.org/10.14569/IJACSA.2026.0170546 PDF
47

An Explainable XGBoost-Based Framework for Robust Multi-Cohort Prediction of Pancreatic Cancer

Author 1: Nada Ahmed El-Gammal Author 2: Rania Ahmed Abdel Azeem Abul Seoud Author 3: Sayed T. Muhammad

Pancreatic cancer remains a leading cause of cancer-related mortality due to its asymptomatic progression and late-stage diagnosis. Early detection is critical for improving patient prognosis and clinical outcomes. Traditional diagnostic approaches and previous computational models often struggle with molecular heterogeneity and technical variations across different genomic platforms. These batch effects… Read full abstract & cite →

Pancreatic cancer gene expression analysis XGBoost SHAP explainability pathway enrichment Explainable AI (XAI)
Abstract doi.org/10.14569/IJACSA.2026.0170547 PDF
48

A Hybrid PCA–Random Forest Model for Predicting Employee Performance from Work Ethics and Work Values

Author 1: Nova E. Arquillano

This study examined the predictive relationship of work ethics and work values with employee task performance using a hybrid PCA–Random Forest model. Data were obtained from 231 PSU employee respondents and 81 questionnaire variables covering task performance, work ethics, and work values. A quantitative, cross-sectional predictive design was used; therefore… Read full abstract & cite →

Employee performance work ethics work values principal component analysis random forest hybrid model machine learning PSU employees
Abstract doi.org/10.14569/IJACSA.2026.0170548 PDF
49

A New Approach for 3D Shape Retrieval Based on an Explainable Boosting Classifier

Author 1: Fatima Rafii Zakani Author 2: Mohcine Bouksim Author 3: Khadija Arhid Author 4: Taoufiq Gadi Author 5: Mohamed Aboulfatah

In the past decade, the number of available 3D models has grown rapidly. This growth is mainly driven by the development of scanning devices and increasing demand for 3D meshes in many application fields. As a consequence, it becomes crucial to have consistent content-based retrieval systems of large 3D mesh… Read full abstract & cite →

3D shape retrieval content-based indexing shape descriptor explainable boosting classifier
Abstract doi.org/10.14569/IJACSA.2026.0170549 PDF
50

HBC-Glicko: A Bloom-Constrained Adaptive Assessment Architecture with Uncertainty-Aware Progression

Author 1: Wissal EL Fougour Author 2: Mohamed Erradi

Adaptive assessment systems typically model learner ability as a single continuous latent trait and select items accordingly. Although effective for efficient measurement, such models rarely encode hierarchical cognitive progression as a formal constraint within the learner model itself. In educational domains where higher-order performance depends on sufficiently stable prerequisite knowledge… Read full abstract & cite →

Adaptive assessment Bloom's revised taxonomy Glicko rating system learner modeling non-compensatory progression uncertainty modeling formative assessment
Abstract doi.org/10.14569/IJACSA.2026.0170550 PDF
51

A Secure Chaotic DNA-Based Framework for Satellite Image Encryption

Author 1: Asim Seedahmed Ali Osman Author 2: Ibrahim Rizqallah Alzahrani Author 3: Aeshah Khalil I Alotaibi Author 4: Adil Mahmoud Mohamed Mahmoud Author 5: Daifallah Zaid Alotaibe

With the growing use of satellite imaging in environmental monitoring, defense, and remote sensing applications, protecting satellite images during storage and transmission has become increasingly important. Unlike textual data, satellite images contain high spatial redundancy and strong correlations among neighboring pixels, which can limit the effectiveness of conventional encryption methods… Read full abstract & cite →

Chaotic cryptography DNA computing satellite image security image encryption information security secure image transmission
Abstract doi.org/10.14569/IJACSA.2026.0170551 PDF
52

A Diffusion-Based Generative AI Framework: An Exterior House Design from Textual Descriptions

Author 1: Muhammad Amirul Akmal bin Ajusin Author 2: Noor Hasimah Ibrahim Teo Author 3: Rosniza Roslan Author 4: Raseeda Hamzah Author 5: Anita Ahmad Kasim

The architectural design process is often iterative, time-consuming, and heavily dependent on effective communication between clients and professionals. Existing design tools, such as Computer-Aided Design (CAD) systems, require technical expertise, limiting accessibility for non-professional users. This study proposes a generative artificial intelligence framework for exterior house design using a diffusion-based… Read full abstract & cite →

Generative artificial intelligence stable diffusion text-to-image generation architectural design image inpainting
Abstract doi.org/10.14569/IJACSA.2026.0170552 PDF
53

Integration of Random Forest and Hough Transform for Cancer Classification Using Microarray Gene Expression Data

Author 1: Hibah Alatawi Author 2: Hechmi Shili

Cancer classification poses a significant challenge owing to the intricate nature and diversity of the disease. This study introduces a novel methodology for cancer classification leveraging microarray gene expression data. The proposed approach integrates Random Forest (RF) and Hough Transform (HT), where RF performs feature selection and classification, and HT… Read full abstract & cite →

Cancer classification microarray gene expression random forest Hough transform feature selection hypergraph model
Abstract doi.org/10.14569/IJACSA.2026.0170553 PDF
54

Detection of Unauthorized Use in SIEM Through Behavioral Analysis and Adaptive Rules

Author 1: Julio Armando Landázuri Castro Author 2: Renato M. Toasa Author 3: Maryory Urdaneta Herrera

Higher education institutions in Ecuador face a growing exposure to unauthorized access and data exfiltration, compounded by fragmented log infrastructures that obstruct real-time threat visibility. This study addresses those gaps through the design and deployment of a Security Information and Event Management (SIEM) architecture at the Compu Sur Higher Technological… Read full abstract & cite →

Adaptive rules behavioral analysis cybersecurity data traceability event logs higher education security security information and event management user and entity behavior analytics
Abstract doi.org/10.14569/IJACSA.2026.0170554 PDF
55

Semantic Modeling of Medical Specialty Relationships Using Large Language Models

Author 1: Ismail Bouajaja Author 2: Omar Elfahim Author 3: Omar Bouattane Author 4: Oussama Barakat Author 5: Abdelaziz Daaif

This work proposes a computational framework for modeling semantic relationships between medical specialties using large language models. Forty-four medical specialties officially recognized in France were analyzed using Claude 4 Sonnet, GPT-4.1, and LLaMA 3.2 3B. Each model evaluated the relevance of 307 ICD-11 disease families, 260 educational teaching items, and… Read full abstract & cite →

Large language models semantic similarity computational modeling cluster analysis medical specialties
Abstract doi.org/10.14569/IJACSA.2026.0170555 PDF
56

An Intelligent Semantic-Aware Academic Library System Using Natural Language Processing and Knowledge Graphs

Author 1: Mohamad Shady Alrahhal Author 2: Bandar Abdulwahed Fallatah Author 3: Omar F. Aloufi Author 4: Abdullah M. Barakeh

Traditional library systems designed for academic research suffer from poor integration when employing AI-based techniques for enhancement purposes. These limitations are mainly related to ignoring semantic relationships during search, overlooking hidden relationships among words, and lacking the generalization capability required to enhance researchers’ experience. To address these issues, the Intelligent… Read full abstract & cite →

Library systems academic search generalization knowledge graph similarity accuracy
Abstract doi.org/10.14569/IJACSA.2026.0170556 PDF
57

Rice Pest Detection Using Enhanced YOLOv8n with Multilayer Contextual Attention and Deformable Snake Convolution

Author 1: Shuangyuan Li Author 2: Jianglong Lin Author 3: Jiaming Liang Author 4: Tianyu Li

Accurate and intelligent detection of rice pests is critical for ensuring food security and advancing precision agriculture. However, due to the small size, irregular morphology, dense distribution, and complex backgrounds of pest targets, traditional lightweight detection models often suffer from low recall and poor localization accuracy in real-world paddy field… Read full abstract & cite →

Rice pest detection YOLOv8n deep learning real-time detection
Abstract doi.org/10.14569/IJACSA.2026.0170557 PDF
58

Data-Driven Excimer LiDAR Framework for Joint Surface Reflectivity Mapping and Atmospheric Pollutant Profiling

Author 1: Sandugash Dospanbetova Author 2: Gulzat Ziyatbekova Author 3: Murat Baktybayev Author 4: Botakoz Smagul Author 5: Yermakhan Zhabayev Author 6: Zhanar Bidakhmet

This study proposes a data-driven excimer LiDAR framework for joint surface reflectivity mapping and atmospheric pollutant profiling, integrating physics-based sensing with deep learning-based multi-source data fusion. The system utilizes ultraviolet excimer LiDAR measurements in combination with auxiliary data from UAV platforms, satellite observations, and ground-based sensors to construct a unified… Read full abstract & cite →

LiDAR data-driven modeling multi-source data fusion deep learning remote sensing
Abstract doi.org/10.14569/IJACSA.2026.0170558 PDF
59

Cervical Cytology Classification Using Multiple CNN Architectures with Transformer-Based Feature Enhancement

Author 1: Mehreen Sirshar Author 2: Omama Shakeel Author 3: Nayyab Asim Author 4: Fakeeha Jafari Author 5: Hani Almoamari Author 6: Adnan Nadeem Author 7: Mohammad Zubair Khan Author 8: Ibrahim Aljubayri

Cervical cancer is the fourth most common cancer among women worldwide and remains a major public health challenge, particularly in regions with limited access to early screening and diagnosis. Accurate classification of cervical cytology images is critical for early detection of cervical cancer, which remains a major health burden in… Read full abstract & cite →

Cervical cancer CNN architectures vision transformer
Abstract doi.org/10.14569/IJACSA.2026.0170559 PDF
60

Methodology for Identifying Soiling on PV Panels Using RGB Images and Deep Learning

Author 1: Katerina Gabrovska-Evstatieva Author 2: Tsvetelina Kaneva Author 3: Irena Valova Author 4: Dimitar Trifonov Author 5: Nikolay Valov Author 6: Ventsislav Keseev Author 7: Nicolay Mihailov Author 8: Boris Evstatiev

Soiling is one of the major factors that can affect the performance of PV installations in many regions of the world. On the other hand, the variety of PV models, types of soiling, and climate conditions makes it very difficult to create a universal model. To address this problem, this… Read full abstract & cite →

PV soiling semantic segmentation pixel-based classification neural network RGB images
Abstract doi.org/10.14569/IJACSA.2026.0170560 PDF
61

Gas-Efficient Smart Contract Design: Quantifying Refactoring Impact on EVM Execution Costs

Author 1: Nur Haliza Abdul Wahab Author 2: Juniardi Nur Fadila Author 3: Nur Faszha Razali Author 4: Keng Yinn Wong

High transaction costs remain a major barrier to the scalability of Ethereum-based decentralized applications (DApps), particularly when smart contracts are computationally inefficient. Although the Solidity compiler optimizer can reduce bytecode size and improve some low-level patterns, it does not fully address structural inefficiencies in storage layout and state mutation. This… Read full abstract & cite →

Ethereum smart contract solidity refactoring gas optimization Ethereum Virtual Machine (EVM) opcode tracing
Abstract doi.org/10.14569/IJACSA.2026.0170561 PDF
62

Agent-Oriented Fuzzy Decision Support System for Multi-Criteria Evaluation of Sustainable Investments in the Agro-Industrial Sector

Author 1: Aigerim Omurtayeva Author 2: Ulzhan Makhazhanova Author 3: Akmaral Kulamanova Author 4: Dinara Kargabaeva Author 5: Bolat Tassuov Author 6: Adilbek Tanirbergenov

Sustainable development of the agri-food sector in emerging economies requires the use of analytical tools capable of taking into account climate risks, environmental constraints, and investment flow instability when making management decisions. Given the fragmentary nature of statistical information and the high volatility of the external environment, traditional econometric methods… Read full abstract & cite →

Sustainable investments agro-industrial sector decision support system agent-oriented systems fuzzy logic ESG factors climate risks emerging economies multicriteria evaluation
Abstract doi.org/10.14569/IJACSA.2026.0170562 PDF
63

Phonetic Completeness Over Prosodic Diversity: Syllable-Level Synthetic Corpus Construction for Low-Resource Penang Hokkien Speech Synthesis

Author 1: Yu Liang Lai Author 2: Yen Min Jasmina Khaw Author 3: Seng Poh Lim Author 4: Tien Ping Tan

This study presents the first Text-to-Speech (TTS) model for Penang Hokkien, a low-resource tonal dialect at risk of extinction. To address phonological sparsity in the collected speech corpus, we propose a two-stage fine-tuning approach that emphasizes comprehensive phonetic coverage through syllable-level synthetic augmentation while subsequently refining prosodic naturalness using real… Read full abstract & cite →

Low-resource language speech synthesis text-to-speech data augmentation techniques
Abstract doi.org/10.14569/IJACSA.2026.0170563 PDF
64

Exploring New Possibilities: Virtual Reality in Supporting Children with Autism

Author 1: Brayan J. Lozano-Martel Author 2: Eduardo F. Araujo-Vasquez Author 3: Segundo E. Cieza-Mostacero

Children with autism spectrum disorder (ASD) often require structured opportunities to practice social interaction, following instructions, and recognizing emotions in safe, controlled environments. This study presents and evaluates a mobile virtual reality (VR) application designed to train these skills through simulated everyday scenarios. An applied quantitative study with an experimental… Read full abstract & cite →

Autism Spectrum Disorder virtual reality mobile application social skills training immersive learning special education
Abstract doi.org/10.14569/IJACSA.2026.0170564 PDF
65

Augmented Reality in Automotive Technical Training: Technological Innovation Through Mobile Applications in Trujillo

Author 1: Julissa B. Polo-Holguin Author 2: Eduardo F. Araujo-Vasquez Author 3: Segundo E. Cieza-Mostacero

This study evaluated the impact of an Augmented Reality (AR)-based mobile application on technical training in automotive maintenance at a company in Trujillo, Peru. The application was developed using Unity 3D, the Vuforia marker-based recognition engine, and C#, enabling technicians to interact with three-dimensional models of automotive components — including… Read full abstract & cite →

Augmented reality marker-based AR automotive maintenance training mobile application technical comprehension training efficiency experimental design
Abstract doi.org/10.14569/IJACSA.2026.0170565 PDF
66

CFD-Based Aerothermal Design Verification of Coffee Cabinet Dryer

Author 1: Gerry M. Castillo Author 2: Aeron R. Mojica Author 3: Jhon Heron P. Carsocho Author 4: Ray Rojan C. Romeroso Author 5: Sheryl Dinglasan-Fenol Author 6: Al Eugene L. Torres Author 7: Gee Jay C. Bartolome

Traditional sun drying remains the primary practice among smallholder coffee farmers in the Philippines, often leading to an inefficient and inconsistent drying process. While transitioning to mechanical drying improves product quality, achieving uniform airflow in cabinets with multiple trays often requires costly post-fabrication modifications, resource-intensive trials, and repetitive field testing… Read full abstract & cite →

COMSOL Multiphysics coffee dryer heat transfer design verification aerothermal simulation performance characterization
Abstract doi.org/10.14569/IJACSA.2026.0170566 PDF
67

Unveiling Temporal Dynamics of Consumer Emotions in Online Reviews: An Emotion Mining Approach Using Ekman’s Emotion Model

Author 1: Azra Shamim Author 2: Abdulwahab Ali Almazroi

Currently, business organizations are using Electronic Word-of-Mouth (EWOM) from consumer-opinion platforms to elevate their marketing strategies using semantic analysis and emotion mining. Existing efforts are devoted to analyzing EWOM using fine-grained emotion classification; however, the well-known and well-established emotion models that provide a basic set of psychological and biological emotions… Read full abstract & cite →

Emotion emotion mining semantic analysis online reviews
Abstract doi.org/10.14569/IJACSA.2026.0170567 PDF
68

Privacy Preserving Federated Graph Learning with Data Envelopment Analysis Driven Interpretable Customer Segmentation Framework

Author 1: Pentareddy Ashalatha Author 2: G. Krishna Mohan

Customer segmentation plays a critical role in retail analytics by enabling personalized marketing, optimized resource allocation, and data-driven strategic decision-making. However, customer data is often distributed across multiple retail branches and contains sensitive transactional information, creating significant challenges related to privacy preservation, regulatory compliance, and model interpretability. Traditional segmentation approaches… Read full abstract & cite →

Customer segmentation federated learning graph neural networks data envelopment analysis privacy preservation
Abstract doi.org/10.14569/IJACSA.2026.0170568 PDF
69

Explainable Cognitive Graph Intelligence Framework for Multi-Task Graduate Employability and Salary Prediction

Author 1: Hameeda Khatoon Author 2: Chandra Prakash Vudatha

Accurate prediction of graduate employability and expected salary is essential for enabling timely, data-driven career interventions in academic institutions. However, most existing methods rely on standalone machine learning or deep learning models that fail to jointly capture cognitive traits, temporal academic progression, and peer-level relational structures within a unified and… Read full abstract & cite →

Cognitive properties prediction of placement salary regression graph neural network bolas spider optimization explainable AI MLP-BiLSTM-Transformer
Abstract doi.org/10.14569/IJACSA.2026.0170569 PDF
70

Large Language Models for Arabic Automated Essay Scoring

Author 1: Leena Najjar Author 2: Liyakathunisa Syed

Automated Essay Scoring (AES) has become an important research area in educational artificial intelligence due to its potential to support scalable and consistent assessment. The developments within the realm of transformers and large language models (LLMs) have led to great improvements within AESs through their ability to comprehend semantics and… Read full abstract & cite →

Arabic automated essay scoring large language models educational AI prompt engineering evaluation metrics natural language processing
Abstract doi.org/10.14569/IJACSA.2026.0170570 PDF
71

Revolutionizing Urban Waste Systems: A Comprehensive Review of IoT, AI, and Optimization Techniques for Smart Waste Management

Author 1: Lahcen GOUSKIR Author 2: Abdelmoula ABOUHILAL Author 3: Mohamed GOUSKIR Author 4: Mohamed BASLAM Author 5: Soufiane BELHOUIDEG Author 6: Hanaa Hachimi

Urban waste management is shifting from fixed, reactive collection toward data-driven and adaptive service models. This review synthesizes 33 recent studies and technical contributions on Artificial Intelligence of Things (AIoT) for smart waste management. The synthesis is organized around six analytical dimensions: IoT sensing and communication, real-time monitoring performance, dynamic… Read full abstract & cite →

Smart waste management Internet of Things Artificial Intelligence AIoT edge computing dynamic routing circular economy robotic sorting
Abstract doi.org/10.14569/IJACSA.2026.0170571 PDF
72

Deep Learning-Enhanced Excimer Laser LiDAR System for High-Resolution Earth Surface Monitoring and Multi-Platform Validation

Author 1: Sandugash Dospanbetova Author 2: Gulzat Ziyatbekova Author 3: Murat Baktybayev Author 4: Botakoz Smagul Author 5: Yermakhan Zhabayev Author 6: Zhanar Bidakhmet

This study proposes a deep learning-enhanced excimer laser LiDAR framework for high-resolution Earth surface monitoring through the integration of multi-platform data sources, including UAV measurements, satellite imagery, and ground-based observations. The study introduces LiDARFormer-Net, a transformer-based architecture designed to effectively capture complex spatial, spectral, and atmospheric dependencies using multi-head attention… Read full abstract & cite →

Deep learning LiDAR transformer architecture earth surface monitoring environmental monitoring remote sensing feature extraction attention mechanism
Abstract doi.org/10.14569/IJACSA.2026.0170572 PDF
73

Federated and Cross-Domain Student Performance Prediction

Author 1: Sam Zhe Xuan Author 2: P. Ganesh Kumar Author 3: C. Rani Author 4: K. Kanagalakshmi Author 5: R. RajiniGanth Author 6: Atif Mahmood

Accurate student performance prediction is critical for data-driven educational decision-making; however, it is often hindered by data heterogeneity, privacy constraints, and domain shift across academic contexts. This study investigates student final grade (G3) prediction using three complementary machine learning paradigms: centralized learning, cross-domain generalization, and federated learning with personalization. Experiments… Read full abstract & cite →

Federated learning cross-domain generalization educational data mining student performance quality education
Abstract doi.org/10.14569/IJACSA.2026.0170573 PDF
74

Intelligent Traffic Surveillance: Machine Learning-Based Detection of Vehicle Speed Violations

Author 1: Niloy Kanti Paul Author 2: Dipanwita Saha Author 3: Kaushik Biswas Author 4: Sultanul Arifeen Hamim Author 5: Tanvir Ahmed Author 6: Rifath Mahmud

With the rapid increase in the number of vehicles on roads, traffic management, and safety enforcement have become significant challenges worldwide. Traditional speed violation detection systems either employ high-end hardware, expensive computational resources, or post-processed video data, which are inefficient to implement in real time. This study presents a real-time… Read full abstract & cite →

Machine learning speed detection object detection vehicle tracking violation
Abstract doi.org/10.14569/IJACSA.2026.0170574 PDF
75

Malak: A Python Toolkit for Edge AI Model Optimization

Author 1: Mohammed Hassan Alnemari

Deploying deep learning models on resource-constrained edge devices demands systematic model compression and optimization. PyTorch supplies low-level quantization and pruning primitives, yet a typical quantization-aware training workflow still requires approximately 40 to 60 lines of observer wiring, calibration, and conversion boilerplate, which slows iteration for researchers and students. We present… Read full abstract & cite →

Edge AI model compression quantization pruning knowledge distillation TinyML
Abstract doi.org/10.14569/IJACSA.2026.0170575 PDF
76

RGB-D Bin-Picking System for Ergonomic Automotive Clip Assembly: 3D Annotation, Deep Learning, and 6-DOF Pose

Author 1: Brahim Bergor Beguiel Author 2: Ibrahim Hadj Baraka Author 3: Yassir Zardoua

Automotive seat cover assembly entails the manual collection of plastic J-shaped clips from boxes located at a non-ergonomic distance from the sewing stations. Operators are compelled to inadvertently pick up multiple components at once, and make actions that involve repetitive stretching. The daily, consistent repetition leads to misassembly of the… Read full abstract & cite →

Automated pick-and-place RGB-D vision YOLOv8 segmentation point cloud matching 6-DOF pose estimation industrial robotics automotive manufacturing deep learning quality inspection PLC control
Abstract doi.org/10.14569/IJACSA.2026.0170576 PDF
77

Urdu-ClimateGPT: Adapting LLM for Climate Data in Urdu Language

Author 1: Muhammad Farooq Author 2: Muhammad Asif Habib Author 3: Jabeen Sultana Author 4: Muhammad Umar Aftab

Climate change is increasingly recognized as a major global challenge that affects environmental systems, weather patterns, and human societies around the world. Rising global temperatures have been linked to more frequent extreme weather events and long-term shifts in climate patterns. Communicating climate information effectively, therefore, becomes essential, especially in a… Read full abstract & cite →

Climate change communication domain-specific AI large language models low-resource languages natural language processing retrieval augmented generation Urdu language processing
Abstract doi.org/10.14569/IJACSA.2026.0170577 PDF
78

Leveraging MCESTA and Large Language Models for Next-Generation Similarity Learning

Author 1: Mohamedou Cheikh Tourad Author 2: Abdelmounaim Abdali Author 3: Mohamed Dhleima Author 4: Naoual Mouhni Author 5: Sana Chakri Author 6: Ibtissam Amalou Author 7: Saadbouh Cheikh El Mehdy

Large Language Models (LLMs) have reshaped how machines read and compare text, yet most similarity learning pipelines built on top of them still behave like black boxes: a single cosine score is returned without any indication of why two sentences were deemed close. This study proposes a different route. We… Read full abstract & cite →

Similarity learning MCESTA fuzzy aggregation Natural Language Processing Large Language Models Sentence-BERT explainable AI
Abstract doi.org/10.14569/IJACSA.2026.0170578 PDF
79

Enhanced Pied Kingfisher Optimization Algorithm with Hovering Scouts and Foraging Flocks Mechanisms

Author 1: Aarhus Dela Cruz

Population-based metaheuristic algorithms are widely used for solving nonlinear, nonconvex, constrained, and high-dimensional optimization problems. However, many swarm-based optimizers still suffer from premature convergence, loss of population diversity, and weak exploitation of multiple promising regions. To address these limitations, this study proposes the Hovering Scouts and Foraging Flocks Pied Kingfisher… Read full abstract & cite →

Exploration-exploitation balance metaheuristic optimization numerical optimization Pied Kingfisher Optimizer swarm intelligence
Abstract doi.org/10.14569/IJACSA.2026.0170579 PDF
80

A Lightweight Explainable Hybrid Deep Learning Approach for Early Skin Disease Detection

Author 1: Mohammad Barr

Early detection of skin pathology is critical for patient survival and treatment effectiveness, particularly in the case of aggressive malignancies such as melanoma. In this study, we propose a lightweight and explainable hybrid deep learning approach for early identification of skin diseases. The proposed approach combines a transformer-based module to… Read full abstract & cite →

Medical image analysis skin disease detection explainable AI hybrid model Vision Transformer
Abstract doi.org/10.14569/IJACSA.2026.0170580 PDF
81

Intrusion Detection System for Ransomware in Network Traffic Using Supervised Machine Learning

Author 1: Ziad Almulla Author 2: Moath Alamri Author 3: Mounir Frikha

Ransomware is one of the most dangerous cyber threats today, as it can disrupt systems and cause serious financial losses. Traditional detection methods often fail to catch newer attacks because they can hide within normal network traffic. In this study, we used machine learning to detect ransomware based on network… Read full abstract & cite →

Ransomware detection machine learning IDS network traffic analysis real-time detection
Abstract doi.org/10.14569/IJACSA.2026.0170581 PDF
82

Domain Knowledge-Enhanced Welding Robot Path Planning Algorithm

Author 1: Ming Liu Author 2: Peng Shao

Welding Robot Path Planning (WRPP) is a core technology for welding automation that seeks an optimal path that satisfies the requirements of the welding process in complex obstacle environments. However, existing algorithms generally lack the integration of knowledge in the welding domain and exhibit insufficient adaptability to complex working conditions… Read full abstract & cite →

Path planning intelligent optimization algorithm welding robot Large Language Models
Abstract doi.org/10.14569/IJACSA.2026.0170582 PDF
83

Optimizing Whiteboard Digitization with Clarix: An Automated Vision-Based System for Real-Time Text Extraction and Surface Cleaning

Author 1: Abdellah NABOU Author 2: Ameksa Mohammed Author 3: Ezzahoud Hajar Author 4: Bazgour Yassine Author 5: Abouhane Zahra

Whiteboard digitization in educational settings grapples with challenges posed by handwritten text, mathematical notation, and unstructured layouts that confound traditional Optical Character Recognition (OCR) systems. Clarix, a low-cost embedded system, addresses these issues by integrating a Raspberry Pi, high-resolution camera, and servo-driven erasure mechanism to automate content capture, text extraction… Read full abstract & cite →

Whiteboard digitization multimodal Large language Models (LLMs) Optical Character Recognition
Abstract doi.org/10.14569/IJACSA.2026.0170583 PDF
84

Adaptive Temporal Windowing for Streaming Outlier Detection Under Dynamic Arrival Rates

Author 1: Hend Maher Author 2: Mohamed Khafagy Author 3: Heba Nagaty

Streaming outlier detection requires adaptive mechanisms capable of handling continuously evolving data streams under dynamic arrival rates. Existing count-based approaches fail under bursty and irregular stream arrival patterns commonly observed in real-world systems, since they trigger model updates after a fixed number of instances without considering temporal dynamics. In this… Read full abstract & cite →

Streaming outlier detection time-based windowing Density Incremental Local Outlier Factor data streams anomaly detection fog computing
Abstract doi.org/10.14569/IJACSA.2026.0170584 PDF
85

Deep Learning-Based Road Damage Detection Using Improved YOLOv8: Model Performance and Implementation

Author 1: Aulia Rahman Author 2: Hwa Jen Yap Author 3: Rusdha Muharar

Road infrastructure monitoring plays a crucial role in ensuring safety and economic efficiency; however, conventional manual inspection methods are expensive and resource-intensive. This study presents the development and evaluation of a real-time road damage detection system using the improved YOLOv8 architecture. Building upon the strengths of previous YOLO models, YOLOv8… Read full abstract & cite →

Road damage detection YOLOv8 deep learning computer vision mobile application
Abstract doi.org/10.14569/IJACSA.2026.0170585 PDF
86

Smarter Bridges: Leveraging Artificial Intelligence to Reshape University-Industry Technology Transfer

Author 1: Khaouja Mohammed Author 2: Sanaa DFOUF Author 3: Kaoutar ERRAKHA Author 4: Hanan ELHARISSI Author 5: Fekkak HAMDI

University-industry technology transfer (UITT) is essential for converting academic research into commercial use, yet traditional strategies often fail to address the knowledge gap. Literature suggests that institutional inertia, communication barriers, and ineffective marketing strategies hinder the commercialization of technology. This study proposes a conceptual framework that incorporates AI-driven marketing to… Read full abstract & cite →

Collaboration university-industry technology transfer AI-enhanced marketing innovation knowledge sharing economic growth strategic partnerships research initiatives entrepreneurial mindset
Abstract doi.org/10.14569/IJACSA.2026.0170586 PDF
87

Development of an Arabic Pet Adoption System with Hybrid Recommendations

Author 1: Hailah Alballaa

Pet adoption platforms often face challenges in effectively matching adopters with suitable pets due to limited personalization and the lack of localized language support. This study presents EWAA, an Arabic-enabled mobile platform for pet adoption in Saudi Arabia that integrates a hybrid recommender system combining content-based and collaborative filtering techniques… Read full abstract & cite →

Recommender systems pet adoption mobile applications collaborative filtering content-based filtering user experience
Abstract doi.org/10.14569/IJACSA.2026.0170587 PDF
88

State-AttentNet: A Dynamic Volatility-Adaptive Hybrid Framework for Market State Classification in Frontier Economies

Author 1: Md. Abul Kalam Azad Author 2: Abdul Kadar Muhammad Masum Author 3: Najmus Saadat Author 4: Esrat Jahan Author 5: Ramona Birau Author 6: Virgil Popescu Author 7: Iuliana Carmen Barbacioru Author 8: Stefan Margaritescu

The proper identification of latent market states is important for effective risk management. However, existing frameworks are often not good at distinguishing the difference between stable situations and systemic crashes in the high entropy environments of frontier economies. To address this challenge, this study presents State-AttentNet, an application-driven market-state classification… Read full abstract & cite →

Frontier markets hybrid deep learning adaptive attention mechanism dynamic volatility labeling Explainable AI (XAI)
Abstract doi.org/10.14569/IJACSA.2026.0170588 PDF
89

Cognitively Aligned Assessment Item Generation with Open-Source LLMs: A Comprehensive Evaluation on LearningQ

Author 1: Mahmoud Badry Author 2: Walaa Medhat Author 3: Shereen A. Taie Author 4: Asmaa Hashem Sweidan

Automated generation of high-quality educational assessment items is still difficult, especially when it comes to higher-order cognitive skills. Although Large Language Models (LLMs) show promise, their structural validity and cognitive alignment are limited. This study systematically evaluates fine-tuning open-source LLMs using an enriched LearningQ dataset that includes Bloom’s cognitive labels… Read full abstract & cite →

Automated question generation Large Language Models educational assessment Bloom’s Taxonomy Parameter-Efficient Fine-Tuning LearningQ
Abstract doi.org/10.14569/IJACSA.2026.0170589 PDF
90

A Machine Learning Approach for Targeting Lucrative Customer Segments

Author 1: Leen Almajed Author 2: Haifa Almakhdhoub Author 3: Jana Alzeydan Author 4: Alaa Bin Sleem Author 5: Ouiem Bchir

Effective marketing plays a pivotal role in modern businesses, where strategic allocation of resources is essential for maximizing return on investment (ROI). Customer segmentation involves dividing users into distinct sub-groups based on common characteristics, enabling each segment to receive tailored promotions according to their behavior. However, identifying which customer segments… Read full abstract & cite →

Return on investment customer segmentation machine learning deep learning profitability prediction tabular data Gradient Boosting XGBoost
Abstract doi.org/10.14569/IJACSA.2026.0170590 PDF
91

A Human-Centered Evaluation of AI-Generated Guidance: Integrated Statistical and Machine Learning Analysis with a Risk Framework for High-Stakes Domains

Author 1: Omar Al-Turki Author 2: Felwah Alqahtani Author 3: Eman Alqahtani Author 4: Sarah Alswedani Author 5: Sami Alshmrany Author 6: Rashid Mehmood

The increasing use of large language models (LLMs) in domains requiring interpretation and judgment has raised critical questions about trust, reliability, and account-ability, particularly in contexts where decisions carry significant consequences. While prior work has focused primarily on improving system performance, limited attention has been given to how users evaluate… Read full abstract & cite →

Large language models AI-generated guidance user perception trust in AI perceived risk source credibility human-AI interaction high-stakes domains risk taxonomy risk framework
Abstract doi.org/10.14569/IJACSA.2026.0170591 PDF
92

D-HAN-Net: A Hybrid Dual-Stream Architecture for Corporate Bankruptcy Prediction via Multimodal Fusion

Author 1: Md. Abul Kalam Azad Author 2: Abdul Kadar Muhammad Masum Author 3: Najmus Saadat Author 4: Esrat Jahan Author 5: Ramona Birau Author 6: Virgil Popescu Author 7: Iuliana Carmen Barbacioru Author 8: Stefan Margaritescu

Early detection of corporate bankruptcy is essential for maintaining financial market stability and reducing systemic risk. However, existing predictive models often struggle under conditions of extreme class imbalance. Traditional approaches either analyze financial ratios or textual disclosures in isolation, while conventional cross-modal fusion strategies tend to dilute rare distress signals… Read full abstract & cite →

Bankruptcy prediction multimodal deep learning corporate disclosures cross-modal fusion class imbalance early warning system
Abstract doi.org/10.14569/IJACSA.2026.0170592 PDF
93

A Reliability-Aware Visual-Inertial Odometry for Dynamic and Low-Texture Environments

Author 1: Yelu Liu Author 2: Ruokun Qu Author 3: Mengcheng Xu Author 4: Chenglong Li Author 5: Hui Jiang

Visual-inertial odometry (VIO) tends to degrade in aggressive dynamic and low-texture environments, where rapid motion, weak visual structure, and moving objects reduce the reliability of visual observations. This study presents TRAIL-VIO, a temporal reliability-aware visual-inertial odometry framework with line feature enhancement. The method estimates temporal observation reliability by combining semantic… Read full abstract & cite →

Visual-inertial odometry point-line feature fusion dynamic environments observation reliability estimation marginalization UAV navigation
Abstract doi.org/10.14569/IJACSA.2026.0170593 PDF
94

Trustworthy App Detection in Saudi Mobile Finance: Bridging Deep Learning and Interpretability

Author 1: Raed Alharbi Author 2: Maryam Alghamdi

Understanding user trust in mobile financial applications is crucial as these platforms increasingly shape how users in Saudi Arabia manage finances and engage with digital banking. However, existing deep learning–based detection models often function as black boxes, offering limited interpretability, while traditional machine learning models, though more transparent, fail to… Read full abstract & cite →

XAI trustworthy apps detection encoding
Abstract doi.org/10.14569/IJACSA.2026.0170594 PDF
95

Benchmarking Deep Learning for PM2.5 Forecasting in IoT-Based Smart Cities: GCN Spatial Encoding and Transformer Temporal Modeling

Author 1: Abdessamad BADOUCH Author 2: Kaoutar BELHOUCINE

Graph convolutional networks are widely used in air quality forecasting, yet their benefit over simpler approaches remains insufficiently validated. This study presents a fully reproducible benchmark comparing five model families (ARIMA, XG-Boost, LSTM, CNN-LSTM, and a GCN+Transformer model) on the public Beijing Multi-site Air Quality Dataset. All experiments share identical… Read full abstract & cite →

Air quality forecasting graph neural networks IoT smart cities transformer PM2.5 prediction
Abstract doi.org/10.14569/IJACSA.2026.0170595 PDF
96

A Controlled Benchmark of Open-Source and Proprietary LLMs for Few-Shot Microbiome IBD Classification

Author 1: Nouhaila En Najih Author 2: Soufiane Hamida Author 3: Ahmed Moussa

Phenotyping inflammatory bowel disease (IBD) from gut microbiome profiles remains challenging due to 93% genus-level zero-inflation, skewed amplicon count distributions, and the practical cost of assembling labelled cohorts. Few-shot in-context learning (ICL) with large language models (LLMs) sidesteps the annotation bottleneck, yet existing benchmarks test only proprietary APIs—a deployment model… Read full abstract & cite →

Inflammatory bowel disease gut microbiome few-shot learning large language models open-source LLMs pro-prietary LLMs in-context learning 16S rRNA log-normalisation zero-inflation GPT-4o Claude Gemini LLaMA Mistral DeepSeek
Abstract doi.org/10.14569/IJACSA.2026.0170596 PDF
97

A Lightweight and Robust APBT–LBP Deep Feature Zero-Watermarking Framework with DNA Encryption for Medical Images

Author 1: Ranjan Kumar Senapati Author 2: Prasanth Mankar Author 3: B Padmaja Author 4: Chilamakuru Nagesh Author 5: Pradeep Kumar Author 6: Gandikota Ramu Author 7: Gandharba Swain

The protection of medical images in healthcare services has become important due to the adoption of telemedicine and cloud-based healthcare services. Conventional watermarking methods embed information directly into the host image, which may introduce subtle distortions and potentially affect diagnostic accuracy. This paper addresses the problem by introducing a robust… Read full abstract & cite →

Zero watermarking medical image authentication deep learning APBT transform local binary pattern CNN feature extraction robust watermarking
Abstract doi.org/10.14569/IJACSA.2026.0170597 PDF
98

A Privacy-Aware Federated Hybrid Model for Multimodal Mental Health Analysis

Author 1: Yusra Author 2: Riaz UlAmin

As mental health disorders such as stress, anxiety, depression, and post-traumatic stress disorder (PTSD) affect a substantial part of the world population, current diagnostic methodologies are still centralized, subjective, and sensitive to privacy concerns. To mitigate these limitations, this study presents a new framework for multimodal mental health classification within… Read full abstract & cite →

Mental health detection hybrid CNN-LSTM-transformer federated learning multimodal physiological signals
Abstract doi.org/10.14569/IJACSA.2026.0170598 PDF
99

Design and Evaluation of a Ubiquitous Learning Game for Adolescent E-Cigarette Addiction Prevention

Author 1: Lamyae Bennis Author 2: Khalid Kandali Author 3: Hamid Bennis

This study presents the design, development, and pilot evaluation of Addict-Shield, a mobile educational serious game aimed at preventing electronic cigarette use among adolescents. The proposed approach is grounded in the DICE model (Define, Imagine, Create, Evaluate) to ensure a structured and learner-centered design process. The game integrates narrative-driven scenarios… Read full abstract & cite →

Serious games mobile learning adolescent health vaping prevention gamification DICE model pilot study
Abstract doi.org/10.14569/IJACSA.2026.0170599 PDF
100

MILP for Multimodal Urban Transport: Formal and Informal Sectors

Author 1: Oumar KONE Author 2: Yapi Fiacre Aristide EDI Author 3: Kouassi Hilaire EDI Author 4: Pawoumodom Matthias TAKOUDA

This study presents a mixed-integer linear programming (MILP) formulation for optimizing multimodal urban transportation networks that integrate formal and informal public transport sectors. Whereas prior optimization approaches have predominantly addressed regulated formal services, the proposed model explicitly incorporates informal operators prevalent in many African cities. The objective is to minimize… Read full abstract & cite →

Mixed-integer linear programming multimodal transportation urban mobility formal transport informal transport operations research network optimization
Abstract doi.org/10.14569/IJACSA.2026.01705100 PDF
101

Artificial Intelligence in Motorsport: A Scoping Review of Applications, Challenges, and Research Gaps

Author 1: Radu C. Lucaciu Author 2: Cornelia Aurora Gyorödi

This study presents a state-of-the-art review of Artificial Intelligence and Machine Learning applications in motorsport, with a particular focus on Formula 1. As modern racing generates increasingly large volumes of high-frequency telemetry data, traditional physics-based analysis methods have proven insufficient to capture the complex, non-linear relation-ships between vehicle, driver, and… Read full abstract & cite →

Artificial Intelligence Machine Learning motor-sport Formula 1 scoping review PRISMA-ScR race strategy optimization telemetry analysis
Abstract doi.org/10.14569/IJACSA.2026.01705101 PDF
102

Benchmarking Deep Vision Architectures: From Controlled Datasets to Real-World Clinical Validation

Author 1: Raghda Essam Ali Author 2: Noha Ahmed Saad El-Dien Author 3: Magi Hossam Eldin Mahfouz

Stroke is a leading cause of mortality and long-term disability, making rapid and reliable detection from non-contrast computed tomography (CT) scans essential for timely clinical intervention. This study introduces NeuroCT-Bench, a unified and reproducible benchmark for evaluating deep learning architectures for automated stroke classification from brain CT images. The benchmark… Read full abstract & cite →

Stroke detection brain CT image deep learning Convolutional Neural Networks Vision Transformers clinical validation neuroimaging AI
Abstract doi.org/10.14569/IJACSA.2026.01705102 PDF
103

Smart IoT Framework for Sustainable Cities: Integrating Information Systems and Artificial Intelligence

Author 1: Amarildo Rista Author 2: Alma Stana

IoT architecture is an intelligent green city when it incorporates information systems and Artificial Intelligence (AI) to maximize the utilization of resources, upgrade public services, and produce cities very efficient but also environment-friendly. There remain poor feature selection and extraction, unbalanced load, Quality of Service (QoS) issues, and energy consumption… Read full abstract & cite →

Smart city blockchain IoT artificial intelligence auto encoder Quality of Service
Abstract doi.org/10.14569/IJACSA.2026.01705103 PDF
IJACSA Journal Cover

Call for Papers - Important Dates

Volume 17 No. 11, November 2026
Paper Submission Due October 25, 2026
Review Notification November 15, 2026
Publication Date November 30, 2026
Call for Papers Submit Paper

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