IJACSA Vol. 17 Issue 3 (2026)

1

Empirical Validation of the ASER Framework for Long-Term Knowledge Retention in Augmented Reality

Author 1: Samer Alhebaishi Author 2: Richard Stone Author 3: Ulrike Genschel Author 4: Kris De Brabanter Author 5: Mani Mina Author 6: Anthony M. Townsend Author 7: Mohammed Ameen

Long-term knowledge retention remains a critical challenge in augmented reality (AR) learning environments, which often prioritize novelty and short-term engagement over durable learning outcomes. This study empirically validates the Augmented Sensory Experience and Retention (ASER) Framework, an instructional model integrating emotional memory cues, interactive storytelling, and gamification within AR to… Read full abstract & cite →

Augmented reality (AR) ASER Framework long-term knowledge retention emotional memory interactive storytelling gamification

Abstract doi.org/10.14569/IJACSA.2026.0170301 PDF

2

Enhancing GANomaly-Based Anomaly Detection for X-Ray Cargo Inspection

Author 1: Kholoud Alotaibi Author 2: Nasser Nasrabadi

Anomaly detection in X-ray cargo imagery is challenging due to complex scene structures, object overlap, and limited labeled abnormal data. Reconstruction-based methods address this problem by learning normal cargo patterns and identifying deviations during testing. This study investigates how feature-level reconstruction objective functions influence detection performance within the GANomaly framework… Read full abstract & cite →

Anomaly detection cargo X-ray imaging GANomaly perceptual loss feature-level reconstruction semi-supervised learning generative adversarial networks structural anomaly detection security screening reconstruction-based detection deep learning for X-ray inspection ResNet50

Abstract doi.org/10.14569/IJACSA.2026.0170302 PDF

3

Benchmarking Lightweight Machine Learning Models for Epileptic Seizure Recognition: Accuracy, Calibration, and Robustness Analysis

Author 1: Sairam Tabibu Author 2: Mugdha Abhyankar

Epileptic seizure recognition is a critical task in clinical decision support systems, where both accuracy and reliability of predictions directly affect patient outcomes. While deep learning architectures such as CNNs and LSTMs are widely applied to EEG-based seizure detection, many publicly available seizure datasets consist of precomputed EEG-derived features, making… Read full abstract & cite →

Epileptic seizure recognition EEG classification lightweight machine learning LightGBM calibration robustness biomedical AI

Abstract doi.org/10.14569/IJACSA.2026.0170303 PDF

4

Hybridizing Collaborative Filtering and Knowledge: How do they Work Together? A Scoping Review

Author 1: Alex Martínez-Martínez Author 2: Raul Montoliu Author 3: Inmaculada Remolar

The rapid expansion of digital platforms and the increasing complexity of user preferences have driven the need for more sophisticated recommendation systems. While Collaborative Filtering and Knowledge-Based Filtering have been widely adopted as core techniques for personalized recommendations, their individual limitations have led to the rise of hybrid approaches. Despite… Read full abstract & cite →

Hybrid Recommender Systems collaborative filtering knowledge-based recommenders personalized recommendations scoping review

Abstract doi.org/10.14569/IJACSA.2026.0170304 PDF

5

Federated Gaussian Process Regression with Orthogonal Feature Encryption and Key-Based Access Control

Author 1: Md. Rashedul Islam Author 2: Jannatul Ferdous Akhi Author 3: Takayuki Nakachi

Federated learning (FL) makes it possible to train models across distributed data sources without collecting raw data in one place. However, even in federated settings, trained models may still leak sensitive information at inference time. This problem is particularly evident for Gaussian Process regression (GPR), where predictive uncertainty is explicitly… Read full abstract & cite →

Gaussian process differential privacy Random Unitary Transformation membership inference attack machine learning federated learning

Abstract doi.org/10.14569/IJACSA.2026.0170305 PDF

6

Fast E-Learning Recommendation: Enhancing Model Efficiency with Q-Matrix Complexity Reduction

Author 1: Ismail Menyani Author 2: Ahmed Oussous Author 3: Ayoub Ait Lahcen

Intelligent tutoring systems generate a large volume of data, which becomes particularly valuable when effectively leveraged for learner performance prediction in adaptive learning environments. In this context, the speed and predictive accuracy of machine learning models are crucial, as they determine the system’s ability to deliver timely and relevant insights… Read full abstract & cite →

Learner performance prediction adaptive learning complexity knowledge components Q-matrix machine learning DAS3H PFA AFM IRT

Abstract doi.org/10.14569/IJACSA.2026.0170306 PDF

7

Photoplethysmogram-Based Diabetes Screening via Supervised Machine Learning: A Demographic Study on a Southeast Asian Cohort

Author 1: Nazrul Anuar Nayan Author 2: Mohd Taufik Rezza Mohd Foudzi Author 3: Mohd Zubir Suboh Author 4: Syaza Norfilsha Ishak Author 5: Zazilah May

Diabetes mellitus is a major chronic metabolic disorder that often leads to serious long-term vascular complications. Traditional monitoring methods focus mainly on metabolic indicators and often miss early vascular changes. This study developed and validated a non-invasive framework for classifying diabetic status based on photoplethysmogram (PPG) pulse morphology. The approach… Read full abstract & cite →

Photoplethysmography (PPG) diabetes prediction supervised machine learning signal morphology features non-invasive screening feature selection

Abstract doi.org/10.14569/IJACSA.2026.0170307 PDF

8

Quantum-Resilient Machine Learning and Q-Learning–Driven Priority Time-Slot AODV for Secure MANET Routing

Author 1: Singireddy Sateesh Reddy Author 2: E. Aravind

Mobile Ad Hoc Networks (MANETs) are decentralized in nature and, therefore, they have no centralized control, and consequently, they are highly susceptible to routing attacks like black hole attacks and gray hole attacks, both of which disable data delivery by causing a vicious loss of packets. To address these issues… Read full abstract & cite →

Mobile Ad Hoc Networks secure routing AODV trust management machine learning reinforcement learning MAC layer scheduling black hole attack post-quantum security quantum-resilient routing

Abstract doi.org/10.14569/IJACSA.2026.0170308 PDF

9

Comparing Random Forest and Gradient Boosting for Monkeypox Diagnosis

Author 1: Fahlul Rizki Author 2: Widowati Author 3: Catur Edi Widodo

Early and accurate diagnosis of Monkeypox is essential to limit transmission and support effective treatment. This study aims to compare the performance of Random Forest and Gradient Boosting models for classifying Monkeypox cases using clinical symptom data. A synthetic dataset from Kaggle containing 25,000 records with 11 symptom-based features was… Read full abstract & cite →

Comparative analysis Random Forest Gradient Boosting clinical symptoms machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170309 PDF

10

User Behaviour Analysis for Insider Threat Detection Using Machine Learning: A Case Study in Enterprise Web Application Security

Author 1: Yosep Author 2: Aditya Kurniawan

This study presents a user behaviour analysis approach for detecting insider threats in an enterprise web application environment. The approach applies machine learning techniques to analyze patterns of user activity. Using a primary dataset collected from a leading ICT distributor company in Indonesia with nationwide channel operations over January–June 2025… Read full abstract & cite →

Insider threat user behaviour analytics machine learning anomaly detection cybersecurity

Abstract doi.org/10.14569/IJACSA.2026.0170310 PDF

11

CdbNorm: An Efficient Library for Automatic Database Normalization

Author 1: Ivan Piza-Davila Author 2: Fernando Gutierrez-Preciado Author 3: Victor Ortega-Guzman Author 4: Mildreth Alcaraz-Mejia

This study introduces CdbNorm, a library that provides efficient implementations of the first three normal forms of relational database normalization. CdbNorm makes it quick and straightforward for a data analyst to divide a large dataset into smaller tables free from database anomalies (insert, update, and delete) and duplicate data. This… Read full abstract & cite →

Database normalization functional dependency normal form 1NF 2NF 3NF

Abstract doi.org/10.14569/IJACSA.2026.0170311 PDF

12

Assessment of Different Energy Management Strategies for the Operation of Hybrid Hot-Water Installations in Hotels

Author 1: Boris I. Evstatiev Author 2: Nadezhda L. Evstatieva

With the adoption of the Energy market in Bulgaria, large fluctuations in the price of electrical energy have been occurring, which is a challenge for businesses in the different sectors of the economy. This study is aimed at evaluating the energy and financial performance of three energy management strategies for… Read full abstract & cite →

Energy management energy market evacuated tube collectors hot water consumption strategies

Abstract doi.org/10.14569/IJACSA.2026.0170312 PDF

13

From Rules to Transformers: A Deep Learning Approach for Arabic Natural Language Interfaces to Databases

Author 1: Dahr Laila Author 2: Sahib Mohamed Rida Author 3: Er-Raha Brahim

Natural language interfaces to databases (NLIDBs) enable users to communicate with databases using natural everyday language rather than difficult query languages. This study presents a new approach using deep learning techniques to improve the robustness and accessibility of Arabic NLIDB systems through a new end-to-end framework. A Transformer-based architecture is… Read full abstract & cite →

Sequence-to-sequence (SeqToSeq) natural language to SQL (NL2SQL) semantic parsing arabic NLP Text-to-SQL

Abstract doi.org/10.14569/IJACSA.2026.0170313 PDF

14

A Review on Machine Learning Approaches for Solid Waste Management

Author 1: S. Vidya

The rapid increase in population and the ongoing expansion of urban regions have resulted in a substantial growth in municipal solid waste generation, creating serious challenges for environmental protection and urban management. In response to these problems, recent research has increasingly focused on technological solutions, among which machine learning has… Read full abstract & cite →

Municipal solid waste management machine learning modeling optimization solid waste generation disposal

Abstract doi.org/10.14569/IJACSA.2026.0170314 PDF

15

Sentiment and Emotion Analysis in Textual Data: A Recent Systematic Literature Review Method, Model and Application

Author 1: Wan Azzura Wan Ramli Author 2: Rabiah Abdul Kadir Author 3: Amalia Amalia Author 4: Ang Mei Choo

The analysis of sentiment and emotion has become an important research topic in Natural Language Processing (NLP) due to the rapid growth of textual data generated on digital platforms. Still, despite significant progress, the existing literature remains fragmented across methods, modalities, and application domains, making it difficult to obtain a… Read full abstract & cite →

Sentiment emotion analysis textual data transformer large language models

Abstract doi.org/10.14569/IJACSA.2026.0170315 PDF

16

Capacitated Location-Allocation Model for Emergency Supply Chain: The Case of Morocco

Author 1: Imane Sassaoui Author 2: Aziz Ait Bassou Author 3: Mustapha Hlyal Author 4: Jamila El Alami

Recently, Morocco has experienced a series of disasters, including the El Haouz earthquake in 2023, which have brought renewed attention to the country’s emergency preparedness and the efficiency of its national emergency supply chain. In addition, this study considers a prospective scenario based on potential flood events in northern Morocco… Read full abstract & cite →

Emergency logistics disaster response location–allocation stochastic demand supply chain resilience

Abstract doi.org/10.14569/IJACSA.2026.0170316 PDF

17

Leveraging Kolmogorov-Arnold Networks (KANs) for Mixed-Domain Satellite Imagery Segmentation

Author 1: Abdul Hadi Mazbah Author 2: Safiza Suhana Binti Kamal Baharin Author 3: Md. Shadman Zoha

Semantic segmentation of satellite imagery requires models that capture global context while preserving sharp object boundaries. Convolutional Neural Networks (CNNs) excel at local feature extraction, but often struggle with long-range dependencies. Transformers provide global context but may blur edges and rely on opaque classifier heads. This study aims to develop… Read full abstract & cite →

Satellite imagery Kolmogorov-Arnold Network semantic segmentation attention mixed-domain

Abstract doi.org/10.14569/IJACSA.2026.0170317 PDF

18

AFLBCRS: Blockchain-Enabled Federated Learning with Ring Signatures

Author 1: Menna Mamdouh Orabi Author 2: Osama Emam Author 3: Hanan Fahmy

With the explosive development of machine learning and increased concern about data privacy, federated learning (FL) has emerged as a major area of study. Despite the benefits of FL, it deals with certain obstacles, including the risk of indirect data leaking via reverse engineering, the compromise of model architectural privacy… Read full abstract & cite →

Machine learning federated learning blockchain security ring signature

Abstract doi.org/10.14569/IJACSA.2026.0170318 PDF

19

Exploring Employability Factors: A Machine Learning Approach Using Association Rules in Business and Economics Graduates at Qassim University

Author 1: Hussain Mohammad Abu-Dalbouh Author 2: Osman Abdalla Mohamed Elhadi Author 3: Ajlan Suliman Al-Ajlan Author 4: Leenah Sulaiman Almuhanna Abalkhail Author 5: Abdullah Suliman Almutlaq Author 6: Wejdan Aamer Alasqah Author 7: Mayadah Shikh Othman Author 8: Sulaiman Abdullah Alateyah

The growing number of business and economics graduates raises concerns about employability in a competitive job market. Furthermore, scrutiny from the Saudi Education and Training Evaluation Commission on educational outcomes highlights the relevance of this research for university administrations. Current literature often overlooks the factors affecting employment outcomes for recent… Read full abstract & cite →

Machine learning prediction hidden patterns employment rates academic performance data analysis modeling

Abstract doi.org/10.14569/IJACSA.2026.0170319 PDF

20

Improving Decision-Making Processes in Retail Through Artificial Intelligence for Advanced Management Information Systems: A Study on Consumer Behavior in Qassim

Author 1: Hussain Mohammad Abu-Dalbouh Author 2: Mushira Mustafa Freihat Author 3: Rayah Ismaeel Jawarneh Author 4: Osman Abdalla Mohamed Elhadi Author 5: Mortada Ibrahim Elimam Author 6: Leenah Sulaiman Almuhanna Abalkhail Author 7: Ghadi Mohammed Al Nafesah Author 8: Soliman Aljarboa Author 9: Sulaiman Abdullah Alateyah

In today’s rapidly evolving retail environment, the sheer volume of consumer data presents both opportunities and challenges for businesses striving to maintain a competitive edge. This study explores the pivotal role of artificial intelligence and sophisticated data mining techniques within management information systems. The study aims to transform decision-making processes… Read full abstract & cite →

Analytics machine learning data-driven observations predictive modeling strategic marketing

Abstract doi.org/10.14569/IJACSA.2026.0170320 PDF

21

Integrating Heterogeneous Data for Stock Market Prediction: A Systematic Literature Review

Author 1: Abdullah Almusned Author 2: Mohammad Mehedi Hassan Author 3: Bader Alkhamees Author 4: Muhammad Al-Qurishi

This systematic literature review examines recent developments in stock market prediction using heterogeneous data sources that combine technical indicators, fundamental attributes, and sentiment-driven signals. Despite the growing adoption of machine learning in financial forecasting, existing research remains fragmented across data modalities, fusion strategies, and evaluation protocols, limiting comparability and practical… Read full abstract & cite →

Stock prediction heterogeneous data machine learning quantitative and qualitative data systematic review

Abstract doi.org/10.14569/IJACSA.2026.0170321 PDF

22

An Ontological Design Model for Integrating Notification, Appointment, and Queue in Healthcare Queue Systems

Author 1: Nik Mohd Habibullah Nik Mohd Nizam Author 2: Shafrida Sahrani Author 3: Mohd Nazri Kama Author 4: Abdul Ghafar Jaafar Author 5: Mohd Yazid Bajuri Author 6: Mohammad Nazir Ahmad

Healthcare queue systems frequently suffer from prolonged waiting times, overcrowding, and inefficient patient flow management. Although various Queue Management Systems (QMS) have been developed, most existing solutions treat notification, appointment scheduling, and queue management as independent components. This fragmented design limits semantic clarity, adaptability, and reusability. This study proposes an… Read full abstract & cite →

Ontology-based design queue management system appointment scheduling notification system healthcare information systems design science research

Abstract doi.org/10.14569/IJACSA.2026.0170322 PDF

23

Multi-Objective Intelligent Control of Bi-Directional V2X Charging Using NSGA-II in an Integrated Energy Management System

Author 1: Muhammad Aqmal Bin Abu Hassan Author 2: Ezmin Abdullah Author 3: Muhammad Umair Author 4: Nik Hakimi Nik Ali Author 5: Roslina Mohamad Author 6: Nabil M. Hidayat

This study presents the development and evaluation of an intelligent control system for a real-time bi-directional Electric Vehicle (EV) charging infrastructure integrated with solar Photovoltaic (PV), Energy Storage Systems (ESS), and the power grid. The proposed system aims to optimize energy flow decisions such as cost minimization, energy efficiency maximization… Read full abstract & cite →

Genetic algorithm NSGA-II optimization Non-dominated Sorting Genetic Algorithm EV charging bi-directional EV charger V2X energy management system

Abstract doi.org/10.14569/IJACSA.2026.0170323 PDF

24

Rule-Based Myanmar Herbal Recommendation System Using Ontology

Author 1: Nang Saing Horm Author 2: Nikom Suvonvorn

Myanmar herbal medicine is recognized as a vital component of traditional healthcare; however, its documentation remains disorganized and primarily available in the local language. Identifying appropriate herbs for individual users from existing records is inefficient and may result in medication errors. This study presents a formalized, digitized representation of Myanmar… Read full abstract & cite →

Myanmar herbal medicine ontology recommendation system

Abstract doi.org/10.14569/IJACSA.2026.0170324 PDF

25

Real-Time LiDAR SLAM-Driven Navigation and Collision Avoidance for Mobile Robots in Unstructured Environments

Author 1: Amandyk Tuleshov Author 2: Anar Adilkhan Author 3: Moldir Kuatova Author 4: Gaukhar Seidaliyeva

Autonomous navigation in unknown environments requires accurate simultaneous localization and mapping, reliable obstacle detection, and efficient path planning within a unified framework. This study proposes a real-time LiDAR-based SLAM-driven navigation system for mobile robots operating in structured indoor environments. The developed architecture integrates three-dimensional LiDAR sensing, ego-motion estimation, scan registration… Read full abstract & cite →

LiDAR SLAM autonomous navigation obstacle avoidance path planning mobile robots real-time mapping 3D point cloud processing loop closure optimization sensor fusion robotic perception

Abstract doi.org/10.14569/IJACSA.2026.0170325 PDF

26

Simulation Study on the Proposed Multi-Agent Backdoor Detection System

Author 1: Kohei Arai

The proposed multi-layered backdoor detection system was evaluated across 10 diverse scenarios, including benign tasks, keyword-triggered attacks, semantic backdoors, and distributed multi-agent attacks. In the simulation experiments, Total Scenarios: 10 | Attack Scenarios: 5 | Benign Scenarios: 5, are prepared and Detection Mechanisms: 5 | Agent Architecture: 3-agent pipeline with… Read full abstract & cite →

Multi-layered backdoor detection system keyword-triggered attack semantic backdoor distributed multi-agent attack multi-agent LLM Byzantine fault tolerance

Abstract doi.org/10.14569/IJACSA.2026.0170326 PDF

27

Traffic Sign Classification Under Varying Lighting Conditions in the Philippines Using Transfer Learning with ResNet50 and Zero-DCE

Author 1: John Paul Q. Tomas Author 2: Carlo Miguel P. Legaspi Author 3: Karl Anthony S. Dalangin Author 4: Gabriel Paul Q. Lim

This study presents a multi-stage transfer learning approach for improving traffic sign recognition performance under both normal and low-light conditions, addressing the gap between existing datasets and the real-world road environments of the Philippines, where poor lighting, faded signs, and unstructured roads are common. A curated local dataset of 7… Read full abstract & cite →

Traffic Sign Recognition (TSR) Traffic Sign Classification (TSC) Advanced Driver-Assistance System (ADAS)

Abstract doi.org/10.14569/IJACSA.2026.0170327 PDF

28

Evaluating ChatGPT for Grading Programming Assignments: Effectiveness, Fairness, and Student Perceptions

Author 1: Abedallah Zaid Abualkishik Author 2: Sherzod Turaev Author 3: Ali A. Alwan Author 4: Mohamed Elhoseny Author 5: Mohsin Murtaza

This study investigates ChatGPT as an automated grading tool for programming assignments in higher education. Three datasets comprising Python, C++, and Java assignments were graded three times by ChatGPT and compared with faculty evaluations. Results show that ChatGPT achieves high grading accuracy, closely aligning with faculty scores and demonstrating statistically… Read full abstract & cite →

AI-assisted grading ChatGPT automated grading programming assignments higher education grading reliability rubric-based evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170328 PDF

29

Reinforcement Learning-Based Adaptive Penetration Testing Framework for Wireless Communication

Author 1: Saken Tleuberdin Author 2: Konstantin Malakhov Author 3: Nurlan Tashatov Author 4: Dina Satybaldina Author 5: Didar Yedilkhan

Wireless Fidelity (Wi-Fi) technology is widely used in the environment of Internet of Things (IoT), and the importance of security assessment has increased. Nowadays, Wi-Fi security assessment is based on security tools that are operated manually, and some methods face issues due to the lack of automation. In this study… Read full abstract & cite →

Internet of things security penetration testing reinforcement learning wireless communication

Abstract doi.org/10.14569/IJACSA.2026.0170329 PDF

30

Machine Learning-Based Web System for Predicting and Classifying Financial Incentives in the Automotive Sector

Author 1: Antony Jesus Ramirez Rivas Author 2: Rosalynn Ornella Flores-Castañeda

This research presents the development of a web-based system using machine learning to predict and classify financial incentives in the automotive sector, contributing to Sustainable Development Goal 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production). The main objective was to design and implement an intelligent system… Read full abstract & cite →

Artificial intelligence fiscal policy forecasting sustainable development automobile

Abstract doi.org/10.14569/IJACSA.2026.0170330 PDF

31

Segmentation of Convective Initiation Based on Spatio-Temporal Feature Joint Modeling

Author 1: Runzhe Tao Author 2: Rui Chen Author 3: Peibei Zheng Author 4: Zibo Hong

As a key indicator of the occurrence of severe convection, convective initiation (CI) exhibits characteristics such as fragmentation, scale heterogeneity, and susceptibility to confusion with other cloud systems in single-temporal remote sensing imagery, posing significant challenges for accurate CI detection. Traditional threshold-based methods inadequately capture spatial representations and have limited… Read full abstract & cite →

Semantic segmentation remote sensing imagery convective initiation spatiotemporal feature fusion

Abstract doi.org/10.14569/IJACSA.2026.0170331 PDF

32

Immersive Educational Application Based on Unity for Learning the Quechua Language

Author 1: Giancarlo Eliseo Arrieta Villarreal Author 2: Rosalynn Ornella Flores-Castañeda

Learning indigenous languages such as Quechua helps preserve cultural identity by narrowing educational gaps, in line with Sustainable Development Goal 4, which promotes inclusive and quality education. The objective was to develop an immersive educational application based on Unity to improve the learning of the Quechua language. This was an… Read full abstract & cite →

Quechua computer application information technology (software)

Abstract doi.org/10.14569/IJACSA.2026.0170332 PDF

33

Weight Trajectory Prediction in Precision Livestock Farming Using Machine Learning: A Comparative Approach

Author 1: Moad Hakem Author 2: Zakaria Boulouard Author 3: Mohamed Kissi

Accurate livestock body weight prediction is a key component of precision livestock farming, as it supports herd monitoring, production management, and planning in response to the increasing global demand for meat. Existing approaches for weight prediction include age-based regression models, growth trajectory modelling, average daily gain estimation, and methods relying… Read full abstract & cite →

Machine learning data science precision livestock farming weight trajectory prediction ensemble learning

Abstract doi.org/10.14569/IJACSA.2026.0170333 PDF

34

Prickly Pear Disease Classification Using Deep Convolutional Neural Networks: A Case Study

Author 1: Raghiya Elghawth Author 2: Wafae Abbaoui Author 3: Soumia Ziti

Prickly pear (Opuntia ficus-indica) is a member of the Cactaceae family. Because of its anti-inflammatory, anti-oxidant, antibacterial, hypoglycemic, and neuroprotective properties, prickly pears are a magical fruit. Both the fruit and its stem are utilized in value-added products. Deep learning (DL) applications are required for prickly pear disease detection and… Read full abstract & cite →

Plant disease classification data augmentation deep learning prickly pear disease MobileNetV2 DenseNet121

Abstract doi.org/10.14569/IJACSA.2026.0170334 PDF

35

CEEMDAN–SSA–RWKV–SMA: A Robust Hybrid Model for Long-Term Wind Speed Forecasting in India

Author 1: S. Vidya

Reliable long-term wind speed forecasting is a critical requirement for the strategic deployment and operational stability of wind energy systems, particularly in meteorologically diverse regions like India. This study proposes a novel hybrid framework, CEEMDAN–SSA–RWKV–SMA, which integrates advanced signal decomposition, deep sequence modeling, and metaheuristic optimization. Initially, the raw wind… Read full abstract & cite →

Wind speed forecasting Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Singular Spectrum Analysis RWKV neural network Slime Mould Algorithm Renewable energy integration

Abstract doi.org/10.14569/IJACSA.2026.0170335 PDF

36

Scaled Agile Process Improvement Recommendations with CMMI 2-Based Agile Scaling Model: A Case Study of the Indonesia National Single Window Agency

Author 1: I Made Aditya Pradnyadipa Mustika Author 2: Betty Purwandari Author 3: Alex Ferdinansyah

The Indonesia National Single Window System (SINSW) is a platform developed by the Indonesia National Single Window Agency (LNSW) using Agile methodology, specifically the Scrum framework. Several challenges were identified during its development, including deviations from the Scrum Guide and the absence of formal, regular events necessary for effective team… Read full abstract & cite →

Scaling Agile CMMI Scrum KPA rating software engineering

Abstract doi.org/10.14569/IJACSA.2026.0170336 PDF

37

Explainable Deep Learning for Automated Skin Cancer Detection Using Advanced CNN Architectures on Dermoscopic Images

Author 1: Adel Rajab

Skin cancer is a considerable health issue worldwide, occurring when pigment cells turn malignant. However, diagnosing skin lesions is difficult for dermatologists because most lesions have similar characteristics. Initial detection is essential because it significantly increases the success rate of treatment and survival rates. In the past few decades, the… Read full abstract & cite →

Deep learning models skin cancer detection image processing Grad-CAM

Abstract doi.org/10.14569/IJACSA.2026.0170337 PDF

38

Energy-Efficient Cluster Head Rotation in WSNs Using Bee Colony Optimization

Author 1: Azamuddin Bin Ab Rahman Author 2: Sakib Iqram Hamim

In this study, we present a method designed to improve energy efficiency and balance the workload across Wireless Sensor Networks (WSNs). Our approach dynamically selects and rotates cluster heads (CHs) based on factors such as remaining energy, node mobility, distance to the base station, and data processing needs. By focusing… Read full abstract & cite →

Energy efficiency load balancing cluster head rotation bee colony optimization metaheuristic clustering Wireless Sensor Network

Abstract doi.org/10.14569/IJACSA.2026.0170338 PDF

39

An Improved Hybrid CURE–SNE Model for High-Dimensional Data Clustering

Author 1: Dewi Sartika Br Ginting Author 2: T. H. F. Harumy Author 3: Ade Sarah Huzaifah Author 4: Ivanny Putri Marianto

Stunting remains a critical public health issue in rural communities, largely driven by inadequate nutrition, poor sanitation, and unfavorable socioeconomic conditions. This study proposes a hybrid clustering approach by integrating Clustering Using Representatives (CURE) with t-distributed Stochastic Neighbor Embedding (t-SNE) to analyze stunting prevalence and support the optimization of child… Read full abstract & cite →

Hybrid clustering CURE-SNE stunting davies bouldin index silhouete score

Abstract doi.org/10.14569/IJACSA.2026.0170339 PDF

40

Pedagogical Mediation Through Prompt Engineering: An Expert Evaluation of AI-Generated Feedback on Islamic-Integrated EFL Argumentative Writing

Author 1: Sari Dewi Noviyanti Author 2: Rudi Hartono Author 3: Hendi Pratama Author 4: Seful Bahri

This research tested whether prompt engineering could act as a type of pedagogical mediation to enhance the quality of AI-generated student feedback on EFL students' argumentative essays in an Islamic education system. An initial pool of eight expert raters (four primary raters and four inter-raters) scored the AI-generated feedback from… Read full abstract & cite →

AI-generated feedback pedagogical mediation prompt engineering

Abstract doi.org/10.14569/IJACSA.2026.0170340 PDF

41

Graph Neural Networks and Ensemble Learning for Mineral Prospectivity Mapping Using Geochemical Data

Author 1: Kholod M. Alzubidi Author 2: Alaa O. Khadidos Author 3: Adil O. Khadidos Author 4: Haitham M. Baggazi Author 5: Fahad M. Alharbi Author 6: Razan Alamoudi

Mineral exploration is inherently challenging because geological formations are complex and geochemical relationships are often nonlinear and spatially variable. Although artificial intelligence has recently shown strong potential in improving mineral potential mapping, many existing approaches struggle to fully capture spatial relationships within geochemical data. In this study, an integrated framework… Read full abstract & cite →

Rare mineral mapping geochemical data geospatial analysis GNN ensemble learning

Abstract doi.org/10.14569/IJACSA.2026.0170341 PDF

42

Emotion Prediction in Performance-Critical Tasks: A Systematic Review of Physiological Signals and Deep Learning Models

Author 1: Norhawani Ahmad Teridi Author 2: Tengku Mohd Tengku Sembok Author 3: Muhammad Fairuz Abd Rauf Author 4: Nurhafizah Moziyana Mohd Yusop Author 5: Zuraini Zainol Author 6: Shahrulfadly Rustam Author 7: Azlinda Abdul Aziz Author 8: Hazri Haidar Author 9: Mohd Fahmi Mohamad Amran

Emotions strongly influence how people think, decide, and perform, making reliable emotion forecasting essential in performance-critical environments. Traditional methods such as facial expressions, speech, and self-reports often lack reliability and continuity. Physiological signals offer a more objective alternative, providing continuous indicators of emotional states, while deep learning models are well-suited… Read full abstract & cite →

Emotion prediction physiological signals deep learning multimodal fusion performance-critical tasks

Abstract doi.org/10.14569/IJACSA.2026.0170342 PDF

43

A Novel Latent-Representation-Based Algorithm with Dynamic Obstacle Avoidance (LADy) for Autonomous Mobile Robots (AMRs)

Author 1: Harishma Prakash Author 2: Prasina A Author 3: Samuthira Pandi V Author 4: Naregalkar Akshaykumar Rangnath Author 5: Rajalingam A Author 6: Sundar R

Conventional path-planning algorithms are often tailored to industrial and warehouse settings, creating the necessity to integrate two or more planners, which requires more memory and computation limitations. To overcome this limitation, this study aims to develop a novel algorithm, semantic cost encoding-based A* with Dynamic obstacle avoidance, specifically designed for… Read full abstract & cite →

Path planning semantic encoding Autonomous Mobile Robots (AMRs) warehouse navigation dynamic environment

Abstract doi.org/10.14569/IJACSA.2026.0170343 PDF

44

Design and Experimental Evaluation of Adaptive Load Balancing Strategies in Software-Defined Networks Using Mininet

Author 1: M Shona Author 2: Rinki Sharma

Software-Defined Networking (SDN) introduces centralized control and programmability, enabling more flexible and efficient network management compared to traditional architectures. Load balancing is a serious SDN application that improves resource utilization, reduces latency, and enhances service reliability. However, implementing SDN-based load balancers involves several challenges, such as controller overhead, scalability issues… Read full abstract & cite →

Load balancing control plane data plane OpenFlow Mininet

Abstract doi.org/10.14569/IJACSA.2026.0170344 PDF

45

Machine Learning-Based Air Quality Monitoring in Indian Metropolitan Cities: A Comparative Study

Author 1: Khushbu Chauhan Author 2: Kruti Sutaria

Pure and clean air is essential to make the ecosystem healthy. Air pollution is becoming a critical global concern for both the environment and human health. Presence of harmful pollutants such as PM2.5, PM10, CO2, NO2, SO2, and O3 continuously degrades air quality and influences climatic conditions. This study aims… Read full abstract & cite →

AQI COVID SVM Gradient Boosting Machine (GBM) Extreme Gradient Boosting (XGBoost)

Abstract doi.org/10.14569/IJACSA.2026.0170345 PDF

46

Cloud-Based Replication Models Using AI Techniques for Enhanced Data Management

Author 1: Moneef M. Jazzar Author 2: Aws I. Abueid

Elastic cloud infrastructure relies on dynamic replication mechanisms to maintain service availability and performance under fluctuating, non-stationary workloads. However, conventional threshold-based and static replication strategies frequently fail to maintain latency stability and Service Level Agreement (SLA) compliance in highly dynamic environments characterised by bursty, peak-stress traffic. This study introduces a… Read full abstract & cite →

Q-learning elastic cloud replication SLA-aware control latency optimization adaptive replication reinforcement learning

Abstract doi.org/10.14569/IJACSA.2026.0170346 PDF

47

Gradient-Guided Data Augmentation with mBERT and MuRIL for Malayalam Offensive Language Detection

Author 1: Munawwar K V Author 2: Nandhini K

The widespread adoption of social media platforms has facilitated increased usage of offensive content, particularly in native languages where users express themselves more freely. Automated offensive language detection in low-resource languages such as Malayalam faces significant challenges due to severe class imbalance, where non-offensive samples substantially outnumber offensive instances, resulting… Read full abstract & cite →

Offensive comment detection gradient guided augmentation NLPAUG back translation paraphrasing with MultiIndicParaphraseGeneration

Abstract doi.org/10.14569/IJACSA.2026.0170347 PDF

48

Predictive Modeling of Lung Cancer Risk in Workers Using Dropouts Meet Multiple Additive Regression Trees

Author 1: Haewon Byeon

Lung cancer remains a leading cause of cancer mortality, and preventable occupational and environmental exposures may compound risk in working-age populations. This study developed and compared predictive models for lung cancer risk using a publicly available tabular dataset (Kaggle; n = 1,000) containing demographic, lifestyle, symptom, and exposure-related variables. After… Read full abstract & cite →

Lung cancer predictive modeling occupational exposure gradient boosting risk stratification

Abstract doi.org/10.14569/IJACSA.2026.0170348 PDF

49

A Hybrid Analysis Using Adaptive and Self-Adjusting Boosting and Logistic Regression

Author 1: Haewon Byeon

This study investigates factors influencing the employment outcomes of sports science graduates, specifically their ability to secure decent jobs. Utilizing data from the Graduates Occupational Mobility Survey (GOMS) from 2015 to 2019, the study analyzed a sample of 1,019 sports science graduates aged 19 to 34. Both traditional statistical methods… Read full abstract & cite →

Employment outcomes machine learning adaptive & self-adjusting boosting gender disparities

Abstract doi.org/10.14569/IJACSA.2026.0170349 PDF

50

A Real-Time Multi-Scale Feature Pyramid YOLO Architecture for Accurate and Deployment-Efficient Road Damage Detection

Author 1: Olzhas Olzhayev Author 2: Bakhytzhan Kulambayev Author 3: Nurly Sakenkyzy Author 4: Madina Belisbek

Automated road damage detection has become a critical component of intelligent transportation systems, enabling timely infrastructure maintenance and enhanced traffic safety. However, detecting pavement defects such as cracks, potholes, and surface degradation remains challenging due to significant scale variation, irregular geometries, illumination changes, and class imbalance. This study proposes a… Read full abstract & cite →

Road damage Multi-Scale Feature Pyramid YOLO architecture intelligent transportation systems small-object detection real-time deployment pavement defect analysis

Abstract doi.org/10.14569/IJACSA.2026.0170350 PDF

51

Interior Windshield Moisture Management Using a Vapor-Assisted Wiping Model: System Architecture and Design

Author 1: Abdelrahim Fathy Ismail

Fogging and moisture accumulation on the interior side of vehicle windshields continue to affect driving visibility despite the development of various defogging and climate-control approaches. While previous studies have mainly addressed the problem through HVAC optimization, airflow management, or intelligent monitoring systems, direct moisture handling at the interior glass surface… Read full abstract & cite →

Interior windshield moisture management vapor-assisted wiping windshield defogging vehicle visibility enhancement

Abstract doi.org/10.14569/IJACSA.2026.0170351 PDF

52

A Detailed Classification of the Scheduling Algorithms in Fog Computing Environment, Challenges, and Future Directions

Author 1: Hend Gamal El Din Hassan Ali Author 2: Imane Aly Saroit Author 3: Amira Mohamed Kotb

Fog computing is considered as a distributed computing and storage resource. It is placed near to users rather than cloud computing to reduce latency in Internet of Things sensitive time applications. Several scheduling algorithms were proposed in fog environment to achieve better performance in terms of execution time, cost, latency… Read full abstract & cite →

Cloud computing fog computing edge computing Internet of Things scheduling algorithms directed acyclic graph

Abstract doi.org/10.14569/IJACSA.2026.0170352 PDF

53

Construction and Application Analysis of a Response Model for Improper Customer Behaviors in Service Enterprises Based on Cognitive Evaluation Theory

Author 1: Enhou Zu Author 2: Chun-Wei Lu Author 3: Jui-Chan Huang Author 4: Tien-Shou Huang Author 5: Cheng-Ju Liu

This study investigates customer misbehavior in tourism services through the lens of Cognitive Evaluation Theory and Game Theory, contributing to both management research and social psychology. A dual-path model, tested via a multi-experiment design optimized with a machine learning algorithm, examines mediation versus defense strategies across violation types (interpersonal/transactional). The… Read full abstract & cite →

Cognitive evaluation customer misbehavior management research social psychology tourism services game theory

Abstract doi.org/10.14569/IJACSA.2026.0170353 PDF

54

Convolutional Neural Network for Chili Plant Disease Classification: A Deep Learning Approach

Author 1: Erna Dwi Astuti Author 2: Widowati Author 3: Aris Sugiharto

Chili peppers are a high-value horticultural crop that is highly susceptible to foliar diseases, which can significantly reduce yield and market quality. This study proposes and evaluates a Convolutional Neural Network (CNN) model based on the MobileNet-V2 architecture for chili leaf disease classification. A combined dataset consisting of 2,690 images… Read full abstract & cite →

Convolutional neural network MobileNet-V2 chili plant disease image processing

Abstract doi.org/10.14569/IJACSA.2026.0170354 PDF

55

Blockchain-Based Secure Data Sharing Framework: Dual Validation Through Content Validity and Thematic Analysis

Author 1: Azman Azmi Author 2: Farashazillah Yahya Author 3: Nur Afrina Azman

Blockchain technology applied to digital government services platforms has introduced new possibilities for secure data sharing for public sector agencies. However, the verification and validation of critical security factors remain underexplored, leading to inconsistent security factors for implementations and theoretical gaps. This study addresses this issue by conducting a double… Read full abstract & cite →

Blockchain content validity data sharing e-government thematic analysis

Abstract doi.org/10.14569/IJACSA.2026.0170355 PDF

56

One Decade of Artificial Intelligence (AI) Research in Public Health Stunting Prediction and Intervention

Author 1: Nurjoko Author 2: Admi Syarif Author 3: Favoriten R. Lumbanraja Author 4: Khairun Nisa Berawi

Stunting attributable to malnutrition remains a global public health problem impacting the long-term physical and cognitive growth of children. In recent years, artificial intelligence (AI) has been applied in public health research to help diagnose and predict stunting. This study seeks to review trends in AI research on stunting prediction… Read full abstract & cite →

Artificial intelligence stunting public health machine learning systematic review

Abstract doi.org/10.14569/IJACSA.2026.0170356 PDF

57

Students’ Perspectives of AI and Academic Integrity in Higher Education Institutions in Oman

Author 1: Alaa Edein Qoussini Author 2: Shaima Al Tabib Author 3: Akel Freij

The rapid adoption of generative artificial intelligence (AI) in higher education offers significant learning benefits while raising serious concerns about academic integrity. This study was conducted to examine undergraduate students’ perspectives and their awareness, attitudes, usage behaviors, perceived educational impact, policy awareness, and intentions to misuse AI tools in academic… Read full abstract & cite →

Artificial intelligence academic integrity student perspectives ethical AI use policy awareness

Abstract doi.org/10.14569/IJACSA.2026.0170357 PDF

58

Web System Based on Convolutional Neural Networks to Support Early Identification of Ocular Pterygium

Author 1: Justo Oscar Salcedo-Enriquez Author 2: Keyla Guadalupe Yataco-Argomedo Author 3: Rosalynn Ornella Flores-Castañeda

This research aligned with SDG No. 9, “Industry, Innovation, and Infrastructure,” as it promotes health and well-being using innovative technologies. The objective was to determine whether the development of a web-based system based on convolutional neural networks improves the early identification of pterygium. Sensitivity, specificity, and accuracy metrics were used… Read full abstract & cite →

Ocular pterygium web system convolutional neural networks early identification technology

Abstract doi.org/10.14569/IJACSA.2026.0170358 PDF

59

Mobile Application Based on Convolutional Neural Networks for the Initial Evaluation of Cutaneous Melanoma

Author 1: Julio Guillermo Farro-Llanos Author 2: Manases Sabteca Juan De Dios-Arango Author 3: Rosalynn Ornella Flores-Castañeda

Cutaneous melanoma is a dermatological disease that affects a large portion of the world's population and is characterized by its high capacity for dissemination and aggressiveness, especially when not detected early. Given this need, the objective was to develop a mobile application based on convolutional neural networks for the initial… Read full abstract & cite →

Melanoma skin disease convolutional neural networks mobile app

Abstract doi.org/10.14569/IJACSA.2026.0170359 PDF

60

A Survey of AI-Based Methods for Cloud Resource Allocation and Optimization

Author 1: Rim Doukha Author 2: Abderrahmane Ezzahout

Cloud computing has become essential for modern digital services, yet efficiently allocating compute, storage, and network resources in large-scale and highly dynamic environments remains a significant challenge. Traditional rule-based approaches often struggle to cope with workload variability, multi-tenancy, and the need for real-time multi-objective optimization. In response, recent research has… Read full abstract & cite →

AI techniques heuristics metaheuristic cloud resource management sustainability survey

Abstract doi.org/10.14569/IJACSA.2026.0170360 PDF

61

Hate Speech Detection on Multiple Social Networks Using Deep Learning and Optimization Techniques: A Hybrid Approach

Author 1: Vishu Tyagi Author 2: Sourabh Jain

The use of social media networks as a source of hate speech is another emerging factor that complicates the possibility of a comprehensive organization of an environment suitable for promoting healthy communication. Automating the detection of hate speech in various social media networks has turned out to be a very… Read full abstract & cite →

Natural language processing sparrow search algorithm hate speech deep neural network social media

Abstract doi.org/10.14569/IJACSA.2026.0170361 PDF

62

Enhancing the Successful Microservices Implementation

Author 1: Dinda Ayu Hapsari Author 2: Teguh Raharjo Author 3: Anita Nur Fitriani Author 4: Bob Hardian

Despite the widespread adoption of microservices across major global companies, a knowledge gap persists between best practices and real-world implementation challenges faced by practitioners. While existing literature provides extensive coverage of microservices patterns, limited evidence exists regarding how the practices are actually implemented in various organizational contexts. Thus, this study… Read full abstract & cite →

Microservice practice systematic literature review Kitchenham PRISMA 2020

Abstract doi.org/10.14569/IJACSA.2026.0170362 PDF

63

Real-Time Person Re-Identification Using Image Generation-Based Data Augmentation

Author 1: Yuya Ifuku Author 2: Kohei Arai Author 3: Oda Mariko

Person Re-identification (Re-ID) in single-gallery scenarios—where each individual has only one registration image—suffers from severe viewpoint sensitivity due to insufficient pose diversity. This study introduces ViewSynthReID, a pioneering generative augmentation framework that leverages Wan2.2, the latest diffusion-based video generation model, to synthesize complete 360° viewpoint coverage from a single input… Read full abstract & cite →

Person re-identification generative AI data augmentation OSNet real-time systems

Abstract doi.org/10.14569/IJACSA.2026.0170363 PDF

64

Transformer-Enhanced Soft Actor-Critic with EV-Aware Reward Shaping for Maize Optimization

Author 1: Xuan Lim Author 2: Hock Guan Goh Author 3: Shen Khang Teoh Author 4: Peh Chiong Teh Author 5: Ivan Andonovic

Optimizing fertilization and irrigation strategies is essential for improving productivity and resource efficiency in precision agriculture. Artificial intelligence (AI), particularly reinforcement learning (RL), has been increasingly explored for adaptive crop management under uncertain environmental conditions. However, many existing approaches rely on single-action formulations that struggle with joint input control, leading… Read full abstract & cite →

Precision agriculture maize optimization fertilization and irrigation management reinforcement learning Soft Actor-Critic transformer reward shaping explainable artificial intelligence

Abstract doi.org/10.14569/IJACSA.2026.0170364 PDF

65

Web Application to Improve Advertising Order Management Based on Cloud Computing: Case Study of a Television Company

Author 1: Ariadna Gisselle Ledesma-Sánchez Author 2: Ricardo Alejandro Gamarra-Valle Author 3: Ernesto Adolfo Carrera-Salas

Television companies face significant problems in managing advertising orders due to manual processes that cause transcription errors, delays, and a lack of traceability. This study proposes a web application based on cloud computing to automate this process. The solution implements a hybrid architecture that allows advertising agencies to enter orders… Read full abstract & cite →

Advertising order management cloud computing process automation television companies web applications

Abstract doi.org/10.14569/IJACSA.2026.0170365 PDF

66

Dynamic Multilevel User Allocation in MEC Using CESO for Resource Efficiency and QoE

Author 1: V Arun Author 2: M Azhagiri

Mobile Edge Computing (MEC) has become one of the key paradigms to enable next-generation networks in supporting applications that are latency sensitive and computation-intensive. Nevertheless, the resourceful placement of heterogeneous and dynamically incoming user tasks with distributed edge servers is a problematic issue to be achieved because of network fluctuation… Read full abstract & cite →

Mobile Edge Computing distributed edge server Quality of Experience Cognitive Evolutionary Synergy Optimization game-theoretic equilibrium

Abstract doi.org/10.14569/IJACSA.2026.0170366 PDF

67

A Deployment-Oriented Framework for Machine Learning-Based Learning Style Identification: A Systematic Computational Analysis

Author 1: Sarafa Olasunkanmi Adeyemo Author 2: Mohd Shahizan Othman Author 3: Chan Weng Howe Author 4: Muteb Sinhat Almarshadi Author 5: Siti Zaiton Mohd Hashim Author 6: Taofik Olasunkanmi Tafa Author 7: Abdulaziz Saidu Yalwa

This study presents a systematic and deployment-oriented analysis of machine learning (ML) techniques for learning style identification in adaptive digital environments. A total of 57 peer-reviewed studies published between 2020 and 2025 were analysed using a PRISMA-guided methodology. Beyond descriptive synthesis, the review systematically examines algorithmic paradigms, multimodal data integration… Read full abstract & cite →

Machine learning learning style identification multimodal data fusion deep learning ensemble learning explainable artificial intelligence deployment maturity adaptive learning systems

Abstract doi.org/10.14569/IJACSA.2026.0170367 PDF

68

An Automated Shrimp Feeding System Using Passive Acoustic Monitoring and Faster R-CNN

Author 1: Huynh Viet Hung Author 2: Huynh Vi Khang Author 3: Luong Vinh Quoc Danh

Shrimp aquaculture plays a vital role in global seafood production, contributing substantially to food security, economic growth, and export revenue. Feed typically accounts for 40–60% of total production costs, making efficient feed management crucial for improving farm profitability and the sustainability of culture operations. Acoustic-based feeding strategies offer a promising… Read full abstract & cite →

Automated feeding system faster R-CNN passive acoustic monitoring shrimp culture whiteleg shrimp

Abstract doi.org/10.14569/IJACSA.2026.0170368 PDF

69

The DAGC-ATS Database for Arabic Grammar Correction for Arabic Summaries

Author 1: Nada Essa Author 2: Mostafa. M. El-Gayar Author 3: Eman M. El-Daydamony

Arabic Grammar Correction is a comprehensive open-domain. Modern methods of correcting Arabic language errors rely on a database specific to a particular field and containing specific words and phrases, which leads to the emergence of the problem of out-of-context words. Due to the growth in recent work for Arabic text… Read full abstract & cite →

Arabic grammar correction Arabic natural processing open domain database

Abstract doi.org/10.14569/IJACSA.2026.0170369 PDF

70

An Enhanced Framework Using XLM-R with Optimized TF-IDF and Positional Encoding for Intra-Sentential Code Mixing Malay-English Sentiment Analysis

Author 1: Surendran Selvaraju Author 2: Nilam Nur Amir Sjarif Author 3: Nurulhuda Firdaus Mohd Azmi Author 4: Wan Noor Hamiza Wan Ali Author 5: Norshaliza Kamaruddin

The increasing use of online platforms, especially in social media, has led to a rapid growth of user-generated content that frequently exhibits intra-sentential code mixing between Malay and English language. Sentiment analysis remains challenging due to linguistic heterogeneity, frequent language switching, non-standard syntax and limited availability of adequate representations for… Read full abstract & cite →

Sentiment analysis code mixing feature extraction contextual embeddings XML-R TF-IDF positional encoding

Abstract doi.org/10.14569/IJACSA.2026.0170370 PDF

71

Empirical Quantitive Investigation of the Effect of GoF Design Patterns on the Quality of Software Systems

Author 1: Somia Abufakher

Design patterns are universal, reusable fixes for common issues in software design. They are supposed to encourage better design choices by rep-urposing already-established successful solutions, saving cost and time. The purpose of the current study is to present quantitive evidence about the expected implications on software quality by employing GoF… Read full abstract & cite →

Gang-of-four patterns empirical quantitive investigation software systems quality

Abstract doi.org/10.14569/IJACSA.2026.0170371 PDF

72

D-LexeCan: A Dynamic Lexicon-Based Framework for Sentiment Analysis in Tarifit, a Low-Resource Multiscript Language

Author 1: Amar Amakssoum Author 2: Fadwa Bouhafer Author 3: Anass El Haddadi Author 4: Abdelkhalak Bahri

Sentiment analysis for low-resource languages remains challenging due to limited annotated data, orthographic instability, informal writing practices, and the lack of dedicated linguistic resources, challenges that are particularly acute for Tarifit (Tamazight of the Rif), an under-resourced Amazigh language characterized by strong dialectal variation, pervasive multi-script usage, and highly noisy… Read full abstract & cite →

Sentiment analysis low-resource languages dynamic lexicon-based static lexicon baseline deep learning machine-learning transformer-based models

Abstract doi.org/10.14569/IJACSA.2026.0170372 PDF

73

Framework for Implementing ERP Integrating Client-Consultant Agency Management within Moroccan SMEs

Author 1: Yassine Zouhair Author 2: Younous El Mrini Author 3: Mustapha Belaissaoui

Nowadays, Enterprise Resource Planning (ERP) is a preferred solution for Small and Medium-sized Enterprises (SMEs) wishing to modernize and integrate their information Systems (IS). However, implementing an ERP remains a complex process due to the organizational constraints specific to SMEs and the difficulties associated with the implementation process. In the… Read full abstract & cite →

ERP ERP implementation client-consultant Moroccan SMEs IS success system benefits

Abstract doi.org/10.14569/IJACSA.2026.0170373 PDF

74

A Hybrid Machine Learning Algorithm for Pipeline Leak Detection and Localisation in Water Distribution Networks

Author 1: Giresse M. Komba Author 2: Topside E. Mathonsi Author 3: Pius A. Owolawi

Water Distribution Networks (WDNs) frequently experience significant water losses due to pipeline leakages. These losses not only create economic challenges for water utilities but also intensify global concerns regarding water scarcity. This study aims to enhance the accuracy and reliability of leak detection and localisation within WDN infrastructures. Traditional leak… Read full abstract & cite →

SVM-ANN-GT leak detection and localisation EPANET WDNs ML

Abstract doi.org/10.14569/IJACSA.2026.0170374 PDF

75

Parameter-Driven Evaluation of Zero Trust Security in Blockchain Networks Under Dynamic Threats

Author 1: Samuthira Pandi V Author 2: T. Vijayanandh Author 3: A. Jeyamurugan Author 4: M. D. Boomija Author 5: V. Parimala Author 6: S. Preena Jacinth Shalom Author 7: Lavanya. M Author 8: Veena. K

In this study, we investigate a parameterized method to evaluate the Zero Trust (ZT) security model integrated with blockchain networks under dynamic cyberattack conditions. Existing static trust-based security models fail to adapt to evolving adversarial behavior and dynamic attack conditions. To address these deficiencies, a discrete-time simulation model is constructed… Read full abstract & cite →

Zero trust blockchain security trust management cyber-attack simulation access control financial networks

Abstract doi.org/10.14569/IJACSA.2026.0170375 PDF

76

Thread-Sensitive Shared Instruction Cache Analysis for Precise WCET Estimation of Multithreaded Programs

Author 1: Naveeta Rani Author 2: P Padma Priya Dharishini Author 3: PVR Murthy

Real-time systems require strict adherence to task deadlines, making Worst-Case Execution Time (WCET) analysis essential. WCET estimation typically involves static analysis of instructions in a program, making use of models of hardware and architectural units such as shared instruction caches that are as precise as possible. Shared instruction caches pose… Read full abstract & cite →

Synchronization concurrency multicore systems multithreaded program shared instruction cache analysis worst-case execution time

Abstract doi.org/10.14569/IJACSA.2026.0170376 PDF

77

A Deterministic ANN–CA Computational Framework for Spatial Simulation Using Socioeconomic Data

Author 1: Álvaro Peraza Garzón Author 2: René Rodríguez Zamora Author 3: Mónica Avelina Gutiérrez Haros Author 4: Iliana Amabely Silva Hernández Author 5: Juan Francisco Peraza Garzón

Hybrid approaches combining Cellular Automata (CA) and Artificial Neural Networks (ANN) have been widely applied to spatial simulation; however, most implementations rely on stochastic components that limit reproducibility and interpretability. This study proposes a deterministic ANN–CA computational framework in which the stochastic perturbation term of a constrained CA model is… Read full abstract & cite →

Artificial neural networks cellular automata spatial simulation deterministic modeling hybrid computational framework

Abstract doi.org/10.14569/IJACSA.2026.0170377 PDF

78

An FMA-Based Action Research Framework for Blockchain-Driven Scholarship Management: A Diagnostic Perspective

Author 1: Chetna Achar Author 2: Bharati Wukkadada Author 3: Harshali Patil

For equity in access to higher education, scholarship schemes play a vital role. This is particularly the case with developing countries, where economic problems may become an impediment to academic betterment. In India, in the state of Maharashtra, the Shikshan Shulk Scholarship scheme was designed with the same goal –… Read full abstract & cite →

Blockchain scholarship shikshan shulk scholarship scheme e-governance smart contracts action research FMA (Framework Methodology Applications)

Abstract doi.org/10.14569/IJACSA.2026.0170378 PDF

79

Clustering Analysis for Extracting Moroccan Health Provinces Typology According to Breast and Cervical Cancer Early Screening

Author 1: Meryem Chakkouch Author 2: Merouane Ertel Author 3: Aziz Mengad Author 4: Said Amali Author 5: Majda Frindy

Cancer remains a major global concern, and its screening is a complex public health intervention. In Morocco, breast and cervical cancers are the most frequent malignancies among women, accounting for about half of all diagnosed cases. However, screening participation and coverage still vary across provinces. This study proposes a provincial… Read full abstract & cite →

Clustering PCA ICA KPCA t-SNE LLE K-Means ACH GMM breast and cervical cancers early screening

Abstract doi.org/10.14569/IJACSA.2026.0170379 PDF

80

A Lightweight Smart Contract Blockchain Platform for Secure and Efficient SME Transaction Systems

Author 1: Sabam Parjuangan Author 2: Suhardi Author 3: I Gusti Bagus Baskara Nugraha

Small and medium enterprises (SMEs) require secure, efficient, and low-cost digital transaction systems. However, many blockchain-based platforms are designed for large-scale applications and impose significant computational overhead, making them unsuitable for resource-constrained SMEs. This study proposes a lightweight smart contract blockchain platform tailored for SME-scale service environments. The system implements… Read full abstract & cite →

Smart contract blockchain SME digitalization performance evaluation transaction systems

Abstract doi.org/10.14569/IJACSA.2026.0170380 PDF

81

An Efficient Computational Framework for Scalable Learning in Complex Data Environments Using Deep Neural Networks

Author 1: Priyanto Author 2: Heri Nurdiyanto

This study introduces an efficient computational framework designed to support scalable learning in complex data environments using deep neural networks. In many real-world settings, data are not only large in volume but also diverse in structure, noisy in quality, and constantly evolving. These conditions often make conventional deep learning pipelines… Read full abstract & cite →

Scalable learning deep neural networks computational framework complex data environments efficient training

Abstract doi.org/10.14569/IJACSA.2026.0170381 PDF

82

Enhanced Grey Wolf Optimization Dimension Learning for Energy-Efficient Task Scheduling in Edge Computing Environment

Author 1: Jafar Aminu Author 2: Rohaya Latip Author 3: Zurina Mohd Hanapi Author 4: Shafinah Kamarudin Author 5: Mustapha Abubakar Giro

The development of edge computing has facilitated the development of numerous applications with diverse characteristics and stringent quality of service (QoS) requirements; these applications demand significant computational power and have strict time-sensitive constraints. While cloud computing offers seemingly unlimited computational resources, it often fails to meet the real-time demands of… Read full abstract & cite →

Edge computing energy consumption execution time task Scheduling grey wolf optimization dimension learning

Abstract doi.org/10.14569/IJACSA.2026.0170382 PDF

83

A Leakage-Aware and Reproducible Evaluation Framework for Predictive Maintenance Classification

Author 1: Abdulrahman M. Qahtani

Predictive maintenance classification is widely used to support industrial maintenance planning; however, reported model performance is often influenced by evaluation practices that allow unintended information leakage between training and testing data, resulting in optimistic and difficult-to-reproduce estimates. This study examines predictive maintenance classification from the perspective of evaluation design, with… Read full abstract & cite →

Predictive maintenance evaluation methodology information leakage reproducible machine learning cross-validation class imbalance performance metrics

Abstract doi.org/10.14569/IJACSA.2026.0170383 PDF

84

Deep Learning-Based Model to Predict Personality Traits of Social Media Users

Author 1: Faiza Abid Author 2: Mazni Binti Omar Author 3: Mohamad Sabri Bin Sinal

The rapid expansion of social media platforms has created enormous amounts of user-created content and behavioral information, providing the computational means to study human personality and psychology. This study creates a temporal deep learning model and Gated Recurrent Units (GRUs) to predict personality traits with behavioral and content-based features obtained… Read full abstract & cite →

Personality trait prediction deep learning gated recurrent units human psychology social media analytics digital behavior analysis

Abstract doi.org/10.14569/IJACSA.2026.0170384 PDF

85

A Multi-Vector Framework for Injection Attack Detection Using NLP Lexical–Semantic Fusion with Reinforcement Learning DQN–Based Calibration

Author 1: Carlo Jude P. Abuda Author 2: Cristina E. Dumdumaya

Injection attacks persist as dominant threats in modern web systems due to obfuscation, polymorphism, and multi-vector exploitation across SQLi, XSS, LDAP Injection, and Command Injection. Existing defenses often rely on static signatures or single-vector models, which limit generalization under adversarial payload mutation. This study addressed that limitation by designing and… Read full abstract & cite →

Deep Q-network reinforcement learning injection attack detection machine learning for cybersecurity multi-vector attack detection Natural Language Processing payload analysis web application security

Abstract doi.org/10.14569/IJACSA.2026.0170385 PDF

86

Machine Learning Application in Healthcare: A Case Study Using Ensemble Methods for Hospital Length of Stay Prediction

Author 1: Hakima Reddad Author 2: Maria Zemzami Author 3: Norelislam El Hami Author 4: Nabil Hmina Author 5: Farouk Yalaoui

Artificial intelligence is driving digital transformation across multiple sectors, including healthcare, pharmaceuticals, industrial production, and the automotive industry. In healthcare specifically, AI-powered predictive analytics offer significant potential for optimizing operational efficiency and resource allocation. To demonstrate this potential, we present a case study focused on hospital length of stay (LOS)… Read full abstract & cite →

Machine learning XGBoost healthcare operations hospital resource management ensemble methods predictive analytics SHAP analysis

Abstract doi.org/10.14569/IJACSA.2026.0170386 PDF

87

A Socio-Technical Analysis of Enterprise Architecture Misalignments

Author 1: Ayed Alwadain

Modern organizations must adapt to competitive environments through the analysis of their current state and plan for a future state through the use of Enterprise Architecture (EA). EA is a management discipline to align business and IT strategies. However, many organizations face challenges in effectively implementing and using EA due… Read full abstract & cite →

Enterprise Architecture EA EA issues EA misalignments socio-technical systems

Abstract doi.org/10.14569/IJACSA.2026.0170387 PDF

88

Bibliometric Mapping and Systematic Review of Deep Learning Approaches in Film and Multimedia Recommendation Systems within New Media

Author 1: Linlin Hou

The rapid growth of film, video, and multimedia content on new media platforms has intensified information overload, increasing the importance of effective recommender systems. Traditional recommendation approaches face limitations in modeling complex content semantics and dynamic user preferences. Deep learning techniques have been widely adopted to enhance film and multimedia… Read full abstract & cite →

Deep learning multimedia recommendation systems film recommendation new media platforms multimodal learning graph-based recommender systems

Abstract doi.org/10.14569/IJACSA.2026.0170388 PDF

89

LoRA-Based Fine-Tuning of Local LLMs for Hallucination Detection in Indonesian RAG Systems

Author 1: I Ketut Resika Arthana Author 2: Nyoman Gunantara Author 3: Made Sudarma Author 4: Made Sukarsa

Retrieval Augmented Generation (RAG) improves the factual grounding of Large Language Models (LLMs) by incorporating external knowledge. However, RAG systems may still generate hallucinated responses, and this issue remains underexplored in Indonesian language settings, particularly in settings where local deployment is preferred. This study proposes a hallucination detection approach for… Read full abstract & cite →

Hallucination detection Retrieval-Augmented Generation LoRA fine-tuning

Abstract doi.org/10.14569/IJACSA.2026.0170389 PDF

90

Noncommunicable Eye Diseases Trend Related to Artificial Intelligence: A Bibliometric and Visualization Analysis

Author 1: Marizuana Mat Daud Author 2: W Mimi Diyana W Zaki Author 3: Laily Azyan Ramlan Author 4: Fazlina Mohd Ali Author 5: Jun Kit Chaw

In recent years, artificial intelligence (AI) has transformed numerous sectors, including healthcare, and ophthalmology is no exception. The field has seen remarkable progress in using AI to detect, diagnose, and manage noncommunicable eye diseases (NCEDs), such as cataract, keratoconus, glaucoma, diabetic retinopathy, and age-related macular degeneration. This study presents a… Read full abstract & cite →

Artificial intelligence noncommunicable eye disease cataract keratoconus glaucoma diabetic retinopathy age-related macular degeneration

Abstract doi.org/10.14569/IJACSA.2026.0170390 PDF

91

An Algorithmic Model Based on Optimization of the Production Rules for Phishing Attacks

Author 1: Anvar Kabulov Author 2: Erkin Urinbaev Author 3: Inomjon Yarashov Author 4: Alisher Otakhonov

Phishing cyber-hazards, which are a correct cyber threat to audit, monitoring, control, and data acquisition systems in the digitalization environment, are aimed at misleading participants in a complex system and editing personal digitalization data through unauthorized access. In the research work, functioning tables, production rules, algorithmic and mathematical modeling apparatus… Read full abstract & cite →

Petri nets phishing production rules URL functioning table

Abstract doi.org/10.14569/IJACSA.2026.0170391 PDF

92

Deep Learning and Optimization-Driven Intrusion Detection Systems for Internet of Things Security: A Systematic Literature Review

Author 1: Rosilawati Mohamad Author 2: Muhammad Arif Mohamad Author 3: Mohd Faizal Ab Razak Author 4: Imam Riadi Author 5: Sri Winiarti Author 6: Herman Yuliansyah

The rapid expansion of Internet of Things (IoT) deployments has increased the exposure of interconnected devices to cyber threats, particularly in heterogeneous and resource-constrained environments. Although recent research increasingly emphasizes learning-based detection, classical intrusion detection system (IDS) paradigms remain widely deployed in practical IoT settings due to their interpretability, deterministic… Read full abstract & cite →

Internet of Things (IoT) intrusion detection system (IDS) deep learning (DL) metaheuristic optimization systematic literature review (SLR) IoT security

Abstract doi.org/10.14569/IJACSA.2026.0170392 PDF

93

ProGem: A Hybrid AI Framework for Task Effort Estimation

Author 1: Shahid Islam Author 2: Shazia Arshad Author 3: Natasha Nigar Author 4: Jose Lukose

Accurate effort estimation at the task level is essential for effective project planning, resource allocation, and meeting delivery timelines in software development. Traditional approaches have focused primarily on project-level estimation, leaving a critical gap in predicting the duration of individual tasks. This study presents ProGem, a novel hybrid framework that… Read full abstract & cite →

Task effort estimation software project management time-series forecasting real-time task insights

Abstract doi.org/10.14569/IJACSA.2026.0170393 PDF

94

Trend-Based Encoding of Exogenous Time-Series for Interpretable Financial Prediction

Author 1: Khudran M. Alzhrani

Integrating heterogeneous exogenous data into financial prediction models is challenging due to scale mismatches and semantic ambiguity. We propose a trend-encoding framework that transforms raw exogenous time-series into directional binary representations, improving predictive robustness while preserving interpretability. Using Saudi stock market data with COVID- 19 indicators, we evaluate predictive models… Read full abstract & cite →

Trend-based encoding exogenous time-series interpretable machine learning financial prediction forecasting

Abstract doi.org/10.14569/IJACSA.2026.0170394 PDF

95

Overcoming Temporal Shuffling in Non-Profiled SCA: A Translation-Invariant Deep Learning Approach

Author 1: Ahmed Ismail Author 2: Eid Emary Author 3: Hala Abbas

Side-Channel Analysis (SCA) utilizing Deep learning has demonstrated significant potential in recovering secret keys from cryptographic implementations. However, the efficiency of these attacks is often severely compromised by hardware countermeasures such as temporal shuffling, which desynchronizes leakage traces. Existing non-profiled collision attacks successfully mitigate shuffling, but often rely on a… Read full abstract & cite →

Side-Channel Analysis Deep Learning collision attack shuffling countermeasure Global Average Pooling AES

Abstract doi.org/10.14569/IJACSA.2026.0170395 PDF

96

A Retrieval-Augmented Generation System for Automated Functional Safety Analysis of AUTOSAR Basic Software Module Dependencies

Author 1: Mohand Hammad Author 2: Ahmed Moro Author 3: Mohamed Taher

This study presents an advanced Retrieval-Augmented Generation (RAG) system designed to assist functional safety engineers in performing safety analysis of AUTOSAR Classic Platform Basic Software (BSW) module dependencies. The system extracts structured dependency information from 128 AUTOSAR Software Specification (SWS) documents in ARXML format and generates draft Failure Mode and… Read full abstract & cite →

AUTOSAR RAG Retrieval-Augmented Generation functional safety FMEA FTA DFA ISO 26262 Langhain vector database automotive software safety analysis

Abstract doi.org/10.14569/IJACSA.2026.0170396 PDF

97

Domain-Agnostic Knowledge Graph Construction for Systematic Hallucination Reduction and Knowledge Reusability in Large Language Models

Author 1: Durvesh Narkhede Author 2: Rama Gaikwad Author 3: Saniya Jadhav Author 4: Pratiksha Ovhal Author 5: Nigam Roy Author 6: Prasad Dhanade

Large Language Models (LLMs) have rapidly advanced the capabilities of automated reasoning and text generation, yet they continue to hallucinate when responding to domain-specific or rapidly evolving queries due to limitations in their static, parametric knowledge. This challenge is especially significant in high-stakes domains where factual accuracy is critical. To… Read full abstract & cite →

Large Language Models knowledge graph construction hallucination reduction Retrieval-Augmented Generation web-grounded reasoning decentralized knowledge systems

Abstract doi.org/10.14569/IJACSA.2026.0170397 PDF

98

ANN-Based Employee Performance Prediction: A Comparative Analysis of Optimization Techniques

Author 1: Rahaf Mohammed Bajhzer Author 2: Yousef Alsenani Author 3: Sahar Jambi Author 4: Tawfiq Hasanin

With the increasing use of artificial intelligence in decision-making systems, predicting employee performance has attracted growing attention in human resource analytics. This study aims to systematically evaluate the impact of data preprocessing and model optimization techniques on artificial neural network (ANN)-based prediction of employee performance in HR analytics. Three publicly… Read full abstract & cite →

Employee performance prediction artificial neural networks data preprocessing model optimization HR analytics

Abstract doi.org/10.14569/IJACSA.2026.0170398 PDF

99

RollupFL: An Auditable Federated Learning Framework for Byzantine Client Accountability

Author 1: Md Tahmid Ashraf Chowdhury Author 2: Fasee Ullah Author 3: Shanjida Islam Labonno Author 4: Shahid Kamal Author 5: Mohammad Ahsanul Islam

Federated learning (FL) trains a shared model without sending raw data, but some clients can be Byzantine and send harmful updates. Robust aggregation methods like Median and Krum can reduce poisoning damage, but they do not clearly show which client attacked. In this study, we propose RollupFL, an audit layer… Read full abstract & cite →

Federated learning Byzantine attacks audit layer accountability attacker attribution tamper detection robust aggregation FedAvg blockchain audit sign-flip attack model-replacement attack

Abstract doi.org/10.14569/IJACSA.2026.0170399 PDF

100

Predicting Concession Curves of Negotiating Agents Using Machine Learning

Author 1: Khalid Mansour

Accurate opponent modeling is critical for effective automated negotiation, enabling agents to adapt their strategies based on the type of opponent. This study investigates machine learning approaches for classifying negotiation agent strategies from offer sequences across three scenarios: time-dependent agents following predetermined concession functions, strategic agents adapting to opponent behavior… Read full abstract & cite →

Automated negotiation strategy classification machine learning feature engineering strategic agents

Abstract doi.org/10.14569/IJACSA.2026.01703100 PDF

101

Comparative Study of Supervised Machine Learning Models for Fake News Detection with Interpretability and Statistical Validation

Author 1: Bayan M. Alsharbi

The rapid proliferation of fake news across digital platforms has intensified the need for reliable and computationally efficient automated detection systems. While deep learning models have demonstrated strong performance, their high computational cost and limited interpretability restrict practical deployment in real-time systems. This study proposes a structured comparative framework that… Read full abstract & cite →

Fake news detection supervised learning Decision Tree

Abstract doi.org/10.14569/IJACSA.2026.01703101 PDF

102

A Systematic Review on Crowd Density Estimation Using Deep Learning Techniques: State-of-the-Art Methods and Future Challenges

Author 1: Norah Aloufi Author 2: Liyakathunisa Syed

Estimating crowd density is a cornerstone of modern urban management and public safety, particularly in the aftermath of catastrophic incidents, such as the 2015 Mina stampede. With the rapid advancement of artificial intelligence (AI) technologies, deep learning (DL) has emerged as a powerful tool for addressing these challenges. This systematic… Read full abstract & cite →

Crowd density estimation computer vision deep learning PRISMA 2020 systematic literature review

Abstract doi.org/10.14569/IJACSA.2026.01703102 PDF

103

Lightweight Human Parsing with Multi-Scale Context for Edge Devices

Author 1: Abderrahim Ouza Author 2: Mohamed El Ghmary Author 3: Ali Choukri

For human parsing in wild and cluttered environment, deep architectures are widely utilized since they yield strong segmentation performance, while with the price of huge model size and computational complexity. These properties are highly limiting for them if to be deployed on resource-limited platforms, in particular for edge intelligence in… Read full abstract & cite →

Human parsing lightweight networks multi-scale representation edge computing real-time segmentation

Abstract doi.org/10.14569/IJACSA.2026.01703103 PDF

104

Time-Aware Hierarchical Attention Recurrent Neural Networks for Multi-Criteria Recommender System

Author 1: Manogna Vankayalapati Author 2: V Ramanjaneyulu Yannam Author 3: Sarada Korrapati Author 4: Murali Krishna Enduri

Recommendation systems are an important component for various online platforms, especially in the e-commerce domain. Recommendation systems suggest items to users using information from their past interactions such as reviews, ratings, and purchase history. Traditional recommendation systems allow users to give only a single rating for an item. Recently, deep… Read full abstract & cite →

Recommendation system multi-criteria ratings time-aware hierarchical attention recurrent neural networks user preferences

Abstract doi.org/10.14569/IJACSA.2026.01703104 PDF

105

PicLingo: A GenAI-Based System for Language-Disabled Children

Author 1: Razan Alatawi Author 2: Shahad Alamri Author 3: Renad Almaghthawi Author 4: Shada Alofi Author 5: Ghada Alharbi Author 6: Rehab Albeladi

Children with language disorders often face challenges in understanding sentences and communicating effectively with others. While previous studies have utilized static digital games and automated feedback to support vocabulary and spelling, there remains a significant gap in leveraging generative models to provide dynamic, personalized visual reinforcement for verbal tasks. This… Read full abstract & cite →

PicLingo Generative AI text-to-image generation speech recognition

Abstract doi.org/10.14569/IJACSA.2026.01703105 PDF

106

An Evidence-Aware and Risk-Sensitive Retrieval-Augmented Generation Framework for Internal Auditing

Author 1: Tareq Fahad Aljabri Author 2: Mariam Abdulaziz Alnajim

Large Language Models (LLMs) that are enhanced with Retrieval-Augmented Generation(RAG) can aid in internal auditing, particularly in search and analysis of documents. However, in general, most RAG-based audit tools focus more on quick document access and being easy to use, then about deeper auditing reasons. They don’t do much to… Read full abstract & cite →

LLM RAG Audit digitalization automation

Abstract doi.org/10.14569/IJACSA.2026.01703106 PDF

107

A Hybrid Modeling and Control Framework for Intelligent Wheelchairs Using Timed Petri Nets and Machine Learning

Author 1: Ayoub Elbazzazi Author 2: Ikram Dahamou Author 3: Cherki Daoui

This study proposes a hybrid modeling and control framework for intelligent wheelchair systems that integrates formal methods with adaptive artificial intelligence to ensure safety, robustness, and real-time performance. The approach combines Timed and Colored Petri Nets for formal safety enforcement with machine learning techniques, including a Multi-Layer Perceptron, Q-learning, and… Read full abstract & cite →

Timed Petri Nets assistive robotics adaptive control neural networks fuzzy logic FPGA human-machine interaction

Abstract doi.org/10.14569/IJACSA.2026.01703107 PDF

108

Bi-Transformers-Aided Contextual Contrastive Learning for Sequential Recommendation

Author 1: Adel Alkhalil Author 2: Ikhlaq Ahmed Author 3: Zafran Khan Author 4: Mazhar Abbas Author 5: Aakash Ahmad Author 6: Abdulrahman Albarrak

Contrastive learning (CL) based on Transformer sequence encoders offers a robust framework for sequential recommendation by effectively addressing data noise and sparsity issues. By utilizing the advantages of CL, these models are able to learn rich representations from sequences of user historical interactions, leading to improved recommendation and user satisfaction… Read full abstract & cite →

Contextual sequential recommendation bidirectional transformers contrastive learning auxiliary information

Abstract doi.org/10.14569/IJACSA.2026.01703108 PDF