IJACSA Vol. 17 Issue 9 (2026)

1

Adversarial Asymmetric Agents for Network Intrusion Detection Using Deep Reinforcement Learning

Author 1: Curtis Rookard

Machine learning-based network intrusion Detection systems (NIDS) increasingly operate in environments where adversaries can adapt their behavior in response to deployed defenses. However, most empirical IDS studies evaluate defenders against static, dataset-defined attack distributions and do not explicitly model an adaptive attacker with a distinct learning strategy or action space… Read full abstract & cite →

Network intrusion Detection cybersecurity deep reinforcement learning deep q-networks adversarial machine learning multiagent learning

Abstract doi.org/10.14569/IJACSA.2026.0170901 PDF

2

Assisted Robot Arm with Voice Recognition for Digital Healthcare

Author 1: Ting Zhang Author 2: Oanh Phan Author 3: Jiang Lu

Healthcare systems face increasing pressure from nursing shortages, growing patient-care demands, and the substantial amount of time healthcare professionals spend performing routine and repetitive bedside activities. These challenges can increase caregiver workload and reduce the time available for clinical assessment, decision-making, and direct patient interaction. Patients with limited mobility may… Read full abstract & cite →

Healthcare Robot Arm LLMs automation voice recognition healthcare innovation

Abstract doi.org/10.14569/IJACSA.2026.0170902 PDF

3

When Agents Delegate: Accountability Chains in Multi-Agent AI Systems

Author 1: Ornella Bahidika

Ethical frameworks for the oversight of autonomous AI systems generally assume a dyadic relationship: one human principal authorizing and supervising one artificial agent. Deployed practice has moved on. Contemporary agentic systems decompose tasks and delegate them onward, to sub-agents they instantiate, to third-party agents discovered through interoperability protocols, and to… Read full abstract & cite →

Agentic AI accountability delegation multiagent systems human oversight AI governance responsibility gap

Abstract doi.org/10.14569/IJACSA.2026.0170903 PDF

4

An Experimental Study on Credit Score Prediction Using K-Nearest Neighbors

Author 1: Taylor Stonelake

Credit score classification is a vital component of risk management in the financial sector. It conventionally relies on obsolete models that fail to capture dynamic patterns. This study utilizes K-Nearest Neighbor (KNN) on a dataset comprised of demographic and financial features. The methodology involved preprocessing steps, followed by model training… Read full abstract & cite →

Correlation matrix dataset k-nearest neighbors machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170904 PDF

5

Leveraging Ancestral Health Records for Early Prediction of Cardiovascular Risk in Future Generations

Author 1: Suresh Kurumalla Author 2: Bal Virdee Author 3: Ashish Khanna Author 4: Siva Shankar S

Cardiovascular disease (CVD) is the leading global cause of morbidity and mortality, with onset driven by a complex interplay between genetic susceptibility, demographic characteristics, and modifiable lifestyle factors. Most current risk-assessment systems rely heavily on just a patient’s existing clinical markers—cholesterol, blood pressure, body-mass index, and lifestyle factors—while largely neglecting… Read full abstract & cite →

Cardiovascular disease prediction ancestral health records hereditary risk big data Hadoop distributed file system distributed latent Dirichlet allocation distributed non-negative matrix factorization precision medicine

Abstract doi.org/10.14569/IJACSA.2026.0170905 PDF

6

Design and Verification of an Interpretable Low-Code Maintenance Decision Support Application for Automotive Assembly

Author 1: Adriana ALDEA Author 2: Adriana FLORESCU Author 3: Catrina CHIVU

Industrial maintenance digitalization is often associated with sensor-rich predictive models, yet brownfield factories may first need reliable decision support from sparse historical records and familiar software. This study designs and verifies an interpretable low-code application for maintenance scenario assessment in automotive assembly. The application combines planned production and cyclic calendar… Read full abstract & cite →

Decision support system maintenance management low-code application spreadsheet engineering explainable analytics automotive assembly

Abstract doi.org/10.14569/IJACSA.2026.0170906 PDF

7

OptiGeoRisk: A Spatially Localized and Optimized Geo-XGBoost Framework for Credit Default Risk Classification

Author 1: Claher Prastian Author 2: Alexander Agung Santoso Gunawan

Credit default risk prediction is important for financial institutions in geographically diverse, collateral-based lending, where early identification of risky borrowers supports preventive monitoring. Most credit scoring models treat borrower records as independent observations and rarely capture local spatial heterogeneity. This study proposes OptiGeoRisk, a spatially localized Geo-XGBoost framework for imbalanced… Read full abstract & cite →

Credit default risk Geo-XGBoost spatial machine learning class imbalance early-warning system financial risk management sustainable economic growth

Abstract doi.org/10.14569/IJACSA.2026.0170907 PDF

8

Collaborative Development Method for Improving Uncertainty Management in Agile Software Development

Author 1: Maha Makkass Author 2: Youness Laghouaouta Author 3: Adil Anwar

Software development projects frequently face uncertainty arising from evolving requirements, communication gaps, and inconsistent coordination among stakeholders. This study presents the Collaborative Development Method (CDM), an agile framework designed to strengthen collaboration, improve requirements engineering practices, and support uncertainty management throughout the software development lifecycle. CDM integrates collaborative activities, requirements… Read full abstract & cite →

Agile leadership uncertainty management agile software development requirements engineering Collaborative Development Method (CDM) software process improvement

Abstract doi.org/10.14569/IJACSA.2026.0170908 PDF

9

Smart IoT Robot Toy with AI-Based Safety Judgment and Parental Monitoring Features

Author 1: Check-Yee Law Author 2: Wee-Yang Goh Author 3: Yong-Wee Sek

Smart Internet of Toys (IoToys) can provide interactive learning and companionship for children, but many systems still provide limited support for guardian awareness and safety notification. This study presents the design and development of a smart IoT robot toy with AI-based safety judgment and parental monitoring features. The prototype integrates… Read full abstract & cite →

Child safety internet of toys IoToy IoT robot parental monitoring privacy concerns smart toy Telegram notification

Abstract doi.org/10.14569/IJACSA.2026.0170909 PDF

10

Real-Time Anomaly Detection Using ML, DL, and XAI for Financial Fraud: A Systematic Review

Author 1: Padilla-Lopez Jimmy Andres Author 2: Lazo-Ordoñez Pedro Antonio Author 3: Juan Jesús Soria-Quijaite

Digital payments, fintech, and online banking are now part of our daily lives. Making a transfer, paying for a service, or shopping online is faster, but this progress has also given rise to new forms of fraud. Rule-based systems remain useful when the pattern is already known; the problem arises… Read full abstract & cite →

Financial fraud machine learning deep learning XAI digital payments

Abstract doi.org/10.14569/IJACSA.2026.0170910 PDF

11

Fusion of Large Language Model and Knowledge Graph for Intelligent Power-System Q&A

Author 1: Zijie Meng Author 2: Xinlei Cai Author 3: Lizhou Jiang Author 4: Kai Dong Author 5: Bingzhao Zhu Author 6: Junhong Guo

Intelligent question-answering (Q&A) models are needed for modern power systems that can provide accurate, reliable, and explainable answers to engineers and operators. Conventional Q&A systems cannot always understand the question semantically or combine domain knowledge, and stand-alone Large Language Models (LLMs) can generate hallucinated or non-grounded answers. To tackle these… Read full abstract & cite →

Large language model intelligent question answering knowledge graph reasoning smart grid power systems knowledge management

Abstract doi.org/10.14569/IJACSA.2026.0170911 PDF

12

Adapting Ephemeral ECC Authentication for Decentralized Web 3.0: A Threat Model and Security Analysis

Author 1: Adarsh S V Nair Author 2: Rathnakar Achary

Web 3.0 shifts control of digital identity and data away from centralized platforms, moving authentication onto resource-constrained end-user devices, wallets, and smart contracts. RSA and ElGamal are computationally expensive at the key sizes required for adequate security, making them a poor match for this setting. This study presents a user-centric… Read full abstract & cite →

Elliptic curve cryptography decentralized authentication Web 3.0 security privacy-preserving authentication self-sovereign identity decentralized identifiers verifiable credentials forward secrecy formal verification resource-constrained devices post-quantum cryptography

Abstract doi.org/10.14569/IJACSA.2026.0170912 PDF

13

Automated Generation of Arabic Procedural Court Orders in the Moroccan Legal Context

Author 1: Saliha Yassine Author 2: Mohammed Essoujaa Author 3: Mustapha Esghir Author 4: Ouafaa Ibrihich

Automating the drafting of legal documents can reduce repetitive judicial workload and support procedural efficiency in e-Justice systems. However, Arabic legal text generation remains underexplored, particularly for Moroccan procedural court orders, which are characterized by highly structured language and recurring legal formulations. This study compares retrieval-based and transformer-based approaches for… Read full abstract & cite →

Arabic legal NLP legal text generation moroccan procedural court orders retrieval-based generation AraBART AraT5 e-Justice

Abstract doi.org/10.14569/IJACSA.2026.0170913 PDF

14

Semi-Supervised Rice Leaf Disease and Pest Classification Using IoT Field Images

Author 1: Abdul Azis Author 2: Abdul Fadlil Author 3: Tole Sutikno

Rice is a vital crop for global food security, but its productivity is frequently threatened by diseases and pests, necessitating rapid and accurate Detection. This study presents a semi-supervised deep learning approach to automate the classification of ten categories of rice leaf conditions, including seven diseases, two pests, and a… Read full abstract & cite →

Semi-Supervised Learning (SSL) CNN rice leaf disease pest classification IoT-based agriculture deep learning dataset

Abstract doi.org/10.14569/IJACSA.2026.0170914 PDF

15

Constraint-Aware Automated Daily Meal Planning: A Controlled Comparison of Genetic Algorithm and Particle Swarm Optimization

Author 1: Kohei Arai Author 2: Diva Kurnianingtyas Author 3: Nathan Daud

This study presents a controlled proof-of-concept comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for automated daily meal-plan generation. The evaluation is deliberately restricted to the data available in the current prototype: a 24-item Japanese-style food database, one nutritional profile, one random seed (42), 100 iterations, and an… Read full abstract & cite →

Automated meal planning genetic algorithm particle swarm optimization constrained combinatorial optimization personalized nutrition computational intelligence

Abstract doi.org/10.14569/IJACSA.2026.0170915 PDF

16

Critical Vendor-Related Failure Factors in Outsourced ICT Projects: Insights from the Malaysian Public Sector

Author 1: Masittah Musatapa Author 2: Fazlina Mohd Ali Author 3: Nurhizam Safie Mohd Satar Author 4: Azran Ahmad Author 5: Surya Sumarni Hussein

ICT projects in the Malaysian public sector continue to experience failure, especially in outsourced application system projects. In striving to overcome this issue, the government is leading a significant push toward the digitization of public services. In previous studies, the challenges of ICT implementation have been analyzed from the perspective… Read full abstract & cite →

ICT project ICT project failure project management ICT procurement vendor characteristics information systems

Abstract doi.org/10.14569/IJACSA.2026.0170916 PDF

17

Empowering Educators to Develop Augmented Reality Applications by Integrating Computational Thinking and Experiential Learning

Author 1: Najwa Abd Ghafar Author 2: Nazatul Aini Abd Majid Author 3: Mohd Nor Akmal Khalid Author 4: Shinobu Hasegawa Author 5: Tan Siok Yee Author 6: Lam Meng Chun

Augmented reality (AR) promises rich, immersive experiences in learning, yet many educators lack the technical expertise to develop AR content independently. Existing authoring environments often emphasise functionality over instructional design, leaving educators reliant on pre-built resources that seldom match their learning goals. This study proposes EduCATE-AR, a template-based authoring model… Read full abstract & cite →

Augmented reality content creation instructional design template-based authoring experiential learning computational thinking

Abstract doi.org/10.14569/IJACSA.2026.0170917 PDF

18

A Bidirectional Cross-Modal Attention Framework for Multimodal Sentiment Analysis

Author 1: B S Harish Author 2: C K Roopa Author 3: M S Kendagannaswamy Author 4: T S Kaveri

Multimodal sentiment analysis on social media data presents unique challenges due to label noise, modality conflicts, and class imbalance inherent in annotated image-text datasets. The proposed work presents CLIP-CrossFusion Net, a novel multimodal sentiment analysis framework that integrates the Contrastive Language-Image Pre-Training (CLIP) ViT-B/32 image encoder with a RoBERTa-Base text… Read full abstract & cite →

Multimodal sentiment analysis CLIP RoBERTa cross-modal attention social media deep learning transformer

Abstract doi.org/10.14569/IJACSA.2026.0170918 PDF

19

Artificial Intelligence for Rheumatoid Arthritis and Osteoporosis Diagnosis from Radiography, Computed Tomography, and Multimodal Clinical Data: A Systematic Review

Author 1: Moulay Youssef ICHAHANE Author 2: Mohamed LACHGAR Author 3: Abdelouafi IKIDID Author 4: Ahmed DOURHRI Author 5: Mohamed EL GHAZOUANI Author 6: Noureddine ASSAD

Artificial intelligence (AI) is increasingly used to support musculoskeletal diagnosis from routine imaging, yet evidence remains fragmented across diseases, modalities, tasks, and validation designs. This systematic review synthesizes AI-based diagnosis, screening, grading, scoring, and structural assessment of rheumatoid arthritis (RA) and osteoporosis/osteopenia using radiography, computed tomography (CT), ultrasound, DXA-related imaging… Read full abstract & cite →

Rheumatoid arthritis osteoporosis osteopenia radiography X-ray CT multimodal learning deep learning convolutional neural networks transfer learning systematic review

Abstract doi.org/10.14569/IJACSA.2026.0170919 PDF

20

A Hybrid System with Automatic Classification and RAG for Moroccan Court Rulings

Author 1: Sara IBRIHICH Author 2: Ahmed OUSSOUS Author 3: Ouafaa IBRIHICH Author 4: Mustapha ESGHIR

The Accessibility of Moroccan court judgments and decisions is significantly limited due to the lack of a specialized Arabic Judicial information retrieval architecture. Current NLP technologies focus on Modern Standard Arabic and fail to capture the specialized procedural language of judicial rulings from the courts, resulting in inadequate search and… Read full abstract & cite →

Legal information retrieval BM25 FAISS Arabic NLP TF-IDF text classification LinearSVC Moroccan legal judgments

Abstract doi.org/10.14569/IJACSA.2026.0170920 PDF

21

IoT-Enabled Smart Cap for Real-Time Stress Monitoring

Author 1: Bairavanathan Maha Sri Author 2: Kala Devi Managuran Author 3: Rajermani Thinakaran Author 4: Narayana Lokesh T Author 5: Punitha Shri M Author 6: Jeya Mithra K Author 7: Meenakshi B Author 8: Raja Subramanian R Author 9: Tong Mingjie

Stress is a physiological and psychological response that can affect the well-being and daily functioning of an individual. In this study, we propose an IoT-enabled smart cap for real-time stress monitoring using multiple physiological signals. The system uses electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and pulse measurements to… Read full abstract & cite →

Stress monitoring EEG ECG GSR IoT Blynk cloud platform mental health management real-time monitoring personalized recommendations

Abstract doi.org/10.14569/IJACSA.2026.0170921 PDF

22

A Hybrid Deep Learning Framework for Scientific Correction Text Classification

Author 1: Garima Sharma Author 2: Vikas Tripathi Author 3: Vijay Singh

Ensuring research integrity and maintaining the reliability of scientific communication requires a clear and comprehensive understanding of the underlying causes of article correction reasons across domains. Correction notices issued to authors are often unstructured with inconsistent textual information, making their automatic classification a challenging task. Existing approaches primarily rely on… Read full abstract & cite →

Correction analysis research integrity BERT natural language processing text classification

Abstract doi.org/10.14569/IJACSA.2026.0170922 PDF

23

AttendEase: Fraud-Resistant Attendance Tracking in Higher Education Using Dynamic Tokens and Geofencing

Author 1: Silvia Gaftandzhieva Author 2: Gabriela Lyavova

Preparing qualified specialists depends directly on student engagement during the educational process. For this reason, many higher education institutions (HEIs) view attendance as a key element of their educational policy and mandate strict requirements in internal regulatory documents. Traditional student attendance tracking is often time-consuming and susceptible to proxy-scanning fraud… Read full abstract & cite →

Attendance tracking QR code geofencing flutter cloud systems mobile security educational technology smart campus

Abstract doi.org/10.14569/IJACSA.2026.0170923 PDF

24

CMST-FL: Cross-Missingness Stable Trust with Evidence-Guided Preprocessing for Federated Healthcare Signal Imputation

Author 1: Benachir Rigalma Author 2: Soumia Ziti Author 3: Meryam Belhiah Author 4: Hamid Ouhnni

Missing physiological samples create preprocessing uncertainty and uneven client reliability in federated learning. This study proposes Cross-Missingness Stable Trust federated learning (CMST-FL), a two-stage framework that selects an imputer using development patients and then weights local updates from their marginal validation-quality gains across MCAR, MAR, and contiguous-block masks. Twelve BIDMC… Read full abstract & cite →

Federated learning missing data trust aggregation physiological signals multiple imputation healthcare analytics

Abstract doi.org/10.14569/IJACSA.2026.0170924 PDF

25

A Fairness–Utility Evaluation Framework for Assessing Large Language Models (LLMs)

Author 1: Samah Alhazmi

Large language models are increasingly used in contexts where their outputs can affect people directly, including hiring, admissions, and lending. This growing role makes it important to consider not only how well these models perform, but also whether their behavior is fair. Although many fairness metrics, bias benchmarks, and mitigation… Read full abstract & cite →

Large language models fairness–utility evaluation AI governance natural language processing

Abstract doi.org/10.14569/IJACSA.2026.0170925 PDF

26

Accuracy, Stability, and Computational Efficiency of Deep CNN Architectures for Apple Leaf Disease Classification

Author 1: Brahim Ouben Hssain Author 2: Khalil Ladrham Author 3: Noureddine El Barbri Author 4: Rachid El Ayachi Author 5: Abdellah Halimi

Reliable apple leaf disease classification requires high predictive performance and transparent computational reporting under heterogeneous field conditions. This study presents an orchard-image benchmark of four ImageNet-initialized convolutional architectures—Xception, EfficientNetB0, DenseNet121, and ResNet50V2—using images acquired at two Moroccan orchard locations (Midelt and Azrou), four cultivars, and three mobile devices. From a… Read full abstract & cite →

Apple leaf disease convolutional architectures field conditions Xception EfficientNetB0 DenseNet121 ResNet50V2 computational efficiency

Abstract doi.org/10.14569/IJACSA.2026.0170926 PDF

27

Confidence-Gated Indonesian Grammatical Error Correction Under Synthetic-to-Authentic Domain Shift

Author 1: Eko Suroso Author 2: Onok Yayang Pamungkas Author 3: Laily Nurlina Author 4: Sudiyono Author 5: Jimat Susilo Author 6: Eko Priyanto Author 7: Hera Septriana

Indonesian grammatical error correction (GEC) requires decisions about when to apply a proposed edit under synthetic-to-authentic domain shift. We evaluate a selective correction framework using a reconstructed, fully executable experiment. An audit of 1,955 authentic records removed 36 incomplete pairs, 14 duplicates, 45 string identities, and five additional to-ken identities… Read full abstract & cite →

Indonesian grammatical error correction domain shift synthetic data selective prediction abstention overcorrection trustworthy natural language processing writing feedback

Abstract doi.org/10.14569/IJACSA.2026.0170927 PDF

28

CompCap-Stego: Computable Capability-Pointer Steganography with Hidden Policy Evaluation and Verifiable External Payload Retrieval

Author 1: Mohammad Othman Nassar

CompCap-Stego is a capability-pointer steganography frame-work in which the concealed object is a compact dual-zone ca-pability capsule rather than the protected payload. The sealed access zone stores receiver binding, wrapped key material, a revocation handle, an audit nonce, and a payload-verification root, while the computable policy zone supports validity, expiry… Read full abstract & cite →

Steganography capability-based access control hidden policy evaluation verifiable external retrieval

Abstract doi.org/10.14569/IJACSA.2026.0170928 PDF

29

A Machine Learning–Driven Information Systems Readiness Index for Predicting Strategic Cloud Adoption in Ghanaian Tertiary Education

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

Adopting cloud computing within the tertiary education sector is still problematic, especially in developing countries, because of the low level of readiness and the absence of predictive decision support tools. In this study, we propose the Machine Learning-driven Information System Readiness Index (ISRI) that merges Structural Equation Modeling (SEM) with… Read full abstract & cite →

Cloud computing information system readiness index higher education structural equation modeling machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170929 PDF

30

Color and Emotion in User Experience Design for Older Adults' Health Applications: A Multi-Database Bibliometric and Evidence-Mapping Study

Author 1: YangFan He Author 2: Azizah Che Omar Author 3: Sobihatun Nur Abdul Salam

Color is routinely present in health applications, but evidence about its role in older adults' user experience remains dispersed across design, health informatics, and human-computer interaction. This five-database study maps an affect-or-color focal corpus and applies a structured design-evidence audit to its color-term candidates. Searches of Web of Science Core… Read full abstract & cite →

Older adults health applications user experience interaction design color emotion bibliometrics science mapping

Abstract doi.org/10.14569/IJACSA.2026.0170930 PDF

31

Ablation-Based Climate-Aware Feature Engineering for Rice Productivity Prediction Using Multi-Source Agricultural Data

Author 1: R. Hadapiningradja Kusumodestoni Author 2: Fatchul Arifin Author 3: Mutiara Nugraheni Author 4: Desti Setiyowati

Climate-aware feature engineering is important for rice productivity prediction because rainfall effects may be delayed, accumulated, standardized, or expressed as extreme anomalies. This study evaluates the incremental contribution of rainfall-derived features for rice productivity prediction using multi-source agricultural data from districts/cities in Central Java, Indonesia, during 2010-2025. The analysis used… Read full abstract & cite →

Ablation study climate-aware feature engineering multi-source agricultural data random forest rainfall lag rice productivity prediction standardized rainfall anomaly index XGBoost

Abstract doi.org/10.14569/IJACSA.2026.0170931 PDF

32

Deep Neural Network Architectures for Real-Time Drone Detection in Aerial Surveillance Systems: A Comparative Evaluation of YOLOv7, YOLOv8, and YOLOv11

Author 1: Nazerke Abylay Author 2: Elvira Kadylbekkyzy Author 3: Bakhytzhan Kulambayev Author 4: Bayan Moldakalykova Author 5: Zharkyn Bimoldina Author 6: Saken Mambetov Author 7: Kuanysh Dossanbek

The proliferation of consumer drones has raised security concerns for critical infrastructure, airports, and urban surveillance, creating a need for reliable real-time Detection. This study presents a controlled comparative evaluation of selected YOLO generations — YOLOv7x, YOLOv8x, and YOLOv11x — for single-class drone Detection under matched high-level settings: 640 by… Read full abstract & cite →

UAV Detection drone Detection YOLO deep learning real-time object Detection comparative evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170932 PDF

33

Next-Gen Agriculture: Enhancing Animal Welfare Through IoT, Edge Devices, and Artificial Intelligence

Author 1: Benlahsiniya Maroua Author 2: Ait Abdelouahid Rachida Author 3: Marzak Abdelaziz

Agriculture has always been a field of innovation, evolving alongside technological advancements to boost productivity and address sector-specific challenges. Today, the Internet of Things (IoT), edge computing, and artificial intelligence (AI) are revolutionizing farming practices, particularly in livestock management. These technologies enable real-time monitoring of animal health, facilitate early disease… Read full abstract & cite →

Smart farming animal welfare IoT edge computing artificial intelligence connected livestock farming

Abstract doi.org/10.14569/IJACSA.2026.0170933 PDF

34

Predicting Multi-KPI Warehouse Dynamics: A Simulation-Based Evaluation of Fuzzy-Integrated Neural Architectures

Author 1: Mohamed Amine Frikha

Warehouse order fulfillment is governed by complex, nonlinear interactions among order arrivals, picking workload, congestion, replenishment, labor, equipment, and dispatch capacity. This study proposes a simulation-based predictive framework that compares a global multilayer perceptron (MLP) with a fuzzy-integrated modular architecture comprising three regime-specific local neural networks. The framework simultaneously predicts… Read full abstract & cite →

Warehouse operations order fulfillment artificial neural networks fuzzy fusion modular neural networks nonlinear simulation predictive analytics

Abstract doi.org/10.14569/IJACSA.2026.0170934 PDF

35

Benchmarking ML-Based DDoS Detection Algorithms: Efficiency, Effectiveness, and Generalization

Author 1: Zhilbert Tafa Author 2: Aurora Uka

Distributed Denial of Service (DDoS) attacks are among the most significant cybersecurity threats today. Traditional DDoS Detection systems struggle to cope with emerging attack vectors, while Deep Learning (DL)-based approaches are limited by their high computational complexity. Shallow Machine Learning (ML) techniques have been widely explored, either as standalone solutions… Read full abstract & cite →

Anomaly Detection autoencoder DDoS isolation forest LOF machine learning OCSVM RF XGBoost

Abstract doi.org/10.14569/IJACSA.2026.0170935 PDF

36

A Confidence-Calibrated Adaptive Ensemble Voting Framework for Stock Price Direction Prediction

Author 1: Yuan Lukito Author 2: Nugroho Agus Haryono Author 3: Raden Gunawan Santosa

Predicting stock price direction is crucial for investment decisions, yet most machine learning models optimize classification metrics while ignoring probability calibration. Uncalibrated probabilities often produce overconfident predictions, increasing financial risk. To address this gap, we introduce Confidence-Calibrated Adaptive Ensemble Voting (CAEV-C), a framework combining temperature scaling calibration with dynamic confidence-weighted… Read full abstract & cite →

Stock market prediction ensemble learning confidence-calibrated ensemble probability calibration

Abstract doi.org/10.14569/IJACSA.2026.0170936 PDF

37

Leveraging Cloud Computing Engineering for Sustainable Education: A Computational Evaluation Framework

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 Author 6: Adade Sedom Percy

As the world confronts complex climate-change challenges, integrating sustainability principles into engineering and computing curricula has become imperative. Traditional learning-system infrastructures, however, often lack the scalability, energy efficiency, and computational capacity required for modern data-driven educational environments. This study develops a computational evaluation framework that models sustainability integration as a… Read full abstract & cite →

Cloud computing engineering sustainability education green computing energy-efficient cloud systems process innovation

Abstract doi.org/10.14569/IJACSA.2026.0170937 PDF

38

An Energy-Efficient and Modeling Attack-Resilient SNN-Driven PUF Security for Internet of Medical Things

Author 1: Agila Harshini T Author 2: Harini Sriraman

The healthcare industry is rapidly evolving with the digitalisation of medical devices. Despite the advantages of IoMT devices, they remain vulnerable to cyberattacks that threaten patient data security. Authentication typically relies on a static key, making it susceptible mainly to modelling attacks. Existing techniques fail to balance security with lightweight… Read full abstract & cite →

PUF SNN biometric authentication IoMT security modeling attack

Abstract doi.org/10.14569/IJACSA.2026.0170938 PDF

39

A Machine Learning Framework for Wheat Seed Variety Classification Using GLCM-Based Texture Analysis and Feature Optimization

Author 1: Muhammad Munawar Ahmed Author 2: Malik Muhammad Saad Missen Author 3: Hannan Adeel Author 4: Ahamad Zaki Bin Mohamed Noor Author 5: Muzamil Malik

In Pakistan, wheat is one of the important crops, and the production of wheat is highly reliant upon the use of pure and certified seed varieties. Hence, wheat seeds should be properly identified to ensure the quality of the crop and better production. Farmers typically rely on a visual assessment… Read full abstract & cite →

Gray-Level Co-occurrence Matrix (GLCM) Principal Component Analysis (PCA) Correlation-based Feature Selection (CFS) Random Forest (RF) BayesNet (BN)

Abstract doi.org/10.14569/IJACSA.2026.0170939 PDF

40

Perceptual Evaluation of Garment Drape Intensity in AI-Generated Garment Images

Author 1: Noriaki Kuwahara Author 2: Yuta Yamamoto Author 3: Dongeun Choi Author 4: Kazunari Hirakoso Author 5: Takashi Satou

Recent advances in generative artificial intelligence have enabled fine-grained manipulation of visual attributes in garment images, but consistency in automated feature space does not necessarily guarantee consistency in human perception. This study investigates the perceptual validity of garment drape intensity control in AI-generated garment images using a semi-automated Human-in-the-Loop evaluation… Read full abstract & cite →

Drape perception generative AI human-in-the-loop CLIP bradley–terry model fashion image generation

Abstract doi.org/10.14569/IJACSA.2026.0170940 PDF

41

SGRL: A Hybrid Semantic-Graph Representation Learning Framework for Software Vulnerability Detection

Author 1: Cong Bui Van

The increasing scale and complexity of software increase the risk of security vulnerabilities, creating a need for automated and effective Detection methods. Transformer-based methods can learn semantic representations but do not fully exploit structural information, whereas graph neural network-based methods remain limited in representing token-level semantics. In addition, data imbalance… Read full abstract & cite →

Source code vulnerability Detection CodeT5 graph convolutional network code property graph representation learning deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170941 PDF

42

Cross-Cohort Reliability of Machine Learning for Breast Cancer Survival Prediction

Author 1: NADIR Younes Author 2: RACHDI Mohamed Author 3: AMHAIMAR Lahcen Author 4: BAKHOUYI Abdellah Author 5: KHALIDI Abderrahim Author 6: AZOUAZI Mohamed

Transportability limitations can affect discrimination, probability calibration, and uncertainty estimates even when machine-learning pipelines use the same input variables. This study evaluated 5-year breast cancer survival models across METABRIC and TCGA-BRCA using age and ER, PR, and HER2 status. Logistic regression, radial-basis-function support vector machine, random forest, XGBoost, and soft… Read full abstract & cite →

Machine learning transportability external validation probability calibration conformal prediction selective prediction cohort shift few-shot recalibration survival prediction

Abstract doi.org/10.14569/IJACSA.2026.0170942 PDF

43

Unmasking the Unknown: Feature-Orchestrated Image Synthesis for Deep Learning-Based Threat Detection

Author 1: Sruthi C K Author 2: Harini Sriraman

IoT cybersecurity is crucial as a vast number of linked, susceptible devices serve as possible entry points into bigger networks. Recently, deep learning and machine learning have been used to detect both known and unknown threats. These models suffer from false positives, outdated datasets, biased evaluations against similar attacks, and… Read full abstract & cite →

Unknown attack pretrained CNN vision transformer feature selection correlation coefficient SHAP

Abstract doi.org/10.14569/IJACSA.2026.0170943 PDF

44

Arabic Text Simplification and Readability Assessment for Educational Applications: A Systematic Literature Review

Author 1: Khalid Essaadani Author 2: Soumia Ziti

Arabic text simplification and readability assessment support level-appropriate material selection, language learning, early literacy, teacher adaptation, and accessibility, but the degree of educational validation remains uncertain. This PRISMA 2020 systematic review searched Scopus, Web of Science Core Collection, and IEEE Xplore on 6 August 2026 and updated coverage through a… Read full abstract & cite →

Arabic text simplification Arabic readability assessment educational natural language processing systematic literature review

Abstract doi.org/10.14569/IJACSA.2026.0170944 PDF

45

An Adaptive AI-Driven Code Optimization Framework Using Machine Learning and Explainable AI for Scalable Software Systems

Author 1: Kavya B K Author 2: S.Annamalai Author 3: A Suresh Kumar

The increasing complexity of embedded computing systems has made it difficult to achieve efficient code execution while effectively utilizing limited hardware resources. Conventional optimization methods mainly rely on compiler-driven techniques and fixed transformation rules, which may not adapt well to different embedded architectures and varying workload conditions. This research introduces… Read full abstract & cite →

Hardware-aware optimization artificial intelligence code optimization machine learning explainable AI scalability

Abstract doi.org/10.14569/IJACSA.2026.0170945 PDF

46

Enhanced Distributed Bandwidth Allocation Algorithm for Network Latency Reduction in IEEE 802.17 Rings

Author 1: Fahd Alharbi

The IEEE 802.17 Resilient Packet Ring (RPR) technology is a cornerstone for the rapid expansion of metropolitan digital infrastructure. It is recognized for its robustness and spatial reuse capabilities. However, under highly dynamic and asymmetric traffic loads, standard RPR fairness algorithms often suffer from bandwidth oscillations, leading to unpredictable queuing… Read full abstract & cite →

RPR IEEE 802.17 bandwidth latency fairness

Abstract doi.org/10.14569/IJACSA.2026.0170946 PDF

47

Hybrid Resource Allocation Technique Based on Double Q Learning and Metaheuristic Algorithms in Optimizing the Allocation of Tasks in Cloud Computing

Author 1: V. Mahalakshmi Author 2: S. Poornima

Cloud computing (CC) has grown as a core component of modern computing facilities, providing users with adaptable, on-demand services. In dynamic environments, the effective utilization of resources is significant for performance, cost-effectiveness, and user satisfaction. The optimization of resource allocation is important in CC due to the dynamic and complex… Read full abstract & cite →

Cloud computing resource allocation double Q-learning meta-heuristic algorithms Whale Optimization Algorithm (WOA) Particle Swarm Optimization (PSO) Ant Colony Optimization (ACO) Genetic Algorithms (GA)

Abstract doi.org/10.14569/IJACSA.2026.0170947 PDF

48

Generative AI for Data-Informed Educational Decision-Making: A Systematic Review and Framework

Author 1: Husna Hafiza Razami Author 2: Abtar Darshan Singh Author 3: Roslina Ibrahim Author 4: Ali Najeeb Author 5: Helio Augusto da Costa Xavier Mauquei

Generative artificial intelligence (GenAI) is creating new possibilities for data-informed decision-making in higher education, yet how it translates educational data into actionable decisions for different stakeholders remains undertheorised. This study reports a PRISMA-guided systematic review of 38 peer-reviewed empirical studies addressing three questions: what educational decisions GenAI supports in higher… Read full abstract & cite →

Educational decision-making generative artificial intelligence higher education data-informed decisions large language models systematic review

Abstract doi.org/10.14569/IJACSA.2026.0170948 PDF

49

Correlation-Aware Hybrid Feature Selection for Efficient Machine Learning-Based Intrusion Detection in Smart Home IoT Networks

Author 1: Samar M. Alqhtani

With the emergence of smart homes based on the Internet of Things, network-based attacks have become more prevalent and require effective and lightweight intrusion Detection (ID). In this study, a Correlation-Aware Hybrid Feature Selection (CAHFS) framework for smart home IDS is proposed. The proposed approach fuses correlation analysis and Mutual… Read full abstract & cite →

Smart home intrusion Detection machine learning- feature representation correlation-aware hybrid feature selection

Abstract doi.org/10.14569/IJACSA.2026.0170949 PDF

50

AI-Based Preprocessing and R-Squared Weighted Federated Aggregation for Robust Healthcare Signal Imputation

Author 1: Benachir Rigalma Author 2: Hamid Ouhnni Author 3: Meryam Belhiah Author 4: Soumia Ziti

Federated learning is an emerging paradigm for collaborative model training in healthcare applications where sensitive patient data cannot be centralized due to privacy concerns; however, missing values in physiological signals remain a major challenge, often leading to degraded model accuracy and unstable convergence. To address this issue, this work presents… Read full abstract & cite →

Missing data imputation healthcare signals weighted aggregation AI‑based preprocessing federated learning

Abstract doi.org/10.14569/IJACSA.2026.0170950 PDF

51

Development and Simulation of a Hue-Based HMI Application for Vegetable Leaf Greenness Analysis

Author 1: Djohar Syamsi Author 2: Mimin Muhaemin Author 3: Oviyanti Mulyani Author 4: Hanif Fakhrurroja Author 5: Puput Dani Prasetyo Adi

This study aims to design, develop, and evaluate, through simulation, a Visual Basic 6.0-based HMI (Human-Machine Interface) application to support the analysis of leaf greenness using the Hue feature in the HSV (Hue, Saturation, Value) color space. The system consists of a camera, a Wi-Fi modem/router, a PC, and the… Read full abstract & cite →

HMI visual basic 6.0 Hue HSV RGB leaf green simulation black-box testing image processing

Abstract doi.org/10.14569/IJACSA.2026.0170951 PDF

52

An Architecture-Aware Flutter Code Generation Framework for Enforcing Clean Architecture and Behavioral Design Patterns

Author 1: Desy Intan Permatasari Author 2: Adam Shidqul Aziz Author 3: Maulidan Bagus Afridian Rasyid Author 4: Ahmad Jarir At-Thobari Author 5: Umi Sa’adah Author 6: Nailussa’ada Author 7: Muhammad Fajrul Falah Subakti

Large-scale Flutter applications can accumulate direct inter-module dependencies, inconsistent responsibility boundaries, and inflexible data-access implementations as their functionality grows. Conventional scaffolding approaches primarily automate project structures and repetitive code, while architectural relationships commonly remain dependent on developer-level implementation decisions. This study proposes an architecture-aware Flutter code generation framework that transforms… Read full abstract & cite →

Architecture-aware code generation specification-driven development flutter clean architecture mediator pattern strategy pattern software modularity

Abstract doi.org/10.14569/IJACSA.2026.0170952 PDF

53

A Unified Agent-Based Simulation Framework for Epidemic and Wildfire Propagation in Complex Adaptive Systems

Author 1: Natasha Stojkovikj Author 2: Aleksandra Stojanova Ilievska Author 3: Limonka Koceva Lazarova Author 4: Daniel Eftimov Author 5: Irena Eftimova

Spreading phenomena in complex adaptive systems often exhibit similar dynamic characteristics despite originating from different application domains. This study investigates heterogeneous spreading processes using a common agent-based modeling methodology implemented in the AnyLogic simulation platform. Two representative models were developed: influenza transmission within a university population and wildfire propagation in… Read full abstract & cite →

Agent-based modeling AnyLogic complex adaptive systems influenza transmission wildfire propagation

Abstract doi.org/10.14569/IJACSA.2026.0170953 PDF

54

Play-Centric Design and Usability Evaluation of a Block-Based Programming Serious Game for Low Vision Children

Author 1: Huang Sheng Author 2: Chau Kien Tsong Author 3: Wan Ahmad Jaafar Wan Yahaya

Serious games are effective tools for learning beyond entertainment, yet children with low vision face obstacles when playing block-based programming games because these environments rely on small graphical elements, low colour contrast, and drag-and-drop interaction, a barrier specific to block-based programming that does not arise in other game genres. This… Read full abstract & cite →

Serious game block-based programming low vision accessibility usability testing computational thinking assistive technology

Abstract doi.org/10.14569/IJACSA.2026.0170954 PDF

55

Hybridization of FA-YOLOv8 Model with Zebra Optimization Algorithm for Improving Traffic Light Detection and Tracking Under Low-Light Conditions

Author 1: Adhwa Salemi Author 2: Muhammad Arif Mohamad

Traffic-light Detection and tracking under low-light conditions remain difficult because signal heads can be small, dim, blurred, low in contrast, or affected by glare and reflections. This study develops a hybridization of Firefly Algorithm–You Only Look Once version 8 (FA-YOLOv8) with the Zebra Optimization Algorithm (ZOA) for offline analysis of… Read full abstract & cite →

Traffic light Detection traffic light tracking YOLOv8 firefly algorithm zebra optimization algorithm low-light conditions adaptive parameter optimization identity tracking

Abstract doi.org/10.14569/IJACSA.2026.0170955 PDF

56

Enhanced Explainable Multi-Agent Generative Artificial Intelligence Framework for Personalized Education

Author 1: Hesham M. A. Abdullah Author 2: Elham Alzain

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have enabled personalized education via intelligent tutoring, adaptive learning, automated assessments, and personalized feedback. Nevertheless, educational AI systems are still confronted with challenges, including high computational complexity, long inference time, unreliable content generation, limited explainability, and ineffective multiagent orchestration. To this… Read full abstract & cite →

Generative artificial intelligence artificial intelligence in education personalized learning multiagent systems explainable artificial intelligence trustworthy AI educational optimization lightweight artificial intelligence library and readers algorithm learning analytics

Abstract doi.org/10.14569/IJACSA.2026.0170956 PDF

57

Data Analytics Framework for Crop Yield Prediction Based on the Impact of Climate Using a Hybrid Model

Author 1: Narinder Kaur Author 2: Vishal Gupta

In the modern Indian economy, agriculture is the most vital sector. Agriculture is an important source of revenue for the majority of the population of the world. Proper management of crops, crop species, crop field conditions, livestock, etc., can improve agricultural systems. The four primary subcategories of crop management are… Read full abstract & cite →

Crop yield prediction impact of climate change decision tree support vector regression linear regression and random forest particle swarm optimization whale optimization algorithm Harris Hawks optimization grey wolf optimization

Abstract doi.org/10.14569/IJACSA.2026.0170957 PDF

58

Student-Disjoint Cross-Module Benchmarking for Calibrated Early Warning in Online Learning

Author 1: Mika Lim

Early-warning models in online learning are commonly evaluated with random data partitions, even though deployment often requires transfer to a course that supplied no training data. This study establishes a leakage-resistant benchmark for cross-module risk prediction using the Open University Learning Analytics Dataset. The analysis covered 32,593 enrollments from 28,785… Read full abstract & cite →

Learning analytics educational data mining early warning student risk prediction cross-course generalization probability calibration data leakage online learning

Abstract doi.org/10.14569/IJACSA.2026.0170958 PDF

59

Dynamic Ant Colony Optimization–Driven Feature Selection for GRU-Based Network Intrusion Detection

Author 1: Zhaosheng Yang

Modern network environments generate large-scale, high-dimensional traffic data that can be effectively interpreted as complex temporal signals. Efficiently analyzing such data requires robust feature selection and accurate temporal modeling techniques. In this study, we propose a signal-driven intrusion Detection framework that integrates a Dynamic Ant Colony Optimization (DyACO) algorithm with… Read full abstract & cite →

Ant colony optimization feature selection network intrusion Detection deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170959 PDF

60

Data-Driven Modeling and Optimization of Wind Turbine Performance

Author 1: Naim Baftiu Author 2: Ana Atanasova Author 3: Enis Baftiu Author 4: Tatjana A. Pachemska Author 5: Egzon Baftiu Author 6: Vladimir Zdraveski Author 7: Petre Lameski

This study proposes an integrated data-driven framework that couples wind power forecasting with the operational optimization of wind turbines under real meteorological conditions. Unlike conventional approaches that focus primarily on improving the predictive accuracy of energy production models, the proposed framework extends forecasting toward proactive operational decision-making, establishing a direct… Read full abstract & cite →

Wind energy wind power forecasting machine learning operational optimization wind turbine performance random forest long short-term memory

Abstract doi.org/10.14569/IJACSA.2026.0170960 PDF

61

ATLAS-FL: Active Timing-Adversary-Resilient Secure Aggregation for Gateway-Assisted IoT, IIoT, and IoMT Federated Intrusion Detection

Author 1: Mohammad Othman Nassar

Federated learning (FL) enables distributed intrusion Detection without centralizing raw Internet of Things (IoT), Industrial IoT (IIoT), or Internet of Medical Things (IoMT) traffic. Secure aggregation protects update contents, but it does not authenticate the temporal context in which a protected contribution is admitted. An active network adversary may delay… Read full abstract & cite →

Federated learning secure aggregation active timing adversary IoT intrusion Detection IIoT security Internet of medical things buffered asynchronous aggregation differential privacy

Abstract doi.org/10.14569/IJACSA.2026.0170961 PDF

62

Adaptive Virtual Reality Exposure Therapy Using AI-Driven Anxiety Prediction from Physiological and Behavioral Data

Author 1: Fayaz Hussain G Author 2: P. Chenna Reddy

All mental health disorders; anxiety disorders rank as the most common ones. They affect the cognitive functions of an individual as well as their ability to socialize or live in a quality manner. Exposure Therapy happens to be one of the most effective methods aimed at treating fears, phobias, and… Read full abstract & cite →

Virtual reality exposure therapy LSTM anxiety prediction physiological computing closed-loop systems multimodal fusion

Abstract doi.org/10.14569/IJACSA.2026.0170962 PDF

63

Predictive RFID Tour Monitoring Dashboard Integrating Multimodal Machine Learning and Contextual Data

Author 1: Heng Jin Koey Author 2: Siti Zuraidah Ibrahim Author 3: Ismahayati Adam Author 4: Megat Syahirul Amin Megat Ali Author 5: Rashidah Che Yob Author 6: Sugchai Tantiviwat Author 7: Sazwan Syafiq Mazlan

Traditional tourism monitoring systems rely on manual tracking and overlook dynamic factors influencing travel duration. To address this limitation, this research presents an intelligent web dashboard integrating RFID infrastructure and multimodal machine learning to assess the risk of delay in tourist activities. The project encompasses the design and physical prototyping… Read full abstract & cite →

RFID predictive analytics multimodal machine learning contextual data smart tourism

Abstract doi.org/10.14569/IJACSA.2026.0170963 PDF

64

Design and Formalization of a Distributed Intelligent Edge-Fog-Cloud Architecture for Industry 4.0 Systems

Author 1: Hafsa AMALIK Author 2: Hicham MEDROMI

Industry 4.0 systems increasingly require distributed decision-making capabilities to manage heterogeneous resources, large volumes of data, and time-sensitive operational constraints. This study proposes a distributed intelligent Edge-Fog-Cloud architecture combining a structural seven-component intelligent-entity model, a distributed cognition mechanism based on the Perceive, Analyze, Decide, and Act (PADA) cycle, hierarchical coordination… Read full abstract & cite →

Industry 4.0 distributed intelligence edge-fog-cloud architecture cyber-physical systems PADA multiagent systems graceful degradation IIoT

Abstract doi.org/10.14569/IJACSA.2026.0170964 PDF

65

A Vision-Based Error Compensation Method for Accurate Robotic Positioning in Automated Marking

Author 1: Olzhas Olzhayev Author 2: Akhanserі Ikramov Author 3: Sayat Ibrayev

Accurate robotic positioning is essential in automated marking because residual pose error directly shifts the permanent mark. This study presents a one-step vision-based residual error compensation method that measures the displacement between the target and the marking-tool position after the initial robot motion, transforms that displacement into the robot base… Read full abstract & cite →

Vision-based error compensation robotic positioning automated marking RGB-D camera tool center point industrial robotics positioning accuracy

Abstract doi.org/10.14569/IJACSA.2026.0170965 PDF

66

Fair Graduate Employment Prediction for Chinese Universities: DML Assisted Stratified Panel XGBoost with Time Varying Loss

Author 1: Wang Yanlai Author 2: Ting Tin Tin Author 3: Jia Jinlong Author 4: Zhang Jingren Author 5: Xu Lin

The existing employment-prediction models overlook key challenges including panel-data endogeneity, cross-tier prediction unfairness, and year-on-year temporal distribution drift. To address these issues, this study proposes a novel deconfounded and fair employment-prediction framework, built upon panel stratification and a time-varying loss function within a DML-enhanced panel XGBoost architecture. From the Input-Process-Output… Read full abstract & cite →

Employment policy graduate employment prediction enhanced xgboost time-sequence fairness DML

Abstract doi.org/10.14569/IJACSA.2026.0170966 PDF

67

Design and Validation of an Agentless Web Application for IT Infrastructure Operations Management

Author 1: Carlos Otárola-Mermau Author 2: Alex Pacheco-Pumaleque

The manual management of information technology (IT) infrastructure operations at an IT services company in Lima, Peru, was characterized by fragmented procedures, delays, and limited traceability, which hindered service continuity and compromised information security. This study presents the design, development, and validation of an on-premises web application for the centralized… Read full abstract & cite →

Web application infrastructure orchestration agentless automation digital transformation

Abstract doi.org/10.14569/IJACSA.2026.0170967 PDF

68

The Impact of Gamified Escape Room Activities on Student Learning and Engagement in a Computer Database Course

Author 1: Po Chan Chiu Author 2: King Kuok Kuok Author 3: Hamizan Sharbini Author 4: Mohammad bin Hossin Author 5: Chih How Bong Author 6: Noor Hazlini Borhan

This study examines the impact of gamified escape room activities on student learning performance and engagement in a university-level database design course. A quasi-experimental repeated-measures design was used to compare the traditional learning approach with a gamified escape room incorporating quizzes, points, leaderboards, and nicknames. The gamified escape room featured… Read full abstract & cite →

Gamified learning escape room quiz database course engagement motivation

Abstract doi.org/10.14569/IJACSA.2026.0170968 PDF

69

Fall Detection System Using Accelerometer and Gyroscope Sensors with Backpropagation Neural Networks

Author 1: Helmy Fitriawan Author 2: Alby Dzaky Hadi Pamungkas Author 3: Ezza Ahmad Faturohman Author 4: Sri Purwiyanti Author 5: Anisa Ulya Darajat Author 6: Teddy Surya Gunawan

A fall is an incident in which a person suddenly loses their balance and falls uncontrollably to the ground or another surface. This is particularly dangerous for the elderly, as it can lead to serious injury or even death if not treated immediately. This study suggests utilizing a Backpropagation Neural… Read full abstract & cite →

Backpropagation neural network fall Detection human activity recognition accelerometer gyroscope

Abstract doi.org/10.14569/IJACSA.2026.0170969 PDF

70

Double Deep Q-Network-Based Smart Channel Hopping for Reactive Jamming Mitigation in IoT Networks: A Reliability and Energy-Aware Approach

Author 1: Enos Letsoalo Author 2: Topside Mathonsi Author 3: Tshimangadzo Tshilongamulenzhe Author 4: Daniel du Duplesis

Reactive jamming attacks are prevalent in low power Internet of Things networks and are severely affecting the performance of IoT nodes. Existing anti-jamming studies focus primarily on communication reliability. Limited attention has been given to the joint optimization of packet delivery ratio (PDR), energy consumption, and network lifetime in mobile… Read full abstract & cite →

DDQN SCHM MDP reactive jamming attacks PDR energy consumption network lifetime

Abstract doi.org/10.14569/IJACSA.2026.0170970 PDF

71

Comparative Evaluation of Synthetic Data Generation Methods for Binary Classification Problems with Class Imbalance

Author 1: Jhonatan Esquivel Author 2: Christian Humpiri Author 3: Jose Vega Author 4: Nemias Saboya

Class imbalance is a critical challenge in the classification of tabular data, since it affects the diagnostic capacity of models in domains such as health and finance. This research compares four synthetic data generation paradigms: traditional interpolation (SMOTE-NC), deep generative models (CTGAN and TVAE), and a hybrid scheme (SMOTE-NC-CTGAN) in… Read full abstract & cite →

Class imbalance synthetic data SMOTE-NC CTGAN hybrid models machine learning tabular classification

Abstract doi.org/10.14569/IJACSA.2026.0170971 PDF

72

A Deep Learning-Based Real-Time Surveillance Framework for Mitigating Herbivorous Animal Intrusions in Rice Nursery Fields

Author 1: Muhammad Juman Jhatial Author 2: Riaz Ahmed Shaikh Author 3: Rafaqat Hussain Arain

The rice nursery stage is a very critical stage of the rice growing cycle, as intrusion of herbivorous animals can lead to substantial economic losses and yield losses. Manual guarding and physical fencing are inefficient and ineffective. Real-Time Animal Intrusion Detection Network (RAID-Net) is an end-to-end intelligent agricultural surveillance system… Read full abstract & cite →

Precision agriculture deep learning yolo model convolutional neural networks

Abstract doi.org/10.14569/IJACSA.2026.0170972 PDF

73

Interpretable Deep Convolutional Neural Network Framework for Online Reputation Management Using Customer Reviews and Social Media Feedback

Author 1: Abdullah Saad AL-Malaise Author 2: Bandar A. Alrami Alghamdi Author 3: Mahmoud Ragab

Social media has become a significant channel for communication and interaction, significantly influencing how organizations are perceived in the digital era. Online reputation management (ORM), as an addition to public relations, concentrates on shaping and maintaining an organization’s image through continuous surveillance of customer reviews and online feedback. Traditional methodologies… Read full abstract & cite →

Online reputation management social media feedback adabelief optimizer explainable artificial intelligence deep convolutional neural network

Abstract doi.org/10.14569/IJACSA.2026.0170973 PDF

74

Design of a Mobile Application with a Hybrid Recommendation System for Connecting Andean Farmers and Urban Consumers in Peru

Author 1: Jorge Mayta Author 2: Astrid Boronda Author 3: Juan Montes

The dependence on intermediaries within agricultural marketing chains not only reduces producers' profits but also generates consumer distrust regarding the quality and traceability of the products they obtain. This work proposes a mobile application with a hybrid recommendation system aimed at connecting farmers and consumers in Peru. The combination of… Read full abstract & cite →

Andean agriculture direct trade hybrid recommendation system order tracking mobile application offline-first

Abstract doi.org/10.14569/IJACSA.2026.0170974 PDF

75

AI-Driven Personalized Learning Framework for Computing Fields Recommendations in Secondary and Pre-University Contexts

Author 1: Anas Inuwa Usman Author 2: Shahida Sulaiman Author 3: Ruhaidah Samsudin

Students or learners, especially those at the secondary and pre-university levels, face challenges in identifying suitable computing fields for personalized educational resources towards their tertiary education specialization pathways. Traditional advising methods/approaches such as guidance from school counselors and teachers often lack personalization and practical relevance because their knowledge of the… Read full abstract & cite →

Artificial intelligence machine learning models pre-university level personalized learning computing fields recommender systems

Abstract doi.org/10.14569/IJACSA.2026.0170975 PDF

76

An Enhanced Deep Learning Framework Integrating Adaptive Whale Optimization and Natural Language Processing for Mental Health Disorder Diagnosis

Author 1: Nameer El.Emam Author 2: Issa Qabajeh Author 3: Mhammed Almakadmeh

Mental health disorders are among the biggest global public health challenges, affecting over 1 billion people worldwide and placing a burden on healthcare systems, economies, and societies. Despite progress in clinical psychiatry, mental health professionals still rely heavily on subjective judgments to make diagnoses, leading to delays and inconsistency in… Read full abstract & cite →

Psychiatric disorder classification Natural Language Processing (NLP) Enhanced Deep Learning (E-DL) Adaptive Whale Optimization Algorithm (AWOA) reddit mental health text depression anxiety

Abstract doi.org/10.14569/IJACSA.2026.0170976 PDF

77

MedViT-MT: A Multi-Task Medical Vision Transformer for Glioblastoma Brain Analysis with Temporal Encoding and Clinical Data Fusion

Author 1: Sana Cheema Author 2: Ghulam Gilanie Author 3: Muhammad Faheem Mushtaq Author 4: Rania Aboalela Author 5: Ali Daud

Glioblastoma Multiforme (GBM) is the most invasive form of primary brain tumor. The average survival time after diagnosis is only 15 months. Accurately and automatically analyzing GBM MRI scans remains a major clinical challenge. It requires the tumor to be localized, a report to be generated in a structured way… Read full abstract & cite →

Glioblastoma multi-task learning deep learning transformer clinical data fusion temporal encoding

Abstract doi.org/10.14569/IJACSA.2026.0170977 PDF

78

Predicting Next-Trading-Session Stock Movements from Arabic Corporate Announcements: A Hybrid Transformer-Boosted Ensemble

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

Predicting next-day stock movements from corporate announcements is a complex task, especially in markets where financial text is published in low-resource languages such as Arabic. In this study, we develop a hybrid, multimodal forecasting framework designed to capture both the semantic depth of Arabic announcements and the quantitative structure of… Read full abstract & cite →

Arabic corporate announcements ensemble learning financial forecasting stock movement prediction temporal transformer text embeddings

Abstract doi.org/10.14569/IJACSA.2026.0170978 PDF

79

Meta-Probabilistic Stacking for Eco-Driving Behavior Profiling

Author 1: Md Mohibul Alam Author 2: Abdullateef Oluwagbemiga Balogun Author 3: Noreen Izza Arshad Author 4: Hussaini Mamman Author 5: Duc Minh Le Author 6: Van Dai Pham Author 7: Manh Toan Nguyen

Automated driver behavior profiling is a strategically important problem within intelligent transportation systems, road safety engineering, and fleet-based emissions governance. This study presents a Heterogeneous Out-of-Fold (OOF) Stacking Ensemble augmented with inter-model probabilistic disagreement as a first-class meta-feature. Five architecturally diverse base learners — Gradient Boosting, LightGBM, Random Forest, AdaBoost… Read full abstract & cite →

Driver behavior profiling meta-probabilistic stacking ensemble second-order meta-entropy telematics-based eco-driving out-of-fold stacking intelligent transportation systems eco-driving classification XGBoost meta-learner uncertainty quantification

Abstract doi.org/10.14569/IJACSA.2026.0170979 PDF

80

How Emergent Architectures Solve the Bird’s Eyes View Shortcomings: A Systematic Review

Author 1: Abdelaziz Sahbani Author 2: Rihem Sebai Author 3: Tarek Bejaoui Author 4: Hela Mahersia

Autonomous driving requires robust, accurate and real-time environmental perception. Although cameras, LiDAR, and radar provide complementary sensing capabilities, individual modalities remain vulnerable to limitations such as depth ambiguity, point-cloud sparsity, adverse weather, and restricted angular resolution. Multi-sensor fusion therefore represents a key approach for improving 3D object Detection and scene… Read full abstract & cite →

Autonomous driving multi-sensor fusion Bird’s-Eye View (BEV) multimodal perception deep learning transformers 3D object Detection adverse weather conditions

Abstract doi.org/10.14569/IJACSA.2026.0170980 PDF

81

Enhancing Trustworthy Mining Trend Analytics Through Hybrid Semantic and Advertisement Detection Models

Author 1: Mohamed Ayari Author 2: Khulud Alashjaee Author 3: Balsam Alzamam Author 4: Aryam Alshammari Author 5: Milaf Alshammari Author 6: Aadil Alshammari

Mining-related social media discussions provide valuable information regarding operational risks, environmental concerns, equipment failures, workforce safety, and industrial market trends. However, large-scale mining discussion streams are increasingly affected by irrelevant promotional content, spam campaigns, and semantically unrelated posts that reduce analytical reliability. This study proposes a hybrid natural language processing… Read full abstract & cite →

Mining analytics semantic relevance Detection advertisement Detection industrial social media analysis NLP transformer embeddings trustworthy analytics

Abstract doi.org/10.14569/IJACSA.2026.0170981 PDF

82

MedEdge-Conf: A Lightweight Confidence-Scoring Framework for Edge-Deployed LLMs in IoMT Health Monitoring

Author 1: Fahd Koraiche Author 2: Rachid Dehbi Author 3: Amine Dehbi

Remote patient monitoring systems based on the Internet of Medical Things (IoMT) increasingly use large language models (LLMs) to convert vital signs into readable alerts, but lightweight edge-deployed LLMs may hallucinate, contradict abnormal vitals, or overstate confidence. This study proposes MedEdge-Conf, a lightweight confidence-scoring frame-work combining physiological coherence aligned with… Read full abstract & cite →

Edge AI internet of medical things medical LLM hallucination Detection confidence scoring

Abstract doi.org/10.14569/IJACSA.2026.0170982 PDF

83

HTBM: A Hybrid-Transformer-Based Method with Double Deep Q-Learning and Reptile Meta-Learning for Dynamic MEC Task Offloading

Author 1: Nouhaila Moussammi Author 2: Mohamed El Ghmary

Mobile edge computing (MEC) enables computation-intensive applications to be processed close to end users. In dynamic MEC environments, task-offloading decisions must account for changing communication conditions, heterogeneous computing resources, and dependencies among fine-grained subtasks. Conventional reinforcement-learning approaches may require substantial interaction data and may suffer from unstable value estimation. This… Read full abstract & cite →

Mobile edge computing task offloading Double Deep Q-Network (DDQN) hybrid transformer deep reinforcement learning meta-reinforcement learning reptile Directed Acyclic Graph (DAG) latency minimization

Abstract doi.org/10.14569/IJACSA.2026.0170983 PDF

84

XraySafe-YOLO: Compact Scale-Aware Fusion and Contour-Motivated Attention for Recall-Oriented Detection of Occluded Prohibited Items in X-ray Images

Author 1: Liu Liu Author 2: Wei Huang

Occlusion, weak texture, and scale variation make prohibited-item Detection difficult in X-ray security images. This study presents XraySafe-YOLO, a compact detector built on YOLO11n that combines Contour-Guided Occlusion Attention with Lightweight Scale-Aware Feature Fusion. On OPIXray, at a fixed confidence threshold of 0.25 and intersection-over-union threshold of 0.50, the full… Read full abstract & cite →

X-ray imaging prohibited item Detection object Detection YOLO occlusion-aware Detection multi-scale feature fusion recall-oriented evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170984 PDF

85

Individualizing an Agent-Based Flood Evacuation Model from Survey Data

Author 1: Tatsuki Fukuda

Earlier agent-based models (ABMs) of flood evacuation by the author, first on an abstract grid and later extended to real 3D city-model geography, use a survey of Japanese residents to assign each simulated agent to one of a few discrete behavioral groups; within a group, the evacuation probability P and… Read full abstract & cite →

Agent-based model flood evacuation evacuation rate survey-based simulation evacuate now button

Abstract doi.org/10.14569/IJACSA.2026.0170985 PDF

86

Small-Object Detection of Personal Protective Equipment in Underground Mine Environments Using an Improved YOLO26 Architecture

Author 1: Taoufik Saidani Author 2: Refka Ghodhbani Author 3: Yahia Said

Reliable Detection of Personal Protective Equipment (PPE) in underground mines is a key enabler of automated safety monitoring, and it is also one of the hardest small-object problems in industrial computer vision: helmets, ear-defenders, dust masks, eye-shields and reflective vests occupy only a tiny fraction of the image and are… Read full abstract & cite →

Personal protective equipment small-object Detection YOLO26 underground coal mine attention mechanism edge deployment worker safety

Abstract doi.org/10.14569/IJACSA.2026.0170986 PDF

87

A Hybrid NLP Framework for Topic Relevance and Advertisement Detection in Trending Hashtags on X (Twitter)

Author 1: Aadil Alshammari Author 2: Khulud Alashjaee Author 3: Balsam Alzamam Author 4: Aryam Alshammari Author 5: Milaf Alshammari Author 6: Mohamed Ayari

Trending hashtags on X (formerly Twitter) are widely used for real-time news tracking and public discourse, yet they are increasingly polluted by advertisements and off-topic posts that exploit hashtag visibility. This study proposes a hybrid natural language processing (NLP) framework for improving the reliability of hashtag streams through two complementary… Read full abstract & cite →

Twitter hashtags advertisement Detection topic relevance Detection NLP machine learning Arabic text processing social media analytics

Abstract doi.org/10.14569/IJACSA.2026.0170987 PDF

88

Enhanced Harris Hawks Optimization Algorithm with Integrated Tabu Search for 3D Bin Packing Problems: Application in Logistics Industries

Author 1: Yasser M. Ayid Author 2: Mohamed Meselhy Eltoukhy Author 3: Alaa Mokhtar Author 4: Rabie Mosaad

The Harris Hawks Optimization (HHO) algorithm has proven to be an effective metaheuristic tool for addressing complex optimization challenges. This study outlines significant modifications and enhancements proposed for the standard HHO algorithm to improve its performance on complex three-dimensional bin packing problems (3D-BPP). The proposed modifications introduce a hybrid metaheuristic… Read full abstract & cite →

Logistics container loading problems harris hawks optimization tabu search wall-building

Abstract doi.org/10.14569/IJACSA.2026.0170988 PDF

89

Energy Personalized Recommender for the PowerEye Home Energy Management System

Author 1: Reem Hejazi Author 2: Sofianiza Abd Malik Author 3: Anees Ara Author 4: Layla Alfawzan

The Energy Personalized Recommender (EPR) is a novel backend module designed to optimize energy management within home energy management systems (HEMSs). Existing research explores various energy optimization strategies but is often constrained by synthetic datasets, simulation-based validation, or limited personalization. To address these limitations, EPR is designed for real-world deployment… Read full abstract & cite →

Home energy management systems machine learning energy goal peak time phantom mode energy baseline k-means clustering XGBoost forecasting energy sustainability

Abstract doi.org/10.14569/IJACSA.2026.0170989 PDF

90

Anatomia: An Interactive 3D Visualization Tool for Enhancing Medical Anatomy Education

Author 1: Iranga Mudalige Author 2: Hiruna Jayasuriya Author 3: Yeshan Gunawardane Author 4: Hashen Cooray Author 5: Imashi Dissanayake Author 6: Damitha Sandaruwan Author 7: Chamath Keppitiyagama Author 8: Mahappuge Shasika Eranda Karunadasa Author 9: Tony Mahadevan

Anatomy education continues to rely on cadaver dissection, which is constrained by donor shortages, costs, and limited hands-on access. Digital 3D anatomy tools offer alternatives, but most do not collaborate well across multiple users, keep upright content legible as viewers move around the table, or support hand gestures specific to… Read full abstract & cite →

3D anatomy visualization anatomy education gesture interaction multi-touch interaction content alignment table-top display collaborative learning

Abstract doi.org/10.14569/IJACSA.2026.0170990 PDF

91

Does It Generalize? A Cross-Dataset Study of Graph-Based Insider Threat Detection Beyond CERT

Author 1: QASIM Mohamed Muhanna Alriyami Author 2: Mohd Murtadha Bin Mohamad

Graph-based insider threat Detection methods increasingly report strong performance by modelling relationships among users, hosts, resources and security events. However, many studies still evaluate within a single benchmark family—usually CERT—or report separate within-dataset results, so it is unclear whether the learned representations capture transferable insider behaviour or dataset-specific artefacts. This… Read full abstract & cite →

Insider threat Detection cross-dataset generalization zero-shot evaluation graph neural networks temporal graphs self-supervised learning CERT SPEDIA LANL

Abstract doi.org/10.14569/IJACSA.2026.0170991 PDF

92

Empirical Evaluation of Language Models for Arabic Financial Sentiment Analysis: A Comparative Study of Pretrained and General Large Language Models

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

This study presents a comparative study on the effectiveness of large and pre-trained language models in analyzing sentiment in Arabic financial news. While sentiment analysis using AI has advanced significantly in English-language finance, similar progress in Arabic remains limited. Most existing Arabic models, such as AraBERT and CAMeLBERT, are trained… Read full abstract & cite →

Arabic financial news stock news financial news domain specific ChatGPT gemini large language model prompt strategies saudi stocks sentiment analysis

Abstract doi.org/10.14569/IJACSA.2026.0170992 PDF

93

Adaptive Differential Privacy in Federated Learning for Privacy-Preserving ICU Clinical Decision Support at the Edge

Author 1: Moaad Almania Author 2: Anazida Zainal Author 3: Nurfazrina Binti Mohd Zamry Author 4: Muhammad Haris Author 5: Ahmad Alnawasrah Author 6: Fuad A. Ghaleb

Clinical decision assistance that protects patient privacy is a prominent research issue in federated learning, edge computing, and intensive care analytics. However, typical differential privacy algorithms allocate equal privacy budgets to all clinical parameters regardless of sensitivity and predictive power. This may lower privacy-utility trade-offs. This study develops an adaptive… Read full abstract & cite →

Federated learning differential privacy clinical decision support edge computing ICU data

Abstract doi.org/10.14569/IJACSA.2026.0170993 PDF

94

An Efficient and Reliability-Oriented Deep Learning Framework for Alzheimer’s MRI Classification

Author 1: Yashwant A Author 2: Rajaprakash S

Reliable Alzheimer’s disease (AD) magnetic resonance imaging (MRI) classification requires evaluation beyond accuracy alone. This study investigates a four-class image-level framework based on ImageNet-pretrained ResNet-18 using 6,400 public MRI images labelled Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Original images were assigned to seventy percent training, fifteen percent… Read full abstract & cite →

Alzheimer’s disease magnetic resonance imaging ResNet-18 confidence calibration robustness Grad-CAM computational efficiency

Abstract doi.org/10.14569/IJACSA.2026.0170994 PDF

95

Multi-Level Contextual Semantic Modeling for Implicit Hate Speech Recognition

Author 1: Rajkumar Panchal Author 2: Megha Jonnalagedda

Social media platforms are key spaces for sharing opinions, experiences, and daily events. However, the rapid rise in hate speech, especially implicit hate, has become a significant concern. Implicit hate is subtle, context-dependent, and more difficult to detect than explicit hate. Despite the growing body of work on Hate Speech… Read full abstract & cite →

Attention network BiLSTM deep learning explicit hate speech Detection hate speech Detection implicit hate speech Detection natural language processing

Abstract doi.org/10.14569/IJACSA.2026.0170995 PDF

96

TrustEdge-AI: Uncertainty-Aware Dynamic Trust Intelligence for Adaptive Cyber Defense in IoT Edge Networks

Author 1: Mohamed Ayari Author 2: Ahmed Alhomoud Author 3: Atef Gharbi Author 4: Yamen El Touati Author 5: Zeineb Klai

Conventional Internet-of-Things (IoT) intrusion Detection typically maps a model score directly to an alert, although autonomous defense must also determine whether the evidence is reliable, whether suspicious behavior persists over time, and whether a disruptive response is justified. This study proposes TrustEdge-AI, an uncertainty-aware dynamic-trust framework for adaptive cyber defense… Read full abstract & cite →

Internet of things edge intelligence intrusion Detection dynamic trust uncertainty estimation adaptive cybersecurity anomaly Detection calibration

Abstract doi.org/10.14569/IJACSA.2026.0170996 PDF

97

FPGA-SoC Implementation of 3D-HEVC Encoder Based on MD-CNN

Author 1: Nacir Omran Author 2: Amna Maraoui Author 3: Imen Werda Author 4: Hamdi Belgacem

Convolutional Neural Networks (CNNs) are the most common deep learning architecture used for video pro-cessing enhancement. Particularly, the Multi-Deep Convolutional Neural Network (MD-CNN) model, embedded into the 3D-HEVC encoder, was able to extract the optimal CTU partition structure efficiently in the depth map and successfully substitute the time-consuming rate-distortion optimisation… Read full abstract & cite →

Three-dimensional high efficiency video coding field-programmable gate array system-on-chip multi-deep convolutional neural network hardware-software co-design

Abstract doi.org/10.14569/IJACSA.2026.0170997 PDF

98

The Coordination Complexity Index: A Parameter-Free Checkpoint Diagnostic for Silent Coordination Failure in Cooperative Multi-Agent Reinforcement Learning

Author 1: Chatchitsanu Pothisakha Author 2: Pudsadee Boonrawd Author 3: Siranee Nuchitprasitchai

Cooperative multiagent reinforcement learning (MARL) can fail silently while standard reward and loss curves remain inconclusive. This study introduces the Coordination Complexity Index (CCI), a checkpoint statistic with no learned parameters that summarizes dispersion among per-agent greedy Q-values. A frozen checkpoint cannot by itself establish a temporal failure; it can… Read full abstract & cite →

Cooperative multiagent Reinforcement Learning (MARL) value decomposition coordination signatures coordination collapse Coordination Complexity Index (CCI) check-point screening Centralized Training with Decentralized Execution (CTDE)

Abstract doi.org/10.14569/IJACSA.2026.0170998 PDF

99

Predicting Crowd Agitation from Raw Video: A Lightweight Multimodal Regression Framework for Social Tension Estimation

Author 1: Abderrahim Ouza Author 2: Khalid Ounachad Author 3: Mohamed El Ghmary Author 4: Ali Choukri

Continuous, quantitative awareness of crowd tension – a perceptually grounded, continuous measure of crowd agitation, distinct from a binary violence label, an unnormalized abnormal-event score, or raw motion magnitude – is required for proportionate public-safety response; however, the dominant paradigm in computer vision approaches remains restricted to post-hoc binary violence… Read full abstract & cite →

Social tension index multimodal video analysis deep visual embeddings optical-flow motion dynamics violence Detection real-time surveillance LightGBM CLIP crowd behavior territorial risk monitoring

Abstract doi.org/10.14569/IJACSA.2026.0170999 PDF

100

Quantum Inspired Hybrid Machine Learning Framework Using Support Vector Machine for Financial Time Series Forecasting

Author 1: Visalakshi Palaniappan Author 2: Iskandar Ishak Author 3: Hamidah Ibrahim Author 4: Fatimah Sidi Author 5: Mohamad Yusnisyahmi Yusof

This article proposes a quantum inspired Support Vector Machine (QISVM) framework that employs simulated quantum circuit features to address these challenges. To encapsulate such features, we use a four-qubit encoding protocol using X-axis rotation and entanglement created from Controlled-NOT (CNOT). Quantum inspired elements from simulated circuit output include entropy and… Read full abstract & cite →

Quantum inspired machine learning support vector machines financial time series forecasting hybrid learning frame-work quantum feature extraction

Abstract doi.org/10.14569/IJACSA.2026.01709100 PDF

101

Eliminating Reference Mixing in Automotive Manufacturing: A DMAIC and Machine Vision Integration Approach

Author 1: Morad Wafi Author 2: Mouad Danane Author 3: Abderrahim Bouzid Author 4: Abdelmajid El Ouadi

Purpose: Reference mixing in series windshield manufacturing creates severe downstream risks. This study investigates the operational causes of mixing in an automotive glass plant and implements an engineering solution to eliminate customer escapes. Design/methodology/approach: A DMAIC framework is utilized. The Define and Measure phases quantified baseline escape rates and attribute… Read full abstract & cite →

Quality management DMAIC PFMEA lean six sigma machine vision automotive manufacturing Poka-Yoke

Abstract doi.org/10.14569/IJACSA.2026.01709101 PDF

102

Hodge-Guided Active Preference Elicitation for Efficient Pairwise Ranking

Author 1: Eskander Bejaoui Author 2: Mohamed Ould-Elhassen Aoueileyine Author 3: Ridha Bouallegue

Pairwise comparison is a standard method for eliciting user preferences in recommender systems and group decision-making. The number of required comparisons grows quadratically with the number of alternatives, creating a substantial burden for users and platforms. This paper asks whether Hodge decomposition, which splits a preference flow into gradient (consensus)… Read full abstract & cite →

HodgeRank hodge decomposition active learning preference elicitation pairwise comparisons uncertainty sampling behavioural cloning recommender systems

Abstract doi.org/10.14569/IJACSA.2026.01709102 PDF

103

EfficientNetB0-Based DeepFake Image Detection: A Transfer-Learning Framework for Robust and Deployable Media Verification

Author 1: Marwa Hamza Author 2: Mohammad Abdallah

As the realism of DeepFakes has increased significantly, there is an emerging necessity to develop efficient, reliable, and robust automatic Detection frameworks that are feasible to apply in the wild. This study proposes an image-based DeepFake Detection system using EfficientNetB0 and transfer learning. Facial images were acquired from the FaceForensics++… Read full abstract & cite →

DeepFake Detection EfficientNetB0 transfer learning convolutional neural networks FaceForensics++ Celeb-DF digital forensics

Abstract doi.org/10.14569/IJACSA.2026.01709103 PDF

104

N-DQN-RRT: A Hybrid Deep Q-Network Guided RRT Algorithm for an Efficient Path Planning in Two-Dimensional Environments

Author 1: Wai Yan Naing Win Author 2: K. kanagalakshmi Author 3: K. Priyadharshini Author 4: Zeyad A. T Ahmed Author 5: R. RajiniGanth Author 6: P. Ganesh Kumar Author 7: Atif Mahmood

Autonomous navigation requires path-planning algorithms that can find safe and efficient routes while avoiding obstacles. Classical sampling-based planners such as Rapidly-exploring Random Tree (RRT) and RRT* are widely used because they are flexible and easy to apply in different environments. However, these methods often produce longer paths and require many… Read full abstract & cite →

Path planning Rapidly-Exploring Random Trees (RRT) RRT* Deep Q-Network (DQN) deep reinforcement learning resilient infrastructure

Abstract doi.org/10.14569/IJACSA.2026.01709104 PDF

105

Deep Learning and Computer Vision-Based Modeling for a Real-Time Traffic Congestion Monitoring Framework

Author 1: Neil Dustin Benedict A. Agner Author 2: Angelo B. Dela Cruz Author 3: Aster Benedict A. Mangabat Author 4: Raphael B. Alampay Author 5: Patricia R. Angela Abu

As intelligent traffic systems evolve to manage complex urban mobility, conventional congestion estimation techniques, such as the time-windowed Volume-to-Capacity (V/C) ratio, fail to capture capture the real-time traffic situation. Because these methods rely on vehicles crossing a specific point, they often fail to register stopped cars, creating a ‘zero flow’… Read full abstract & cite →

Traffic congestion estimation level of service volume-to-capacity ratio RT-DETR real-time object Detection

Abstract doi.org/10.14569/IJACSA.2026.01709105 PDF