IJACSA Vol. 17 Issue 4 (2026)

1

EU Public Procurement Contract Value Prediction: A Machine Learning Approach

Author 1: Muhammad Azizur Rahman Author 2: Anna Hlinska

Using modern analytical tools, such as machine learning, organisations can collect and analyse large amounts of data related to suppliers, pricing, demand structure, and market trends. This study uses machine learning models to predict the value of EU public procurement contracts based on the Tenders Electronic Daily (TED) database. The… Read full abstract & cite →

Public procurement EU procurement machine learning Random Forest Contract Value Prediction TED database institutional economics OLS regression K-means clustering predictive modelling data quality EU AI Act

Abstract doi.org/10.14569/IJACSA.2026.0170401 PDF

2

Unified Modular Architectural and Software Model for Educational XR Systems

Author 1: Miroslav GALABOV

This article presents the development and validation of a unified architectural and software model for educational Extended Reality (XR) systems, which integrates the technological, pedagogical, and interface-related aspects of virtual and augmented reality into a coherent, modular, and interoperable framework. The proposed model addresses key limitations of existing XR solutions… Read full abstract & cite →

Extended reality virtual reality augmented reality educational XR systems software architecture WebXR immersive learning modular architecture interactive simulations

Abstract doi.org/10.14569/IJACSA.2026.0170402 PDF

3

Interruptible Multi-Agent Debate: Sentence-Level Disclosure and Urgency-Based Turn-Taking for Early Error Correction

Author 1: Akikazu Kimura Author 2: Ken Fukuda Author 3: Yasuyuki Tahara Author 4: Yuichi Sei

Multi-agent debate (MAD) has emerged as a promising approach for improving the reasoning ability of large language models (LLMs). However, existing turn-taking schemes typically disclose a speaker’s entire utterance before other agents can respond, allowing erroneous premises to spread through the shared context and making early correction difficult. This study… Read full abstract & cite →

Large Language Models (LLMs) multi-agent debate turn-taking early error correction misinformation propagation

Abstract doi.org/10.14569/IJACSA.2026.0170403 PDF

4

Secure User Authentication Model Using Identity-Based Encryption (IBE) Scheme: Challenges, Techniques, and Trends

Author 1: Raja Farah Sharima Raja Muhamad Danial Author 2: Nazhatul Hafizah Kamarudin Author 3: Abdul Ghafar Jaafar

User authentication is the crucial component in ensuring that digital identities are securely authenticated when performing sensitive digital transactions. Building resilience against emerging cyber threats like identity theft and Man-in-the-Middle (MITM) attacks remains an ongoing challenge. Identity-Based Encryption (IBE) is a cryptographic method in the category of public key cryptography… Read full abstract & cite →

User authentication Identity-Based Encryption (IBE) digital identity network security

Abstract doi.org/10.14569/IJACSA.2026.0170404 PDF

5

Improving Heart Sound Diagnosis with a Combined CNN-LSTM and Dual-Attention Deep Learning Model

Author 1: Arshad Jamal Author 2: R. Kanesaraj Ramasamy Author 3: Junaidi Abdullah

Accurate classification of heart sounds is critical for the early detection and diagnosis of cardiovascular diseases. This research presents an automated technique for classifying heart sounds into normal, murmur, and extrasystolic categories. The approach initiates with a bandpass filtering preprocessing phase, aimed at improving the quality of heart sound recordings… Read full abstract & cite →

Heart sound classification cardiovascular disease diagnosis CNN-LSTM Dual-Attention deep learning signal preprocessing

Abstract doi.org/10.14569/IJACSA.2026.0170405 PDF

6

Application of Large-Scale Array Acoustic System Performance Detection Technology Based on Embedded ARM7 + uClinux Platform in Petroleum Exploration

Author 1: Zhiyuan Sun Author 2: Cheng Yang Author 3: Guixu Xu

With the continuous development of petroleum exploration, the requirements for acoustic wave detection technology are getting higher and higher. Aiming at the limitations of traditional acoustic wave detection technology in performance detection, this study proposes a large-scale array acoustic system performance detection method based on an embedded ARM7 + Linux… Read full abstract & cite →

Embedded ARM7 uClinux platform array acoustic system detection technology petroleum exploration

Abstract doi.org/10.14569/IJACSA.2026.0170406 PDF

7

Factors Influencing Generative AI-Enabled e-Government Services (GAIGS) Information Quality: A Systematic Literature Review

Author 1: Azwan Abd Aziz Author 2: Rozi Nor Haizan Nor Author 3: Yusmadi Yah Jusoh Author 4: Wan Nurhayati Wan Ab. Rahman Author 5: Khairi Azhar Aziz Author 6: Nur Ilyana Ismarau Tajuddin Author 7: Raditya Muhammad

The integration of Generative Artificial Intelligence (GAI) into electronic government (e-Government) services has transformed the delivery of public information, raising critical questions about the quality of AI-generated content. This study presents a systematic literature review (SLR) to identify and categorise the key factors influencing information quality in GAI-enabled e-Government Services… Read full abstract & cite →

Generative AI e-government services information quality PRISMA PICo

Abstract doi.org/10.14569/IJACSA.2026.0170407 PDF

8

Balancing Accuracy Robustness and Explainability in E-Commerce Recommender Systems

Author 1: Mansor Alohali

Recommender systems are essential to digital marketplaces, shaping how users discover products and engage with platforms. While AI has significantly improved accuracy, critical concerns about robustness and explainability remain. This study introduces and empirically validates the “Recommender’s Trilemma”—an inherent trade-off between accuracy, robustness, and explainability. Through comparative analysis of NeuMF… Read full abstract & cite →

Recommender systems E-commerce explainable AI adversarial robustness personalization digital platforms

Abstract doi.org/10.14569/IJACSA.2026.0170408 PDF

9

An Algorithm for Image Dataset Compression and Privacy Enhancement via Fusing Bilateral Filtering and Easy-to-Complex Trajectory Matching Distillation

Author 1: Qiao Zhou Author 2: Lei Zhang Author 3: Longjie Li Author 4: Tong Wang Author 5: Jun Cheng

Accurate engineering vehicle detection is the core part of intelligent construction. Aiming at the problems of high training resource consumption and prominent privacy leakage risk of engineering vehicle image data, this paper proposes an image dataset compression and privacy enhancement algorithm for construction site engineering vehicles, which fuses bilateral filtering… Read full abstract & cite →

Data distillation trajectory matching distillation dataset compression privacy enhancement engineering vehicle dataset

Abstract doi.org/10.14569/IJACSA.2026.0170409 PDF

10

Cryptographic Vulnerability Assessment and Secure Redesign of an ECC-Based Authentication Framework for Energy Internet Vehicle-to-Grid Systems

Author 1: Haewon Byeon

This study re-examines the protocol introduced in A robust ECC-based authentication framework for energy internet (EI)-based vehicle to grid communication system by Itoo et al. The target paper claims low-cost mutual authentication for electric vehicles, charging stations, and a service provider by combining ECC registration with hash- and XOR-based online… Read full abstract & cite →

Energy internet vehicle-to-grid security ECC authentication protocol analysis compromise containment

Abstract doi.org/10.14569/IJACSA.2026.0170410 PDF

11

An AI-Powered Approach for Medical Specialty Triage Using Natural Language Processing and Transformer Models

Author 1: Anas Chahid Author 2: Ismail Chahid Author 3: Wafae Mrabti Author 4: Mohamed Emharraf Author 5: Mohammed Ghaouth Belkasmi

Upon arrival at a hospital, patients require an initial assessment to determine the urgency of their condition and the appropriate medical specialty for their needs. This manual triage process, however, is often time-consuming and resource-intensive, leading to potential delays in care, patient dissatisfaction, and inefficient allocation of specialized medical staff… Read full abstract & cite →

Medical triage natural language processing BERT deep learning healthcare AI text classification

Abstract doi.org/10.14569/IJACSA.2026.0170411 PDF

12

Multi-View Behavioral Probing for Political Bias in Arabic and Multilingual Transformers Before and After Domain Adaptation

Author 1: Ahmad Abdelhameed Author 2: Ensaf Hussein Mohamed Author 3: Walaa Medhat

Political bias in transformer-based language models poses a critical challenge for applications involving politically sensitive Arabic news, yet systematic evaluation remains limited. This paper presents a multi-view behavioral framework to detect political bias in four pre-trained transformer models: AraBERTv2, CAMeLBERT, mBERT, and XLM-R. The framework integrates four complementary probes: sentiment… Read full abstract & cite →

NLP political bias Arabic transformers domain-adaptive pretraining masked probing actor-swapping bias detection behavioral evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170412 PDF

13

Trust and Hallucinations: A Study of 39 Experts on AI-Assisted Requirements Reverse Engineering

Author 1: Abdullah A H Alzahrani

With regard to the evolution of software systems, the process is hindered by the poor state of documentation, as software systems continuously evolve, which thereafter increases the maintenance costs to around 90% of development lifecycle spending. In addition, although the extraction of embedded business logic through the reverse engineering of… Read full abstract & cite →

Software engineering (SE) requirements engineering (RE) requirements reverse engineering (RRE) large language models (LLMs) natural language processing (NLP)

Abstract doi.org/10.14569/IJACSA.2026.0170413 PDF

14

Generative Monocular Perception Pipeline-Based Framework for Accurate Stem Detection in Automated Strawberry Harvest

Author 1: Kohei Arai Author 2: Jin Sawada Author 3: Mariko Oda

Horticulture faces a growing labor crisis, driving demand for autonomous harvesting robots, but reliable strawberry peduncle detection remains a critical unsolved challenge due to their fine, millimeter-scale structure and severe intertwining with leaves and stems. Existing single-view RGB imaging struggles with occlusions and ambiguities, while depth sensors falter in reflective… Read full abstract & cite →

Monocular depth estimation marigold easy wan22 strawberry harvesting robot stem detection

Abstract doi.org/10.14569/IJACSA.2026.0170414 PDF

15

Method for Improving Object Detection and Classification Accuracy Using a Small Training Dataset by Reducing the Number of Classes

Author 1: Kohei Arai

This study investigates a class-splitting strategy for improving object detection under limited training data using YOLOv11n with transfer learning and data augmentation for agricultural images containing leaves and peppers. The proposed approach evaluates leaf-only, pepper-only, and combined-class configurations using mAP@0.5, mAP@0.5:0.95, precision, recall, and F1-score to examine how class splitting… Read full abstract & cite →

YOLOv11n COCO2017 HSV/Flip/Crop transfer learning pipeline SPDarknet C2PSA Self-Attention SPPF

Abstract doi.org/10.14569/IJACSA.2026.0170415 PDF

16

Enhancing Communication Accessibility: Real-Time Recognition and Synthesis of Arabic Sign Language Gestures Using Long Short-Term Memory

Author 1: Mina Nagy Gaber Sorial Author 2: Rodaina Abdelsalam Author 3: Mayar Ali Author 4: Hesham Hassan

The Arabic Sign Language Recognition Research aims to develop a real-time system that accurately recognizes Arabic Sign Language (ArSL) gestures and translates them into both text and speech. This Research leverages the KArSL-502 Dataset, which contains 502 unique Arabic signs, to train a deep learning model using Bidirectional Long Short-Term… Read full abstract & cite →

Arabic Sign Language bidirectional LSTM machine learning text-to-speech real-time processing accessibility sign language translation

Abstract doi.org/10.14569/IJACSA.2026.0170416 PDF

17

A Multi-Layer Computational Framework for Predicting Student Performance Ranges in Higher Education Using Machine Learning

Author 1: Abdellatif HARIF Author 2: Moulay Abdellah KASSIMI

Predicting student academic performance constitutes a strategic priority for higher education institutions seeking to reduce attainment gaps and provide timely, targeted support. Existing approaches predominantly generate single-point performance estimates, overlooking the inherent variability in individual academic trajectories. This paper introduces a novel seven-layer computational framework that predicts student performance as… Read full abstract & cite →

Student performance prediction machine learning unsupervised learning performance range higher education educational data mining feature selection

Abstract doi.org/10.14569/IJACSA.2026.0170417 PDF

18

Digital Infrastructure Transformation in the Public Sector: Explaining IPv6 Adoption Through the UTAUT Framework

Author 1: Aydin Koçak Author 2: Ugur Dagtekin Author 3: Ahmet Kamil Kabakus

The Internet, as a product of advanced technological development, has evolved through a dynamic and synergistic process. However, its original architecture was not designed to accommodate such unprecedented growth, resulting in fundamental limitations, particularly in the addressing architecture. The exhaustion of the IPv4 address space has emerged as a major… Read full abstract & cite →

IPv6 UTAUT Structural Equation Modeling (SEM) Technology Acceptance Models internet and network technologies

Abstract doi.org/10.14569/IJACSA.2026.0170418 PDF

19

FIFO Age-Cohort Stochastic MILP for Perishable Inventory Optimization

Author 1: Hirman Rachman Author 2: Saib Suwilo Author 3: Sutarman Author 4: Elvina Herawati

This paper addresses the perishable inventory optimization problem for fish processing SMEs under compound supply-demand uncertainty. We develop a two-stage stochastic Mixed-Integer Linear Programming (MILP) framework comparing four model variants: two employing the conventional fixed deterioration rate (FDR) approach and two incorporating explicit First-In-First-Out (FIFO) age-cohort tracking, each with and… Read full abstract & cite →

Mixed-integer linear programming FIFO age-cohort tracking fixed deterioration rate perishable inventory two-stage stochastic programming cold storage optimization fish processing SME

Abstract doi.org/10.14569/IJACSA.2026.0170419 PDF

20

Detection of Video Anomalies via CNN-LSTM Model for Intelligent Surveillance

Author 1: Mohamed H. Mousa Author 2: Yasser M. Ayid Author 3: Ayman E. Khedr Author 4: Ahmed M. Elshewey

Automated Video Anomaly Detection (VAD) plays a vital role in developing surveillance systems in public spots. Our study develops real-time anomaly detection via a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, which uses the UCSD Pedestrian (Ped2) dataset. It introduces a methodology designed for detection accuracy enhancements by extracting… Read full abstract & cite →

Video Anomaly Detection smart surveillance computer vision CNN-LSTM hybrid deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170420 PDF

21

Evidence-Driven AI Governance for Healthcare: A PEARL-PATHWAY Analysis of Madinah

Author 1: Roba Alsaigh Author 2: Rashid Mehmood Author 3: Iyad Katib Author 4: Abdulaziz A. Almuzaini Author 5: Sami Saad Albouq Author 6: Sami Alshmrany

The rapid integration of artificial intelligence (AI) into healthcare systems has intensified the need for governance frameworks that ensure safety, accountability, ethical, and sustainable deployment. However, existing AI governance approaches are primarily articulated through high-level ethical and regulatory principles, with limited operational guidance tailored to specific healthcare contexts. This challenge… Read full abstract & cite →

AI governance healthcare AI evidence-driven governance PEARL framework PATHWAY framework governance gap analysis domain-specific governance context-aware governance policy intelligence sustainability

Abstract doi.org/10.14569/IJACSA.2026.0170421 PDF

22

KnowRAG: A Zero-Shot Diagnostic Analysis of Knowledge Base Coverage in Scientific Retrieval-Augmented Generation

Author 1: Assmaa MOUTAOUKKIL Author 2: Ali EL MEZOUARY Author 3: Kaoutar BOUMALEK

The "hallucination" problem in Large Language Models (LLMs) remains an unresolved hurdle for scientific researchers who require precise, grounded evidence. While Retrieval-Augmented Generation (RAG) aims to mitigate these errors, standard systems are often unoptimized for the structural complexities of scientific papers. We introduce KnowRAG, a zero-shot RAG pipeline specifically designed… Read full abstract & cite →

Large Language Models Retrieval-Augmented Generation evaluation scientific writing information retrieval knowledge base data engineering GenAI

Abstract doi.org/10.14569/IJACSA.2026.0170422 PDF

23

Determinants of Telemedicine Security Readiness Among ICT Professionals: Extended UTAUT Model

Author 1: Fazlina Mohd Ali Author 2: Afraa Mohammed Mohammed Gaashan Author 3: Marizuana Mat Daud Author 4: Rimaniza Zainal Abidin

Ensuring strong security readiness in telemedicine is essential to protect patients, safeguard their data, and build confidence and satisfaction in today’s digital healthcare platforms. Hence, this research explores and evaluates the maturity of telemedicine services from the perspectives of ICT professionals focused on security readiness. A case study with a… Read full abstract & cite →

Telemedicine security readiness ICT professionals UTAUT healthcare systems

Abstract doi.org/10.14569/IJACSA.2026.0170423 PDF

24

Evaluating Perceptual Reliability of Latent Attribute Control in Diffusion-Based Fashion Generation

Author 1: Noriaki Kuwahara Author 2: Shintaro Kawanami Author 3: Takashi Sato Author 4: Dongeun Choi

Although diffusion-based image generation models enable high-quality synthesis of fashion images, the reliable control of perceptual attributes in these models remains poorly understood. Current evaluation approaches primarily rely on semantic similarity metrics, such as CLIP scores, which may not accurately reflect human perceptual judgments. This study proposes a three-layer evaluation… Read full abstract & cite →

Diffusion models controllable generation latent space analysis human preference modeling perceptual reliability fashion image generation

Abstract doi.org/10.14569/IJACSA.2026.0170424 PDF

25

Performance Comparison of Regularization Methods on Transfer Learning Algorithm for Fish Species Classification

Author 1: Handrie Noprisson Author 2: Anita Ratnasari Author 3: Sri Dianing Asri Author 4: Vina Ayumi Author 5: Hadiguna Setiawan

Automatic fish classification played an essential role in the fisheries sector, particularly in underwater environments where visual quality was often degraded. This study addressed challenges related to low-contrast underwater images and limited dataset conditions by integrating Contrast Limited Adaptive Histogram Equalization (CLAHE) with a VGG16-based transfer learning model with regularization… Read full abstract & cite →

Fish classification underwater image VGG16 CLAHE regularization transfer learning

Abstract doi.org/10.14569/IJACSA.2026.0170425 PDF

26

FEM-KP: A Functional Evaluation Metric for Keyphrase Prediction Models

Author 1: Lahbib Ajallouda Author 2: Ahmed Zellou

Keyphrase prediction models are among the natural language processing (NLP) tasks that have improved their performance with transformers and large language models (LLMs). Instead of extracting present keyphrases in the text, these models also generate absent keyphrases. This improvement has led to significant challenges in the evaluation process of these… Read full abstract & cite →

Document retrieval Document summarization Evaluation metrics Functional evaluation metric Keyphrase prediction models natural language processing

Abstract doi.org/10.14569/IJACSA.2026.0170426 PDF

27

A Neutrosophic Machine Learning-Based Intelligent Ensemble Model for Sustainable Tea Yield Prediction Under Climatic Variability

Author 1: Maitraya Dey Author 2: Pushpita Roy Author 3: Shubhendu Banerjee Author 4: Amrut Ranjan Jena Author 5: Rakesh Naskar Author 6: Suparna Dasgupta Author 7: Soumyabrata Saha Author 8: Sudarshan Nath Author 9: Bikash Mondal

For efficient agricultural planning, resource management, and enhancing farmer livelihoods in significant tea-producing regions, tea production prediction is essential. However, climate variability—including temperature, rainfall, humidity, and sunlight duration—has a significant impact on tea output, making precise forecasting difficult. Using meteorological data from 2015 to 2025, this study suggests a hybrid… Read full abstract & cite →

Random Forest XG Boost Light GBM Cat Boost SLSQP Fuzzy Neutrosophic

Abstract doi.org/10.14569/IJACSA.2026.0170427 PDF

28

Incorporating Generative AI in Foreign Language Teaching Preparation: An Empirical Investigation Into the Effects of AI-Assisted Lesson Planning

Author 1: Xinxin Guo Author 2: Hao Chen

Generative artificial intelligence, particularly large language models such as ChatGPT, has emerged as a promising educational technology tool with extensive application potential in teaching preparation. The research objectives of this study are: 1) to examine how generative AI affects foreign language teachers’ teaching preparation experiences in terms of curriculum design… Read full abstract & cite →

Artificial intelligence ChatGPT generative AI educational technology teacher professional development

Abstract doi.org/10.14569/IJACSA.2026.0170428 PDF

29

Climate Change on Social Media: AI and Deep Learning-Based Analysis of Tweets

Author 1: Bahar URHAN Author 2: Mehmet KAYAKUS Author 3: Dilsad ERDOGAN Author 4: Gülten ADALI Author 5: Emrah BOZKURT Author 6: Zeynep Nihan BAKIR

The study analyses Turkish and English tweets about climate change on the social media platform Twitter and comparatively examines individuals” perceptions, concerns, and emotional reactions to this issue. A total of 2,046 Turkish and 18,000 English tweets were collected; 1,104 Turkish and 6,449 English tweets were analyzed after the cleaning… Read full abstract & cite →

Climate change awareness social media communication machine learning sentiment analysis deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170429 PDF

30

Preference-Controllable Multi-Objective Deep Reinforcement Learning for Human-Robot Task Allocation in Service Environments

Author 1: Asmaa Rashed Alahmari Author 2: Wadee Alhalabi

Human–Robot Collaboration (HRC) has gained increasing attention as it expands from industrial environments to service-oriented settings, where dynamic conditions and diverse operational objectives pose significant challenges for task allocation. Unlike controlled industrial environments, service contexts are characterized by frequent changes, uncertainty, and time-varying priorities, rendering static task allocation strategies ineffective… Read full abstract & cite →

Deep reinforcement learning human–robot collaboration preference-controllable reinforcement learning smart service environments task allocation

Abstract doi.org/10.14569/IJACSA.2026.0170430 PDF

31

Fuzzy-Integrated Modular Neural Networks for Accurate Prediction of On-Time-In-Full Supply Chain Performance

Author 1: Mariem Mrad Author 2: Mohamed Amine Frikha Author 3: Younes Boujelbene Author 4: Soufiene Ben Othman

Accurate prediction of supply chain performance is essential for improving operational efficiency and enabling proactive, data-driven decision-making under dynamic and uncertain conditions. Conventional forecasting methods often struggle to capture the nonlinear relationships between operational factors and performance outcomes. This paper proposes an improved neural modeling framework for predicting supply chain… Read full abstract & cite →

Artificial neural networks supply chain management performance prediction predictive analytics modular-network modeling demand forecasting

Abstract doi.org/10.14569/IJACSA.2026.0170431 PDF

32

Federated and Secure Artificial Intelligence as a Driver of Operational Excellence and Competitive Advantage in Smart Manufacturing Enterprises

Author 1: Adel Saad Assiri

The rapid advancement of Industry 4.0 and adoption of cyber-physical production systems (CPPS) demand real-time, adaptive, and privacy-preserving optimization that conventional centralized AI architectures cannot adequately provide. Such approaches remain susceptible to data privacy vulnerabilities, regulatory non-compliance (GDPR, CCPA), communication latency, and insufficient responsiveness to dynamic production variability within edge… Read full abstract & cite →

Federated learning secure AI smart manufacturing industry 4.0 cyber-physical production systems edge computing digital twin homomorphic encryption neural architecture search operational excellence

Abstract doi.org/10.14569/IJACSA.2026.0170432 PDF

33

Blockchain Governance Framework and Assessment Tools from a Readiness Perspective

Author 1: Nur Indrawati Author 2: Dana Indra Sensuse Author 3: Deden Sumirat Hidayat Author 4: Erisvaha Kiki Purwaningsih

Blockchain has been used in various sectors and use cases, but inadequate blockchain governance can cause failure in blockchain adoption. Previous research on blockchain governance has not been carried out with multiple perspectives and has not yet reached the point of developing governance assessment tools. This research aims to identify… Read full abstract & cite →

Blockchain governance blockchain governance framework blockchain readiness fuzzy Delphi method governance assessment tools STOPE framework

Abstract doi.org/10.14569/IJACSA.2026.0170433 PDF

34

The Untapped Potential of Extended Reality for Indigenous Medicinal Knowledge: A Review of Cross-Disciplinary XR Applications

Author 1: Giampearo Anak Peter Author 2: Suriati Khartini Jali Author 3: Jane Labadin Author 4: Abby Lian Hendrick Author 5: Ally Dian Hendrick Author 6: Nurul Farizah Ridzuan

Many sectors are quickly moving toward XR for education and professional training. These technologies, which include Virtual, Augmented, and Mixed Reality, are not being applied equally across different types of knowledge preservation. This study aims to review how effective these technologies are and whether they have overlooked the preservation of… Read full abstract & cite →

Extended Reality Indigenous Medicinal Knowledge Virtual Reality Augmented Reality Mixed Reality gamification cultural heritage preservation Task-Technology Fit PRISMA

Abstract doi.org/10.14569/IJACSA.2026.0170434 PDF

35

IFDA-EMA-YOLOv9: Wheat Disease Detection Integrating Flow Optimization and Auxiliary Supervision

Author 1: Jiaxiang Fan Author 2: Leixiao Li

To address the challenges of feature extraction in complex field environments, the limited sensitivity of YOLOv9 to subtle disease features, and the lack of adaptive hyperparameter optimization, this paper proposes an improved high-precision detection model, named IFDA-EMA-YOLOv9. First, to enhance feature extraction capabilities, an Efficient Multi-scale Attention (EMA) mechanism and… Read full abstract & cite →

Wheat disease detection Object detection YOLOv9 Flow algorithm

Abstract doi.org/10.14569/IJACSA.2026.0170435 PDF

36

Energy-Efficient Multi-Hop LoRa Communication in Forested Environments via Proximal Policy Optimization

Author 1: Muhd Kahfi Bin Jumali Author 2: Lim Kit Guan Author 3: Ervin Gubin Moung Author 4: Lorita Angeline Author 5: Tianlei Wang Author 6: Kenneth Teo Tze Kin

Multi-hop LoRa networks extend coverage for large-scale Internet of Things deployments but are severely limited by interference-induced collisions, retransmissions, and rapid battery depletion of relay nodes. Conventional routing strategies that minimize hop count or rely on static heuristics fail to account for dynamic medium contention and its impact on energy… Read full abstract & cite →

LoRa Multi-Hop PPO ADR MDP-Markov Decision Process Reinforcement-Learning PDR

Abstract doi.org/10.14569/IJACSA.2026.0170436 PDF

37

Comparative Analysis of Fixed vs Machine Learning Dynamic Pricing Models: A Computational Performance Study

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

The rise of e-commerce and digital offerings has generated a need for ultra-adaptable pricing policies seeking to maximize revenue while optimizing competitive advantage. Traditional fixed pricing schemes are inherently flawed due to a lack of responsiveness to instantaneous fluctuations in the marketplace, inventory levels, as well as demand inelasticity. This… Read full abstract & cite →

Computational performance deep learning dynamic pricing Machine Learning (ML) process innovation

Abstract doi.org/10.14569/IJACSA.2026.0170437 PDF

38

Machine Learning-Driven Resource Provisioning in Modern Cloud Environments: A Taxonomic Survey

Author 1: Stefanus Albert Kosim Author 2: Bagus Jati Santoso Author 3: Deka Julian Arrizki Author 4: Riki Mi'roj Achmad Author 5: I Nyoman Gede Artadana Mahaputra Wardhiana Author 6: Royyana Muslim Ijtihadie

Dynamic resource provisioning is a critical challenge in cloud computing, offering the necessary elasticity to guarantee reliable services within a usage-based payment framework. With the evolution of distributed systems, traditional threshold-based provisioning methods are increasingly inadequate for managing highly dynamic workloads. This inadequacy necessitates adaptive, machine learning (ML)-driven approaches capable… Read full abstract & cite →

Deep learning cloud computing machine learning resource provisioning taxonomy

Abstract doi.org/10.14569/IJACSA.2026.0170438 PDF

39

A Machine Learning-Driven Framework for Accurate Brain Image Registration in Multimodal and Noisy Environments

Author 1: M. S. Minu Author 2: Mutharasu M Author 3: S. Hemamalini Author 4: Sunitha T Author 5: Mohanaprakash T A Author 6: Justindhas Y

Brain image registration is fundamental for medical imaging to allow the matching of images from multiple modalities, temporal sequences, and people to offer spatial correlation. This is crucial for activities such as cohort studies, intervention recommendations, and treatment monitoring, where exact alignment assures consistent analysis. Notwithstanding their importance, modern brain… Read full abstract & cite →

Machine learning algorithms brain image registration convolutional neural networks deep learning brain disease

Abstract doi.org/10.14569/IJACSA.2026.0170439 PDF

40

On the Security of Authentication Protocols for Remote Healthcare Systems Through Cryptographic Vulnerability Analysis and Secure Protocol Redesign

Author 1: Haewon Byeon

This paper revisits a previously proposed authentication scheme for remote healthcare systems in Cloud-IoT. Although that protocol was introduced as a repair of an earlier healthcare design and was claimed to satisfy the usual confidentiality and mutual-authentication goals, a closer reconstruction of its registration, login, authentication, and password-update logic reveals… Read full abstract & cite →

Remote Healthcare Security Authentication Protocol Internet of Medical Things Cryptographic Vulnerability Analysis Secure Session Key Establishment Privacy-Preserving Authentication

Abstract doi.org/10.14569/IJACSA.2026.0170440 PDF

41

An Ensemble Boosting Approach with Boruta Feature Selection for Predicting E-Payment Adoption

Author 1: Mariana Purba Author 2: Junaidi Junaidi Author 3: Lemi Iryani Author 4: Nia Umilizah Author 5: Handrie Noprisson Author 6: Nur Ani

This study examined the factors influencing the adoption of electronic payment systems among Micro, Small, and Medium Enterprises (MSMEs) and developed a predictive model to evaluate the suitability of e-payment implementation. The research applied an ensemble machine learning approach consisting of AdaBoost, Binomial Boosting, L2 Boosting, GLM Boosting, and Random… Read full abstract & cite →

E-payment AdaBoost binomial boosting L2 boosting GLM boosting Boruta procedure

Abstract doi.org/10.14569/IJACSA.2026.0170441 PDF

42

Beyond Paper and Pencil: Evaluating the Effectiveness and User Perception of a Digital Reading Proficiency Assessment Platform

Author 1: Ivy M. Tarun

This study investigated the effectiveness of a digital reading proficiency assessment platform compared with traditional paper-and-pencil methods and examined users’ perceptions of its performance. It employed a quasi-experimental pretest–posttest control group design involving two comparable groups of secondary school students: a control group assessed using conventional reading assessment procedures and… Read full abstract & cite →

Digital reading assessment reading proficiency quasi-experimental design user perception software quality evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170442 PDF

43

Hybrid Learning-to-Rank Approach for Complex Information Retrieval Systems

Author 1: Fatma Zohra Bessai-Mechmache Author 2: Yasmine Hanifi Author 3: Damia Lyna Ait Idir

Biomedical question answering presents significant challenges due to the complexity of biomedical language and the need for precise information retrieval. This study aims to improve the performance of a biomedical information retrieval system through a hybrid learning-to-rank framework. Specifically, we combine lexical (BM25) and semantic (BioBERT) representations to form hybrid… Read full abstract & cite →

Learning-to-rank information retrieval hybrid learning-to-rank transformer-based ranking model biomedical question answering

Abstract doi.org/10.14569/IJACSA.2026.0170443 PDF

44

Optimizing Rainfall Prediction in Settat, Morocco, Through Machine Learning

Author 1: Oussama Zemnazi Author 2: Sanaa El Filali Author 3: Sara Ouahabi Author 4: Abderrahim Mouhtadi

Rainfall prediction is still a difficult challenge because rainfall is nonlinear, intermittent, and highly variable, especially in semi-arid climates. Accurate rainfall prediction is crucial for water resource management, agricultural planning, climate-driven decision-making, and more. This study proposes a comparative framework based on machine learning and ensemble learning techniques to predict… Read full abstract & cite →

Rainfall forecasting machine learning gradient boosting LightGBM semi-arid climate ensemble learning

Abstract doi.org/10.14569/IJACSA.2026.0170444 PDF

45

A Systematic Literature Review on Artificial Intelligence Applications for Breast Cancer Classification

Author 1: Nursakinah Abdullah Author 2: Qi Wei Oung Author 3: Chee Chin Lim Author 4: Chiew Chea Lau Author 5: Vrshni Menaka R Siva Nathan Author 6: Hui Wen Tiu

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, needing to be diagnosed early and properly classified for effective treatment. Advancements in artificial intelligence (AI), deep learning, and machine learning techniques have shown great potential in automating breast cancer diagnosis and molecular subtyping using medical imaging. This… Read full abstract & cite →

Artificial intelligence breast cancer classification Convolutional Neural Network deep learning machine learning mammography medical imaging molecular subtypes Vision Transformer

Abstract doi.org/10.14569/IJACSA.2026.0170445 PDF

46

Integrating Big Data and Machine Learning for Effective Cyberattack Prediction in e-Health Information Systems

Author 1: Mohamed Abdelbaki Author 2: Latif Adnane Author 3: Charaf Eddine Ait Zaouiat

This study proposes an intrusion-prediction framework for e-Health information systems that combines structured web-log analysis, supervised machine learning, and Apache Spark-based distributed processing. A corpus of 1,000,000 labeled HTTP log instances collected from a university hospital web environment was preprocessed into security-relevant features, including request method, request/response type, packet size… Read full abstract & cite →

Artificial intelligence big data cybersecurity hospital information systems log files

Abstract doi.org/10.14569/IJACSA.2026.0170446 PDF

47

Solar Irradiance Forecasting Approaches Based on Machine Learning: A Systematic Literature Review

Author 1: Sempe Thom Leholo Author 2: Chunling Tu Author 3: Topside Ehleketani Mathonsi

The prediction of solar irradiance plays a crucial role in the design, performance, and stability of renewable energy sources, and especially photovoltaic (PV) power generation. Accurate forecasting helps in managing energy, grid stability, and integration of solar energy in contemporary power systems. The study is a Systematic Literature Review (SLR)… Read full abstract & cite →

Solar irradiance forecasting Machine Learning Deep Learning hybrid models Systematic Literature Review

Abstract doi.org/10.14569/IJACSA.2026.0170447 PDF

48

Efficient Computation of Parametric Exponentiation in Parametric Algebra

Author 1: Khudoykulov Zarifjon Author 2: Khudoynazarov Umidjon Author 3: Muminova Mastura

This study analyzes the computational efficiency of parametric exponentiation operations in parametric algebra and proposes an algorithmic approach for their fast computation. By leveraging the properties of parametric algebra, the possibility of reducing parametric exponentiation to fast exponentiation methods is established, and a corresponding computational algorithm is developed. The proposed… Read full abstract & cite →

Parametric algebra parametric multiplication parametric exponentiation computational efficiency algebraic structures computational complexity fast exponentiation

Abstract doi.org/10.14569/IJACSA.2026.0170448 PDF

49

An Interoperable Multi-Agent Architecture for Personalized Smart Learning Using Generative AI and Learning Analytics

Author 1: Al Mahdi Khaddar Author 2: Youssef Said Author 3: Amine Dehbi Author 4: Tarik Chafiq

Learning Management Systems (LMSs) remain central to digital education, but they still provide limited support for adaptive and personalized learning across heterogeneous platforms. This study proposes an interoperable smart learning architecture that integrates a Multi-Agent System (MAS), generative artificial intelligence, and learning analytics to support context-aware interventions while preserving LMS… Read full abstract & cite →

Smart learning multi-agent systems generative AI learning analytics LMS interoperability adaptive learning

Abstract doi.org/10.14569/IJACSA.2026.0170449 PDF

50

Mapping Tourist Sentiments Through Lexicon-Based Analysis of Social Media Reviews: The Case of Salak Sibetan Agritourism

Author 1: Paula Dewanti Author 2: Putu Adi Guna Permana Author 3: I Gusti Ayu Widari Upadani Author 4: Ellyn Ly Maramento Author 5: Alfred John G. Borreros

Salak Sibetan, Bali's emblematic snake fruit cultivated in Sibetan Village, Karangasem, has gained increasing digital visibility through user-generated content across social media platforms. This study applies a bilingual lexicon‑based sentiment analysis framework integrating Indonesian and English sentiment lexicons, explicit negation handling, domain‑specific agritourism vocabulary, and a mean‑based sentiment scoring function… Read full abstract & cite →

Sentiment analysis lexicon-based methods Salak Sibetan agritourism GIAHS social media analytics

Abstract doi.org/10.14569/IJACSA.2026.0170450 PDF

51

Optimization of Access Point Location Using an Integer Programming Model

Author 1: Joan Marie M. Panes Author 2: Marilou O. Espina Author 3: Jovelin M. Lapates

To address the increasing demand for reliable connectivity in educational environments, strategic access point (AP) placement is necessary to support coverage and bandwidth requirements while controlling installation cost. This study applies a binary integer programming model to the third floor of the College of Technologies at Bukidnon State University in… Read full abstract & cite →

Access points (APs) optimization wireless network coverage integer programming

Abstract doi.org/10.14569/IJACSA.2026.0170451 PDF

52

AI and Blockchain for Secure Healthcare Data Management: A Bibliometric Analysis of Research Trends and Thematic Clusters (2020–2025)

Author 1: Maroufi Mohammed Author 2: Lamzabi Siham Author 3: Ziti Soumia

The convergence of artificial intelligence (AI) and blockchain has become an active axis of interdisciplinary research in healthcare data security. This paper reports a bibliometric analysis of 434 Scopus-indexed articles published between 2020 and 2025, with data collection and processing performed on 20 April 2026. The objective is to map… Read full abstract & cite →

Artificial intelligence blockchain healthcare data security bibliometric analysis federated learning internet of medical things data privacy cybersecurity science mapping VOSviewer

Abstract doi.org/10.14569/IJACSA.2026.0170452 PDF

53

A Bidirectional LSTM–Sentiment Fusion Framework for Dynamic Financial Market Prediction

Author 1: Minal Dhankar Author 2: Neha Gupta

Financial market prediction can be said to be a great challenge because of the intrinsic fluctuation, non-stationarity and multi-faceted influence of the economic indicators, world events, as well as the voter sentiment. Conventional models can easily miss the time dependence and emotional aspects inherent in market data, and the results… Read full abstract & cite →

Bidirectional LSTM deep learning financial market modelling sentiment analysis

Abstract doi.org/10.14569/IJACSA.2026.0170453 PDF

54

A Systematic Review of Graph Neural Networks and Social Network Analysis Techniques for Public Sentiment Uncovering

Author 1: Adi Wibowo Author 2: Wijayanto Author 3: Henri Tantyoko Author 4: Ari Wibisono Author 5: Usman Ependi

The rapid growth of social media has produced large-scale, highly interconnected user-generated data, creating the need for analytical approaches that can capture both textual meaning and relational structure. This systematic literature review examines the integration of Graph Neural Networks (GNNs) and Social Network Analysis (SNA) for public sentiment uncovering in… Read full abstract & cite →

Graph Neural Networks (GNNs) Social Network Analysis (SNA) public sentiment analysis hybrid models Systematic Literature Review (SLR)

Abstract doi.org/10.14569/IJACSA.2026.0170454 PDF

55

AI-Based Process Mining Framework for Process Business Integration in an Enterprise System

Author 1: Mardhani Riasetiawan Author 2: Ahmad Ashari

The accelerated progression of information technology has necessitated the adoption of more intelligent and adaptive Enterprise Systems (ES) to sustain and optimize organizational processes. The ES serve as critical infrastructures for resource management, operational efficiency, and sustaining competitive advantage; however, their deployment frequently encounters persistent challenges. These include discrepancies between… Read full abstract & cite →

Enterprise System process mining artificial intelligence process business

Abstract doi.org/10.14569/IJACSA.2026.0170455 PDF

56

Intelligent ECU Load Management in Electric Vehicles Using a Gated Multi-Stage Machine Learning Framework

Author 1: Vaishali Mishra Author 2: Sonali Kadam

The growing adoption of software-defined and electrified vehicle architectures has significantly increased the computational burden on electronic control units, leading to dynamic and non-stationary load conditions that can compromise real-time performance and system reliability. Conventional ECU load-management strategies are largely static or address isolated aspects of the problem, such as… Read full abstract & cite →

Electric vehicles electronic control units intelligent load management machine learning overload detection resource allocation

Abstract doi.org/10.14569/IJACSA.2026.0170456 PDF

57

Hybrid Feature Learning with TF-IDF and SBERT for Ambiguous Requirement Classification

Author 1: Fariha Khalid Author 2: Muhammad Yaseen Author 3: Gohar Rahman Author 4: Nauman Mazhar Author 5: Muhammad Asif Nauman Author 6: Aida Mustapha

Ambiguity in Software Requirement Specifications (SRS) remains a major source of project delay, rework, and misinterpretation in software engineering. Traditional ambiguity detection approaches rely on lexical or rule-based techniques that capture surface-level patterns but fail to model contextual meaning. Recent transformer-based models improve semantic representation; however, when applied independently, they… Read full abstract & cite →

Ambiguity detection software requirements engineering hybrid feature learning TF-IDF Sentence-BERT Support Vector Machine

Abstract doi.org/10.14569/IJACSA.2026.0170457 PDF

58

QR Code-Based Access Control Systems: Architectural Taxonomy, Security Landscape, and Future Research Directions

Author 1: Worood Alsawi Author 2: Dina M. Ibrahim

Access control systems secure physical and digital settings, especially in colleges, businesses, and restricted areas. Physical key, magnetic card, and biometric access control techniques have loss, duplication, high deployment costs, and maintenance complexity. With the widespread adoption of smartphones, QR code-based access control systems (QR-ACS) have emerged as a flexible… Read full abstract & cite →

QR codes access control systems security mechanisms authentication Internet of Things

Abstract doi.org/10.14569/IJACSA.2026.0170458 PDF

59

Real-Traffic-Trained Intelligent IDS for Advanced Cyberattack Detection in Enterprise Networks

Author 1: Dalila Naira Chinchay Author 2: Rodrigo Calderón Ari Author 3: Liset S. Rodriguez-Baca

Early detection of cyberattacks remains a major challenge in enterprise networks due to encrypted traffic, protocol diversity, and highly dynamic service behavior. This study evaluates a machine learning-based intrusion detection system trained on real enterprise traffic captured over 20 working days under operational conditions. A total of 1,163,014 packets were… Read full abstract & cite →

IDS machine learning cyberattacks cybersecurity intrusion detection

Abstract doi.org/10.14569/IJACSA.2026.0170459 PDF

60

Evaluating Open-Source LLMs for Thai Clinical Information Extraction

Author 1: Somkiat Kosolsombat Author 2: Phatnattachat Chatsiraphon Author 3: Taratep Si-Aksorn Author 4: Chiabwoot Ratanavilisagul

Electronic medical records (EMRs) in sports medicine contain rich clinical insights but often remain in unstructured, bilingual formats. While locally-deployed large language models (LLMs) offer a privacy-preserving solution for data extraction, their performance in handling Thai-English clinical shorthand remains under-explored. This study evaluated five open-source LLMs for extracting structured clinical… Read full abstract & cite →

Locally-deployed LLMs Open-Source Models bilingual NLP Thai Language Processing privacy-preserving clinical NLP

Abstract doi.org/10.14569/IJACSA.2026.0170460 PDF

61

Two-Phase Transfer Learning Framework for Automated Depression Classification in the Elderly via Facial Expression Recognition

Author 1: Muhammad Daffa Zahrandika Wibisono Author 2: Marizuana Mat Daud Author 3: Wan Mimi Diyana Wan Zaki

Automatic detection of depression in the elderly through Facial Expression Recognition faces a fundamental challenge in the form of domain shift due to skin deformation and facial structural changes due to aging, such as ptosis and deep wrinkles. This study proposes a Two-Phase Transfer Learning framework that integrates high-density facial… Read full abstract & cite →

Elderly depression Facial Expression Recognition transfer learning VGG19 texture bias spatiotemporal network

Abstract doi.org/10.14569/IJACSA.2026.0170461 PDF

62

Comparison of Time-Domain and Frequency-Domain EMG Features for Gait Phases Classification Using Machine Learning

Author 1: Muhamad Amirul Sunni Rohim Author 2: Nurhazimah Nazmi Author 3: Shin-Ichirou Yamamoto Author 4: Muhammad Kashfi Shabdin Author 5: Mohd Asyadi Azam

Accurate gait phase detection is essential for biomechanical analysis and the control of wearable assistive devices such as powered prostheses and exoskeletons. Electromyography (EMG) provides a direct representation of neuromuscular activation and offers potential advantages for low-latency, anticipatory gait phase recognition. However, the effectiveness of different EMG feature representations for… Read full abstract & cite →

Electromyography (EMG) gait phase detection stance–swing classification time-domain features frequency-domain features support vector machine (SVM) wearable assistive devices

Abstract doi.org/10.14569/IJACSA.2026.0170462 PDF

63

A Framework for Digital Technology and AI Adoption in Slovak Firms: Evidence from Qualitative Analysis

Author 1: Martina Chrancokova Author 2: Ludmila Mitkova Author 3: Lukas Vartiak

This study examines the adoption of digital technologies and artificial intelligence (AI) in Slovak firms, with particular attention to technological integration, employee adaptation, and organizational change. The study is based on qualitative semi-structured interviews conducted with managers and employees from 20 companies across multiple sectors and firm-size categories. The data… Read full abstract & cite →

Digital technology adoption artificial intelligence digital transformation process automation thematic analysis Slovak firms

Abstract doi.org/10.14569/IJACSA.2026.0170463 PDF

64

Feature-Level Analysis and Robust Baselines for EEG-Based Imagined Speech Recognition on the ASU Dataset

Author 1: Hatem T M Duhair Author 2: Masrullizam Mat Ibrahim Author 3: Jamil Abedalrahim Jamil Alsayaydeh Author 4: Mazen Farid Author 5: Safarudin Gazali Herawan

Imagined speech decoding from non-invasive electroencephalography remains a challenging problem, especially when moving beyond small vocabularies and optimistic evaluation protocols. This work revisits the Arizona State University (ASU) imagined speech dataset and treats it as a rigorous ten-class benchmark, with a focus on offline, corpus-level analysis rather than real-time deployment… Read full abstract & cite →

EEG-based imagined speech ASU imagined speech dataset brain–computer interface feature extraction logistic regression

Abstract doi.org/10.14569/IJACSA.2026.0170464 PDF

65

A Multivocal Literature Review of Metadata Governance: Conceptual Foundations and Research Gaps

Author 1: Dana Indra Sensuse Author 2: Alivia Yulfitri Author 3: Erisva Hakiki Purwaningsih Author 4: Anton Satria Prabuwono

As digital ecosystems grow and cross-organizational data sharing intensifies, metadata governance has become increasingly important. It plays a critical role in supporting interoperability, accountability, and sustainable data ecosystems. However, metadata governance remains conceptually fragmented and insufficiently structured as a distinct research domain. This study conducts a multivocal literature review (MLR)… Read full abstract & cite →

Metadata governance multivocal literature review data governance metadata management conceptual synthesis

Abstract doi.org/10.14569/IJACSA.2026.0170465 PDF

66

Advanced Forensic Analysis Techniques for Insider Threat Detection in Database Management Systems

Author 1: Kholod Saeed Talea AlQahtani Author 2: Mounir Frikha Author 3: M. M. Hafizur Rahman

This paper presents a real-time database forensic framework designed to detect insider threats within database management systems (DBMSs). Existing database forensic approaches, as identified through a systematic literature review employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, are predominantly reactive and post-mortem in nature, lacking real-time… Read full abstract & cite →

Database management systems insider threat detection SQL server forensic auditing ELK stack digital forensics

Abstract doi.org/10.14569/IJACSA.2026.0170466 PDF

67

A Hybrid Explainable Ensemble Learning Framework for Health Risk Prediction

Author 1: Hussain AlSalman

Early prediction of patients’ health risk is a crucial component of the safe and effective implementation of clinical triage and timely intervention. Health risk data in the real world often tends to be small in size and limited, with class imbalance, making the overall accuracy and transparency of Machine Learning… Read full abstract & cite →

Health risk prediction hybrid explainable ensemble learning meta-learner LightGBM SHAP

Abstract doi.org/10.14569/IJACSA.2026.0170467 PDF

68

A Digital Twin-Enabled Approach to Optimize Freight Fleet Operations in a Peruvian Transportation Company

Author 1: Deyber Flores Cabezas Author 2: Liset S. Rodriguez-Baca

The study evaluates the impact of a system enabled by Digital Twin on the optimization of the operations of a cargo fleet belonging to a transport company in Lima, Peru, during the year 2025. The proposed solution integrates OBD-II sensors, a Python processing engine, and a MongoDB-based data layer to… Read full abstract & cite →

Digital Twin fleet management IoT transportation operational optimization

Abstract doi.org/10.14569/IJACSA.2026.0170468 PDF

69

Real-Time Thought-to-Vision Generation Using Low-Channel EEG and Feature-Fusion Learning

Author 1: Abha Marathe Author 2: Medha Wyawahare Author 3: Milind Rane Author 4: Vrinda Parkhi

Severe motor disabilities and paralysis make it hard for individuals to communicate their thoughts and express their imagination using standard interfaces. Recent methods that convert EEG signals into images, using diffusion models, have shown superior results. However, these methods usually depend on high-density EEG systems with 32 to 128 channels… Read full abstract & cite →

Brain computer interface feature fusion learning diffusion models EEG to image generation wavelet EEG signal processing

Abstract doi.org/10.14569/IJACSA.2026.0170469 PDF

70

Adoption of Blockchain Technology in Electronic Records Management in the Malaysian Public Sector

Author 1: Aida Ruzana Ahmad Yani Author 2: Umi Asma’ Mokhtar

As the Malaysian public sector undergoes digital transformation, its electronic records management faces challenges, including security issues, maintaining record integrity and record authenticity, audit trails, and trust in existing systems. Blockchain technology has the potential to solve these challenges through features such as distributed records, transparency, restricted immutability, and cryptographic… Read full abstract & cite →

Electronic records management blockchain adoption public sector TAM UTAUT

Abstract doi.org/10.14569/IJACSA.2026.0170470 PDF

71

Reproducible Prediction Framework of Customer Churn Using Machine Learning, Advanced Data Science and Business Intelligence Techniques

Author 1: Younes KOULOU Author 2: Norelislam EL HAMI

The telecommunications sector has evolved in recent years, resulting in intense competition and high customer acquisition costs. As a result, retaining customers has become a key concern for telecom operators. In this work, we propose the design and implementation of a complete customer churn prediction system that combines data science… Read full abstract & cite →

Churn prediction telecommunications machine learning random forest business intelligence ETL Power BI feature engineering decision support

Abstract doi.org/10.14569/IJACSA.2026.0170471 PDF

72

Attribute-Conditioned Attention Scaling for Text-to-Image Diffusion Models

Author 1: Rabia Tahir Author 2: Bilal Ahmed Memon

Many text-to-image diffusion models have attained significant success in the generation of images from textual prompts; however, they still face some challenges, such as limited fine-grained control over different semantic attributes. To overcome this issue, this study proposes an Attribute-Conditioned Attention Scaling (ACAS), which modulates the cross-attention layers of the… Read full abstract & cite →

Diffusion models attribute-control CLIP LPIPS text-to-image generation cross-attention modulation

Abstract doi.org/10.14569/IJACSA.2026.0170472 PDF

73

Adaptive Multi-Layer Encryption for Enhancing Security in Smart Home IoT Ecosystems

Author 1: Dancan Obuya Machuki Author 2: Kennedy Ronoh

The proliferation of Internet of Things (IoT) devices in smart home environments has introduced significant security challenges, largely due to device heterogeneity, constrained computational resources, and limited native support for robust encryption mechanisms. This study presents an Adaptive Multi-Layer Encryption (AMLE) model designed to enhance security across the device, network… Read full abstract & cite →

Smart home security IoT encryption adaptive security multi-layer protection resource-constrained devices

Abstract doi.org/10.14569/IJACSA.2026.0170473 PDF

74

Workload-Aware Storage Reduction for Multi-Tenant SIEM on ClickHouse

Author 1: Nutthakorn Chalaemwongwan

Security Information and Event Management (SIEM) platforms ingest terabytes of heterogeneous telemetry daily—Windows event logs, DNS queries, HTTP transactions, EDR alerts, and network metadata from Zeek—yet the majority of stored records are never queried for threat-hunting or incident-response workflows. This study presents a workload-aware storage reduction framework that tailors data… Read full abstract & cite →

ClickHouse SIEM storage reduction workload analysis column importance scoring multi-tenant PDPA compliance

Abstract doi.org/10.14569/IJACSA.2026.0170474 PDF

75

Web Server Seizure and Live Acquisition: A Legally Compliant Forensic Framework for Indonesia

Author 1: Irwan Hariyanto Author 2: Yudi Prayudi Author 3: Rimba Whidiana Ciptasari

The proliferation of cybercrime activities, particularly those leveraging web servers for illicit purposes such as distributing hoaxes, hosting illegal online gambling, and spreading malware, underscores a pressing demand for a standardized digital forensic framework. Existing methodologies, like simple IP blocking, have proven insufficient in guaranteeing the integrity and admissibility of… Read full abstract & cite →

Digital forensics web server seizure live acquisition evidence integrity chain of custody

Abstract doi.org/10.14569/IJACSA.2026.0170475 PDF

76

Adaptive Phishing Website Detection Using Incremental Machine Learning: A Dynamic Approach to Cybersecurity Threats

Author 1: Ajla Kulaglic Author 2: Mutaz A. B. Al-Tarawneh

The rapid expansion of internet services and cloud-based platforms has increased cybersecurity threats, particularly phishing attacks that deceive users into disclosing sensitive information. Traditional phishing detection methods, including blacklists and batch-learning models, often struggle to adapt to the continuously evolving nature of these attacks. In order to address this challenge… Read full abstract & cite →

Phishing detection incremental learning online machine learning cybersecurity threats real-time classification

Abstract doi.org/10.14569/IJACSA.2026.0170476 PDF

77

Automated Interactance Near-Infrared Spectral Acquisition System for Mandarin Quality Assessment

Author 1: Van-Linh Lam Author 2: Dinh-Tri Nguyen Author 3: Thanh-Trung Le Author 4: Hoang-Tien Nguyen Author 5: Phuoc-Loc Nguyen Author 6: Quoc-Khanh Huynh Author 7: Nhut-Thanh Tran Author 8: Chanh-Nghiem Nguyen

Intelligent fruit grading systems require automated sensing solutions capable of rapid and reliable non-destructive internal quality assessment. While near-infrared spectroscopy has been widely used for soluble solids content (SSC) prediction, most existing studies rely on manually acquired spectra, limiting scalability in smart agricultural environments. Although online Vis–NIR systems based on… Read full abstract & cite →

Automated spectral acquisition interactance spectroscopy near-infrared spectroscopy fruit quality assessment soluble solids content chemometric modeling smart agriculture sensing systems

Abstract doi.org/10.14569/IJACSA.2026.0170477 PDF

78

The Impact of Modern AI on Software Development: A Systematic Literature Review

Author 1: Meryem Bensaid Author 2: Marouane Achbari Author 3: Younesse Ouahbi Author 4: Soumia Ziti

As large language models, and agentic AI systems are increasingly being integrated into software engineering, an expanding amount of empirical evidence surrounding these technologies has emerged. This systematic literature review examines the impact of modern AI techniques and tools in software development lifecycle phases and related activities, covering studies published… Read full abstract & cite →

Large language models generative AI agentic AI software development lifecycle systematic literature review

Abstract doi.org/10.14569/IJACSA.2026.0170478 PDF

79

A Survey on Extended Reality Technologies for Industrial Maintenance

Author 1: Mouad Danane Author 2: Abdelmajid El Ouadi Author 3: Youssef Rochdi

Industrial maintenance increasingly relies on extended reality (XR) technologies, yet comprehensive analysis comparing AR, VR, and MR implementations with human-centric evaluation remains limited. This systematic literature review employs the PRISMA methodology to analyze 95 primary studies (2014–2025) that investigate implementation patterns, benefits, challenges, and evaluation criteria across XR modalities. AR… Read full abstract & cite →

Industrial maintenance maintenance 5.0 extended reality augmented reality virtual reality mixed reality human-centric design artificial intelligence systematic literature review

Abstract doi.org/10.14569/IJACSA.2026.0170479 PDF

80

Design and Implementation of a Trust-Aware Fog Computing Simulation Framework Using WorkflowSim

Author 1: Chia Chuan Wu Author 2: Selvakumar Manickam Author 3: Shams Ul Arfeen Laghari Author 4: Shankar Karuppayah

Fog computing extends cloud capabilities to the net-work edge, providing low-latency services for mission-critical applications such as healthcare and industrial automation. However, validating security-aware scheduling policies in this distributed paradigm remains a challenge due to the lack of native support for trust modeling in standard simulation tools like CloudSim and… Read full abstract & cite →

Fog computing simulation tools WorkflowSim trust modeling task scheduling software architecture

Abstract doi.org/10.14569/IJACSA.2026.0170480 PDF

81

User-Centered Ergonomic Design of a Portable EEG System for Evaluating Dolphin-Assisted Therapy in Children with Neurodevelopmental Disorders

Author 1: Brenda Lorena Flores Hidalgo Author 2: Urim Rarael Pérez Bernal Author 3: Jesús Jaime Moreno Escobar Author 4: Hugo Quintana Espinosa Author 5: Ana Lilia Coria Páez

Dolphin-Assisted Therapy (DAT) is a therapeutic alternative used in comprehensive neurorehabilitation, aimed at improving the quality of life and social integration of patients with neurodevelopmental disorders through interaction with dolphins. However, the lack of defined standards makes it difficult to objectively evaluate its effectiveness. In this context, it was identified… Read full abstract & cite →

User-centered design ergonomics portable EEG system Dolphin-Assisted Therapy neurodevelopmental disorders 3D printing TGAM1

Abstract doi.org/10.14569/IJACSA.2026.0170481 PDF

82

Automated Medical Image De-Identification via U-Net++ Segmentation and Conditional GAN Inpainting

Author 1: Ismail Chahid Author 2: Anas Chahid Author 3: Yassine Chahid Author 4: Aissa Kerkour Elmiad Author 5: Mohammed Badaoui

The acceleration of multi-centric medical AI studies hinges on the ability to share imaging data without exposing burnt-in Protected Health Information (PHI). Manual redaction remains the dominant practice, but it erases diagnostically relevant context, violates harmonization guidelines issued by large consortia, and cannot keep up with the petabyte-scale repositories envisioned… Read full abstract & cite →

Medical image de-identification U-Net++ conditional GAN generative inpainting patient privacy PHI DICOM deep learning synthetic data perceptual loss

Abstract doi.org/10.14569/IJACSA.2026.0170482 PDF

83

Learning Structural Regularities over Mobile UI Flows from Interaction Traces

Author 1: Iskandar Salama Author 2: Masayasu Atsumi

Mobile applications exhibit rich user interface (UI) flows composed of sequences of screens connected through user interactions. While prior work has made significant progress in understanding individual UI screens, reusable flow-level regularities across applications remain underexplored. In this study, we learn a role-based probabilistic prior over mobile UI flows from… Read full abstract & cite →

UI Flow modeling causal transformer n-grams clustering multimodal embeddings next-step prediction cross-app generalization

Abstract doi.org/10.14569/IJACSA.2026.0170483 PDF

84

Risk Assessment and Risk Management Challenges in Intelligent IoT-Based Smart City Infrastructures

Author 1: Abdullah Alessa Author 2: Yaseen Alduwayl Author 3: M M Hafizur Rahman

Smart Internet of Things (IoT) technologies, which include artificial intelligence (AI) are increasingly being implemented in the infrastructures of smart cities to enhance the efficiency, sustainability, and service delivery of cities. Nonetheless, with the implementation of such intelligent and interconnected systems, there are complex security, privacy, and safety risks that… Read full abstract & cite →

Artificial intelligence cybersecurity Internet of Things risk assessment risk management smart cities

Abstract doi.org/10.14569/IJACSA.2026.0170484 PDF

85

A Cryptographic Framework Using AES-ECC with Threshold Key Management for Cloud Storage Systems

Author 1: Abdulsalam Ibrahim Almirdasi Author 2: Mohamed Tahar Ben Othman

Cloud storage systems have become an essential platform for storing and managing large volumes of data, but their security depends not only on confidentiality but also on integrity, controlled key management, and resistance to active attacks. Many existing protection approaches emphasize encryption of data while giving less attention to context–aware… Read full abstract & cite →

Cloud storage system hybrid encryption AES ECC Object-Centric threshold key management integrity verification Merkle root verification

Abstract doi.org/10.14569/IJACSA.2026.0170485 PDF

86

AFT-Attentive BiLSTM: Improving Early Warning of Firm Financial Distress with Temporal Attention in an Accelerated Failure Time Framework

Author 1: Muhammad Ali Chohan Author 2: Suresh Ramakrishnan Author 3: Mohammad Abrar Author 4: Shamaila Butt Author 5: Shahid Kamal

Early warning systems (EWS) for firm-level financial distress are essential for identifying potential bankruptcies or insolvencies before their realization. While traditional statistical models such as Z-score and logistic regression offer interpretability, they lack the ability to capture nonlinear and temporal dependencies in financial data. Recent deep learning approaches improve predictive… Read full abstract & cite →

Financial distress prediction early warning systems survival analysis accelerated failure time bidirectional LSTM temporal attention deep learning corporate bankruptcy

Abstract doi.org/10.14569/IJACSA.2026.0170486 PDF

87

TrustPatch-X: Multi-Stage Explainable Framework for Reliable LLM Patch Validation

Author 1: Sheetal Madhukar Parate Author 2: Jasmine Selvakumari Jeya I

Large Language Models (LLMs) have shown strong potential in automated vulnerability repair; however, generated security patches often lack reliability, semantic guarantees, and interpretability. Purely generative approaches may remove super-ficial patterns while failing to eliminate root-cause vulnerabilities or preserve program behavior. To address this limitation, this study proposes an Explainable Multi-Stage… Read full abstract & cite →

Large Language Models security patch validation automated vulnerability repair explainable AI semantic consistency analysis static analysis software security Graph Neural Networks

Abstract doi.org/10.14569/IJACSA.2026.0170487 PDF

88

An Identity-Aware Privacy-Preserving Deep Learning Framework for Culturally Sensitive Image Sharing

Author 1: Mahmoud Obaid Author 2: Hadeel Bkhaitan Author 3: Duha Maali Author 4: Saja Hammad Author 5: Thaer Thaher

The blistering development of digital image sharing raises privacy concerns, especially in cultural contexts where image exposure could be ethically and socially provocative. In Islamic societies, sharing images of women without hijab can be deeply sensitive. This study presents SITR, an identity-sensitive privacy-preserving deep learning system aimed at reducing un-intended… Read full abstract & cite →

Cultural sensitivity deep learning DenseNet121 Efficient Channel Attention FaceNet hijab detection MTCNN

Abstract doi.org/10.14569/IJACSA.2026.0170488 PDF

89

Stability-Weighted Feature Selection with Adaptive PSO-SGD for Neural Network-Based Predictive Hiring

Author 1: Yassine Temsamani Khallouk Author 2: Said Achchab

Predictive hiring leverages machine learning to forecast candidate success, yet existing approaches suffer from two limitations: reliance on single-method feature selection that lacks robustness, and sensitivity to neural network initialization that impairs convergence. This study introduces two contributions integrated into a unified framework. First, Stability-Weighted Multi-Criteria Feature Selection (SW-MCFS) is… Read full abstract & cite →

Predictive hiring Multi-Criteria Feature Selection Adaptive Particle Swarm Optimization stability-weighted consensus neural networks HR analytics ensemble methods

Abstract doi.org/10.14569/IJACSA.2026.0170489 PDF

90

GTMedoids: A New Grey Sheep Users Detection Approach

Author 1: Bouchra Boualaoui Author 2: Ahmed Zellou Author 3: Lahbib Ajallouda

Recommender systems have been developed to serve users and provide them with the best suggestions. Despite their success, offering fully identical recommendations to users’ preferences remains a difficult task, where the complexity of human taste results in different challenges. Grey sheep user phenomena continues to be one of the most… Read full abstract & cite →

Recommender system grey sheep users detection clustering Kmeans Kmedoids dissimilarity metric

Abstract doi.org/10.14569/IJACSA.2026.0170490 PDF

91

A Proxy-Based DevSecOps Framework for Multi-Tier Web Applications

Author 1: Abderrahim Rida Author 2: Abdelaziz Bakhil Author 3: Ayoub Ait Lahcen

Multi-tier web applications face significant implementation challenges in DevSecOps, including tool integration complexity, automation gaps, and cultural resistance. This study presents a proxy-based DevSecOps framework grounded in a formal architectural decomposition that transforms classical O(n × m) pipeline integration complexity into additive O(n1 × m1) + O(n2 × m2) components… Read full abstract & cite →

DevSecOps proxy architecture multi-tier applications PHP AWS security testing CI/CD pipeline optimization ecosystem coherence

Abstract doi.org/10.14569/IJACSA.2026.0170491 PDF

92

Hybrid Shape Descriptor Fusion with LightGBM for Robust 3D Mesh Classification and Retrieval

Author 1: Khadija Arhid Author 2: Youness Ghazi Author 3: Ilham Kachbal Author 4: Fatima Rafii Zakani Author 5: Mohcine Bouksim Author 6: Said El Abdellaoui Author 7: Taoufiq Gadi Author 8: Mohamed Aboulfatah

Recent advances in 3D mesh acquisition and the development of interactive modeling tools have significantly increased both the quantity and diversity of available 3D model databases. Therefore, the task of searching, querying, and re-trieving models in large-scale 3D databases has become a focus of research in this area. Indexing 3D… Read full abstract & cite →

3D object 3D mesh retrieval classification machine learning shape matching feature fusion LightGBM

Abstract doi.org/10.14569/IJACSA.2026.0170492 PDF

93

A Survey of FPGA Floorplanning for Dynamic Partial Reconfiguration: From Heuristic Approaches to Autonomous AI-Driven Methods

Author 1: Ibrahim LIMEM Author 2: Sadok BAZINE Author 3: Abdessalem BEN ABDELALI

Dynamic Partial Reconfiguration (DPR) has emerged as a key enabler of runtime adaptability and hardware-software co-design in modern FPGA-based heterogeneous systems. However, with the transition toward 5nm technologies and multi-die 3D-IC architectures, spatial resource management faces a “complexity wall,” where traditional manual floorplanning techniques struggle to satisfy timing, utilization, and… Read full abstract & cite →

Field Programmable Gate Arrays (FPGA) Floor-planning Dynamic Partial Reconfiguration (DPR) bitstream relocation hardware autonomy reinforcement learning heterogeneous computing 2D shape-adaptive placement

Abstract doi.org/10.14569/IJACSA.2026.0170493 PDF

94

SentimentPulse: A Concurrent Multi-Platform System for Social Media Sentiment Monitoring with LLM-Based Interpretation

Author 1: Gabriel A. León-Paredes Author 2: Erika C. Villa-Quishpi Author 3: Jorge E. Márquez-Chávez Author 4: Erick F. Zhigue-Granda

Monitoring public perception on social media is increasingly important for detecting reputational risks and communication opportunities in rapidly evolving digital environments. However, operational sentiment monitoring remains challenging due to platform fragmentation, heterogeneous data formats, and the need to generate interpretable reports quickly when specialized analysts are not available. This study… Read full abstract & cite →

Sentiment analysis social media monitoring large language models social listening systems multi-platform analytics

Abstract doi.org/10.14569/IJACSA.2026.0170494 PDF

95

Improving Performance and Accuracy in Decision Trees: A Literature Survey on Impurity Functions

Author 1: Abed Alsulami Author 2: Reda Khalifa Author 3: Wajdi Alghamdi

Decision tree algorithms have remained some of the most popular tools for supervised learning tasks. This has been because of their comprehensibility, malleability, and robustness to accommodate varying types of data. Nevertheless, their accuracy and performance are dependent on the use of impurity measures to guide the process of tree… Read full abstract & cite →

Decision trees impurity functions split criteria decision tree optimization literature review

Abstract doi.org/10.14569/IJACSA.2026.0170495 PDF