IJACSA Vol. 16 Issue 12 (2025)

1

ML to Predict Effectiveness of the MCP Authorization Model for LLM-Powered Agent

Author 1: Upakar Bhatta

In today’s AI-driven world, unlocking AI potential and enabling AI models to communicate with external data sources is vital for enhancing the efficiency and security of AI-driven applications. The Model Context Protocol (MCP) serves as a standard for maximizing AI potential. This study leverages a machine learning approach to predict… Read full abstract & cite →

Model Context Protocol artificial intelligence machine learning large language model

Abstract doi.org/10.14569/IJACSA.2025.0161201 PDF

2

Efficient Multi-Class Analysis of Consumer Complaints Using Frozen MiniLM Embeddings and Neural Networks

Author 1: Sri Vishnu Gopinathan Author 2: Muhammad Faraz Manzoor

Text classification is a critical task in domains generating large volumes of unstructured text, such as finance, healthcare, and consumer services. However, accurately classifying such data remains challenging due to its noisy, imbalanced, and context-dependent nature. While pre-trained language models have improved general text classification, their direct application often overlooks… Read full abstract & cite →

Consumer complaints text classification sentence embeddings MiniLM class imbalance sentiment analysis domain adaptation contextual embeddings

Abstract doi.org/10.14569/IJACSA.2025.0161202 PDF

3

TransAneu-Net: A Hybrid Radiomics and Contrastive Deep Learning Framework for Automated Brain Aneurysm Diagnosis

Author 1: Zhadra Kozhamkulova Author 2: Shirin Amanzholova Author 3: Bella Tussupova Author 4: Yelena Satimova Author 5: Mukhamedali Uzakbayev Author 6: Kenzhekhan Kaden Author 7: Dastan Kambarov

Accurate and early detection of intracranial aneurysms is critical for preventing life-threatening subarachnoid hemorrhage and improving clinical outcomes. This study proposes a hybrid diagnostic framework that integrates radiomics-based feature engineering with a transformer-driven deep learning architecture enhanced by teacher–student contrastive representation learning. The workflow incorporates region-of-interest segmentation, handcrafted radiomic feature… Read full abstract & cite →

Aneurysm deep learning radiomics transformer networks contrastive learning MR imaging MRA medical image analysis aneurysm detection neurovascular diagnostics

Abstract doi.org/10.14569/IJACSA.2025.0161203 PDF

4

Improving Emergency Preparedness with a Mobile Application for Respiratory Therapy Resource Coordination

Author 1: Rahaf Katib

Mass gatherings such as Hajj and Umrah, along with pandemic outbreaks, place a significant strain on healthcare systems, particularly respiratory therapy (RT) services, where shortages of respiratory therapists, ventilators, and specialized equipment can compromise emergency response due to increased patient volume, environmental exposure, and heightened risk of respiratory diseases. This… Read full abstract & cite →

Respiratory therapy mass gatherings Hajj and Umrah emergency preparedness health information systems

Abstract doi.org/10.14569/IJACSA.2025.0161204 PDF

5

TrustGraph: A Heterogeneous GNN for Dynamic Zero-Trust Policy Enforcement in Microservices

Author 1: Nurmyrat Amanmadov Author 2: Jemshit Iskanderov Author 3: Tarlan Abdullayev

Securing cloud microservices requires a unified understanding of how services behave, authenticate, and interact in real time. Unlike existing methods that analyze telemetry signals in isolation, this work presents a heterogeneous graph-based Zero-Trust framework that represents microservices using multi-modal telemetry—logs, metrics, traces, and authentication flows—embedded directly into graph nodes and… Read full abstract & cite →

Graph neural networks zero-trust security microservices anomaly detection heterogeneous graphs multi-modal telemetry dynamic policy enforcement

Abstract doi.org/10.14569/IJACSA.2025.0161205 PDF

6

Hybrid Optimization and CNN-Transformer Framework for Hot Topic Detection in Social Media

Author 1: Hemasundara Reddy Lanka Author 2: Vinodkumar Reddy Surasani Author 3: Nagaraju Devarakonda Author 4: Sarvani Anandarao

The rapid growth of Twitter as a real-time communication platform has created an urgent need for effective hot topic detection. Traditional statistical and machine learning models often fail to capture contextual semantics and long-range dependencies, while deep learning approaches such as CNNs and LSTMs improve representation but face challenges in… Read full abstract & cite →

Hot topic detection Twitter trend analysis CNN-Transformer Modified Bald Eagle Search (MBES) Particle Swarm Optimization (PSO)

Abstract doi.org/10.14569/IJACSA.2025.0161206 PDF

7

Intelligent Diagnostic Model for Early Malaria Symptoms

Author 1: Phoebe A Barraclough Author 2: Charles M Were Author 3: Hilda Mwangakala Author 4: Philip Anderson Author 5: Dornald O Ohanya Author 6: Harison Agola Author 7: Philip Nandi

One of the most significant worldwide health concerns in low-middle-income nations over the past few decades is Malaria, especially in Kenya. In Kenya, seventy per cent of people reside in areas where malaria is widespread, and most of them face obstacles getting access to medical care because of social culture… Read full abstract & cite →

Malaria diagnosis system malaria symptoms classifier ANFIS fuzzy rules

Abstract doi.org/10.14569/IJACSA.2025.0161207 PDF

8

Latent-Topology Graph State-Space Model (LT-GSSM) for Robust Traffic Fore-Casting

Author 1: Selma Kerdous

Accurate traffic forecasting remains challenging when sensor data are noisy, incomplete, or non-stationary. Recent advances in spatio-temporal learning have combined Graph Neural Networks (GNNs) with recurrent, convolutional, or attention mechanisms to capture spatio-temporal dependencies. However, most existing approaches remain largely deterministic and rely on fixed or pre-learned adjacency matrices, limiting… Read full abstract & cite →

Traffic forecasting graph neural networks state-space models latent topology dynamic adjacency learning spatio-temporal modeling noise and missing data robustness probabilistic modeling

Abstract doi.org/10.14569/IJACSA.2025.0161208 PDF

9

Dynamic Trust Modulation and Human Oversight in AI-Driven AML Systems: A Conceptual Framework for Compliance

Author 1: Julian Diaz Author 2: Abeer Alsadoon Author 3: Oday D. Jerew Author 4: Ahmed Hamza Osman Author 5: Hani Moetque Aljahdali Author 6: Albaraa Abuobieda Author 7: Abubakar Elsafi

This literature review investigates how human trust, decision fatigue, explainability (XAI), and human oversight interrelate to influence analyst decision-making in AI-driven anti-money laundering (AML) systems. While prior research has predominantly emphasized algorithmic performance, detection accuracy, or regulatory compliance in isolation, a critical gap remains in understanding the human-centered dynamics that… Read full abstract & cite →

Artificial intelligence anti-money laundering (AML) Trust Calibration Explainability decision fatigue human oversight AUSTRAC Compliance transaction monitoring false positives Analyst–System Interaction Regulatory Technology (RegTech)

Abstract doi.org/10.14569/IJACSA.2025.0161209 PDF

10

A Systematic Review of Functional Requirements, Modelling Practices, and Validation Strategies in IoT Application Development

Author 1: Nor Haniza Ramli Author 2: Nur Atiqah Sia Abdullah Author 3: Nur Ida Aniza Rusli

The rapid development of the Internet of Things (IoT) requires systematic development methods that address complex functional, architectural, and validation concerns. This review synthesized research published between 2016 and 2023 to characterize common functional requirements (FRs), current modelling techniques, and validation practices. From an initial corpus of 1,598 articles, 425… Read full abstract & cite →

Functional requirements Unified Modeling Language validation Internet of Things systematic review

Abstract doi.org/10.14569/IJACSA.2025.0161210 PDF

11

CAT-TODNet: A Contextual Transformer-Based Optimized Deformable Convolution Framework for Efficient ECG-Based Heart Failure Detection

Author 1: Vinitha V Author 2: V. Parthasarathy Author 3: R. Santhosh

Heart Failure detection using Electrocardiogram (ECG) signals is a critical clinical task, as continuous analysis of cardiac waveforms supports early diagnosis and effective intervention. Despite advancements in machine learning and deep learning techniques, existing approaches often suffer from limited contextual representation, sensitivity to noise, and in-adequate handling of non-stationary temporal… Read full abstract & cite →

Heart Failure (HF) Electrocardiogram (ECG) Artificial Intelligence (AI) Contextual Auxiliary Transformer (CAT) deformable convolution optimization algorithm

Abstract doi.org/10.14569/IJACSA.2025.0161211 PDF

12

Application of Improved YOLO-LSTM with Combined MQTT-LoRaWAN for AI Surveillance in Tea Plantations to Prevent Elephant Intrusion

Author 1: Rabin Kumar Mullick Author 2: Rakesh Kumar Mandal

Elephant-human conflict is a growing problem in tea garden areas of Dooars in North Bengal, resulting in massive cost for crops, infrastructures and sometimes human life as well. Each year, these mild-mannered giants destroy crops, destroy fences and even threaten the locals, which raises the costs of repairs and endangering… Read full abstract & cite →

Tea garden machine learning artificial intelligence

Abstract doi.org/10.14569/IJACSA.2025.0161212 PDF

13

Creative Guidance of Intelligent Emotion Recognition in Video Art

Author 1: Weixing Chen Author 2: Yubo Zhou

This study optimizes the existing emotion recognition model to improve the application effect of emotion recognition technology in the guidance of video art creation. It also compares the performances of Multimodal Sentiment Analysis (MMSA), Multimodal Sequence Encoder (MuSE), and the optimized model through a series of simulation experiments. In the… Read full abstract & cite →

Emotional recognition image art artistic expressiveness creative feedback

Abstract doi.org/10.14569/IJACSA.2025.0161213 PDF

14

NeuroFusionNet Adaptive Deep Learning for Intelligent Real-Time Industrial IoT Decisions

Author 1: Ghayth AlMahadin

The rapid development of Industrial IoT (IIoT) has facilitated real-time observation and decision-making in smart factories, even though current methods suffer from constraints like processing noisy, high-dimensional sensor data and modeling both spatial and temporal relationships well. Classical models like CNN, LSTM, and GRU tend to fail in handling sequential… Read full abstract & cite →

Deep learning hybrid CNN-BiGRU OptiSenseNet sensor data synthesis smart manufacturing

Abstract doi.org/10.14569/IJACSA.2025.0161214 PDF

15

Speckle Denoising in Breast Ultrasound Images Using Multi-Filter Pseudo-Clean Targets and Deep Learning

Author 1: Omar Ayad Alani Author 2: Muhammad Moinuddin

Ultrasound imaging is widely used in breast cancer diagnosis, but suffers from speckle noise, which reduces contrast and obscures fine structures. Supervised deep learning methods for speckle reduction/denoising typically require clean ground truth, which is unattainable in vivo. To address this, this study proposes a multi-filter pseudo-ground-truth strategy combined with… Read full abstract & cite →

Speckle noise breast ultrasound denoising U-Net++ multi-filter pseudo-clean targets deep supervision

Abstract doi.org/10.14569/IJACSA.2025.0161215 PDF

16

From Bits to Qubits: Comparative Insights into Classical and Quantum Computing Systems

Author 1: Tariq Jamil

The rapid development of computing hardware has been driven by an ever-emerging need for high throughput, scalable performance, and computation capabilities to be able to address increasingly complex problems. The paradigm of classical computing, centered on deterministic binary logic and the von Neumann architecture, has long favored modern information processing… Read full abstract & cite →

Classical computing high-performance computing quantum computing qubit

Abstract doi.org/10.14569/IJACSA.2025.0161216 PDF

17

Histogram Gradient Boosting Classifier-Based UWSN Cyber Attack Detection Incorporating Environmental Factors (HGBoostUCAD)

Author 1: Hamid OUIDIR Author 2: Amine BERQIA Author 3: Siham AOUAD

Underwater Wireless Sensor Networks (UWSNs) are commonly employed for exploring and exploiting aquatic areas, and its role is very important and more beneficial precisely in hostile and constrained marine environments. However, their security is more critical than terrestrial wireless sensor networks (TWSNs) due to the space in which they are… Read full abstract & cite →

UWSN security intrusion detection system cyber-attack detection cybersecurity machine learning histogram gradient boosting

Abstract doi.org/10.14569/IJACSA.2025.0161217 PDF

18

Human–Technology Interaction in Generative AI: A Theoretical Review of Technology Acceptance and Cognitive Response

Author 1: Ugur Dagtekin Author 2: Ahmet Kamil Kabakus

The rapid rise of Generative Artificial Intelligence (GenAI) has transformed the way humans interact with technology and has revealed cognitive mechanisms that extend beyond the explanatory scope of traditional technology acceptance models, such as the Technology Acceptance Model (TAM), Technology Acceptance Model 2 (TAM2), and the Unified Theory of Acceptance… Read full abstract & cite →

Generative Artificial Intelligence Technology Acceptance Model Cognitive Response Theory Human-AI Interaction cognitive trust

Abstract doi.org/10.14569/IJACSA.2025.0161218 PDF

19

Optimized Dimensionality Reduction Using Metaheuristic and Class Separability

Author 1: Eman Abdulazeem Ahmed Author 2: Malek Alzaqebah Author 3: Sana Jawarneh

The high dimensionality of modern datasets presents significant challenges for machine learning, including increased computational cost, model complexity, and risk of overfitting. This study introduces a metaheuristic framework for optimized dimensionality reduction to identify the highly discriminative feature subsets. The proposed method (KDR-PSO) combines a Particle Swarm Optimization (PSO) algorithm… Read full abstract & cite →

Dimensionality reduction Particle Swarm Optimization metaheuristics K-Nearest Neighbors class separability high-dimensional data

Abstract doi.org/10.14569/IJACSA.2025.0161219 PDF

20

Dynamic Assessment and Goal Optimization of Corporate ESG Performance Based on DEA-CCR-GML and Inverse DEA Integration Framework

Author 1: Hui Liu Author 2: Tsung-Xian Lin Author 3: Yaqing Hu Author 4: Yingxi Xiao Author 5: Chengze Ou Author 6: Yayi Lao Author 7: Wenchao Pan

Since the Ministry of Ecology and Environment issued the "Reform Plan for the System of Environmental Information Disclosure in Accordance with the Law" in 2021, the nation has set forth new requirements for sustainable development. Against this backdrop, how enterprises enhance their value across all dimensions through ESG in compliance… Read full abstract & cite →

Corporate ESG performance DEA financing constraints green innovation

Abstract doi.org/10.14569/IJACSA.2025.0161220 PDF

21

Development and Evaluation of a Mobile-Based Local Food Information System for Elderly Nutrition Support

Author 1: Renuka Khunchamnan Author 2: Kewalin Angkananon

This research aimed to: 1) study information needs regarding local foods and information systems for the elderly, 2) develop a local food information system for the elderly, and 3) evaluate system effectiveness. The quantitative study included 235 senior caregivers selected via purposive sampling. The research tools were an interview form… Read full abstract & cite →

Information system LINE OA elderly care system local food elderly nutrition

Abstract doi.org/10.14569/IJACSA.2025.0161221 PDF

22

Human-Centered Behavioral Analysis of Window Operation Using AI-Based Skeletal Recognition

Author 1: Jewon Oh Author 2: Daisuke Sumiyoshi Author 3: Takahiro Yamamoto Author 4: Takahiro Ueno Author 5: Tatsuto Kihara

This study presents a quantitative approach to analyzing window opening and closing behaviors using skeletal recognition technology. Video data of five participants performing these actions were captured and processed using the Openpose model, which detects 25 human joints. Focusing on the shoulder, elbow, and wrist, the study analyzed time-series joint… Read full abstract & cite →

Image processing skeletal recognition behavioral analysis Openpose

Abstract doi.org/10.14569/IJACSA.2025.0161222 PDF

23

Agentic AI as the Orchestrator of Mobile Ecosystems: A Review of the Trade-off Between Performance and Drawbacks

Author 1: Ayat Aljarrah Author 2: Mustafa Ababneh

This system review explores the transformational role of agentic artificial intelligence (AI) as an orchestrator in mobile ecosystems. Agentic AI systems proactively plan, execute, and adapt across applications, devices, and services, unlike traditional and generative AI. These systems offer autonomous, context-aware coordination by integrating reasoning engines, tool orchestration, memory, retrieval-augmented… Read full abstract & cite →

Agentic AI orchestrator mobile ecosystems on-device

Abstract doi.org/10.14569/IJACSA.2025.0161223 PDF

24

A Novel Fuzzy Logic System for Real-Time Text Difficulty Assessment in Mobile Reading Apps for Dyslexia

Author 1: Enrique Lee Huamaní Author 2: Brian Andreé Meneses-Claudio Author 3: Carlos Fidel Ponce Sánchez Author 4: Jehovanni F. Velarde-Molina

Automated text difficulty assessment in mobile reading applications remains an underexplored challenge for dyslexia support systems. This study presents the development and validation of an intelligent fuzzy logic system engineered for real-time text complexity analysis in mobile web environments. Our approach integrates six computational variables: sentence length patterns, lexical complexity… Read full abstract & cite →

Fuzzy logic systems mobile web applications dyslexia support technology automated text analysis accessibility engineering

Abstract doi.org/10.14569/IJACSA.2025.0161224 PDF

25

Ubiquitous Computing Framework for Reducing Ambiguity in the Lanna Thai Dialect Using Transformer Models and Fuzzy Logic

Author 1: Wongpanya S. Nuankaew Author 2: Pathapol Jomsawan Author 3: Pratya Nuankaew

This research focuses on developing a speech-recognition model that can better handle the unique sounds and vocabulary of the Lanna Thai dialect while supporting translation between Lanna and Standard Thai. A dataset of spoken Lanna Thai was collected from native and fluent speakers between 2023 and 2025, refined from 200… Read full abstract & cite →

Ambiguities in Lanna Thai vocabulary Lanna Thai vocabulary pervasive computing Speech Transformer Thai speech recognition ubiquitous computing

Abstract doi.org/10.14569/IJACSA.2025.0161225 PDF

26

The Retrieval-Augmented Pedagogical Assistant (RAPA): A Methodology for Enhancing Critical Thinking and Equity in AI-Augmented Education

Author 1: Shohel Pramanik Author 2: Mohd Heikal Bin Husin

This study presents the Retrieval-Augmented Pedagogical Assistant (RAPA) methodology, an integrated framework designed to overcome the core limitations of general Large Language Models (LLMs)—specifically factual instability (hallucination) and static knowledge bases—by deploying a specialized, institutional Retrieval-Augmented Generation (RAG) architecture. The methodology addresses three critical challenges to the responsible integration of… Read full abstract & cite →

RAG AI literacy critical thinking equitable education professional development

Abstract doi.org/10.14569/IJACSA.2025.0161226 PDF

27

Automated Quality Evaluation of Panoramic Dental Radiographs Using a Domain-Adapted Transfer Learning

Author 1: Nur Nafiiyah Author 2: Rifky Aisyatul Faroh Author 3: Eha Renwi Astuti Author 4: Rini Widyaningrum Author 5: Agus Harjoko Author 6: Kang-Hyun Jo Author 7: Alhidayati Asymal Author 8: Youan Nhareswary Dwike Prasetya

Assessing the quality of panoramic dental radiographs is essential to ensure diagnostic accuracy and patient safety. However, existing CNN-based approaches for radiograph quality assessment often emphasize architectural comparisons, while providing limited discussion on training stability and generalization, particularly when applied to relatively small and heterogeneous datasets. To address this gap… Read full abstract & cite →

Batch Normalization image quality panoramic radiograph transfer learning

Abstract doi.org/10.14569/IJACSA.2025.0161227 PDF

28

A Multi-View Classification Method for Distribution Network Towers Based on Improved EfficientNet

Author 1: Gao Liu Author 2: Changyu Li Author 3: Junsheng Lin Author 4: Xinzhe Weng Author 5: Qianming Wang Author 6: Zhenbing Zhao

View recognition of distribution network towers is a key technology in UAV intelligent inspection. To address the problem of low accuracy of existing deep learning methods in complex background interference, this paper proposes a tower view classification method based on EfficientNet that integrates foreground perception, multi-scale feature fusion, and dual-dimensional… Read full abstract & cite →

Multi-view classification of power towers mask-guided feature fusion BirefNet multi-scale feature fusion convolutional block attention

Abstract doi.org/10.14569/IJACSA.2025.0161228 PDF

29

Accessible Application Prototype for Improving Digital Reading in People with Dyslexia: User-Centered Design and Usability Validation

Author 1: Enrique Lee Huamaní Author 2: Brian Andreé Meneses-Claudio Author 3: Carlos Fidel Ponce Sánchez Author 4: Jehovanni F. Velarde-Molina

Digital reading remains a challenge for individuals with dyslexia due to the limited availability of accessible tools tailored to their cognitive and perceptual needs. Although many digital reading applications offer basic personalization options, they often lack integrated mechanisms to support reading comprehension and user autonomy. This study presents the design… Read full abstract & cite →

Dyslexia digital accessibility digital reading usability user-centered design

Abstract doi.org/10.14569/IJACSA.2025.0161229 PDF

30

Vegetation Identification in Hyperspectral Images of Cartagena City Using the Haar Wavelet Transform

Author 1: Gabriel Elías Chanchí Golondrino Author 2: Manuel Alejandro Ospina Alarcón Author 3: Manuel Saba

Hyperspectral imaging is one of the most widespread remote sensing techniques in earth observation, corresponding to images with high spectral and spatial resolution that enable material detection through the identification of their spectral signature. A key challenge in hyperspectral imaging is the definition of novel and efficient computational methods that… Read full abstract & cite →

Vegetation detection hyperspectral images remote sensing wavelet transform earth observation

Abstract doi.org/10.14569/IJACSA.2025.0161230 PDF

31

Impact of Climate Change on Animal Diseases Based on Machine Learning

Author 1: Gehad K. Hussien Author 2: Mohamed H. Khafagy Author 3: Hussam M. Elbehiery

The rapid pace of climate change has altered the distribution of animal diseases, increased their frequency, and dispersed them over a larger geographic area. Rising temperatures, fluctuating humidity, and erratic rainfall patterns have increased the risk of illness in cows. These modifications have facilitated the growth of diseases and vectors… Read full abstract & cite →

Climate change environmental health convolutional neural network (CNN) animal diseases graphical user interface (GUI)

Abstract doi.org/10.14569/IJACSA.2025.0161231 PDF

32

Linking Leadership Styles to Corporate ESG Performance: A Novel Sierpinski Triangle Fuzzy Decision-Making Modelling

Author 1: Serkan Eti Author 2: Çagla Özgen Safak Author 3: Serhat Yüksel Author 4: Hasan Dinçer

Existing research generally addresses the factors affecting ESG performance at a general level, but fails to examine the relative impact of leadership approaches on this performance with a holistic decision-making model. This deficiency makes it difficult for businesses to align their sustainability strategies with the right leadership styles and creates… Read full abstract & cite →

Leadership approaches ESG performance z-NIDM CIMAS RAM

Abstract doi.org/10.14569/IJACSA.2025.0161232 PDF

33

Temporal-Cross-Modal Intelligence for Detecting Fraudulent Crowdfunding Campaigns

Author 1: Lakshmi B S Author 2: Rekha K S

Reward-based crowdfunding platform fraud has now become a multimodal and temporally dynamic threat, with conventional text-only or snapshot-based detection methods ineffective at detecting more complex deceptive campaigns. In this study, a Temporal Dynamics Aware Multi-Model Fraud Detection Framework (TDMM-FDF) that simultaneously models linguistic indicators, visual discrepancies, and time behavioral changes… Read full abstract & cite →

Crowdfunding fraud detection multimodal learning temporal behavior modeling cross-modal consistency analysis blockchain-based verification

Abstract doi.org/10.14569/IJACSA.2025.0161233 PDF

34

Innovative Approaches to Green Strategy Formulation with a Novel Hybrid AI-Spherical Fuzzy Framework

Author 1: Yasar Gökalp Author 2: Serkan Eti Author 3: Halil Yorulmaz Author 4: Serhat Yüksel Author 5: Hasan Dinçer

This study aims to establish prioritized strategies for businesses to adopt green strategies. In this framework, literature-based criteria are analyzed through a three-stage model. In the first stage of the analysis, an artificial intelligence (AI)-based decision matrix is created. In the second stage, factors affecting green business strategies are weighted… Read full abstract & cite →

Artificial intelligence fuzzy decision-making spherical fuzzy sets ARAS Entropy green strategy

Abstract doi.org/10.14569/IJACSA.2025.0161234 PDF

35

RFM–K-OPT Based Machine Learning Framework for Customer Segmentation and Behavioral Profiling in Direct Marketing

Author 1: Khadija Mehrez

Customer segmentation is an essential element of modern marketing analytics, which helps companies recognize, comprehend, and market to customers depending on their behavioral and transactional attributes. Conventional methods based on Recency, Frequency, and Monetary (RFM) analysis or on simple unsupervised clustering algorithms such as K-Means are very common, but they… Read full abstract & cite →

Customer segmentation behavioral profiling clustering optimization predictive marketing data-driven decision making

Abstract doi.org/10.14569/IJACSA.2025.0161235 PDF

36

Enhancing Organizational Information Systems Through Explainable Artificial Intelligence

Author 1: Kian Jazayeri

This study examines Workplace Perceptions among Finnish employees through the application of Artificial Intelligence within the domain of Human Resource Analytics. An integrated analytical framework combining Clustering Analysis, supervised classification, and Explainable Artificial Intelligence is proposed to uncover and interpret latent employee perception profiles. Using 23 perception-related indicators from the… Read full abstract & cite →

Artificial intelligence Human Resource Analytics Explainable Artificial Intelligence Decision-Support Systems workplace perceptions Clustering Analysis Decent Work and Economic Growth

Abstract doi.org/10.14569/IJACSA.2025.0161236 PDF

37

Business Process Outsourcing and Digitalization in Albania: Challenges, Opportunities, and Strategic Directions

Author 1: Nertila Çika

The rapid expansion of Business Process Outsourcing (BPO) has transformed the global services economy, and Albania is emerging as a competitive nearshoring destination in the Western Balkans. This study examines the intersection of BPO and digitalisation in Albania, exploring how technological innovation, artificial intelligence (AI), and cloud-based automation are reshaping… Read full abstract & cite →

Business Process Outsourcing (BPO) digitalisation artificial intelligence (AI) automation PEST analysis Western Balkans Albania strategic development

Abstract doi.org/10.14569/IJACSA.2025.0161237 PDF

38

Framework for Ethical Acquisition of User-Data to Improve Recommendation Models’ Accuracy in Digital Systems

Author 1: Shaheer Hussain Qazi Author 2: M.Batumalay Author 3: Asheer Hussain Author 4: Ali Abbas

The modern digital ecosystem has evolved into a pervasive, opaque system where platforms collect and infer personal data through nearly every online action, search queries, email content, browsing history, and app usage, without transparency. Justified as a means to deliver “relevant” content and ads, this approach undermines user privacy, introduces… Read full abstract & cite →

Data handling data privacy online tracking data collection user profiling model accuracy ethical AdTech open standards user autonomy SDG 9 SDG 16 process innovation

Abstract doi.org/10.14569/IJACSA.2025.0161238 PDF

39

Towards Quantum-Accelerated Urban Systems: Integrating Quantum Computing into Saudi Smart City Megaprojects

Author 1: Eissa Alreshidi

Quantum Computing (QC), rooted in the principles of superposition and entanglement, enables transformative computational capabilities that surpass classical systems, particularly in solving NP-hard combinatorial optimization, simulation, and machine learning problems. These capabilities are increasingly vital for smart cities, which depend on real-time data from the Internet of Things (IoT) devices… Read full abstract & cite →

Quantum Computing (QC) Hybrid Quantum-Classical Architecture (HQCA) quantum security smart cities NEOM Saudi Vision 2030 combinatorial optimization Urban Digital Twin (UDT) Quantum Machine Learning (QML) roadmap

Abstract doi.org/10.14569/IJACSA.2025.0161239 PDF

40

MetaEdge: A Meta-Learning-Based Auto-Selective Tool for Hardware-Aware Anomaly Detection on Edge Devices

Author 1: Nadia Rashid Author 2: Rashid Mehmood Author 3: Fahad Alqurashi Author 4: Turki Alghamdi

The deployment of anomaly detection systems across heterogeneous edge computing environments faces significant challenges due to varying computational constraints and resource limitations. Existing approaches typically employ static model selection strategies that fail to adapt to diverse hardware capabilities, resulting in suboptimal detection performance and inefficient resource utilization. To address this… Read full abstract & cite →

Anomaly detection edge computing hardware-aware optimization machine learning meta-learning model selection ONNX

Abstract doi.org/10.14569/IJACSA.2025.0161240 PDF

41

A Bio-Inspired Behavior-Based Hybrid Framework for Ransomware Detection

Author 1: Mohammed A. F. Salah Author 2: Mohd Fadzli Marhusin Author 3: Rossilawati Sulaiman

Ransomware remains a critical and evolving cybersecurity threat, increasingly rendering traditional signature-based detection techniques ineffective. While modern machine learning models achieve high detection accuracy, they often operate as opaque “black boxes”, introducing a significant explainability gap that undermines analyst trust. In addition, behavior-based anomaly detection systems frequently suffer from high… Read full abstract & cite →

Ransomware Artificial Immune Systems (AIS) anomaly detection Negative Selection Algorithm Markov chain Random Forest hybrid framework

Abstract doi.org/10.14569/IJACSA.2025.0161241 PDF

42

A Resilient Framework for Industry 5.0 WSNs: Enhancing Network Lifetime via a Lightweight Reputation Ledger and Hybrid AI

Author 1: Padma Sree N Author 2: Malini M Patil

Wireless Sensor Networks (WSNs) play an increasingly important role in Industry 5.0 cyber–physical systems, where resilience, trust, and energy efficiency are essential under dynamic operating conditions. However, their limited resources, scattered deployment, and continuous operation make these networks highly susceptible to unusual behavior and cyberattacks. Such issues can compromise data… Read full abstract & cite →

Wireless Sensor Networks (WSNs) Industry 5.0 anomaly detection lightweight blockchain trust management network lifetime Digital Twin

Abstract doi.org/10.14569/IJACSA.2025.0161242 PDF

43

MTML 1.0: A Novel Interlingua Knowledge Representation Model for Machine Translation

Author 1: M. A. S. T Goonatilleke Author 2: B Hettige Author 3: A. M. R. R Bandara

Machine translation is one of the major areas of both computational linguistics and artificial intelligence that employs computer algorithms to automatically translate text between different natural languages. At present, the advent of Large Language Models (LLMs) has revolutionized this field, marking a significant turning point in its evolution. Despite their… Read full abstract & cite →

Machine translation knowledge representation LLMs rule-based approach hybrid approach

Abstract doi.org/10.14569/IJACSA.2025.0161243 PDF

44

Safety Helmet Wear Detection Algorithm Based on ASG-YOLOv8s

Author 1: Li-Zhen He Author 2: Zhi-Sheng Wang Author 3: Yi-Wei Duan Author 4: Jin-Hai Sa

In the field of industrial safety, the standardised wearing of safety helmets by workers constitutes a core protective measure against head injuries. However, in industrial settings, multi-scale background interference arising from variations in monitoring distance renders traditional detection models ineffective at capturing the contour features of small-sized helmets. This study… Read full abstract & cite →

YOLOv8 safety helmet wearing detection slim- neck attention mechanism

Abstract doi.org/10.14569/IJACSA.2025.0161244 PDF

45

A Comparative Review of AI, IoT, and Big Data in Healthcare: Towards a Data-Centric Approach for Enhanced Data Quality and Contextual Adaptability

Author 1: Imane RAFIQ Author 2: Zahi JARIR Author 3: Hiba ASRI

The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data is revolutionizing healthcare by enabling predictive diagnostics, real-time monitoring, and personalized treatment through data-driven analytics and intelligent decision-making. Despite these advancements, the effectiveness of such systems is significantly hindered by poor data quality, including issues such… Read full abstract & cite →

Data-Centric AI IoT Big Data Analytics healthcare informatics data quality bias mitigation privacy predictive analytics machine learning disease prediction

Abstract doi.org/10.14569/IJACSA.2025.0161245 PDF

46

A Bibliometric Analysis of Blockchain Applications in E-Commerce: Trends and Research Directions

Author 1: Nguyen Thi Phuong Giang Author 2: Le Ngoc Son Author 3: Thai Dong Tan

Blockchain technology has emerged as a transformative force within the e-commerce industry, offering significant potential to address longstanding issues such as data security, transaction transparency, and customer trust. Despite its growing relevance, the academic exploration of blockchain applications in e-commerce remains fragmented and lacks a cohesive research agenda. This study… Read full abstract & cite →

Blockchain e-commerce bibliometric analysis smart contracts digital transformation

Abstract doi.org/10.14569/IJACSA.2025.0161246 PDF

47

Related Multi-Task Allocation Scheme Based on Greedy Algorithm in Mobile Crowdsensing

Author 1: Xia Zhuoyue Author 2: Raja Kumar Murugesan

With the popularity of mobile intelligent devices, the mobile crowdsensing (MCS) network based on wireless sensor networks and crowdsourcing technology came into being. There is more and more research on MCS, and it has been applied in many scenarios. Due to the increase in data volume of the MCS platform… Read full abstract & cite →

Mobile crowdsensing task allocation fuzzy logic greedy algorithm

Abstract doi.org/10.14569/IJACSA.2025.0161247 PDF

48

A Lightweight Rule-Based Detection Approach for ARP Flooding Malware in Office Networks

Author 1: Rizal Fathoni Aji Author 2: Heri Kurniawan Author 3: Nilamsari Putri Utami

Address Resolution Protocol (ARP) is a standard protocol used to map an IP address to its MAC address so the network can send packets to its destination. Office networks, which typically have limited network resources, are vulnerable to ARP flooding attacks launched by malware. ARP flooding can be used by… Read full abstract & cite →

ARP flooding cybersecurity detection rule-based detection lightweight intrusion detection

Abstract doi.org/10.14569/IJACSA.2025.0161248 PDF

49

EfficientNet-Based Melanoma Classification with CBAM Attention and Monte Carlo Dropout for Robust Uncertainty Estimation

Author 1: Soujenya Voggu Author 2: Shadab Siddiqui Author 3: Shahin Fatima

Recent developments in deep learning have demonstrated tremendous potential for enhancing medical picture classification tasks, particularly for the detection of skin malignancies like melanoma. However, it is still a huge challenge to guarantee high accuracy, reliability, and interpretability in real clinical settings. This study attempted to resolve these issues by… Read full abstract & cite →

Deep learning CNN accuracy CBAM EfficientNetB4

Abstract doi.org/10.14569/IJACSA.2025.0161249 PDF

50

Multi-Objective Design Optimization of Ventilation Duct Systems: A Graph-Informed Hybrid Evolutionary Approach

Author 1: Xiangming Liu Author 2: Bin Liu Author 3: Kunze Du Author 4: Da Gao Author 5: Nan Li

Optimizing silencer placement in Heating, Ventilation, and Air Conditioning (HVAC) systems is a complex multi-objective problem due to conflicting objectives (noise, energy, cost) and intricate topological constraints. Conventional Multi-Objective Evolutionary Algorithms (MOEAs) often exhibit inefficient convergence on such problems due to their reliance on random search strategies. Addressing this challenging… Read full abstract & cite →

Multi-objective optimization NSGA-III graph-informed optimization HVAC design heuristic search domain knowledge

Abstract doi.org/10.14569/IJACSA.2025.0161250 PDF

51

AI-Powered Architecture Refactoring: From Legacy Systems to Modern Patterns

Author 1: Mohamed El BOUKHARI Author 2: Nassim KHARMOUM Author 3: Soumia ZITI

This study explores the integration of artificial intelligence (AI), especially large language models (LLMs), into software engineering, particularly the architecture refactoring process, focusing on automated command-query classification for legacy systems transitioning to the Command Query Responsibility Segregation (CQRS) pattern. We present Airchitect, a modular system. NET-based tools that orchestrate legacy… Read full abstract & cite →

Artificial intelligence LLM AI-driven refactoring code-level refactoring legacy systems command and query responsibility segregation CQRS software architecture refactoring software engineering CodeSearchNet

Abstract doi.org/10.14569/IJACSA.2025.0161251 PDF

52

Optimizing Dermatological Image Classification Using Efficient Convolutional Neural Network Architecture

Author 1: Khalil Ladrham Author 2: Hicham Gueddah

Skin diseases represent a global healthcare challenge because of their frequent occurrence and complex diagnosis. However, despite clinical advances, accurately identifying dermatological lesions remains difficult due to significant intra-class variability, overlapping visual patterns, and reliance on clinician expertise. In this study, it presents a complete overview of a number of… Read full abstract & cite →

Convolutional neural networks skin diseases medical image classification Xception clinical

Abstract doi.org/10.14569/IJACSA.2025.0161252 PDF

53

Elaboration Context Graph: A System to Support Understanding the Contexts in Elaboration Processes of Research Documents

Author 1: Sho Onami Author 2: Ryo Onuma Author 3: Hiroki Nakayama Author 4: Hiroaki Kaminaga Author 5: Youzou Miyadera Author 6: Shoichi Nakamura

Elaborating research documents is carried out by repeatedly creating and editing documents while simultaneously performing tasks such as surveys, presentation of results, and discussion of research directions. Although indispensable for advancing research, such work is often challenging because it requires handling diverse documents. Effective execution therefore demands an accurate understanding… Read full abstract & cite →

Elaboration contexts graph elaboration work of research documents elaboration contexts understanding work circumstances and histories screenshots

Abstract doi.org/10.14569/IJACSA.2025.0161253 PDF

54

Machine Learning-Based Dissolved Oxygen Classification Using Low-Cost IoT Sensors for Smart Aquaponic

Author 1: Supria Author 2: Afis Julianto Author 3: Wahyat Author 4: Marzuarman Author 5: M Nur Faizi Author 6: Hardiyanto

Dissolved oxygen (DO) plays a vital role in maintaining balanced aquaponic ecosystems, yet conventional optical and galvanic DO sensors remain costly and impractical for low-budget deployments. However, most existing dissolved oxygen monitoring studies rely on costly sensing infrastructures, regression-oriented prediction approaches, or centralized processing schemes, which limit their applicability in… Read full abstract & cite →

Aquaponic dissolved oxygen IoT machine learning XGBoost low-cost sensors

Abstract doi.org/10.14569/IJACSA.2025.0161254 PDF

55

A Game-Based Learning Model for Basic Life Support Using First-Person Interactive Simulation

Author 1: Nur Raidah Rahim Author 2: Siti Aisyah Mohd Nasron Author 3: Sazilah Salam Author 4: Che Ku Nuraini Che Ku Mohd Author 5: Wan Mohd Ya’akob Wan Bejuri Author 6: Richki Hardi Author 7: Nur Sri Syazana Rahim

Previous BLS and first-aid learning studies largely rely on traditional face-to-face training or low-fidelity digital approaches, which are often costly, time-consuming, and inaccessible to many learners, especially laypersons. Many serious games focus primarily on awareness and conceptual knowledge, rather than procedural mastery and real-time decision-making. In addition, most existing games… Read full abstract & cite →

Basic life support emergency simulation game-based learning serious games

Abstract doi.org/10.14569/IJACSA.2025.0161255 PDF

56

Enhancing Arabic Biomedical Named Entity Recognition Using Transformer-Based Representations and CRF Sequence Labeling

Author 1: Nassima Gannoune Author 2: Abdellah Madani Author 3: Mohamed Kissi

Electronic health records have witnessed tremendous growth in recent years. To make these documents useful for decision-making, high-performance natural language processing (NLP) systems are essential. Named entity recognition (NER) is a critical task for many biomedical NLP applications that contribute to improving patient care, drug discovery, and disease surveillance. However… Read full abstract & cite →

Named entity recognition electronic health records natural language processing CAMeLBERT CRF

Abstract doi.org/10.14569/IJACSA.2025.0161256 PDF

57

Adaptive Denoising of Partial Discharge Using Absolute Difference Optimization Versus Artificial Neural Networks

Author 1: Kui-Fern Chin Author 2: Chang-Yii Chai Author 3: Ismail Saad Author 4: Yee-Ann Lee

Accurate partial discharge (PD) localization in medium-voltage (MV) power cables is essential for condition-based maintenance, yet it remains unreliable when PD pulses are masked by broadband noise and narrowband interference. The novelty of this work is a controlled denoiser-to-localization benchmarking framework that isolates the denoising front end, while keeping the… Read full abstract & cite →

Partial discharge localization adaptive denoising optimization discrete wavelet transform artificial neural network

Abstract doi.org/10.14569/IJACSA.2025.0161257 PDF

58

Improving YOLO11 Architecture for Reckless Driving Detection on the Road

Author 1: Sutikno Author 2: Aris Sugiharto Author 3: Retno Kusumaningrum

Reckless driving behavior on the road can increase the risk of traffic accidents for drivers and other road users. Currently, supervision remains weak, particularly in direct supervision, due to the limited number of officers. This study developed an automated system to detect reckless drivers based on their road trajectories. This… Read full abstract & cite →

Reckless driving detection improved YOLO11n-cls added convolution blocks added C3k2 blocks

Abstract doi.org/10.14569/IJACSA.2025.0161258 PDF

59

Formal Verification Unified Modeling Language Statechart Using Enhancement Common Modeling Language

Author 1: Muhammad Amsyar Azwarrudin Author 2: Pathiah Abdul Samat Author 3: Norhayati Mohd Ali Author 4: Novia Indriaty Admodisastro

Modern systems are rapidly evolving and increasing in complexity to satisfy growing requirements. Such systems often incorporate multiple hierarchical statecharts within their behavior modeling diagram, which significantly complicates the verification process. To address this challenge, the Common Modeling Language (CML) was introduced as an intermediate modeling language for formal verification… Read full abstract & cite →

CML E-CML formal verification model checkers UML Statechart

Abstract doi.org/10.14569/IJACSA.2025.0161259 PDF

60

Detecting Low-Quality Deepfake Videos Using 3D Residual Vision Transformer

Author 1: Amna Saga Author 2: Lili N. A Author 3: Fatimah Khalid Author 4: Nor Fazlida Mohd Sani Author 5: Hussna E. M. Abdalla Author 6: Zulfahmi Syahputra Author 7: Rian Farta Wijaya

The rapid evolution of deep generative models has facilitated the creation of "Deepfakes", enabling the synthesis of hyper-realistic facial manipulations that threaten the trustworthiness of digital media. While forensic countermeasures have been developed to identify these forgeries, deepfake detection in real-world scenarios is severely hampered by video compression artifacts, which… Read full abstract & cite →

Deepfake detection compressed deepfake videos low-quality deepfakes 3D convolutional neural networks Video Vision Transformer

Abstract doi.org/10.14569/IJACSA.2025.0161260 PDF

61

Contact-Free Cardiovascular Monitoring Using AI-Driven Radar and Sensor Fusion on a Hybrid Edge-Cloud Platform

Author 1: K Ravindra Shetty Author 2: Shanthala K V Author 3: Nishanth A R Author 4: Himani Jain

Access to essential cardiovascular parameters such as heart rate (HR), heart rate variability (HRV), and blood pressure (BP) remains limited in low-income and remote populations, particularly among older adults in developing regions. Continuous, simultaneous, and contact-free monitoring of these parameters beyond close proximity can enhance early detection, screening, and management… Read full abstract & cite →

Wireless sensing radar signal processing sensor fusion contact-free monitoring heart rate heart rate variability blood pressure deep learning

Abstract doi.org/10.14569/IJACSA.2025.0161261 PDF

62

Choosing the Arena: A Systematic Review of Simulators for Deep Reinforcement Learning in Mobile Robot Navigation

Author 1: Zakaria Haja Author 2: Leila Kelmoua Author 3: Ihababdelbasset Annaki Author 4: Jamal Berrich Author 5: Toumi Bouchentouf

This study presents a formal Systematic Literature Review (SLR) to address a critical methodological question in robotics research: "Which simulator is most suitable for a given Deep Reinforcement Learning (DRL) algorithm and mobile robot navigation task?" The choice of a simulation environment profoundly impacts policy robustness, data efficiency, and sim-to-real… Read full abstract & cite →

Simulator mobile robot Deep Reinforcement Learning navigation

Abstract doi.org/10.14569/IJACSA.2025.0161262 PDF

63

Deep Learning Approach for Solar Radiation Forecasting in a Tropical Region Using LSTM Networks

Author 1: Manuel Ospina Author 2: Gabriel Chanchí Author 3: Álvaro Realpe

Solar radiation forecasting is a key task for energy planning, grid management, and photovoltaic deployment, especially in tropical regions where weather variability reduces operational reliability. This work applies deep learning techniques to forecast hourly solar radiation in Mompox, Colombia, using Long Short-Term Memory (LSTM) neural networks. Three temporal windows were… Read full abstract & cite →

Deep learning LSTM networks renewable energy solar radiation forecasting time series prediction

Abstract doi.org/10.14569/IJACSA.2025.0161263 PDF

64

A Hybrid Spherical Fuzzy–Machine Learning Model for Multi-Criteria Decision-Making in Sustainable Water Resource Management

Author 1: Edanur Ergün Author 2: Serkan Eti Author 3: Serhat Yüksel Author 4: Hasan Dinçer

The aim of this study is to develop an innovative, multi-dimensional, and uncertain decision-making model that can identify the most appropriate alternative irrigation method for the efficient use of water resources in agriculture. In this context, the proposed model is based on the integrated use of spherical fuzzy sets, machine… Read full abstract & cite →

Irrigation activities water use decision-making model machine learning MEREC WASPAS

Abstract doi.org/10.14569/IJACSA.2025.0161264 PDF

65

Balancing Privacy and Acceptance: The Role of Anthropomorphism and Information Sensitivity in Autonomous Taxis

Author 1: Jia Fu Author 2: Kyoung-jae Kim

This study investigates how anthropomorphic interface design and information sensitivity influence users’ acceptance of autonomous vehicles (AVS), and examines the underlying role of privacy concern and its boundary conditions in a commercial autonomous taxi context. Addressing prior research that has predominantly examined anthropomorphism or privacy concerns in isolation, this study… Read full abstract & cite →

Anthropomorphism information sensitivity privacy concern technology acceptance individual cultural value technical familiarity autonomous taxis

Abstract doi.org/10.14569/IJACSA.2025.0161265 PDF

66

Intelligent Systems, Machine Learning, and Deep Learning Algorithms for Detecting Banking Fraud: A Review

Author 1: Jessica Vazallo-Bautista Author 2: Allison Villalobos-Peña Author 3: Juan Soria-Quijaite

The increase in unauthorized remote banking fraud has intensified with the expansion of digital channels, creating new risks and highlighting the inadequacy of traditional methods based on fixed rules and manual audits. This review aims to synthesize recent scientific evidence on the use of machine learning and deep learning techniques… Read full abstract & cite →

Deep learning algorithms machine learning fraud detection real-time methods

Abstract doi.org/10.14569/IJACSA.2025.0161266 PDF

67

User Experience Evaluation in Government Applications: A Systematic Review

Author 1: Emmy Hossain Author 2: Noris Mohd Norowi Author 3: Azrina Kamaruddin Author 4: Hazura Zulzalil

Evaluating the User Experience (UX) of government applications is becoming increasingly crucial as governments deploy public services online. Nevertheless, research in this area remains fragmented. Correspondingly, this study presents a systematic review of UX evaluation in government applications to address the following Research Questions (RQs): What UX evaluation approaches and… Read full abstract & cite →

User Experience Evaluation UX evaluation government applications e-government systematic review

Abstract doi.org/10.14569/IJACSA.2025.0161267 PDF

68

Feature Engineering for Machine Learning-Based Trading Systems Using Decision Tree, Random Forest, and Gradient Boosting

Author 1: Nugroho Agus Haryono Author 2: Yuan Lukito Author 3: Aditya Wikan Mahastama

Machine learning-based trading systems require the selection and creation of features that crucially determine the performance level of the trading system. This study introduces an asset-specific, correlation-based feature selection approach for machine learning–based stock trading models. The research conducts a systematic evaluation of the influence of lookup period, the number… Read full abstract & cite →

Feature engineering machine learning trading system decision tree Random Forest Gradient Boosting

Abstract doi.org/10.14569/IJACSA.2025.0161268 PDF

69

Computational Intelligence for Sustainable Banking: A Novel Fermatean Fuzzy LOPCOW–EDAS Framework

Author 1: Majidah Majidah Author 2: Dadan Rahadian Author 3: Anisah Firli Author 4: Suhal Kusairi Author 5: Serkan Eti Author 6: Serhat Yüksel Author 7: Hasan Dinçer

The primary objective of this study is to identify the priority strategies required for banks to achieve their sustainable growth targets and to develop a new fuzzy multi-criteria decision-making model sensitive to uncertainty conditions. The model proposed in this study is designed based on the integration of Fermatean Fuzzy LOPCOW–EDAS… Read full abstract & cite →

Fermatean fuzzy sets multi-criteria decision-making sustainable banking digital transformation decision support systems

Abstract doi.org/10.14569/IJACSA.2025.0161269 PDF

70

Predicting the Duration of Judicial Cases Using Hybrid Systems Based on Language Models

Author 1: Amina BOUHOUCHE Author 2: Saliha YASSINE Author 3: Mustapha ESGHIR Author 4: Mohammed ERRACHID

Recent technological developments in the field of Natural Language Processing (NLP), notably due to Transformer architectures and language models, have made it possible to tackle aspects that were previously inaccessible with traditional tools. The present study addresses the issue of predicting legal case durations using Arabic judicial data. For this… Read full abstract & cite →

Language model judicial case durations legal domain Arabic legal corpus

Abstract doi.org/10.14569/IJACSA.2025.0161270 PDF

71

Reinforcement Learning-Driven Adaptive Aggregation for Blockchain-Enabled Federated Learning in Secure EHR Management

Author 1: Cai Yanmin Author 2: Wang Lei Author 3: Zainura Idrus Author 4: Jasni Mohamad Zain Author 5: Marina Yusoff

With the rapid digitization of healthcare, blockchain-integrated federated learning (FL) for EHR management faces challenges of heterogeneous data, high latency, and adversarial vulnerabilities. This study proposes a novel Reinforcement Learning-Driven Adaptive Aggregation (RL-DAA) in an enhanced blockchain-FL framework, using Q-learning to dynamically optimize model weights based on trust, data quality… Read full abstract & cite →

Federated learning blockchain reinforcement learning electronic health records privacy preservation

Abstract doi.org/10.14569/IJACSA.2025.0161271 PDF

72

Engineering Prompt-Orchestrated LLM Workflows for Automated Test Case Generation in Agile Environments

Author 1: Almeyda Alania Fredy Antonio Author 2: Barrientos Padilla Alfredo Author 3: Siancas Garay Ronald Gustavo

Manual test case generation for agile software development is a critical bottleneck that is costly, inconsistent, and error-prone. This study introduces a prompt-engineering and multi-level orchestration framework to automate this process. The proposed approach explicitly targets the automated generation of high-level acceptance test cases, addressing a gap in existing research… Read full abstract & cite →

Software testing test case generation Large Language Models Generative AI prompt engineering LLM orchestration Behavior-Driven Development (BDD) agile methodology acceptance testing schema-aware prompting Human-in-the-Loop quality assurance Software testing test case generation Large Language Models Generative AI prompt engineering LLM orchestration Behavior-Driven Development (BDD) agile methodology acceptance testing schema-aware prompting Human-in-the-Loop quality assurance automation

Abstract doi.org/10.14569/IJACSA.2025.0161272 PDF

73

Task Scheduling in Cloud Computing Environment Based on Dwarf Mongoose Optimization

Author 1: Olanrewaju Lawrence Abraham Author 2: Md Asri Ngadi Author 3: Johan Bin Mohamad Sharif Author 4: Mohd Kufaisal Mohd Sidik Author 5: Ogunyinka Taiwo Kolawole

The rapid advancement of the Internet and Internet of Things (IoT) technologies has significantly increased the demand for scalable and efficient cloud computing solutions. Task scheduling, a critical aspect of cloud computing, directly impacts system performance by influencing resource utilization, execution time, and operational costs. However, scheduling tasks in large-scale… Read full abstract & cite →

Task scheduling cloud computing virtual machines dwarf mongoose optimization algorithm Cloudsim makespan

Abstract doi.org/10.14569/IJACSA.2025.0161273 PDF

74

H∞ Control Design for Nonlinear Systems via Multimodel Approach

Author 1: Rihab ABDELKRIM

Nonlinear systems are integral to contemporary engineering applications, yet their regulation remains a significant challenge due to complex and highly dynamic behaviors. Robust control frameworks, particularly H∞ methods, provide systematic tools to ensure stability and performance in the presence of disturbances and modeling uncertainties. This study proposes an integrated design… Read full abstract & cite →

Nonlinear systems H∞ loop shaping control multimodel

Abstract doi.org/10.14569/IJACSA.2025.0161274 PDF

75

Modelling Dimensions and Indicators of Readiness for Lean 4.0 Implementation in Indonesian Industries

Author 1: Sarjono Sarjono Author 2: Pudji Hastuti Author 3: Satrio Utomo Author 4: Gani Soehadi Author 5: Budi Setiadi Sadikin Author 6: Manifas Zubair Author 7: Jaizuluddin Mahmud Author 8: Rizki Arizal Purnama Author 9: Hardono Hardono Author 10: Helen Fifianny

The integration of Lean Manufacturing and Industry 4.0, known as Lean 4.0, has emerged as a strategic approach to enhancing operational efficiency, digital transformation, and competitiveness in modern industries. The rapid development of Industry 4.0 has driven a massive transformation in the global manufacturing sector, including Indonesia, which continues to… Read full abstract & cite →

Lean 4.0 Industry 4.0 readiness assessment organizational readiness Indonesian industries digital transformation

Abstract doi.org/10.14569/IJACSA.2025.0161275 PDF

76

Autonomous Blockchain-Enabled Security Framework for Smart Grids Using Adaptive AI

Author 1: Brinal Colaco Author 2: Nazneen Ansari

The increasing interconnectivity of smart grids exposes critical energy infrastructure to more sophisticated cyber threats, necessitating adaptable and auditable security measures. This study presents a blockchain-enabled, self-improving intrusion detection system (IDS) that integrates a permissioned blockchain, autonomous governance loops, and a hybrid CNN–LSTM detector. The platform retrains models across federated… Read full abstract & cite →

Smart Grid Security intrusion detection system (IDS) adaptive AI deep learning false data injection (FDI) attacks cyber-physical systems (CPS)

Abstract doi.org/10.14569/IJACSA.2025.0161276 PDF

77

Automated Question Answering System for FAQ COVID-19 Using Word Embeddings

Author 1: Nazar Elfadil Author 2: Sarah Saad Alanazi

This study's scope includes the development of a Question Answering System for COVID-19 and a review of that system. This is unlike previous work in biomedical QAS, which primarily targets technical users. This work leans towards developing a COVID-19 QAS customized for the general public, especially those who have limited… Read full abstract & cite →

Word embedding Bag of Words BERT Word2Vec Qaviar Question Answering System (QAS) COVID-19 natural language processing public health informatics

Abstract doi.org/10.14569/IJACSA.2025.0161277 PDF

78

Adversarial Robustness of Deep Learning in Medical Imaging: A Comprehensive Survey and Benchmark of State-of-the-Art Architectures

Author 1: Neethunath M R Author 2: Gladston Raj S Author 3: Pradeepan P

The integration of artificial intelligence into medical diagnostics promises to revolutionize healthcare. However, the reliability of these systems is critically undermined by adversarial examples, which are imperceptible perturbations that can lead to misdiagnosis. Ensuring the robustness of AI-driven clinical decisions is paramount for ensuring patient safety and institutional trust. This… Read full abstract & cite →

Adversarial attacks dermatoscopy deep learning robustness benchmark security in medical AI

Abstract doi.org/10.14569/IJACSA.2025.0161278 PDF

79

Enhanced Detection of Acute Lymphocytic Leukemia Using Deep Learning and Hybrid Classifiers on Microscopic Blood Images

Author 1: H. A. El Shenbary Author 2: Amr T. A. Elsayed Author 3: Khaled A. A. Khalaf Allah Author 4: Belal Z. Hassan

There is no doubt that a significant number of individuals worldwide suffer from blood cancer. A lot of people are unaware of the dangers associated with this disease, which can be fatal. When diagnosed, patients may feel intense fear and a sense of powerlessness. In addition, due to the rarity… Read full abstract & cite →

Deep learning transfer learning leukemia Alexnet VGG19 SVM K-NN classification

Abstract doi.org/10.14569/IJACSA.2025.0161279 PDF

80

An AI-Driven Framework for Network Intrusion Detection Using ANOVA-Based Feature Selection

Author 1: Salam Allawi Hussein Author 2: Sándor Répás

In the last few years, cyberattacks have become more complex, and it is becoming increasingly necessary to establish secure networks. This study examines enhancements to intrusion detection systems (IDSs) with the implementation of machine learning for the categorization of network traffic attacks. For the current study, we utilize four publicly… Read full abstract & cite →

Network security intrusion detection machine learning feature selection

Abstract doi.org/10.14569/IJACSA.2025.0161280 PDF

81

DrugCellGNN: Graph Convolutional Networks for Integrating Omics and Drug Similarities in Cancer Therapy Prediction

Author 1: Gehad Awad Aly Author 2: Rania Ahmed Abdel Azeem Abul Seoud Author 3: Dina Ahmed Salem

Predicting drug response in cancer cell lines is a critical step toward precision oncology, enabling more efficient therapeutic discovery and personalized treatment strategies. However, the complexity of drug–cell interactions, driven by diverse omics profiles and structural variability among drugs, poses significant challenges for conventional machine learning approaches. In this study… Read full abstract & cite →

Precision oncology drug sensitivity prediction graph neural networks (GNNs) multi-omics integration focal loss PCA

Abstract doi.org/10.14569/IJACSA.2025.0161281 PDF

82

Personalized Point of Interest in Location-Based Augmented Reality Tourism Application

Author 1: Rimaniza Zainal Abidin Author 2: Ma Boqi Author 3: Rosilah Hassan Author 4: Nor Shahriza Abdul Karim Author 5: Mohamad Hidir Mhd Salim

In recent years, the rapid growth of the tourism industry and increasing demand for efficient and meaningful travel experiences have highlighted the need for smarter travel assistance tools. Many tourists, particularly first-time visitors, often face challenges navigating unfamiliar destinations and identifying relevant points of interest, leading to delays, inconvenience, and… Read full abstract & cite →

Augmented Reality LBAR PutrajayAR AR Discovery AR Recommendation

Abstract doi.org/10.14569/IJACSA.2025.0161282 PDF

83

Multi-Class Object Detection Using Quantized YOLOv11 for Real-Time Inference

Author 1: Yehia A. Soliman Author 2: Amr Ghoneim Author 3: Mahmoud Elkhouly

Real-time multi-class object detection on embedded devices poses significant challenges due to limited computational power, memory capacity, and energy efficiency requirements. Conventional high-precision object detectors, such as YOLOv11, deliver outstanding accuracy but are computationally intensive, making them unsuitable for deployment on resource-constrained hardware. This study presents a quantized implementation of… Read full abstract & cite →

Quantized neural networks YOLOv11 object detection embedded systems real-time inference model optimization

Abstract doi.org/10.14569/IJACSA.2025.0161283 PDF

84

SWAP Optimization for Qubit Mapping Based on the Centric-Shortest Quantum Gate Set in NISQ Devices

Author 1: Shujuan Liu Author 2: Hui Li Author 3: Yingsong Ji Author 4: Jiepeng Wang

In the Quantum computing era of Noisy Intermediate-Scale Quantum (NISQ) devices, conventional qubit mapping strategies typically rely on specific heuristic rules to solve the mapping problem, overlooking the impact of other factors on the mapping, which leads to increased overhead from extra SWAP gates. To address this issue, we propose… Read full abstract & cite →

Quantum computing qubit mapping Centric-Shortest Quantum Gate Set (C-SQGS) executable SWAP gate multi-factor cost function

Abstract doi.org/10.14569/IJACSA.2025.0161284 PDF

85

Predictive Modelling of Flood Dynamics in Malaysia’s East Coast Using an NARX Model

Author 1: Nur Nabilah Zakaria Author 2: Azlee Zabidi Author 3: Mahmood Alsaadi Author 4: Mohd Izham Mohd Jaya

Flood forecasting is critical for improving early warning systems in Malaysia’s East Coast region, particularly in flood-prone Pekan. This study develops a Nonlinear Autoregressive with Exogenous Inputs (NARX) model to predict river water levels using data from four stations: Sungai Pahang, Sungai Pahang Tua, Sungai Paloh Hinai, and Sungai Mentiga… Read full abstract & cite →

Flood prediction NARX model hydrological modelling Pekan

Abstract doi.org/10.14569/IJACSA.2025.0161285 PDF

86

Polarimetric Imaging and Computational Techniques for Identification of Malignant Lesions

Author 1: Mohammed Hachem MEZOUAR Author 2: Abdessamad ACHNAOUI Author 3: Mohammed TBOUDA Author 4: Said CHOUHAM Author 5: Said BELKACIM Author 6: Mohamed NEJMEDDINE Author 7: Driss MGHARAZ

According to the International Agency for Research on Cancer, cervical cancer is a major cause of death among Moroccan women, with high incidence and mortality rates. Early detection remains essential to increasing patients’ chances of recovery. Our study combines polarized light imaging, digital image correlation (DIC), Gray-Level Co-occurrence Matrix (GLCM)… Read full abstract & cite →

Polarized light digital image correlation Gray-Level Co-occurrence Matrix fractal cervix cancer

Abstract doi.org/10.14569/IJACSA.2025.0161286 PDF

87

Implementation of Hybrid Channel-Aware Prioritization (HCAP) Scheduler for a Multi-User MIMO System in 5G Communication

Author 1: Krishna Deshpande Author 2: Virupaxi B. Dalal Author 3: Yedukondalu Udara

The evolution of 5G networks demands highly efficient resource allocation strategies to accommodate burgeoning mobile data traffic, latency-sensitive applications, and diverse user requirements. Multi-User Multiple-Input Multiple-Output (MU-MIMO) technology is a cornerstone of 5G, enabling simultaneous service to multiple users and significantly improving spectral efficiency. However, its performance is critically dependent… Read full abstract & cite →

Multiple input and multiple output HCAP CQI throughput 5G QoS k-means clustering resource scheduling

Abstract doi.org/10.14569/IJACSA.2025.0161287 PDF

88

Adaptive Intelligence in Retail Space Optimization: Modeling the Coffee Shop Dilemma with Q-Learning Agents

Author 1: Siranee Nuchitprasitchai Author 2: Kanchana Viriyapant Author 3: Kanjanee Satitrangseewong Author 4: May Myo Naing

This study models the "coffee shop dilemma", where customer attendance is discouraged by both overcrowding and emptiness. Using an agent-based model with Q-learning reinforcement learning, this study simulates the daily decisions of 100 agents over a one-year period. The results reveal a self-organizing attendance cycle around a $60\%$ capacity threshold… Read full abstract & cite →

El Farol Bar problem agent-based modeling Q-learning reinforcement learning customer behavior congestion paradox decision-making coffee shop operations

Abstract doi.org/10.14569/IJACSA.2025.0161288 PDF

89

AI Readiness as a Pathway to Sustainable Competitiveness in Tourism Transport: Evidence from an Integrative SEM Model

Author 1: Mohamed Amine Frikha

Artificial intelligence (AI) is transforming demand forecasting in the tourism transportation sector, delivering unprecedented accuracy in volatile, seasonal, and customer-sensitive environments. Yet, many firms struggle to translate AI's potential into performance due to gaps in technological and organizational readiness. Drawing on the resource-based-view (RBV) and the Technology-Organization-Environment (TOE) framework, this… Read full abstract & cite →

Artificial intelligence (AI) demand forecasting tourism transport Intelligent Transportation Systems (ITS) digital infrastructure Resource-Based View (RBV) Technology- Organization-Environment (TOE) Framework Structural Equation Modeling (SEM) mediation analysis sustainable mobility data-driven decision making

Abstract doi.org/10.14569/IJACSA.2025.0161289 PDF

90

RoadSCNet: Road Surface Condition Detection Network

Author 1: Sujittra Sa-ngiem Author 2: Kwankamon Dittakan Author 3: Saroch Boonsiripant

The quality of the road is an important issue that contributes to accidents, resulting in the loss of time, resources, and lives. To manually survey the road issue. This is very delayed and costly. Automatic detection of road conditions facilitates surveys more efficiently than human methods. This research identifies three… Read full abstract & cite →

RoadSCNet road surface detect road road condition deep learning crack pothole manhole cover image analysis convolutional neural network CNN

Abstract doi.org/10.14569/IJACSA.2025.0161290 PDF

91

Optimizing Fetal Health Prediction Using Machine Learning on Biocompatible Sensor Data

Author 1: Yuli Wahyuni Author 2: Hadiyanto Author 3: Ridwan Sanjaya Author 4: Nendar Herdianto

Automatic Fetal Health Prediction plays a vital role in supporting early prenatal intervention through continuous and non-invasive monitoring. Recent advances in biocompatible sensors enable the safe long-term acquisition of physiological signals, which can be effectively analyzed using machine learning techniques. This study proposes a comprehensive machine learning pipeline for Fetal… Read full abstract & cite →

Fetal health prediction biocompatible sensors machine learning Random Forest SVM

Abstract doi.org/10.14569/IJACSA.2025.0161291 PDF

92

6G Wireless Networks in the Generative AI Age: Overview, Techniques, and Future Trends

Author 1: Sallar S. Murad Author 2: Rozin Badeel Author 3: Harth Ghassan Hamid Author 4: Reham A. Ahmed

As the world move beyond the 5G era, the emergence of 6G promises a significant integration with innovative communication paradigms and burgeoning technology trends, actualizing previously utopian concepts alongside increased technical complexities. Analytical models offer basic frameworks, but ML and AI now outperform them in solving complex problems, either by… Read full abstract & cite →

GenAI 6G generative models intelligent systems wireless communication

Abstract doi.org/10.14569/IJACSA.2025.0161292 PDF

93

Context-Aware Requirements Prioritization Using Integrated Regression Learning with Ordinal Neural Modeling and Roberta

Author 1: Prasis Poudel Author 2: Noraini Che Pa Author 3: Abdikadir Yusuf Mohamed

Effective prioritization of software requirements is essential for reducing project risks, optimizing resource allocation, and ensuring timely delivery. Conventional approaches such as Analytic Hierarchy Process (AHP) and MoSCoW often suffer from subjectivity, inefficiency, and poor scalability, making them unsuitable for large-scale projects. Although machine learning (ML) based methods improve scalability… Read full abstract & cite →

Requirements prioritization context-aware prioritization machine learning natural language processing ordinal regression dependency analysis Explainable AI

Abstract doi.org/10.14569/IJACSA.2025.0161293 PDF

94

Functions Inverse Using Neural Networks via Branch-Wise Decomposition and Newton Refinement

Author 1: Abdullah Balamash

In this work, a unified framework (using Neural Networks) is proposed to find the inverse of mathematical functions, spanning both simple one-to-one mapping and complex multivalued relations. The approach uses standard multilayer Neural Networks (NN) to approximate the functions’ inverse and introduces a deterministic branch-wise decomposition to handle multi-valued inverses… Read full abstract & cite →

Neural networks function inverse Newton method branch-wise decomposition

Abstract doi.org/10.14569/IJACSA.2025.0161294 PDF

95

Hybrid Diagnostic Approaches Integrating Fuzzy Logic and Neural Networks for Parkinson’s Disease

Author 1: Marwah Muwafaq Almozani Author 2: Hüseyin Demirel

Parkinson’s Disease (PD) is a movement-related and non-motor symptom neurological condition that requires early diagnosis and treatment. Fuzzy Logic and Neural Network Diagnostic hybrids are more accurate and reliable. The diagnostic approaches of PD are not sensitive to early PD, are subjective in assessing symptoms, and lack standardization. Such problems… Read full abstract & cite →

Convolutional neural network disease hybrid diagnostic Parkinson's disease fuzzy logic

Abstract doi.org/10.14569/IJACSA.2025.0161295 PDF

96

Evaluating CTGAN-Generated Synthetic Data for Heart Disease Prediction: Fidelity, Predictive Utility, and Feature Preservation

Author 1: Wan Aezwani Wan Abu Bakar Author 2: Nur Laila Najwa Josdi Author 3: Mustafa Man Author 4: Evizal Abdul Kadir

The increasing scarcity and sensitivity of clinical data necessitate the development of high-quality synthetic datasets. This study evaluated the ability of Conditional Tabular GAN (CTGAN) to generate synthetic heart disease data that preserves the statistical properties and predictive patterns of the Cleveland Heart Disease dataset. It assessed the fidelity of… Read full abstract & cite →

Conditional Tabular GAN (CTGAN) correlation analysis dimensionality reduction feature importance heart disease prediction predictive utility synthetic data tabular data fidelity

Abstract doi.org/10.14569/IJACSA.2025.0161296 PDF

97

Epidemic Modeling with a Hybrid RF-LSTM Method for Healthcare Demand Prediction

Author 1: Budor Alshammari Author 2: Bassam Zafar

Accurate resource demand forecasts are necessary for sustainable healthcare systems to preserve flexibility and efficiency as well as to provide services in a professional manner. In this work, we propose an integrated Random Forest/Long Short-Term Memory (RF-LSTM) model for predicting Saudi Arabia's national healthcare resource demand. It combines non-linear feature… Read full abstract & cite →

Predictive analytics Hybrid modeling digital health Saudi Arabia COVID-19 decision support systems

Abstract doi.org/10.14569/IJACSA.2025.0161297 PDF

98

Intelligent Platform for Employee Retention Prediction

Author 1: Medha Wyawahare Author 2: Milind Rane Author 3: Ashish Rodi Author 4: Samarth Arole Author 5: Aryan Mundra

Employee retention is a very important challenge to the organizations since it raises the cost of recruitment, affects domain knowledge retention, and impacts workforce stability. Presented here is a platform-based intelligent employee retention prediction system as a real-time HR decision support tool. As a part of the research, Feedforward Neural… Read full abstract & cite →

Employee retention feedforward neural network Large Language Model HR analytics intelligent platform

Abstract doi.org/10.14569/IJACSA.2025.0161298 PDF

99

Spatial Classification of Fertilizer Requirements Using Fuzzy C-Means on Shallot Agricultural Land

Author 1: Roghib Muhammad Hujja Author 2: Ahmad Ashari Author 3: Danang Lelono Author 4: Agus Prasekti

Spatial variability in soil fertility constrains productivity in intensive shallot farming, yet fertilizer is frequently applied uniformly across fields. This practice results in nutrient inefficiencies, increased costs, and heightened environmental risks. This study introduces a fertilizer requirement mapping framework utilizing Fuzzy C-Means (FCM) clustering, a machine learning technique for data… Read full abstract & cite →

Fuzzy C-Means (FCM) soil fertility zoning NPK (Nitrogen Phosphorus Potassium) fertilizer recommendation precision agriculture Site-Specific Nutrient Management (SSNM) IoT (Internet of Things) UAV (Unmanned Aerial Vehicle)

Abstract doi.org/10.14569/IJACSA.2025.0161299 PDF

100

CleanCity IoT: A Vehicle-Mounted Platform for Real-Time Urban Air-Quality Monitoring and Forecasting in Resource-Constrained African Cities

Author 1: Eric Nizeyimana Author 2: Damien Hanyurwimfura Author 3: Gabriel Uwanyirigira Author 4: Bonaventure Karikumutima Author 5: Jimmy Nsenga Author 6: Irene Niyonambaza Mihigo

Urban air pollution is a growing public-health challenge in African cities, yet traditional monitoring stations are sparse and expensive. The paper presents CleanCity IoT, a deployed, low-cost, vehicle-mounted air-quality platform that combines IoT sensors, GSM connectivity, cloud aggregation, and machine learning to produce near-real-time exposure maps and 2-hour forecasts for… Read full abstract & cite →

CleanCity IoT air quality mobile sensing multivariate forecasting spike detection

Abstract doi.org/10.14569/IJACSA.2025.01612100 PDF

101

From Consensus to Chaos: A Vulnerability Assessment of the RAFT Algorithm

Author 1: Tamer Afifi Author 2: Abdelfatah Hegazy Author 3: Ehab Abousaif

In recent decades, the RAFT distributed consensus algorithm has become a main pillar of the distributed systems ecosystem, ensuring data consistency and fault tolerance across multiple nodes. Although the fact that RAFT is well known for its simplicity, reliability, and efficiency, its security properties are not fully recognized, leaving implementations… Read full abstract & cite →

RAFT consensus protocol security distributed systems message forgery replay attacks cryptography

Abstract doi.org/10.14569/IJACSA.2025.01612101 PDF

102

EvoNorm-GAN for Adaptive and Interpretable Detection of Ransomware in Windows PE Files

Author 1: G Badrinath Author 2: Arpita Gupta

Ransomware remains a key cybersecurity issue because of its growing amount of obfuscation, polymorphism, and constantly changing patterns of attack that repeatedly circumvent conventional defenses. Traditional systems and standard deep learning may fail, lowering accuracy and increasing false positives. To address these shortcomings, the proposed work proposes EvoNorm-GAN, a dynamic… Read full abstract & cite →

Ransomware detection EvoNorm-GAN feature-wise dynamic normalization portable executable files adversarial learning Explainable AI

Abstract doi.org/10.14569/IJACSA.2025.01612102 PDF

103

An Interpretable Analytical Intelligence Architecture Delivering Reliable Detection of Software Defect Instances

Author 1: Srinivasa Rao Katragadda Author 2: Sirisha Potluri

Software defect prediction plays a crucial role in improving software quality, yet existing approaches still suffer from severe class imbalance, redundant feature spaces, weak generalization, and limited interpretability, making their adoption in real development pipelines difficult. Many current models rely on black-box deep learning architectures or conventional classifiers that fail… Read full abstract & cite →

Contrastive learning explainable artificial intelligence feature optimization Siamese Neural Network software defect prediction

Abstract doi.org/10.14569/IJACSA.2025.01612103 PDF

104

A Hybrid CNN-BiGRU-GAN Framework for Enhanced Automated Analysis of Cervical Cancer in Medical Imaging

Author 1: Donepudi Rohini Author 2: M Kavitha

Cervical cancer screening requires reliable automated systems capable of overcoming variability in staining, morphology, and limited annotated data, which often undermine the performance of traditional machine learning and deep learning approaches. Existing techniques commonly rely on single-modality feature extraction or static fusion, resulting in weak generalization, class imbalance sensitivity, and… Read full abstract & cite →

Cervical cancer detection DiagnoFusionNet medical image analysis Adaptive Triple-Stage Feature Fusion generative adversarial networks

Abstract doi.org/10.14569/IJACSA.2025.01612104 PDF

105

Reinforcement Learning Framework for Missing Data Imputation in IoT Environments

Author 1: Ahmed M. Salama Salem Author 2: Sayed AbdelGaber A Author 3: Ahmed E. Yakoub

Continuous, accurate meteorological sensing underpins many Internet of Things (IoT) applications, from smart irrigation and urban heat-island monitoring to early weather warnings, but data from distributed stations are often disrupted by sensor faults, power loss, or communication noise, causing missing values that degrade analytics and decisions. Existing data imputation methods… Read full abstract & cite →

Data imputation reinforcement learning machine learning deep learning Internet of Things (IoT)

Abstract doi.org/10.14569/IJACSA.2025.01612105 PDF

106

Hierarchical Swin Transformer Encoder-Decoder Architecture for Robust Cerebrovascular Abnormality Segmentation in Multimodal MRI

Author 1: Nazbek Katayev Author 2: Zhanel Bakirova Author 3: Assel Kaziyeva Author 4: Aigerim Altayeva Author 5: Karakat Zhanabaykyzy Author 6: Daniyar Sultan

This study presents a hierarchical Swin Transformer–based framework for automated segmentation of cerebrovascular structures using multimodal magnetic resonance imaging. The proposed architecture integrates patch partitioning, linear embedding, hierarchical windowed self-attention, and a multilevel encoder–decoder design to address the inherent challenges of vascular segmentation, including irregular morphology, small-caliber vessel visibility, and… Read full abstract & cite →

Cerebrovascular segmentation Swin Transformer multimodal MRI deep learning vascular imaging hierarchical attention encoder–decoder architecture medical image analysis

Abstract doi.org/10.14569/IJACSA.2025.01612106 PDF

107

A Multi-Scale ROI-Aligned Deep Learning Framework for Automated Road Damage Detection and Severity Assessment

Author 1: Bakhytzhan Orazaliyevich Kulambayev Author 2: Olzhas Muratuly Olzhayev Author 3: Aigerim Bakatkaliyevna Altayeva Author 4: Zhanna Zhunisbekova

This study presents a multi-scale ROI-aligned deep learning framework designed to advance automated road damage detection and severity assessment using high-resolution roadway imagery. The proposed architecture integrates hierarchical feature extraction, a road-damage proposal network, and refined ROI-aligned encoding to capture both fine-grained local anomalies and broader contextual patterns across diverse… Read full abstract & cite →

Road damage detection deep learning ROI alignment multi-scale features severity assessment RDD2020 dataset intelligent transportation systems

Abstract doi.org/10.14569/IJACSA.2025.01612107 PDF

108

Enhanced Mobile GC Vit Architecture for Efficient Image Classification with Application to Plant Disease Detection

Author 1: Mohamed Jawher Bahrouni Author 2: Faouzi Benzarti Author 3: Mohamed Touati Author 4: Sadok Ben Yahia

Efficient and accurate automated diagnosis of plant diseases remains a challenge for deployment on resource-constrained edge devices. While hybrid vision transformers like GCViT balance accuracy and efficiency, they often lose critical high-frequency details such as fine lesion textures and leaf margins that are essential for fine-grained disease classification. To address… Read full abstract & cite →

Hybrid transformer architecture convolutional refinement block gated convolution edge devices high-frequency features tomato leaf disease classification

Abstract doi.org/10.14569/IJACSA.2025.01612108 PDF

109

Modeling Mixed Gas Reactions in Air Pollution: Stoichiometry, Kinetics, and Hazard Assessment

Author 1: T Somasekhar Author 2: Rekha B. Venkatapur

This study introduces a novel integrated framework for modeling mixed gas reactions relevant to air pollution and industrial safety, demonstrated on the reaction between carbon monoxide and ammonia producing hydrogen cyanide and water. The approach couples closed form stoichiometric mass balances with a transport corrected kinetic ordinary differential equation system… Read full abstract & cite →

Stoichiometric reaction modeling mixed-gas kinetics plug-flow transport correction Bayesian hazard classification air pollution risk assessment environmental process safety probabilistic uncertainty quantification

Abstract doi.org/10.14569/IJACSA.2025.01612109 PDF

110

VidAvDetect: A Deepfake-Inspired Vision Transformer Approach for Detecting Real Humans vs. AI-Avatars in Video Streams

Author 1: Btissam Acim Author 2: Hamid Ouhnni Author 3: Nassim Kharmoum Author 4: Soumia Ziti

The pace of advancement in Generative AI has made it possible to realize highly realistic synthetic identities in the form of avatars for non-existent persons, thus paving the way for a paradigm beyond state-of-the-art deepfake attacks that aim to manipulate real identities in people. This rapidly emerging trend poses a… Read full abstract & cite →

Vision transformer deepfake Artificial Intelligence (AI) Generative AI AI Avatar video streams

Abstract doi.org/10.14569/IJACSA.2025.01612110 PDF

111

Dynamic Sentiment Analysis on the Emergence of Pre-Trained Generative Model-Based Applications in Indonesia

Author 1: Frans Mikael Sinaga Author 2: Jefri Junifer Pangaribuan Author 3: Kelvin Author 4: Ferawaty Author 5: Andree Emmanuel Widjaja

The emergence of pre-trained generative model–based applications has intensified sentiment dynamics within Indonesia’s multi-platform digital ecosystem, where sentiment intensity and temporal fluctuations occur simultaneously. To overcome these challenges, this study extends IndoBERT by incorporating a time-aware tokenization mechanism within a fine-grained dynamic sentiment analysis framework. This mechanism is designed to… Read full abstract & cite →

Dynamic sentiment fine-grained IndoBERT multi-platform big data sentiment analysis

Abstract doi.org/10.14569/IJACSA.2025.01612111 PDF

112

Data-Driven Insights for Moroccan Airports: PCA and Clustering to Enhance Operational Performance

Author 1: H. Fatih Author 2: A. Bentaleb Author 3: M. Lazaar Author 4: B. Bentalha

Following the trend of increasing complexity among systems, in an attempt to meet air passengers’ demands for higher quality service, this paper contributes to this stream of research by studying the operational efficiency of Moroccan airports through a novel multivariate approach. This research examines the following five performance metrics: baggage… Read full abstract & cite →

Principal Component Analysis (PCA) airport performance transportation systems K-means clustering operational optimization airport efficiency airport operations management air traffic passenger experience

Abstract doi.org/10.14569/IJACSA.2025.01612112 PDF

113

Explainable AI Models for Assessing Short-Circuit Propagation in Fire-Exposed Cable Bundles

Author 1: Vijay H. Kalmani Author 2: Kishor S. Wagh Author 3: Kavita Tukaram Patil Author 4: Pallavi Jha Author 5: Tanuja Satish Dhope Author 6: Deepak Gupta Author 7: Chanakya Kumar Jha

Fire-induced short-circuit propagation in cable bundles poses significant safety risks in electrical installations, nuclear facilities, and transportation systems. Traditional fault detection methods often lack interpretability, hindering root cause analysis and preventive maintenance strategies. This paper presents novel explainable artificial intelligence (XAI) models for predicting and analyzing short-circuit propagation in fire-exposed… Read full abstract & cite →

Explainable AI short-circuit propagation fire safety cable testing SHAP values gradient boosting feature importance nuclear safety

Abstract doi.org/10.14569/IJACSA.2025.01612113 PDF

114

A Framework Design and Solutions Taxonomy for Performance Optimization in Internet of Things Network

Author 1: Mariam A. Alotaibi Author 2: Sami S. Alwakeel Author 3: Aasem N. Alyahya

The Internet of Things (IoT) is an exciting, rapidly expanding technology that’s still in its early stages and faces several complex issues. These challenges primarily arise from the limitations of IoT devices (e.g., restricted energy, memory, and processing power), the diversity of communication protocols, and the heterogeneity of interconnected devices… Read full abstract & cite →

IoT performance reliability security scalability quality energy efficiency technology

Abstract doi.org/10.14569/IJACSA.2025.01612114 PDF

115

Q-Learning Guided Local Search for the Traveling Salesman Problem

Author 1: Sanaa El Jaghaoui Author 2: Aissa Kerkour Elmiad

The Traveling Salesman Problem (TSP) remains a fundamental challenge in combinatorial optimization with applications in logistics, routing, and network design. Classical local search methods face a trade-off between solution quality and computational efficiency: while 3-opt delivers better solutions than 2-opt, its O(n3) complexity renders it impractical for large instances. This… Read full abstract & cite →

Traveling salesman problem reinforcement learning Q-Learning local search 2-opt 3-opt

Abstract doi.org/10.14569/IJACSA.2025.01612115 PDF

116

A Two-Step Real-Time Complex Environmental Vehicle Detection Model

Author 1: Zhihui Huo Author 2: Yiqian Liang Author 3: Xingju Wang

In recent years, as a critical pillar supporting the national economy and daily life, the safe and efficient operation of road traffic has highly relied on precise environmental perception capabilities. To address this, this study proposes a two-stage “denoising-detection” framework: the first stage restores clear images using an improved Uformer… Read full abstract & cite →

Object detection vehicle detection image denoising

Abstract doi.org/10.14569/IJACSA.2025.01612116 PDF

117

HCC: A Hierarchical Chart Captioning Model for Enhanced Accessibility of Chart Data for Visually Impaired Users

Author 1: Yoojeong Song Author 2: Kanghyeon Seo Author 3: Svetlana Kim Author 4: Joo Hyun Park

In educational settings, charts and graphs are commonly used to convey complex information in a simple and understandable manner. However, these visual representations often present accessibility challenges regarding Accessibility for Visually Impaired users, as they cannot be directly interpreted by screen readers without proper alternative text. This pa-per proposes a… Read full abstract & cite →

Hierarchical captioning accessibility for visually impaired chart interpretation transformer models

Abstract doi.org/10.14569/IJACSA.2025.01612117 PDF

118

Enhancing Privacy in Databases by Data-Layer

Author 1: Sami Alharbi Author 2: Samer Atawneh Author 3: Hussein Al Bazar Author 4: Roxane Elias Mallouhy

This study addresses the growing challenge of enhancing privacy in enterprise database systems, where excessive privileges and shared service accounts often lead to unauthorized data access and insider threats. The study proposes a data-layer security framework that enforces fine-grained access control based on authenticated user identities, integrating role-based access control… Read full abstract & cite →

Database privacy security model access control data protection privacy enhancing technologies database systems

Abstract doi.org/10.14569/IJACSA.2025.01612118 PDF

119

Multi-Spectral Image Analysis Using Different CNN Models to Detect the Plant Diseases in its Early Stages

Author 1: Dhiraj Bhise Author 2: Sunil Kumar Author 3: Hitesh Mohapatra

The Researchers and academicians are continuously working on minimizing the production losses due to various plant diseases. Therefore, recent technologies such as artificial intelligence (AI), and machine learning (ML) are playing a crucial role in detecting plant diseases in their early stages. These technologies help classifying plant leaves into ‘healthy’… Read full abstract & cite →

Convolutional Neural Network (CNN) Multi-spectral images Alexnet Densenet121 Resnet18 Resnet50 VGG16 VGG19 Effficeienet80 MobilenetV2 Xception InceptionV3 InceptionResnetV2

Abstract doi.org/10.14569/IJACSA.2025.01612119 PDF

120

Model-Driven Transformation of Business Processes into Blockchain Smart Contracts

Author 1: Imane Bouzaidi Tiali Author 2: Zineb Aarab Author 3: Achraf Lyazidi Author 4: Moulay Driss Rahmani

This paper presents a comprehensive Model-Driven Engineering (MDE) methodology for automatically transforming Business Process Model and Notation (BPMN) diagrams into executable blockchain-based smart contracts. The proposed approach defines a set of Atlas Transformation Language (ATL) rules that systematically map BPMN elements to Solidity con-structs, ensuring semantic consistency and traceability through-out… Read full abstract & cite →

Model-driven engineering BPMN smart contracts blockchain ATL automation solidity process transformation

Abstract doi.org/10.14569/IJACSA.2025.01612120 PDF

121

Bridging the Gap Between Text-Based and Visual Programming: A Comparative Study of Efficiency and Student Engagement in Game Development

Author 1: Álvaro Villagómez-Palacios Author 2: Claudia De la Fuente-Burdiles Author 3: Cristian Vidal-Silva

The integration of Low-Code and No-Code (LCNC) tools in higher education challenges traditional text-based programming pedagogies. While visual environments are often relegated to K-12 education, their adoption in professional engines like Unity suggests a need to re-evaluate their role in engineering curricula. This study analyzes the effectiveness, development efficiency, and… Read full abstract & cite →

Visual scripting higher education development efficiency engineering curricula

Abstract doi.org/10.14569/IJACSA.2025.01612121 PDF

122

Confidence-Based Trust Calibration in Human-AI Teams

Author 1: Michael Ibrahim

Effective human-AI collaboration is contingent upon calibrated trust, wherein users depend on AI systems when accuracy is probable and rely on human judgment when errors are likely. In this study, a confidence-based mechanism for trust calibration within human-AI teams is examined. A decision-making strategy is proposed in which task delegation… Read full abstract & cite →

Human-AI collaboration trust calibration confidence-based delegation decision-making strategies

Abstract doi.org/10.14569/IJACSA.2025.01612122 PDF

123

AI-Based Framework for Automated Cell Cleavage Detection and Timing in Embryo Time-Lapse Videos

Author 1: Yasmin Alharbi Author 2: Sultanah Alshammari Author 3: Aisha Elaimi

In vitro fertilization (IVF) has become a primary therapeutic intervention for couples worldwide addressing in-fertility challenges. IVF success depends critically on embryo quality assessment, where cell cleavage timing serves as a key developmental parameter. Traditional morphological evaluation methods suffer from inter-observer variability and laborintensive manual analysis. This study presents an… Read full abstract & cite →

In vitro fertilization Time-Lapse Microscopy (TLM) videos AI-based framework cleavage stage cleavage onset timing optical character recognition Hours Post-Insemination (HPI)

Abstract doi.org/10.14569/IJACSA.2025.01612123 PDF

124

Fine-Tuning Language Models for Pedagogy-Aligned Lesson Plans in Cybersecurity Education

Author 1: Samar Althagafi Author 2: Miada Almasre Author 3: Wafaa Alsaggaf Author 4: Lana Alshawwa

Lesson planning in cybersecurity is time-consuming and cognitively demanding, especially for less experienced instructors, and manual approaches often lack flexibility across courses and contexts. We present a framework for generating pedagogy-aligned lesson plans using a large language model, integrating measurable objectives (Revised Bloom’s Taxonomy), explicit learning theories, and evidence-based teaching… Read full abstract & cite →

Fine-Tuning large language models lesson planning cybersecurity

Abstract doi.org/10.14569/IJACSA.2025.01612124 PDF

125

Advanced Multi-Scale Enhanced U-Net for Efficient Land Cover Classification of Remote Sensing Images

Author 1: Syed Zaheeruddin Author 2: K. Suganthi

For monitoring the environment, building cities, assessing crops, and studying the climate, it is very important to be able to accurately classify land cover from remote sensing images. Deep learning has made semantic segmentation work much better, especially with encoder-decoder designs like U-Net. Still, ordinary U-Net models have trouble capturing… Read full abstract & cite →

Land cover classification remote sensing UNet satellite images AMSE-U-Net multi-scale features semantic segmentation

Abstract doi.org/10.14569/IJACSA.2025.01612125 PDF

126

A Fuzzy Petri Net Approach with Automated ANFIS Rule Learning for Modelling Real-Time Systems

Author 1: Abdelilah Serji Author 2: El Bekkaye Mermri Author 3: Mohammed Blej

In this paper, we propose a modelling approach for real-time intelligent systems using Fuzzy Petri Nets (FPNs), a formalism that generates dynamic fuzzy rules, supports uncertainty, and enables concurrent reasoning. FPNs offer a well-defined tool for dynamically evaluating Fuzzy Production Rules (FPRs), Certainty Factors (CFs), and truth degrees, and for… Read full abstract & cite →

Fuzzy petri net adaptive neuro-fuzzy inference system expert systems fuzzy logic real-time system artificial intelligence

Abstract doi.org/10.14569/IJACSA.2025.01612126 PDF

127

Trajectory Planning of Shipbuilding Welding Manipulator Based on Improved Whale Optimization Algorithm

Author 1: Caiping Liang Author 2: Hao Yuan Author 3: Chen Wang Author 4: Wenxu Niu Author 5: Yansong Zhang

Time-optimal trajectory planning for shipboard welding robotic arms is a challenging problem due to strong kinematic constraints and the nonlinear coupling between trajectory parameters and execution time. Although various intelligent optimization algorithms have been combined with robotic arm trajectory planning in existing studies, most approaches primarily focus on algorithmic performance… Read full abstract & cite →

Shipboard welding robotic arm quintic polynomial Improved Whale Optimization Algorithm time-optimal trajectory planning

Abstract doi.org/10.14569/IJACSA.2025.01612127 PDF

128

An RBAC-Based Access Control and Security Architecture for UAV Networks in Precision Agriculture Using Software-Defined Drone Networking

Author 1: Nadia Kammoun Author 2: Aida Ben Chehida Douss Author 3: Ryma Abassi

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, are widely employed in applications such as surveillance, delivery, mapping, and precision agriculture. Their flexibility, mobility, and cost effectiveness have accelerated their adoption in both civilian and industrial domains. However, the rapid evolution of UAV technologies introduces significant challenges related to… Read full abstract & cite →

Unmanned Aerial Vehicles Software-Defined Drone Network role-based access control security attacks trust management authentication access control

Abstract doi.org/10.14569/IJACSA.2025.01612128 PDF

129

A Soft and Hard Mixture-of-Experts Approach for Improved ADR Extraction from Patient-Generated Narratives

Author 1: Oumayma Elbiach Author 2: Hanane Grissette Author 3: El Habib Nfaoui

Traditional single-architecture neural models, in-cluding monolithic transformer-based and sequence-to-sequence architectures, often struggle to extract Adverse Drug Reactions (ADRs) from patient-generated health narratives due to informal language, high linguistic variability, and complex relationships among drugs, diseases, and adverse events. Although Mixture-of-Experts (MoE) architectures have demonstrated strong performance across various Natural Language… Read full abstract & cite →

Adverse Drug Reaction Mixture-of-Experts Soft and Hard MoE sequence-to-sequence patient narratives biomedical text mining

Abstract doi.org/10.14569/IJACSA.2025.01612129 PDF

130

Achieving Long-Term Autonomy: A Self-Correcting Deep Reinforcement Learning Agent for Edge IoT Using Digital Twin-Based Drift Compensation

Author 1: Jhon Monroy Author 2: Miguel Paco Author 3: Miguel Portella Author 4: Geral Basurco Author 5: Jeymi Valdivia Author 6: Fiorela Jara Author 7: Guido Anco

Ensuring long-term autonomy in Edge AI systems remains one of the most persistent challenges in environmental monitoring and biorisk management. Over time, the degradation of low-cost sensors—particularly sensor drift—leads to cumulative measurement errors, distorted state perception, and catastrophic decision failures in Deep Reinforcement Learning (DRL) agents. This paper proposes a… Read full abstract & cite →

Deep Reinforcement Learning (DRL) Edge AI Internet of Things (IoT) digital twin sensor drift fault tolerance autonomous systems self-correcting systems

Abstract doi.org/10.14569/IJACSA.2025.01612130 PDF

131

Sustainable and Ethical AI-Driven Recognition in Robotics: Integrating ESG Analytics and Human–Robot Interaction

Author 1: Fatma Mallouli Author 2: Lobna Amouri Author 3: Mejda Dakhlaoui Author 4: Nada Chaabane Author 5: Imen Gmach Author 6: Inès Hammami Author 7: Hanen Chakroun Author 8: Ahmed Mellouli Author 9: Sonda Elloumi Author 10: Abdelwaheb Trabelsi Author 11: Heba Elbeh Author 12: Mohamed Elkawkagy

Environmental, Social, and Governance (ESG) in-formation has become an essential component in evaluating corporate responsibility and long-term resilience. However, its incremental value in predicting firm profitability remains insufficiently understood. This study investigates whether integrating ESG analytics with traditional financial ratios enhances the machine-learning classification of firms into high- and low-profitability… Read full abstract & cite →

Artificial intelligence robotic recognition human–robot interaction explainable AI ESG analytics sustainable robotics

Abstract doi.org/10.14569/IJACSA.2025.01612131 PDF

132

Hybrid Deep Learning for Signals Automatic Modulation Classification

Author 1: Muhammad Moinuddin Author 2: Hitham K. Alshoubaki Author 3: Omar Ayad Alani Author 4: Ubaid M. Al-Saggaf Author 5: Karim Abed-Meraim

Classifying signals or modulation classification is a crucial step in developing communication receivers. A common practice is to extract features before categorizing the signal, which requires implementing long preprocessing techniques. Due to breakthroughs in neural network topologies, machine learning (ML) algorithms, and optimization techniques, referred to as "deep learning" (DL)… Read full abstract & cite →

Automatic modulation classification deep learning machine learning EfficientNet Transformer Network

Abstract doi.org/10.14569/IJACSA.2025.01612132 PDF

133

A Comprehensive Analysis of Security Challenges and Solutions in the Internet of Drones: Recent Trends and Development

Author 1: Amine Hedfi Author 2: Aida Ben Chehida Douss Author 3: Ryma Abassi Author 4: Mohamed Aymen Chalouf Author 5: Om Saad Hamdi

The Internet of Drones (IoD) is a decentralized structure that links drones to regulate airspace and offer inter-location navigation services. With the increasing use of drones in both civilian and military applications, the importance of the IoD has grown significantly. It reshapes the current internet landscape, making it more extensive… Read full abstract & cite →

Unmanned Aerial Vehicle Internet of Drones cybersecurity attacks threats

Abstract doi.org/10.14569/IJACSA.2025.01612133 PDF

134

Relationship Management System: A Data-Driven Framework for Modeling, Monitoring, and Restoring Human–AI Relationships

Author 1: Ilia Sedoshkin

We present the Relationship Management System (RMS) a modular framework for modeling, monitoring, and repairing human AI relationships. Grounded in Knapp’s Relational Development Model and Social Penetration Theory, RMS operationalizes ten stages of relationship growth and decline, linking depth of disclosure with stage-appropriate behavior. An Airtable-backed schema Relationship Stages, Conversational… Read full abstract & cite →

Human-AI interaction relationship modeling trust dynamics conversational systems affective computing regression detection recovery protocols

Abstract doi.org/10.14569/IJACSA.2025.01612134 PDF

135

Medical Diagnosis Using Hybrid of Machine Learning and Deep Learning Techniques

Author 1: Raed Alazaidah Author 2: Moath Alomari Author 3: Hamza Mashagba Author 4: Musab Iqtait Author 5: Azlan B. Abd Aziz Author 6: Hayel Khafajeh Author 7: Omar Khair Alla Alidmat Author 8: Ghassan Samara Author 9: Haneen Alzoubi Author 10: Samir Salem Al-Bawri

The rapid development of medical practices and imaging technology tools creates substantial growth in the amount of medical image data each year in our present era. This research aims to develop a hybrid approach that integrates Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy and reliability… Read full abstract & cite →

Classification deep learning feature selection hybrid models machine learning medical diagnosis medical image classification

Abstract doi.org/10.14569/IJACSA.2025.01612135 PDF