IJACSA Vol. 16 Issue 11 (2025)

1

Assessing LXC Containers on Raspberry Pi 4B/5 Boards in a Proxmox Virtual Environment

Author 1: Eric Gamess Author 2: Deuntae Winston

On one hand, containerization is gaining acceptance as a lightweight virtualization alternative to Virtual Machines (VMs). On the other hand, Single-Board Computers (SBCs) are increasingly used due to their affordability, versatility, low energy consumption, and growing computational power. In this work, extensive experiments were conducted to assess the capabilities and… Read full abstract & cite →

Container virtualization Proxmox VE LXC single-board computers Raspberry Pi performance evaluation

Abstract doi.org/10.14569/IJACSA.2025.0161101 PDF

2

Agentic AI in Commodity Trading: A Comparative Simulation Study

Author 1: TarakRam Nunna Author 2: Ananya Samala

Agent Based Modeling (ABM) has long been used to study emergent market behavior, but most prior financial ABM frameworks rely on reactive rule-based or reinforcement learning agents with limited cognitive capability. This study introduces a novel integration of agentic artificial intelligence (AI) featuring autonomous goal setting, persistent memory, and multi-step… Read full abstract & cite →

Agentic AI Agent Based Modeling commodity trading reinforcement learning cognitive agents market simulation

Abstract doi.org/10.14569/IJACSA.2025.0161102 PDF

3

Analysis of Test Access Mechanisms for Improving Scan Compression and Test Time

Author 1: Vijay Sontakke

System-on-Chip (SoC) devices now integrate dozens—and sometimes hundreds—of heterogeneous embedded IP cores, each of which must be verified after fabrication. Industry therefore relies on modular testing so that every core can be exercised and validated without revealing its internal implementation, and so that designers can reuse test patterns efficiently. A… Read full abstract & cite →

Compression ratio scan bandwidth TAM test coverage test scheduling test time

Abstract doi.org/10.14569/IJACSA.2025.0161103 PDF

4

SUNDUS: A Human-Centered Framework for Fostering Human-AI Collaboration Through Transparency

Author 1: Abduljaleel Hosawi Author 2: Richard Stone

Imagine a future where a security operator can understand a complex threat in fractions of a second without specialized training. Not only that, but the operator can instantly understand the logic behind a particular AI warning. This vision of AI-augmented cognition is the focus of the proposed SUNDUS framework. However… Read full abstract & cite →

SUNDUS human-AI collaboration Human-Computer Interaction (HCI) transparency multisensory display cognitive load decision-making trust training OMAR

Abstract doi.org/10.14569/IJACSA.2025.0161104 PDF

5

Leveraging Large Language Models in the Software Development Lifecycle: Opportunities and Challenges

Author 1: Jasdeep Singh Bhalla Author 2: Mansimar Kaur Jodhka

Large Language Models (LLMs) are increasingly integrated into software engineering workflows, yet existing studies provide fragmented or domain-specific examinations of their impact. This survey aims to systematically analyze how LLMs influence the Software Development Lifecycle (SDLC) end-to-end, identifying capabilities, limitations, risks, and emerging opportunities. We review 147 publications from 2017–2025… Read full abstract & cite →

Large Language Models (LLMs) Software Development Lifecycle (SDLC) AI-assisted software engineering automated code generation software testing software architecture DevOps automation

Abstract doi.org/10.14569/IJACSA.2025.0161105 PDF

6

Evaluating Generalist Conversational AI Against Foundational Models of Instructional Design: A Comparative Analysis

Author 1: Abdelmounaim AZINDA Author 2: Mohamed Khaldi

The rapid integration of Generative AI into instructional engineering presents a critical challenge: verifying the capacity of these tools to strictly adhere to systemic theoretical models of learning, despite the risk of generating "pedagogically hallucinated" content that possesses surface plausibility but lacks structural validity. This study addresses this gap by… Read full abstract & cite →

Generative AI large language models (LLMs) instructional design constructive alignment pedagogical evaluation AI ethics in education Human-AI collaboration

Abstract doi.org/10.14569/IJACSA.2025.0161106 PDF

7

Anamel: Children’s Psychological and Mental Health Detection Application by Drawing Analysis Based on AI

Author 1: Amal Alshahrani Author 2: Manar Mohammed Almatrafi Author 3: Jenan Ibrahim Mustafa Author 4: Layan Saad Albaqami Author 5: Raneem Abdulrahman Aljabri

Psychological and mental health issues affect many people worldwide. However, the impact of these issues is stronger on children starting from early ages until their teenage. Using drawing to analyze and detect such feelings is a common way for specialists to help children express their feelings. Due to that, this… Read full abstract & cite →

Application mental health drawings artificial intelligence YOLO deep learning computer vision flutter

Abstract doi.org/10.14569/IJACSA.2025.0161107 PDF

8

Enhanced Android Malware Detection Using Deep Learning and Ensemble Techniques

Author 1: Abdul Museeb Author 2: Yaman Hamed Author 3: Rajalingam Sokkalingam Author 4: Anis Amazigh Hamza Author 5: Atta Ullah Author 6: Iliyas Karim Khan

Android malware continues to pose significant security threats, with evolving tactics that often bypass traditional detection systems. Existing detection mechanisms remain ineffective against obfuscated or novel malware variants, necessitating the development of more robust detection techniques. This study introduces a comprehensive machine learning framework for Android malware detection that leverages… Read full abstract & cite →

Android malware detection machine learning API calls permissions android security malware classification

Abstract doi.org/10.14569/IJACSA.2025.0161108 PDF

9

Dual-Attention ResUNet-GAN for Secure Image Steganography: Optimizing the Trade-off Between Imperceptibility and Payload Capacity

Author 1: Zobia Shabeer Author 2: Muhammad Naeem Author 3: Gohar Rahman Author 4: Mehmood Ahmed Author 5: Muhammad Zeeshan Author 6: Asim Shahzad Author 7: Salamah binti Fattah

Secure and high-capacity data concealment has already become a requirement of modern multimedia communication, particularly with the enhanced protection and privacy levels of concern. The framework introduced in this study—the improved Dual-Attention ResUNet-GAN—helps optimize the trade-off among imperceptibility, robustness, and payload capacity in the field of image steganography. The two… Read full abstract & cite →

Image steganography Generative Adversarial Networks (GANs) payload capacity steganalysis robustness artificial intelligence BOSSbase ALASKA#2

Abstract doi.org/10.14569/IJACSA.2025.0161109 PDF

10

A Method for Planning the Dissemination Path of Traditional Chinese Medicine Culture Based on the Optimized Ant Colony Algorithm

Author 1: Qian Guo Author 2: Ying Ma

Strategic planning improves TCM cultural transmission efficacy, reliability, and impact. Many systems use heuristic or rule-based approaches, which have drawbacks such as path redundancy, low adaptation, and limited scalability in non-static networks. To address these constraints, we suggest RACO-TCM, or Reinforced Ant Colony Optimization for TCM Dissemination. This novel algorithmic… Read full abstract & cite →

TCM dissemination Ant Colony Optimization (ACO) intelligent path planning cultural communication networks knowledge graph optimization algorithmic dissemination strategy

Abstract doi.org/10.14569/IJACSA.2025.0161110 PDF

11

Maximizing Influence and Mitigating Harmful Viral Content in Social Networks

Author 1: Muhammad Mohsin Author 2: Muhammad Yaseen Author 3: Umar Farooq Khattak Author 4: Gohar Rahman

This study presents an integrated framework containing sentiment and network analysis for social media modernization to mitigate the spread of harmful viral posts. The research focuses on detecting harmful content, identifying key influencers, and generating counter-narratives to promote constructive engagement. A Twitter Dataset was preprocessed to remove URLs, special characters… Read full abstract & cite →

Sentiment analysis harmful content counter-narratives network analysis PageRank Vander influence maximization digital ethics social media moderation

Abstract doi.org/10.14569/IJACSA.2025.0161111 PDF

12

Security Vulnerability Analysis and Enhancement of a Lightweight Sensor Node Authentication Framework

Author 1: Kim Kyoung Yee Author 2: Haewon Byeon

This study presents a comprehensive structural and mathematical security analysis of LightAuth, a lightweight authentication framework, specifically designed for smart health sensor networks. We delve into its core components and identify several critical vulnerabilities that could compromise the integrity and security of the system. Our analysis reveals that the framework… Read full abstract & cite →

Lightweight authentication IoMT security ECC timestamp–nonce validation replay resistance formal verification smart healthcare systems

Abstract doi.org/10.14569/IJACSA.2025.0161112 PDF

13

A Proposed IoT System for Monitoring and Controlling Movable Zamzam Tanks in the Holy Mosque

Author 1: Taha M. Mohamed

Zamzam water is very important for all Muslims worldwide, especially for Pilgrims and Umrah performers who are visiting the two holy mosques in Saudi Arabia. Many movable Zamzam tanks are dispersed in many places of the two holy mosques. These tanks are available for pilgrims and visitors for drinking. When… Read full abstract & cite →

Zamzam tanks monitoring IoT smart systems automatic monitoring and control Logistics 5.0

Abstract doi.org/10.14569/IJACSA.2025.0161113 PDF

14

Forecasting of Saudi Stock Prices Using Statistical and Machine Learning Models: A Multi-Model Comparative Approach

Author 1: Eissa Alreshidi

Accurate forecasting of financial time-series data is not just a challenge—it's a critical necessity for investors in emerging markets. This study decisively evaluates the predictive power of seven advanced statistical and machine learning models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest, eXtreme Gradient Boosting (XGBoost), Support… Read full abstract & cite →

Financial time-series forecasting Saudi Stock Market ARIMA XGBoost deep learning ensemble models LSTM stock price prediction

Abstract doi.org/10.14569/IJACSA.2025.0161114 PDF

15

Speech Emotion Recognition via Parallel Dual-Branch Fusion Model

Author 1: Zhongliang Wei Author 2: Chang Ge Author 3: Lijun Zhu Author 4: Jinmin Ye

Speech Emotion Recognition (SER) has become a pivotal topic within affective computing and human–computer interaction, where the core challenge lies in jointly capturing both the time–frequency structure and the semantic context of speech. To overcome the shortcomings of current approaches—including single-view feature representation, the lack of emotional discriminability in self-supervised… Read full abstract & cite →

RAVDESS Speech Emotion Recognition spectrogram modeling probability-space fusion wav2vec 2.0 fine-tuning

Abstract doi.org/10.14569/IJACSA.2025.0161115 PDF

16

An Improved Method Based on YOLOv7 for Detecting the Safety Helmets of Two-Wheeled Bicycle Riders

Author 1: Xufei Wang Author 2: Penghui Wang Author 3: Zishuo Wang Author 4: Jeonyoung Song Author 5: Jinde Song

Convolutional neural networks (CNNs) were widely used in object detection tasks. Usually, CNNs with strong object detection performance were difficult to apply to small, mobile embedded systems with limited computational resources due to the large number of parameters. Aiming at this problem, the lightweight improvement method for the safety helmet… Read full abstract & cite →

Object detection YOLOv7 MobileOne CA module TCHD

Abstract doi.org/10.14569/IJACSA.2025.0161116 PDF

17

Lightweight Multi-Feature Fusion GAN with Deformable Attention for HMD-Occluded Face Reconstruction

Author 1: Yingying Li Author 2: Ajune Wanis Ismail Author 3: Muhammad Anwar Ahmad Author 4: Norhaida Mohd Suaib Author 5: Fazliaty Edora Fadzli

Head-mounted displays (HMDs) enhance virtual reality (VR) experiences, but occlude the upper face, hindering realistic user representation. To address this, some studies employ sensors to capture facial expressions under occlusion, while deep learning methods typically rely on image inpainting to restore missing regions. However, these approaches often suffer from limitations… Read full abstract & cite →

Generative adversarial network Lie group feature learning deformable attention face reconstruction virtual reality head-mounted displays

Abstract doi.org/10.14569/IJACSA.2025.0161117 PDF

18

Adult-Size Bio-Mechatronic Exoskeleton with Robotic Underactuated Mechanism for Continuous Passive Motion Therapy on Hemiplegic Lower Limbs

Author 1: Adrian Nacarino Author 2: Carol Sandoval Author 3: Cesar Martel Author 4: Jose Cornejo Author 5: Margarita Murillo Author 6: Jeanette Borja Author 7: Josue Alata Rey Author 8: Ricardo Palomares

Cerebrovascular accidents (CVA) cause hemiplegia in adult patients, limiting their daily activities and reducing their autonomy, which calls for effective rehabilitation systems for the lower limbs. The study aims to design and simulate a subactuated robotic continuous passive motion system for lower-limb therapy in adult patients with post-stroke hemiplegia. Physical… Read full abstract & cite →

Bio-mechatronics engineering design exoskeleton stroke rehabilitation rehabilitation robotics

Abstract doi.org/10.14569/IJACSA.2025.0161118 PDF

19

Improving Spam Detection with Feature Engineering and Adaptive Learning Approaches

Author 1: Sadeem H. AlHomidan Author 2: Marwah M. Almasri Author 3: Shimaa A. Nagro

Spam email detection is a critical component of securing and maintaining reliable digital communication systems. This study explores the effectiveness of various machine learning algorithms in classifying spam, with an emphasis on enhancing accuracy and precision through systematic preprocessing, advanced feature engineering, and text preprocessing. Six models were evaluated: Logistic… Read full abstract & cite →

Spam detection machine learning Multinomial Naïve Bayes logistic regression ensemble learning text preprocessing feature engineering

Abstract doi.org/10.14569/IJACSA.2025.0161119 PDF

20

A Newton-Raphson-Based Optimizer-Driven Temporal Convolutional Networks for Birth Rate Prediction in a Small Area

Author 1: Shengyi Zhou Author 2: Liang Chen Author 3: Wei Han Author 4: Bin Liu

For economically developed small geographic regions, population forecasting serves as a vital tool for achieving refined regional management. However, due to relying on the subjective experience of experts, traditional methods for predicting birth rates have shortcomings in accuracy, resulting in unreliable results. To address this limitation, this study introduces deep… Read full abstract & cite →

Temporal Convolutional Network Bi-directional Long Short-Term Memory prediction model birth rate meta-heuristic algorithm

Abstract doi.org/10.14569/IJACSA.2025.0161120 PDF

21

A New Advanced Multimodal Sentiment Classification Through Combined Attention Mechanisms: A-MSDA

Author 1: Soukaina FATIMI Author 2: WAFAE SABBAR Author 3: Abdelkrim BEKKHOUCHA

Interest in multimodal sentiment analysis has grown significantly due to the widespread sharing of text and images on social platforms. Existing approaches often emphasize either sentiment features within textual–visual data or the correlation between modalities, leaving gaps in effectively capturing both aspects simultaneously. To address these limitations, we propose the… Read full abstract & cite →

Dual attention self-attention cross-modal attention multimodal sentiment analysis multimodal fusion MVSA dataset image–text interaction deep multimodality

Abstract doi.org/10.14569/IJACSA.2025.0161121 PDF

22

NDN-Based ICN Architecture Design to Improve Data Communication QoS in Kertajati Aerocity

Author 1: Enang Rusnandi Author 2: Tri Kuntoro Priambodo Author 3: Mardhani Riasetiawan

The development plan for Kertajati Aerocity requires adequate support from the data communication infrastructure. The primary challenge lies in the dense data flows, which involve very large volumes and diverse data types. The commonly used host-centric architecture suffers from latency, bandwidth utilization, and scalability issues, making it unsuitable for the… Read full abstract & cite →

Aerocity Information-Centric Networking (ICN) Named Data Networking (NDN) Quality of Service (QoS) ndnSIM

Abstract doi.org/10.14569/IJACSA.2025.0161122 PDF

23

Improving the Performance of TFS with Ensemble Learning for Cross-Project Software Defect Prediction

Author 1: Pathiah Abdul Samat Author 2: Yahaya Zakariyau Bala Author 3: Nur Hamizah Hamidi

Software defect prediction (SDP) plays a key role in improving software quality by identifying defect-prone modules early in the development cycle. While within-project prediction has been widely studied, cross-project defect prediction (CPDP) remains challenging due to differences in datasets, high feature dimensionality, and poor model generalization. To address these challenges… Read full abstract & cite →

Software defect prediction cross-project ensemble learning feature selection

Abstract doi.org/10.14569/IJACSA.2025.0161123 PDF

24

From User Experience Evaluation to Design Guidelines for E-Commerce Websites

Author 1: Layla Hasan Author 2: Beatrice Lim Pei Ying

The evolution of the Internet and advances in information technology have significantly transformed business practices, leading to widespread e-commerce website development. To succeed in a competitive environment, website owners and designers must ensure that their e-commerce websites provide positive user experiences (UX). This research proposes UX guidelines for e-commerce websites… Read full abstract & cite →

User experience UX guidelines e-commerce websites Malaysian e-commerce hedonic features pragmatic features

Abstract doi.org/10.14569/IJACSA.2025.0161124 PDF

25

From External Stakeholder Pressure to Sustainable Practice in HEIs: Mechanisms and Internal Mediating Factors

Author 1: Xue Jin Author 2: S. M. Ferdous Azam Author 3: Jacquline Tham

Sustainable procurement is an important part of sustainable development in HEIs, playing a pivotal role in optimizing resource allocation, fulfilling social responsibilities, and promoting green development. However, existing research has paid insufficient attention to the impact of external stakeholder pressure on HEIs’ sustainable procurement and its intrinsic action mechanism. To… Read full abstract & cite →

Sustainable procurement HEIs external stakeholder pressure affective commitment knowledge

Abstract doi.org/10.14569/IJACSA.2025.0161125 PDF

26

Lexicon-Based Sentiment Analysis of Social Media Reviews for Floating Market Popularity in South Kalimantan

Author 1: Evi Lestari Pratiwi Author 2: Ramadhani Noor Pratama Author 3: Inayatul Ulya Ahyati Author 4: Paula Dewanti

Floating markets in South Kalimantan are culturally significant heritage destinations whose contemporary reputation is increasingly shaped by user-generated content on digital platforms. This study analyzes public perceptions of these markets by applying a lexicon-based sentiment analysis framework to 300 reviews collected from TripAdvisor and Google Maps between 2023 and 2024… Read full abstract & cite →

Sentiment analysis lexicon-based methods floating markets social media analytics cultural tourism

Abstract doi.org/10.14569/IJACSA.2025.0161126 PDF

27

AI-Driven Deep Learning Architectures for Robust Emotion Recognition

Author 1: Hamad Ali Abosaq

Due to an insufficient labeled dataset, class-level variation emotion recognition becomes a challenging task in computer vision. Deep learning (DL) makes it possible to automatically learn meaningful patterns from facial expressions. It captures simple details such as edges, textures at low layers, and gradually builds up to more complex information… Read full abstract & cite →

Deep learning models computer vision emotion recognition image processing Grad-CAM

Abstract doi.org/10.14569/IJACSA.2025.0161127 PDF

28

User Experience Deficiencies in Mobile Tourism Applications: A Preliminary Study from Generation Z and Tourism Practitioners

Author 1: Huang Huihui Author 2: Azliza Othman Author 3: Nadia Diyana Mohd Muhaiyuddin

While previous research has broadly explored the user experience of mobile tourism applications (MTAs), few have examined user experience deficiencies from the dual perspectives of both tourism practitioners and Generation Z adults. This preliminary study sought to investigate user experience deficiencies present in MTA, with an emphasis on Generation Z… Read full abstract & cite →

Mobile tourism applications Generation Z tourism practitioners user experience preliminary study

Abstract doi.org/10.14569/IJACSA.2025.0161128 PDF

29

Data-Driven Model for Optimizing Active Learning

Author 1: Abang Asyraaf Aiman Abang Azahari Author 2: Marshima Mohd Rosli Author 3: Nor Shahida Mohamad Yusop

Data-driven models depend on extensive datasets for precise predictions; yet, acquiring adequate labeled data for training these models is a challenge, especially with medical datasets that are constrained by privacy considerations, resulting in a deficiency of labeled data. Active Learning (AL) has developed as a cost-effective strategy that minimizes the… Read full abstract & cite →

Data-driven models selection model data quality characteristics active learning

Abstract doi.org/10.14569/IJACSA.2025.0161129 PDF

30

A Novel YOLO-Like Multi-Branch Architecture for Accurate Apple Detection and Segmentation Under Orchard Constraints

Author 1: Olzhas Olzhayev Author 2: Nurbibi Imanbayeva Author 3: Satmyrza Mamikov Author 4: Bibigul Baibek

This study introduces a novel YOLO-like multi-branch deep learning architecture designed for accurate apple detection and segmentation in orchard environments, addressing the persistent challenges of occlusion, illumination variability, and fruit clustering. The proposed model integrates an enhanced backbone with C2f modules and a Spatial Pyramid Pooling Fast (SPPF) block to… Read full abstract & cite →

Precision agriculture detection segmentation YOLO-like architecture multi-branch network feature pyramid network real-time inference orchard monitoring

Abstract doi.org/10.14569/IJACSA.2025.0161130 PDF

31

Kinematic Modeling and Design of a Robotic Manipulator System for Automated Fruit Harvesting in Intensive Orchards

Author 1: Nurbibi Imanbayeva Author 2: Bekzat Amanov Author 3: Aiman Nurmaganbetova Author 4: Arman Moldashev Author 5: Akbayan Aliyeva Author 6: Sulukul Dauletbekova

The development of robotic systems for automated fruit harvesting in intensive orchards has emerged as a critical response to labor shortages, high production costs, and the need for efficiency in modern agriculture. This study presents the kinematic modeling and design of a robotic manipulator system integrated into a mobile platform… Read full abstract & cite →

Robotic manipulator kinematic modeling fruit harvesting intensive orchards machine vision deep learning

Abstract doi.org/10.14569/IJACSA.2025.0161131 PDF

32

A Configurable Storytelling System for Emotion Recognition in Children: Design and Pilot Evaluation

Author 1: Chadi Fouad Riman Author 2: Wael Hosny Fouad Aly Author 3: Carmen Mariana Pasca

Computer games are very popular among children, with serious games being widely used for teaching specific skills and rehabilitation across various age groups. This work presents a configurable, story-based serious game designed to teach emotion recognition to young children (ages 6 to 11) through a peer-assisted learning approach. The system… Read full abstract & cite →

Computer gaming serious games education emotions learning peer-assisted learning

Abstract doi.org/10.14569/IJACSA.2025.0161132 PDF

33

Leveraging AI and ML for Enhanced Business Intelligence Systems: A Research Landscape of Trends, Influences, and Future Directions

Author 1: Sunil Mandaliya Author 2: Priti Kulkarni

This research provides a systematic review of AI and ML applied to the BI context from 2014 to 2024. By characterizing the article and citation distribution and by tracing the topics of publications over time, this study outlines major developments, landmark contributions, and the future outlook of the research discipline… Read full abstract & cite →

Artificial intelligence machine learning business intelligence digital transformation data analytics bibliometric analysis

Abstract doi.org/10.14569/IJACSA.2025.0161133 PDF

34

Performance Evaluation of Software Multi-Threshold Decoders for Self-Orthogonal Codes in Modern Broadband Wireless Communication Systems

Author 1: Nurlan Tashatov Author 2: Gennady Ovechkin Author 3: Zhuldyz Sailaukyzy Author 4: Eldor Egamberdiyev Author 5: Dina Satybaldina Author 6: Gulmira Danenova Author 7: Zarina Khassenova

5G and Internet of Things (IoT) wireless systems face challenges to reliable data transmission due to multipath fading, intersymbol interference, and the need for low-complexity Forward Error Correction (FEC). Conventional FEC techniques, such as Low-Density Parity-Check (LDPC) and turbo codes, provide high reliability but are unsuitable for resource-constrained IoT devices… Read full abstract & cite →

Forward Error Correction Multi-Threshold Decoder Self-Orthogonal Codes Orthogonal Frequency-Division Multiplexing Multiple Input Multiple Output Bit Error Rate signal-to-noise ratio LDPC turbo codes space–time coding fading channels GPU acceleration OpenCL

Abstract doi.org/10.14569/IJACSA.2025.0161134 PDF

35

CICA Framework: Harnessing CSR, AI, and Blockchain for Sustainable Digital Culture

Author 1: Danang Danang Author 2: Agustinus Budi Santoso Author 3: Maya Utami Dewi

Digital transformation has created new opportunities for organizations, but it has also intensified cybersecurity risk. In emerging economies, where institutional support and digital literacy remain limited, cybersecurity awareness plays a crucial role in strengthening digital resilience and fostering a sustainable digital culture. This study introduces the CSR-Integrated Cybersecurity Awareness (CICA)… Read full abstract & cite →

Cybersecurity awareness corporate social responsibility artificial intelligence blockchain sustainable digital culture emerging economies

Abstract doi.org/10.14569/IJACSA.2025.0161135 PDF

36

Recursive Gated Convolution-Based YOLOv11 Framework for Operator Safety Management in Live-Line Work

Author 1: Dapeng Ma Author 2: Liang Yang Author 3: Kang Chen Author 4: Feng Yang Author 5: Ao Cui Author 6: Rundong Yang Author 7: Zhilin Wen Author 8: Donghua Zhao

In live-line work scenarios, it is essential for workers to wear electric field shielding clothing to prevent fatal accidents caused by electric shock. Accordingly, this study developed an electric field shielding clothing detection system for live-line working environments based on the YOLOv11 framework. Previous research has explored intelligent wearable detection… Read full abstract & cite →

Detection transformer recursive gated convolution YOLOv11 personal protective equipment live-line work scenarios

Abstract doi.org/10.14569/IJACSA.2025.0161136 PDF

37

Evaluating the Effectiveness and Usability of Microsoft Threat Modelling Tool in Undergraduate Cybersecurity Education

Author 1: Nor Laily Hashim Author 2: Ahmad Zuhairi Bin Mohd Yusri

As cyber threats evolve, equipping students with hands-on experience in identifying and mitigating system vulnerabilities is critical for developing a cybersecurity-aware workforce. There are a variety of threat modelling tools available on the market, and it is challenging for educators to select the best tool for their students to learn… Read full abstract & cite →

Cybersecurity education threat modelling stride usability testing

Abstract doi.org/10.14569/IJACSA.2025.0161137 PDF

38

Robust Detection of Partially Occluded Faces in Low-Light Scenarios Using YOLOv7 and YOLOv6

Author 1: Nayef Alqahtani Author 2: Amina Shaikh Author 3: Imran Khan Keerio

Partial occlusion and low light are significant challenges for face detection, limiting its effectiveness in critical applications such as security, surveillance, and user identification within computer vision. This study evaluates the effectiveness of two influential deep-learning models, YOLOv6 and YOLOv7, in identifying partially occluded faces in uncontrollable, real-world conditions. Training… Read full abstract & cite →

Deep learning partial occlusion YOLOv6 YOLOv7 real-time detection low-light face recognition

Abstract doi.org/10.14569/IJACSA.2025.0161138 PDF

39

Semantic Segmentation Algorithm of Animal Husbandry Image Based on an Improved U⁃Net Network

Author 1: Jia Li Author 2: Jinjing Zhang Author 3: Fengjiao Jiang

Due to the limitations of unclear edges and fuzzy features in image segmentation tasks, this study proposes an enhanced U⁃Net semantic segmentation network utilizing the local and global fusion attention module in response to the drawbacks of fuzzy features and unclear edges in image segmentation tasks. Firstly, a feature extraction… Read full abstract & cite →

Machine vision semantic segmentation feature fusion attention mechanism

Abstract doi.org/10.14569/IJACSA.2025.0161139 PDF

40

AI-Driven Professional Profile Categorization and Recommendation System

Author 1: Marouane CHIHAB Author 2: Hicham BOUSSATTA Author 3: Mohamed CHINY Author 4: Nabil Mabrouk Author 5: Younes CHIHAB Author 6: Moulay Youssef HADI

The exponential growth of applications in digital and information system domains has made the identification of qualified candidates increasingly complex, resulting in longer and less efficient recruitment processes. Recruiters frequently deal with heterogeneous and unstructured résumés, which complicates skill assessment and increases the risk of mismatches between candidates and job… Read full abstract & cite →

Professional profile classification profile recommendation natural language processing (NLP) supervised learning Logistic Regression Random Forest Support Vector Machine (SVM) k-Nearest Neighbors (KNN) Gradient Boosting Naïve Bayes AI-based recruitment

Abstract doi.org/10.14569/IJACSA.2025.0161140 PDF

41

Robotic Process Automation (RPA) Scripting Model Using Machine Learning (ML) for Enterprise Data Validation and Integration

Author 1: Luis Ángel Bendezú Jiménez Author 2: Jorge Luis Juan de Dios Apaza Author 3: Ruben Oscar Cerda García

This research presents an automated data processing model based on RPA Scripting, designed to enhance efficiency in extracting, validating, and integrating information from various web platforms. The automated workflow begins with the use of a tool that simulates human interaction on web platforms to obtain data automatically and reliably. The… Read full abstract & cite →

RPA scripting data automation Machine Learning data validation data integration intelligent workflows

Abstract doi.org/10.14569/IJACSA.2025.0161141 PDF

42

How did the Intelligent Search Engine Become Popular Among Chinese Residents Since the Emergence of Deepseek?

Author 1: Zhixuan Wang Author 2: Yuying Song Author 3: Hanyu Guo

This study, based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), combined with the Stimulus-Organism-Response (SOR) theoretical framework, explores the influencing factors and promotion mechanisms for the popularization of “Deepseek” intelligent search engines in China. Through a questionnaire survey, 343 valid samples were collected, and… Read full abstract & cite →

AI search engine Deepseek SOR Technology Acceptance Model Theory of Planned Behavior

Abstract doi.org/10.14569/IJACSA.2025.0161142 PDF

43

Software Project Effort Estimation Using Formal Method and Model Checker

Author 1: Abdulaziz Alhumam

Software project effort estimation is a critical component of software development, as it determines the time and financial resources required to complete a project. Existing estimation techniques—ranging from empirical models and algorithmic methods to heuristic and expert-based approaches—struggle with inconsistent accuracy due to the inherent complexity, subjectivity, and contextual variability… Read full abstract & cite →

Software effort estimation cost estimation formal methods SMT Solver automated verification Z-specifications

Abstract doi.org/10.14569/IJACSA.2025.0161143 PDF

44

Cross-Lingual Sentiment Analysis in Low-Resource Languages: A Recent Review on Tasks, Methods and Challenges

Author 1: Nor Zakiah Lamin Author 2: Azwa Abdul Aziz

Cross-lingual sentiment analysis (CLSA) has become increasingly important in natural language processing and machine learning, enabling the understanding of opinions across diverse linguistic communities, particularly in low-resource languages (LRLs). Despite growing attention, persistent challenges such as limited annotated data, semantic misalignment, and cultural variation in sentiment expression continue to hinder… Read full abstract & cite →

Cross-lingual sentiment analysis low-resource language natural language processing pre-trained language models transfer learning few-shot learning

Abstract doi.org/10.14569/IJACSA.2025.0161144 PDF

45

Advances in Natural Language Processing for Radiology: State-of-the-Art Techniques, Applications, and Open Challenges

Author 1: Kotha Chandrakala Author 2: Shahin Fatima

Radiology reports encode critical clinical observations from medical imaging in an unstructured textual form that is central to modern clinical diagnosis and decision support. In this context, natural language processing (NLP) has emerged as a key clinical NLP technology for automatically extracting, classifying, and interpreting information from radiology reports. This… Read full abstract & cite →

Natural language processing radiology reports deep learning transformers medical imaging report generation clinical NLP

Abstract doi.org/10.14569/IJACSA.2025.0161145 PDF

46

AI-Driven Multimodal Frameworks for Cardiovascular Diagnostics: Integrating Sensors, Imaging, and Robotic Systems

Author 1: Chandrasekhara Reddy T Author 2: Ramesh Babu P

Cardiovascular disease is still the leading cause of death, and a definitive cure has not yet been found, so this is the time to make important changes in prevention and early diagnosis. Integrating artificial intelligence, machine learning, wearable sensors, and biomedical imaging is changing healthcare for cardiovascular care. Recent breakthroughs… Read full abstract & cite →

Cardiovascular disease (CVD) machine learning (ML) artificial intelligence (AI) wearable sensors deep learning Biomedical Signal Processing microfluidics nano sensors robotic intervention medical imaging predictive modelling causal inference domain adaptation federated learning clinical validation

Abstract doi.org/10.14569/IJACSA.2025.0161146 PDF

47

Sustainable IoT Smart Home Perceptions Across Demographic and Vulnerable User Segments

Author 1: Burhan Mahmoud Hamadneh Author 2: Zeyad Alshboul Author 3: Nabhan Mahmoud Hamadneh Author 4: Turki Mahdi Alqarni Author 5: Malek Turki Jdaitawi

The rapid expansion of Internet of Things (IoT) technologies has transformed smart home systems into essential tools for enhancing safety, independence, and quality of life, particularly for older adults and individuals with disabilities. However, the extent to which these groups understand and adopt IoT-enabled smart homes remains limited. This study… Read full abstract & cite →

Internet of Things smart home vulnerable people smart home energy efficiency

Abstract doi.org/10.14569/IJACSA.2025.0161147 PDF

48

The Evolution of Hackathons as an Innovation Tool: A Systematic Analysis

Author 1: Claudia Marrujo-Ingunza Author 2: Meyluz Paico-Campos

Hackathons have established themselves as dynamic open innovation spaces that promote interdisciplinary collaboration and creative problem-solving. This systematic review, which follows the PRISMA methodology, synthesizes the findings of 73 articles from Scopus, Web of Science, and IEEE Xplore, with the aim of analyzing the evolution, impacts, and knowledge gaps surrounding… Read full abstract & cite →

Hackathon evolution tool innovation review

Abstract doi.org/10.14569/IJACSA.2025.0161148 PDF

49

SBERT-Based Stacking Ensemble Model for Fake News Detection

Author 1: Abdulaziz A Alzubaidi Author 2: Amin A Alawady

Fake news has become a significant global challenge, affecting public opinion, social dynamics, and decision-making processes. Detecting fabricated news accurately and efficiently remains a challenging task due to the diversity of content, writing styles, and subtle semantic nuances. In this study, we propose a stacking ensemble model that uses SBERT-based… Read full abstract & cite →

Fake news detection machine learning SBERT embeddings stacking ensemble Random Forest Logistic Regression MLP XGBoost

Abstract doi.org/10.14569/IJACSA.2025.0161149 PDF

50

Aligning Confidence and Localization: An Enhanced DINO Model for Small Object Detection in Robotic Nursing

Author 1: Yanchen Du Author 2: Qingzhuo Yuan Author 3: Shengli Luo Author 4: XiaoLong Shu Author 5: Xu Wang Author 6: Yuheng Jiang Author 7: Hongliu Yu

In care environments such as nursing homes, robots performing tasks (e.g., feeding assistance) must accurately identify and locate target objects to ensure safe and efficient execution. However, real-world applications face challenges such as numerous small objects, complex backgrounds, and severe occlusions, all of which compromise detection performance. To address these… Read full abstract & cite →

Small object detection matching alignment robotic object detection nursing feeding DINO GhostConv edge deployment transformers

Abstract doi.org/10.14569/IJACSA.2025.0161150 PDF

51

Sleep Quality and Burnout Syndrome in Students at a University in Lima, Peru: A Cross-Sectional Study

Author 1: Yajaira Garay-Castro Author 2: Luis Acosta-Avila Author 3: Ana Flores-Hiyo Author 4: Ana Huamani-Huaracca Author 5: Sebastián Ramos-Cosi Author 6: Gina León-Untiveros Author 7: Alicia Alva-Mantari

The World Health Organization (WHO) reports that 15% of mental health problems develop in people with demanding work and academic conditions. Both sleep quality problems and Burnout Syndrome (BS) are recognized as significant problems in university settings. In Lima, Peru, the situation is critical, as BS affects up to 60%… Read full abstract & cite →

Burnout Syndrome sleep quality university students mental health

Abstract doi.org/10.14569/IJACSA.2025.0161151 PDF

52

A Conceptual Model for an Ontology-Based Dietary Recommendation Plan in Crohn’s Disease

Author 1: Rezan Almehmadi Author 2: Aisha Alsobhi Author 3: Omaima Almatrafi

Crohn's Disease (CD) is a long-term inflammatory bowel disorder that affects the digestive system. It is influenced by geography, diet, genetics, and immune response. Patients often experience difficulties managing CD due to the complexity and heterogeneity of the condition. Despite increasing scientific efforts, knowledge within the domain remains scattered and… Read full abstract & cite →

Conceptual model ontology dietary recommendations Crohn’s Disease domain knowledge

Abstract doi.org/10.14569/IJACSA.2025.0161152 PDF

53

Swarm Intelligence-Based Optimization of FACTS Devices: A Review of Operation, Control and Emerging Algorithms

Author 1: Patricia Khwambala Author 2: Kumeshan Reddy Author 3: Senthil Krishnamurthy

The integration of Flexible AC Transmission System (FACTS) devices into modern power networks plays a pivotal role in enhancing voltage stability, reducing transmission losses, and improving overall power transfer capability. Determining the optimal location and sizing of these devices is a critical task that significantly influences system performance. In recent… Read full abstract & cite →

Swarm intelligence FACTS devices power transfer system voltage stability power losses

Abstract doi.org/10.14569/IJACSA.2025.0161153 PDF

54

SPA-DCN-NET: A Gated Multi-Scale Local Contrast Normalization Network for Ultrasound Image Segmentation of Liver

Author 1: Su Ming Jian Author 2: Afizan Bin Azman Author 3: Afizan.Azman@taylors.edu.my

Segmentation of the liver in ultrasound images is a critical task in medical image analysis, yet it remains challenging due to acoustic speckle noise, brightness instability, and deformations caused by probe pressure. To address these problems, this study presents SPA-DCN-NET, a lightweight framework that integrates three synergized components: first, a… Read full abstract & cite →

Spatial pyramid attention deformable convolutional network ultrasound image medical image analysis soft-gate mechanism local contrast normalization

Abstract doi.org/10.14569/IJACSA.2025.0161154 PDF

55

Generalizing In-Field Plant Disease Diagnosis: A Deep Transfer Learning Approach for Multi-Crop and Heterogeneous Imaging Conditions

Author 1: Khoerul Anwar Author 2: Tubagus Mohammad Akhriza Author 3: Mahmud Yunus

Plant diseases pose a serious threat to agricultural productivity, which can cause significant crop losses if not addressed quickly and appropriately. There are significant opportunities for digital image-based treatment with computer vision and artificial intelligence. The main challenges in recognizing image-based plant diseases are: developing a single model capable of… Read full abstract & cite →

Plant diseases deep learning transfer learning multi-food crops precision agriculture

Abstract doi.org/10.14569/IJACSA.2025.0161155 PDF

56

InfoCore: AI Driven Named Entity Deduplication and Event Categorization

Author 1: Rohail Qamar Author 2: Raheela Asif Author 3: Abdul Karim Kazi Author 4: Muhammad Ali Author 5: Muhammad Mustafa

The exponential growth of digital information presents critical challenges for efficient data management, as conventional manual curation methods remain slow, error-prone, and unable to adapt to evolving data streams. This paper presents InfoCore: AI-Driven Entity Deduplication and Event Categorization, an automated framework that leverages artificial intelligence to identify and remove… Read full abstract & cite →

Data deduplication context-aware event categorization NLP large language model

Abstract doi.org/10.14569/IJACSA.2025.0161156 PDF

57

Two-Level Hierarchical Adaptive Dynamic Fusion for CNN–LSTM Integration in Fatigue Level Prediction

Author 1: Marlince NK Nababan Author 2: Poltak Sihombing Author 3: Erna Nababan Author 4: T Henny Febriana Harum

Driver fatigue is a major contributor to traffic accidents, yet most existing detection systems rely on unimodal inputs or static fusion mechanisms that lack robustness under poor lighting, partially obscured faces, and missing sensor data. This study aims to overcome these limitations by proposing a Hierarchical Adaptive Dynamic Fusion (HADF)… Read full abstract & cite →

Multimodal fusion adaptive dynamic fusion CNN-LSTM fatigue level prediction

Abstract doi.org/10.14569/IJACSA.2025.0161157 PDF

58

Optimizer Algorithms Analysis for Intrusion Detection System on Deep Neural Network

Author 1: H. A. Danang Rimbawa Author 2: Agung Nugroho Author 3: Muhammad Abditya Arghanie

Intrusion Detection Systems (IDS) play a critical role in identifying potential threats and intrusions in real-time within information technology infrastructures. The development of IDS using Deep Neural Networks (DNN) with the UNSW-NB15 dataset has shown significant potential in improving attack classification accuracy. However, the performance of the DNN-based IDS models… Read full abstract & cite →

Deep Neural Networks (DNN) Intrusion Detection System (IDS) optimization algorithms UNSW-NB15 dataset

Abstract doi.org/10.14569/IJACSA.2025.0161158 PDF

59

Shadow IT Transformation in the Post-Pandemic Digital Workplace: A Systematic Literature Review

Author 1: Ginanjar Nugraha Author 2: Munir Author 3: Puspo Dewi Dirgantari

The COVID-19 pandemic has significantly altered organizational work patterns, accelerating digital transformation and the adoption of remote and hybrid work models. These changes have affected the practice of shadow IT, the use of unauthorized IT by employees without formal IT approval. This systematic literature review aims to explore how the… Read full abstract & cite →

Shadow IT COVID-19 remote work digital transformation systematic literature review IT governance

Abstract doi.org/10.14569/IJACSA.2025.0161159 PDF

60

HeritageLM: Culturally-Aware Multimodal Language Modeling with Memory-Enhanced Cross-Dialect Adaptation

Author 1: Pasupuleti Venkata Ramana Author 2: K. K. Sunalini Author 3: A. Swathi Author 4: R. Lakshmi Author 5: Raman Kumar Author 6: Elangovan Muniyandy Author 7: Khaled Bedair

The HeritageLM system solves the acute problem of language loss by proposing a multimodal language model that brings into Generative Memory the cultural context. Manual documentation and linguistic archiving are the traditional ways of preserving dialects, which may not be effective in preserving the phonetic variety and other cultural peculiarities… Read full abstract & cite →

Cultural embeddings Generative Memory dialect revitalization contrastive learning multimodal NLP

Abstract doi.org/10.14569/IJACSA.2025.0161160 PDF

61

Personalized Grammar Refinement Using Meta-Reinforcement Learning and Transformer-Based Framework

Author 1: Bukka Shobharani Author 2: Melito D. Mayormente Author 3: Edgardo B. Sario Author 4: Bernadette R. Gumpal Author 5: S. Farhad Author 6: Jasgurpreet Singh Chohan Author 7: Elangovan Muniyandy

Writing competence is an essential academic and professional proficiency, and grammatical precision and reliability a long-term issue, especially among ESL students. Conventional rule-based and statistical grammar correction models have constraints based on context, whereas contemporary Transformer-based sequence-to-sequence models like BERT, T5, and GPT have strong performance but cannot be customized… Read full abstract & cite →

Grammar correction Transformer models Meta-Reinforcement Learning curriculum learning personalization ESL writing

Abstract doi.org/10.14569/IJACSA.2025.0161161 PDF

62

Graph-Enhanced Transformer Framework for Context-Sensitive English Skill Assessment

Author 1: Anna Shalini Author 2: Myagmarsuren Orosoo Author 3: W. Grace Shanthi Author 4: Prema S Author 5: S. Farhad Author 6: Elangovan Muniyandy Author 7: A. Chrispin Antonieta Dhivya

The integration of Artificial Intelligence (AI) into English Language Teaching (ELT) has enabled personalized and interactive learning, yet most existing systems rely on static, rule-based feedback models, which fail to capture learner history or adapt interventions based on skill interdependencies. These limitations result in generic management, reduced learner engagement, and… Read full abstract & cite →

Memory-augmented networks conversational AI English Language Teaching (ELT) adaptive feedback personalized language learning

Abstract doi.org/10.14569/IJACSA.2025.0161162 PDF

63

Attention-Enhanced Multi-View Graph Convolutional Network for Early Prediction of Chronic Kidney Disease

Author 1: Roshan D Suvaris Author 2: K Nagaiah Author 3: P. Satish Author 4: Hussana Johar R B Author 5: Elangovan Muniyandy Author 6: Manasa Adusumilli Author 7: Khaled Bedair

The prediction of chronic kidney disease (CKD) must have models capable of processing heterogeneous clinical data and being transparent to assist clinical decision making. Current CKD research usually uses single-view data, integrated graph representations, or bivalent deep learning systems that do not reflect view-specific clinical connections or cannot be interpreted… Read full abstract & cite →

Chronic kidney disease progression multi-view graph convolutional network temporal fusion transformer uncertainty-aware AI models personalized medicine in healthcare

Abstract doi.org/10.14569/IJACSA.2025.0161163 PDF

64

Multimodal Cognitive Mapping Framework for Context-Aware Figurative Language Understanding

Author 1: R. Swathi Gudipati Author 2: Neena PC Author 3: K. Ezhilmathi Author 4: M. Durairaj Author 5: S. Farhad Author 6: Elangovan Muniyandy Author 7: Padmashree V

Learning figurative language, including idioms, metaphors, and similes, remains challenging due to subtle cultural, contextual, and multimodal cues that cannot be inferred from literal meanings alone. Traditional unimodal and text-only approaches, such as CLS-BERT, LaBSE, and mUSE, often fail to capture these deeper semantic patterns, resulting in reduced accuracy and… Read full abstract & cite →

Bi-LSTM cognitive mapping cross-lingual understanding idiom acquisition multimodal learning

Abstract doi.org/10.14569/IJACSA.2025.0161164 PDF

65

Unveiling Gender in Malay-English Short Text: A Comparative Study of ML, DL and Sequential Models with XAI Misclassification Analysis

Author 1: Norazlina Khamis Author 2: Nur Shaheera Shastera Nulizairos Author 3: Haslizatul Mohamed Hanum Author 4: Amirah Ahmad Author 5: Nor Hapiza Mohd Ariffin Author 6: Ruhaila Maskat

Gender identification through written text analysis leverages writer-specific characteristics including linguistic patterns and stylistic behaviors, yet research on gender identification in Malay-English (Manglish) using Traditional Machine Learning (ML), Shallow Deep Learning (DL), and Deep Sequential techniques remains limited compared to English-focused studies. This study addresses this gap by investigating gender… Read full abstract & cite →

Gender identification Manglish machine learning shallow deep learning deep sequential model

Abstract doi.org/10.14569/IJACSA.2025.0161165 PDF

66

Causality Aware Multimodal Reasoning Network in Human Emotion Identification and Sentiment Understanding

Author 1: N. K. Thakre Author 2: Yazan Shaker Almahammed Author 3: G. Indra Navaroj Author 4: Mohammed Fahad Almohazie Author 5: Abdullah Albalawi Author 6: Marran Al Qwaid Author 7: G. Sanjiv Rao

Sentiment and emotion recognition in dynamic English communication require intelligent systems capable of reasoning beyond surface correlations among linguistic, acoustic, and visual cues. Traditional multimodal approaches exhibit limited interpretability, weak contextual adaptability, and lack causal understanding of emotional expressions, resulting in inconsistent predictions under ambiguous conditions. To address these challenges… Read full abstract & cite →

Multimodal sentiment analysis knowledge-driven transformer explainable AI dynamic multimodal fusion CMU-MOSEI dataset

Abstract doi.org/10.14569/IJACSA.2025.0161166 PDF

67

Beyond Ensembles: Architecture-Level Fusion for Enhanced Monument Heritage Recognition

Author 1: Mennat Allah Hassan Author 2: Mona M. Nasr Author 3: Alaa Mahmoud Hamdy

Heritage is seen as a key part of nations, including a broad variety of traditions, cultures, monuments, plants and animals, foods, music, and further. Regarding countries, their own heritages are defined by preservation, excavation, and restoration of historical assets that are important and show the nation's history. It comprises a… Read full abstract & cite →

Cultural monument heritage landmarks monument classification monument recognition transformers

Abstract doi.org/10.14569/IJACSA.2025.0161167 PDF

68

Semi-Supervised Learning vs. Few-Shot Learning: Which is Better for Sentiment Analysis on Hotel Reviews Towards a Small Labeled Training Data?

Author 1: Retno Kusumaningrum Author 2: Ahmad Ainun Herlambang Author 3: Wafiq Afifah Author 4: Adi Wibowo Author 5: Sutikno Author 6: Priyo Sidik Sasongko

The massive growth in user reviews on the online travel agent (OTA) website can be automatically processed using sentiment analysis to understand consumer satisfaction or feedback. Sentiment analysis is commonly implemented as a sentiment classification task by applying classical machine learning and deep learning algorithms. However, implementing both strategies has… Read full abstract & cite →

Sentiment analysis hotel reviews semi-supervised learning few-shot learning low-resource language

Abstract doi.org/10.14569/IJACSA.2025.0161168 PDF

69

Linguistically Informed Essay Assessment Framework to Analyze Writing Style Vocabulary Usage and Coherence

Author 1: Sreela B Author 2: B. Neelambaram Author 3: Manasa Adusumilli Author 4: Revati Ramrao Rautrao Author 5: Aseel Smerat Author 6: Myagmarsuren Orosoo Author 7: A. Swathi

Automated essay scoring (AES) has become an essential tool in educational technology, yet many existing approaches rely on black-box models that lack interpretability and adaptability across diverse prompts and writing styles. Conventional transformer-based AES systems demonstrate strong accuracy, but often fail to provide pedagogically meaningful feedback or generalize effectively in… Read full abstract & cite →

COSMET-Net contrastive learning explainable AI meta-learning essay scoring

Abstract doi.org/10.14569/IJACSA.2025.0161169 PDF

70

An Interpretable Dual-Level Feedback Approach for Improving Graded Language Simplification and Readability

Author 1: Pavani G Author 2: Myagmarsuren Orosoo Author 3: W. Grace Shanthi Author 4: Vinod Waiker Author 5: Aseel Smerat Author 6: Bhuvaneswari Pagidipati Author 7: Bansode G. S Author 8: Osama R. Shahin

Text simplification plays a vital role in adaptive language learning, especially when aligned with the Common European Framework of Reference (CEFR) proficiency levels. The purpose of this study is to develop an interpretable and CEFR-aligned text simplification framework that produces pedagogically appropriate simplified texts for learners at different proficiency levels… Read full abstract & cite →

Text simplification CEFR-level alignment reinforcement learning adaptive feedback loop T5 transformer

Abstract doi.org/10.14569/IJACSA.2025.0161170 PDF

71

Meta Learning Enhanced Graph Transformer for Robust Smart Grid Anomaly Detection

Author 1: Layth Almahadeen Author 2: Aseel Smerat Author 3: Sandeep Kumar Mathariya Author 4: G. Indra Navaroj Author 5: Vuda Sreenivasa Rao Author 6: Kamila Ibragimova Author 7: Osama R.Shahin

The increasing complexity of modern smart grids and the heterogeneity of multi-sensor data make anomaly detection extremely challenging, as existing techniques struggle to capture long-range spatial dependencies, cross-sensor interactions, and unseen anomaly patterns. Conventional models such as Isolation Forest, Random Forest, GCAD, AT-GTL, CVTGAD, and hybrid CNN-Transformer approaches often suffer… Read full abstract & cite →

Adaptive detection anomaly detection contrastive learning graph transformer networks smart grid

Abstract doi.org/10.14569/IJACSA.2025.0161171 PDF

72

An Interpretable Deep Learning Framework for Measuring Organizational Digital Transformation Readiness

Author 1: Pravin D. Sawant Author 2: Veera Ankalu Vuyyuru Author 3: B. Arunsundar Author 4: A. Vini Infanta Author 5: Dekhkonov Burkhon Author 6: N. Roopalatha

The accelerating pace of digital transformation (DT) across industries demands accurate, transparent, and adaptable maturity evaluation frameworks capable of capturing complex organizational behaviors. Conventional fuzzy logic and decision tree-based maturity models cannot effectively represent the nonlinear dependencies among DT indicators and often produce inconsistent, opaque assessments. To overcome these limitations… Read full abstract & cite →

Digital transformation FT-Transformer TabNet maturity intelligence deep learning

Abstract doi.org/10.14569/IJACSA.2025.0161172 PDF

73

Clinically Informed Adaptive Multimodal Graph Learning Paradigm for Transparent Temporal and Generalizable Alzheimer’s Disease Diagnosis

Author 1: Padmavati Shrivastava Author 2: V S Krushnasamy Author 3: Guru Basava Aradhya S Author 4: Vinod Waiker Author 5: Peddireddy Veera Venkateswara Rao Author 6: Elangovan Muniyandy Author 7: Khaled Bedair

This is a clinically reliable and explainable diagnostic framework for the early detection of Alzheimer's disease with multimodal data. Current computational methods face challenges in dealing with fragmented clinical information, poor cross-modal integration, limited temporal modelling, and low interpretability, rendering them unsuitable for real-world medical deployment. To overcome these limitations… Read full abstract & cite →

Alzheimer’s detection graph neural network multimodal fusion explainable AI temporal transformer

Abstract doi.org/10.14569/IJACSA.2025.0161173 PDF

74

Transformer Driven Multi-Agent Reinforcement Learning Framework for Integrated Waste Classification Forecasting and Adaptive Routing

Author 1: Ritesh Patel Author 2: Igamberdiyev Asqar Kimsanovich Author 3: Vinod Waiker Author 4: Elangovan Muniyandy Author 5: Swarna Mahesh Naidu Author 6: Nurilla Mahamatov Author 7: Osama R.Shahin

The rapid expansion of urban populations has intensified the challenges associated with municipal solid waste management, particularly where conventional static or ad-hoc routing strategies create operational inefficiencies, excessive fuel usage, and repeated bin overflow. Many existing systems still treat waste classification, fill-level forecasting, and routing as separate processes, which restricts… Read full abstract & cite →

Smart waste management temporal fusion transformer vision transformer predictive analytics route optimization deep learning

Abstract doi.org/10.14569/IJACSA.2025.0161174 PDF

75

Leveraging Intelligent Speech Training to Elevate Phonetic Accuracy and Prosodic Fluency in English Learners

Author 1: Amit Khapekar Author 2: Nidhi Mishra Author 3: Vijaya Lakshmi Mandava Author 4: T K Rama Krishna Rao Author 5: Bhuvaneswari Pagidipati Author 6: Prasad Devarasetty Author 7: Elangovan Muniyandy

The successful teaching of pronunciation, as well as prosody, is the significant challenge that still remains to the English as Foreign Learning (EFL) students. Traditional pedagogical theories tend to focus on segmental phoneme accuracy but ignore suprasegmental components (stress or rhythm and intonation) which are natural and intelligible speech components… Read full abstract & cite →

Automatic speech recognition pronunciation and prosody transformer-based phoneme identification prosody assessment adaptive learning algorithm

Abstract doi.org/10.14569/IJACSA.2025.0161175 PDF

76

An Enhanced Deep Learning Framework for Diabetic Retinopathy Classification Using Multiple Convolutional Neural Network Architectures

Author 1: Zaid Romegar Mair Author 2: Agus Harjoko Author 3: Rendra Gustriansyah Author 4: Septa Cahyani Author 5: Rudi Heriansyah Author 6: Indah Permatasari Author 7: Muhammad Haviz Irfani

Diabetic retinopathy (DR) is a leading cause of blindness, requiring early and accurate diagnosis. Although deep learning, particularly Convolutional Neural Networks (CNNs), has shown promising results in automating DR classification, selecting the optimal architecture and extracting effective features for specific clinical datasets remains a challenge. This study aims to conduct… Read full abstract & cite →

Diabetic retinopathy diabetic retinopathy classification deep learning Convolutional Neural Network (CNN) VGG16

Abstract doi.org/10.14569/IJACSA.2025.0161176 PDF

77

Integrating Causality with Spatio-Temporal Attention for Accurate Airline Delay Prediction

Author 1: Akash Daulatrao Gedam Author 2: Pavaimalar S Author 3: Mercy Toni Author 4: Y. Rajesh Babu Author 5: P. Satish Author 6: Bobonazarov Abdurasul Author 7: Elangovan Muniyandy

Flight delays can cause serious problems for airlines, passengers, and the economy in general. Current prediction methods that use Random Forests, deep neural networks, and recurrent architectures such as GRU can address either time or quantity, pero not both when applied to causal reasoning and assess uncertainty therein, which negatively… Read full abstract & cite →

Flight delay prediction spatio-temporal modeling causal reasoning attention network uncertainty estimation

Abstract doi.org/10.14569/IJACSA.2025.0161177 PDF

78

AI-Enabled Assessed Learning from an Interdisciplinary Educational Perspective: An Empirical Case Study Using ChatGPT LLM

Author 1: Ali Hassan Author 2: Omar Tayan Author 3: Abdurazzag Almiladi Author 4: Adnan Ahmed Abi Sen

The recent developments in artificial intelligence (AI) have introduced transformative tools into the educational landscape, with early signs of its impact on advancing student learning methods and possible outcomes. This paper explores the possible integration of large language models (LLMs) in higher education academic courses, using the popular ChatGPT LLM… Read full abstract & cite →

Educational assessment interdisciplinary learning ChatGPT Large Language Models (LLMs) Artificial Intelligence (AI)

Abstract doi.org/10.14569/IJACSA.2025.0161178 PDF

79

A Privacy Protection Method for IoT Data Based on Edge Computing and Federated Learning Algorithm

Author 1: Ying Wu

This paper proposes a privacy protection method for IoT data integrating edge computing and federated learning. To address challenges including edge node heterogeneity, central server bottlenecks in traditional federated learning, and high overhead of homomorphic encryption, we design a hierarchical architecture comprising requesters, participants, edge nodes, a sensing platform, and… Read full abstract & cite →

Edge computing federated learning Internet of Things privacy protection homomorphic encryption dropout supplementation

Abstract doi.org/10.14569/IJACSA.2025.0161179 PDF

80

TOPSIS-YOLO Decision Fusion with Mel-Spectrogram Analysis for Engine Fault Detection

Author 1: Rommel F. Canencia Author 2: Ken D. Gorro Author 3: Deolinda E. Caparroso Author 4: Marlito V. Patunob Author 5: Jonathan C. Maglasang Author 6: Joecyn N. Archival

Industrial machinery fault detection systems require both high diagnostic accuracy and computational efficiency for real-time deployment. This study presents a novel hybrid approach that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with You Only Look Once (YOLO) deep learning for efficient audio-based fault detection… Read full abstract & cite →

Industrial fault detection audio signal processing spectrogram analysis deep learning multi-criteria decision making (MCDM) TOPSIS YOLO

Abstract doi.org/10.14569/IJACSA.2025.0161180 PDF

81

Kalman-Enhanced Deep Reinforcement Learning for Noise-Resilient Algorithmic Trading in Volatile Gold Markets

Author 1: Amine Kili Author 2: Brahim Raouyane Author 3: Mohamed Rachdi Author 4: Mostafa Bellafkih

Precious metals market, such as gold (XAU/USD), exhibit high volatility and significant microstructure noise in their financial time series, which degrade the reliability of algorithmic trading models. While deep reinforcement learning (DRL) has shown strong results in equities and cryptocurrencies, its application to precious metals remains limited by unstable signals… Read full abstract & cite →

Deep reinforcement learning Kalman filtering algorithmic trading gold markets XAU/USD microstructure noise signal processing financial time series noise reduction institutional trading

Abstract doi.org/10.14569/IJACSA.2025.0161181 PDF

82

Iterative Partition Optimization: A Novel Approach for Feature Selection in NIR Spectroscopy

Author 1: Phuong Nguyen Thi Hoang Author 2: Thinh Ngo Hung Author 3: Tuong Nguyen Huy Author 4: Hieu Nguyen Van

Machine learning for near-infrared (NIR) spectroscopy requires effective feature selection to address high dimensionality and multicollinearity. This study proposes Iterative Partition Optimization (IPO), a framework integrating Model Population Analysis with Weighted Binary Matrix Sampling through segment-wise optimization: partitioning spectra into segments, isolating one active segment while freezing others, and using… Read full abstract & cite →

Machine learning feature extraction near infrared spectroscopy iterative partition optimization

Abstract doi.org/10.14569/IJACSA.2025.0161182 PDF

83

A Secure Tracking System to Prevent Counterfeit Drugs in the Pharmaceutical Supply Chain

Author 1: Huda Alrehaili Author 2: Suhair Alshehri Author 3: Rania M. Alhazmi

Counterfeit drugs represent a serious global risk, accounting for about 10% of the medicines worldwide. This per-centage increases significantly in developing countries, reaching up to 30%, and has become a major cause of child mortality in these regions. Blockchain technology provides a good solution to address this critical issue through… Read full abstract & cite →

Pharmaceutical supply chain blockchain access control InterPlanetary File System (IPFS) Non-Fungible Token (NFT)

Abstract doi.org/10.14569/IJACSA.2025.0161183 PDF

84

Integrating Large Language Models with Deep Reinforcement Learning for Portfolio Optimization

Author 1: Renad Alsweed Author 2: Mohammed Alsuhaibani

This paper explores the application of Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) to portfolio optimization, a critical financial task requiring strategies to balance risk and return in volatile markets. Traditional models often struggle with the complexity of financial markets, whereas Reinforcement Learning (RL) provides end-to-end frameworks for… Read full abstract & cite →

Portfolio optimization Reinforcement Learning (RL) algorithmic trading Markov Decision Process (MDP) Large Language Models (LLM) deep learning

Abstract doi.org/10.14569/IJACSA.2025.0161184 PDF

85

MOON Framework: An Emotionally Adaptive Voice Interaction Model for Older Adults

Author 1: Hasan Sagga Author 2: Richard Stone

As the world experiences a rapidly aging population, it has become a pressing design challenge to ensure that emerging digital technologies remain understandable, supportive, and meaningful for older adults. One of the most promising modalities for promoting inclusive interaction is the Voice User Interface (VUI), supported by artificial intelligence (AI)… Read full abstract & cite →

Voice User Interfaces emotional adaptivity affective computing older adults human-computer interaction empathy accessibility adaptive systems MOON Framework

Abstract doi.org/10.14569/IJACSA.2025.0161185 PDF

86

Informative Inputs Over Complex Features: Long Short-Term Memory Forecasting in the Saudi Stock Market

Author 1: Munira AlBalla Author 2: Arwa Alawajy Author 3: Seetah Alsalamah Author 4: Zahida Almuallem

This study investigates the effectiveness of deep learning, specifically Long Short-Term Memory (LSTM) networks, for forecasting stock closing prices in the Saudi Arabian market. Unlike prior research that focuses on narrow stock subsets or individual technical indicators, we present the first comprehensive evaluation across the top thirty companies by market… Read full abstract & cite →

Deep learning feature engineering stock price forecasting macroeconomic indicators Brent crude oil Saudi stock market Long Short-Term Memory Tadawul Tadawul All Share Index (TASI)

Abstract doi.org/10.14569/IJACSA.2025.0161186 PDF

87

Integrating Artificial Intelligence into Continuous Improvement for Automotive Manufacturing

Author 1: Sara OULED LAGHZAL Author 2: EL OUADI Abdelmajid

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Continuous Improvement (CI) frameworks is redefining the foundations of automotive manufacturing under the Industry 4.0 paradigm. Traditional method-ologies such as Kaizen, Lean Six Sigma, and Total Quality Management (TQM) have long provided structured approaches for quality enhancement, waste reduction… Read full abstract & cite →

Industry 4.0 automotive manufacturing artificial intelligence machine learning CNN computer vision

Abstract doi.org/10.14569/IJACSA.2025.0161187 PDF

88

Managerial Drivers and Performance Outcomes of AI Adoption in Automotive Manufacturing

Author 1: Sara OULED LAGHZAL Author 2: EL OUADI Abdelmajid

This article examines how artificial intelligence and machine learning reshape automotive manufacturing within Industry 4.0. Reported impacts include up to a 200 percent reduction in costs and a 400 percent gain in production efficiency, with controlled studies showing about a 15 percent improvement from process optimization. The largest early wins… Read full abstract & cite →

Industry 4.0 automotive manufacturing artificial intelligence machine learning quality 4.0 predictive maintenance

Abstract doi.org/10.14569/IJACSA.2025.0161188 PDF

89

Mapping Artificial Intelligence Research in Automotive Manufacturing: A Bibliometric Study

Author 1: Sara OULED LAGHZAL Author 2: EL OUADI Abdelmajid

Artificial Intelligence (AI) is transforming the automotive industry by enabling smart manufacturing, optimizing supply chains, enhancing vehicle safety, and accelerating the shift toward autonomous mobility. This bibliometric study provides a systematic overview of the intellectual landscape of AI adoption in the automotive sector. Using data from Scopus, Web of Science… Read full abstract & cite →

Artificial Intelligence automotive industry Industry 4.0 autonomous vehicles supply chain optimization computer vision predictive maintenance digital twins smart manufacturing bibliometric analysis

Abstract doi.org/10.14569/IJACSA.2025.0161189 PDF

90

AI in Web Development: A Comparative Study of Traditional Coding and LLM-Based Low-Code Platforms

Author 1: Abiha Babar Author 2: Nosheen Sabahat Author 3: Arisha Babar Author 4: Nosheen Qamar Author 5: Marwan Abu-Zanona Author 6: Asef Mohammad Ali Al Khateeb Author 7: Bassam ElZaghmouri Author 8: Saad Mamoun AbdelRahman Ahmed Author 9: Lamia Hassan Rahamatalla

Web development supports business, education, and public services online, so speed and reliability are important. Low-code and no-code (LCNC) platforms aim to save time by using visual tools instead of writing all code. The impact of these platforms when combined with large language models (LLMs) has not been well studied… Read full abstract & cite →

Web development artificial intelligence large language models low-code platforms no-code platforms conversational agents software engineering

Abstract doi.org/10.14569/IJACSA.2025.0161190 PDF

91

Towards a Tailored Cybersecurity Education Framework for Malaysia: A Systematic Literature Review

Author 1: Muhammad Asfand Yar Author 2: Hock Guan Goh Author 3: Kiran Adnan Author 4: Ming Lee Gan Author 5: Vasaki Ponnusamy

Developing countries including Malaysia faces urgent challenges in cybersecurity education: preparing graduates who meet industry demands while addressing national cultural and regulatory contexts. Despite global advancements, no localized education framework currently aligns Malaysian higher education curricula with industry-required competencies. This systematic literature review (SLR) analyzed 65 academic and gray literature… Read full abstract & cite →

Cybersecurity education systematic literature review Malaysia industry competencies higher education curriculum model hands-on learning

Abstract doi.org/10.14569/IJACSA.2025.0161191 PDF

92

Bit Stability and Hash-Length Trade-Offs in Binary Face Templates

Author 1: Abdelilah Ganmati Author 2: Karim Afdel Author 3: Lahcen Koutti

Binary face templates are an appealing alternative to floating-point embeddings for face verification in resource-constrained environments because they enable constant-time Hamming matching with minimal storage and input/output (I/O). This paper studies the bit-level behavior of hashes obtained by principal component analysis followed by iterative quantization (PCA–ITQ) at L∈{32, 64, 128}… Read full abstract & cite →

Binary face templates face verification hamming distance PCA-ITQ bit stability eyeglasses attribute resource-constrained verification MORPH CelebA

Abstract doi.org/10.14569/IJACSA.2025.0161192 PDF

93

A Quantized Deep Learning Model for Efficient Plant Leaf Disease Detection on Embedded Systems

Author 1: Balkis Tej Author 2: Soulef Bouaafia Author 3: Mohamed Ali Hajjaji Author 4: Abdellatif Mtibaa Author 5: Mohamed Atri

You Only Look Once (YOLO) object detection network has gained significant adoption in the field of plant leaf disease detection due to its strong detection capabilities. However, deploying YOLO models on resource-constrained devices remains challenging, as they require substantial computational power. The complexity and size of these models pose significant… Read full abstract & cite →

Object detection plant disease quantization technique YOLOv5

Abstract doi.org/10.14569/IJACSA.2025.0161193 PDF

94

AI-Assisted Schema Translation and Verified Migration: From Object-Relational Models to NoSQL Document-Oriented Stores

Author 1: Fouad Toufik Author 2: Abd Allah Aouragh Author 3: Abdelhak Khalil

The transition from object relational databases (ORDBs) to document oriented NoSQL stores offers increased flexibility and scalability in modern data management. However, existing migration processes remain largely manual and heuristic, hindering automation, formal verification, and adaptability to evolving workloads. This paper introduces a principled AI-assisted framework that unifies schema translation… Read full abstract & cite →

Graph Neural Networks (GNN) transformer models neuro-symbolic verification hybrid AI architectures schema translation data migration object-relational databases document-oriented databases

Abstract doi.org/10.14569/IJACSA.2025.0161194 PDF

95

THMI-FS-Stack: A Hybrid Imputation and Feature Selection with Stacking Ensemble for Avian Influenza Outbreak Prediction

Author 1: V. S. V. S. Murthy Author 2: J. N. V. R. Swarup Kumar Author 3: Srinivas Gorla

Timely prediction of zoonotic disease outbreaks, particularly Highly Pathogenic Avian Influenza (HPAI), is critical for real-time epidemiological surveillance and pandemic pre-paredness. However, real-world avian surveillance datasets often suffer from missing values, high dimensionality, and inconsistent feature distributions, leading to unreliable predictions. This study proposes THMI-FS-Stack, a modular and interpretable machine… Read full abstract & cite →

Zoonotic disease outbreaks avian influenza pan-demic prediction hybrid imputation feature selection stacking ensemble classifier wild bird HPAI dataset early-warning systems

Abstract doi.org/10.14569/IJACSA.2025.0161195 PDF

96

Ensuring End-to-End Traceability and Sustainability in the FSC: A Modular Web3 Architecture Integrating Blockchain, IoT, and Machine Learning

Author 1: Addou Kamal Author 2: Mohammed Yassine El Ghoumari

Traceability in food supply chains is crucial for ensuring safety, enabling effective quality control, and maintaining consumer trust. However, traditional paper-based or digital tracking systems often prove too slow and opaque during food safety incidents or investigations into fraud. To address these limitations, this paper presents a modular Web3 architecture… Read full abstract & cite →

Food supply chain traceability blockchain web3 smart contracts IoT machine learning data integrity

Abstract doi.org/10.14569/IJACSA.2025.0161196 PDF

97

A Hybrid Mutual-Information and Heatmap Driven Under-Sampling Algorithm for Imbalanced Binary Classification

Author 1: Sehar Gul Author 2: Syahid Anuar Author 3: Hazlifah Mohd Rusli Author 4: Azri Azmi Author 5: Sadaquat Ali Ruk

Class imbalance is a common problem that occurs in classification where one class has much more instances than the other class. Class imbalance is especially challenging in high-stakes fields like medical diagnosis, fraud detection and predictive maintenance among others. In cases of imbalanced class distribution, models perform well while predicting… Read full abstract & cite →

Mutual information heat-map visualization clustering under-sampling data-level hybrid

Abstract doi.org/10.14569/IJACSA.2025.0161197 PDF

98

STROKECT-BENCH: Evaluating Convolutional and Transformer-Based Deep Models for Automated Stroke Diagnosis Using Brain CT Imaging

Author 1: Raghda Essam Ali Author 2: Reda Abdel-Wahab El-Khoribi Author 3: Ehab Ezzat Hassanein Author 4: Farid Ali Moussa

Stroke detection from computed tomography (CT) images is an important research direction in computer vision. However, prior studies often use different preprocessing steps, model configurations, and evaluation protocols, making it difficult to compare results or assess architectural reliability. This paper presents an exploratory benchmark that evaluates representative convolutional neural networks… Read full abstract & cite →

Stroke detection brain CT image Convolutional neural networks vision transformers exploratory benchmark

Abstract doi.org/10.14569/IJACSA.2025.0161198 PDF

99

Mobile Application Based on Clean Architecture for Sustainable Tourism Recommendation Using BERT

Author 1: Cristhian Paolo Atuncar Yataco Author 2: Marco Antonio Lopez Salinas Author 3: Jos´e Alfredo Herrera Quispe

The development of intelligent systems requires robust architectures and design patterns that ensure scalability, maintainability, and separation of concerns. This paper presents the design and implementation of a mobile application for recommending sustainable tourist destinations in Peru, employing a Clean Architecture approach in the backend and the MVC pattern in… Read full abstract & cite →

Software engineering clean architecture MVC BERT recommendation systems mobile applications FastAPI Kotlin sustainability

Abstract doi.org/10.14569/IJACSA.2025.0161199 PDF

100

A String-Similarity-Oriented Item Swapping Algorithm for Protecting Sensitive Frequent Itemsets in Transaction Databases

Author 1: Fatah Yasin Al Irsyadi Author 2: Dedi Gunawan Author 3: Diah Priyawati Author 4: Wardah Yuspin

Frequent itemset mining is a widely adopted data mining technique. The application of the technique can be found in transaction database analysis, such as exploring a set of purchased items. Presently, the growing concern of privacy protection and security issues in society leads the business parties to be more careful… Read full abstract & cite →

Data hiding sensitive frequent itemset frequent itemset data mining item swapping technique D-Lswap

Abstract doi.org/10.14569/IJACSA.2025.01611100 PDF

101

A Novel Access Control Model with the Skew Tent Map for Decision Making (STM-ABAC)

Author 1: Omessead BenMbarak Author 2: Anis Naanaa Author 3: Sadok ElAsmi

The proliferation of cloud computing exposes sensitive data to the risk of unauthorized access, as traditional access control mechanisms are often inadequate for this dynamic environment. To address these shortcomings, this article proposes a novel access control scheme, named STM-ABAC, which is based on the Skew Tent Map (STM). This… Read full abstract & cite →

Cloud computing skew tent map ABAC ABE attribute token

Abstract doi.org/10.14569/IJACSA.2025.01611101 PDF

102

A Novel Approach for Urban Traffic Congestion Prediction

Author 1: Chaimae Kanzouai Author 2: Abderrahim Zannou Author 3: Soukaina BOUAROUROU Author 4: El Habib Nfaoui Author 5: Abdelhak Boulaalam

Traffic congestion is a global problem in urban areas that creates longer travel times, increased fuel consumption, and elevated levels of pollution. Traffic congestion occurs because of the exponential growth of vehicles along with a finite number of roadways and the inability to manage traffic effectively. This paper studies the… Read full abstract & cite →

Traffic congestion traffic management traffic factors congestion level DBSCAN GCN

Abstract doi.org/10.14569/IJACSA.2025.01611102 PDF

103

Lightweight Cryptography for Energy-Conscious Authentication in IoT Systems

Author 1: Ashwag Alotaibi Author 2: Huda Aldawghan Author 3: Mounir Frikha

The proliferation of IoT devices has introduced new challenges in ensuring secure communication while maintaining computational and energy efficiency. Traditional cryptographic mechanisms such as AES-128, although robust, often fail to meet the stringent constraints of resource-limited IoT environments. This research investigates lightweight cryptographic algorithms and energy-conscious authentication protocols as viable… Read full abstract & cite →

Lightweight cryptography IoT security energy efficiency lightweight authentication SPECK PRESENT AES- 128 cryptographic algorithms resource-constrained devices Multi-Metric Suitability Analysis

Abstract doi.org/10.14569/IJACSA.2025.01611103 PDF

104

Hybrid Reinforcement Learning-Based Hyper-Parameter Optimization with Yolov8 Indoor Fire Recognition

Author 1: Patrick D. Cerna Author 2: Harvey C. Quijada Author 3: Michael Joseph E. Ortaliz Author 4: Kenneth Charles H. Saluna

This study presents an indoor vision-based fire detection system that integrates a YOLOv8n object detection model with a Reinforcement Learning-based Optimization Algorithm (ROA) for hyperparameter tuning. The research investigates three key aspects: 1) the effectiveness of ROA in improving model performance, 2) the optimal smart camera resolution and placement for… Read full abstract & cite →

Fire detection YOLOv8 reinforcement learning hyperparameter optimization convolutional neural network indoor surveillance real-time alert system

Abstract doi.org/10.14569/IJACSA.2025.01611104 PDF

105

Adaptive Open Cyber Intelligence for SOAR: Reduced False Positives in Low-Resource Environments

Author 1: Shunmugam U Author 2: Rajesh D

The increasing use of resource-constrained cyber-physical devices emphasizes the need for effective and flexible methods in the deployment of threat intelligence. The Open Cyber Intelligence Framework (OCIF), an architecture that applies Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) capabilities to resource-constrained environments, is presented… Read full abstract & cite →

Open Cyber Intelligence Framework SOAR SIEM Cyber Threat Intelligence false positive reduction threat mitigation anomaly detection

Abstract doi.org/10.14569/IJACSA.2025.01611105 PDF