IJACSA Vol. 16 Issue 2 (2025)

1

6G-Enabled Autonomous Vehicle Networks: Theoretical Analysis of Traffic Optimization and Signal Elimination

Author 1: Daniel Benniah John

This paper proposes a theoretical framework for optimizing traffic flow in autonomous vehicle (AV) networks using 6G communication systems. We propose a novel technique to eliminate conventional traffic signals through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The article demonstrates traffic flow optimization, density, and safety improvements through real-time management and… Read full abstract & cite →

6G Communication systems autonomous vehicle networks traffic flow optimization signal-free traffic management Vehicle-to-Vehicle Communication (V2V) Vehicle-to-Infrastructure Communication (V2I) multi-agent deep reinforcement learning real-time traffic management

Abstract doi.org/10.14569/IJACSA.2025.0160201 PDF

2

Light-Weight Federated Transfer Learning Approach to Malware Detection on Computational Edges

Author 1: Sakshi Mittal Author 2: Prateek Rajvanshi Author 3: Riaz Ul Amin

With rapid increase in edge computing devices, Light weight methods to identify and stop cyber-attacks has become a topic of interest for the research community. Fast proliferation of smart devices and customer’s concerns regarding the data security and privacy has necessitated new methods to counter cyber attacks. This work presents… Read full abstract & cite →

Alware detection transfer learning light weight transfer learning federated learning alware detection transfer learning light weight transfer learning federated learning

Abstract doi.org/10.14569/IJACSA.2025.0160202 PDF

3

An Automated Mapping Approach of Emergency Events and Locations Based on Object Detection and Social Networks

Author 1: Khalid Alfalqi Author 2: Martine Bellaiche

The high prevalence of cellphones and social networking platforms such as Snapchat are obviously dissolving traditional barriers between information providers and end-users. It is certainly relevant in emergency events, as individuals on the site produce and exchange real-time information about the event. However, notwithstanding their demonstrated significance, obtaining event-related information… Read full abstract & cite →

Machine learning deep learning big data social networks object detection emergency event detection snapchat hotspot map

Abstract doi.org/10.14569/IJACSA.2025.0160203 PDF

4

Resampling Imbalanced Healthcare Data for Predictive Modelling

Author 1: Manoj Yadav Mamilla Author 2: Ronak Al-Haddad Author 3: Stiphen Chowdhury

Imbalanced datasets pose significant challenges in healthcare for developing accurate predictive models in medical diagnostics. In this work, we explore the effectiveness of combining resampling methods with machine learning algorithms to enhance prediction accuracy for imbalanced heart and lung disease datasets. Specifically, we integrate undersampling techniques such as Edited Nearest… Read full abstract & cite →

Imbalanced data resampling machine learning healthcare

Abstract doi.org/10.14569/IJACSA.2025.0160204 PDF

5

Exploiting Ray Tracing Technology Through OptiX to Compute Particle Interactions with Cutoff in a 3D Environment on GPU

Author 1: David Algis Author 2: Berenger Bramas

Particle interaction simulation is a fundamental method of scientific computing that require high-performance solutions. In this context, computing on graphics processing units (GPUs) has become standard due to the significant performance gains over conventional CPUs. However, since GPUs were originally designed for 3D rendering, they still retain several features that… Read full abstract & cite →

CUDA graphics processing unit high-performance computing OptiX particle interactions ray tracing scientific computing

Abstract doi.org/10.14569/IJACSA.2025.0160205 PDF

6

RSCHED: An Effective Heterogeneous Resource Management for Simultaneous Execution of Task-Based Applications

Author 1: Etienne Ndamlabin Author 2: Berenger Bramas

Modern parallel architectures have heterogeneous processors and complex memory hierarchies, offering up to billion-way parallelism at multiple hierarchical levels. Their exploitation by HPC applications greatly boosts scientific discoveries and advances, but they are still not fully utilized, leading to proportionally high energy consumption. The task-based programming model has demonstrated promising… Read full abstract & cite →

Heterogeneous resource management scheduling task-based applications gradient descent StarPU

Abstract doi.org/10.14569/IJACSA.2025.0160206 PDF

7

Enhanced Network Bandwidth Prediction with Multi-Output Gaussian Process Regression

Author 1: Shude Chen Author 2: Takayuki Nakachi

Modern network environments, especially in do-mains like 5G and IoT, exhibit highly dynamic and nonlinear traffic behaviors, posing significant challenges for accurate time series analysis and predictive modeling. Traditional approaches, including stochastic ARIMA and deep learning-based LSTM, frequently encounter difficulties in capturing rapid signal variations and inter-channel dependencies, often due… Read full abstract & cite →

Network traffic prediction Multi-Output Gaussian Process (MOGP) signal processing time series analysis predictive modeling multi-channel data IoT traffic monitoring 5G networks

Abstract doi.org/10.14569/IJACSA.2025.0160207 PDF

8

Automated Subjective Perception of a Driver’s Pain Level Based on Their Facial Expression

Author 1: F. Hadi Author 2: O. Fukuda Author 3: W. LYeoh Author 4: H. Okumura Author 5: Y. Rodiah Author 6: Herlina Author 7: A. Prasetyo

One factor that has a positive correlation with the risk of traffic accidents is the pain experienced by drivers. This pain is sometimes expressed facially by the driver and can be subjectively perceived by others. By observing the facial expression of drivers, it can estimate the pain experienced at that… Read full abstract & cite →

Pain driver convolution neural network facial expression

Abstract doi.org/10.14569/IJACSA.2025.0160208 PDF

9

Mobile Application Based on Geolocation for the Recruitment of General Services in Trujillo, La Libertad

Author 1: Melissa Giannina Alvarado Baudat Author 2: Camila Vertiz Asmat Author 3: Fernando Sierra-Liñan

Currently, there is no technological solution that efficiently facilitates the offering of general services by independent workers in the city of Trujillo. This limitation reduces job opportunities, as workers secure fewer contracts due to reliance on client recommendations, a method that is often inefficient due to long response times and… Read full abstract & cite →

Mobile application recruitment geolocation general services

Abstract doi.org/10.14569/IJACSA.2025.0160209 PDF

10

Development of a Software Tool for Learning the Fundamentals of CubeSat Angular Motion

Author 1: Victor Romero-Alva Author 2: Angelo Espinoza-Valles

The development of tools for understanding and simulating CubeSat angular motion is essential for both educational and research purposes in space technology. In this context, this paper presents the development of a MATLAB-based software tool designed to facilitate the comprehension of CubeSat angular motion. This tool allows users to simulate… Read full abstract & cite →

CubeSat angular motion simulation learning tool MATLAB

Abstract doi.org/10.14569/IJACSA.2025.0160210 PDF

11

Performance Evaluation and Selection of Appropriate Congestion Control Algorithms for MPT Networks

Author 1: Naseer Al-Imareen Author 2: Gábor Lencse

Recent academic research highlights a growing interest in multipath technologies, which offer promising solutions to networking challenges in complex environments. This interest is reflected in the emergence of protocols such as Multipath TCP (MPTCP) and Multipath UDP-in-GRE (MPT-GRE). The development of network protocols, particularly various iterations of the Transmission Control… Read full abstract & cite →

Packet loss congestion control MPT-GRE delay throughput jitter

Abstract doi.org/10.14569/IJACSA.2025.0160211 PDF

12

A Chatbot for the Legal Sector of Mauritius Using the Retrieval-Augmented Generation AI Framework

Author 1: Taariq Noor Mohamed Author 2: Sameerchand Pudaruth Author 3: Ivan Coste-Manière

Mauritius is known to have a hybrid legal system as the logical consequence of being both a former French and English colony. From its independence in 1968 to date, the legal environment has changed to reflect the constant need to provide a framework to address the country’s diverse needs. With… Read full abstract & cite →

Law chatbot retrieval augmented generation large language model OpenAI Mistral AI

Abstract doi.org/10.14569/IJACSA.2025.0160212 PDF

13

Model for Training and Predicting the Occurrence of Potato Late Blight Based on an Analysis of Future Weather Conditions

Author 1: Daniel Damyanov Author 2: Ivaylo Donchev

Plant diseases pose a significant challenge to agriculture, leading to serious economic losses and a risk to food security. Predicting and managing diseases such as potato blight requires an analysis of key environmental factors, including temperature, dew point, and humidity, that influence the development of pathogens. The current study uses… Read full abstract & cite →

Machine learning potato late blight data analysis forecast prediction models

Abstract doi.org/10.14569/IJACSA.2025.0160213 PDF

14

Lung Parenchyma Segmentation Using Mask R-CNN in COVID-19 Chest CT Scans

Author 1: Wilmer Alberto Pacheco Llacho Author 2: Eveling Castro-Gutierrez Author 3: Luis David Huallpa Tapia

During the COVID-19 pandemic, the precise evaluation of lung impairments using computed tomography (CT) scans became critical for understanding and managing the disease; however, specialists faced a high workload and the urgent need to deliver fast and accurate results. To address this, deep learning models offered a promising solution by… Read full abstract & cite →

Mask R-CNN ResNet-50 computed tomography lung parenchyma COVID-19

Abstract doi.org/10.14569/IJACSA.2025.0160214 PDF

15

Impact of the TikTok Algorithm on the Effectiveness of Marketing Strategies: A Study of Consumer Behavior and Content Preferences

Author 1: Raquel Melgarejo-Espinoza Author 2: Mauricio Gonzales-Cruz Author 3: Juan Chavez-Perez Author 4: Orlando Iparraguirre-Villanueva

TikTok has become one of the most widely used platforms, its innovative video format has allowed companies and users to increase their visibility, transforming the way brands communicate their strategies. This systematic literature review (SLR) explored how the TikTok algorithm influences marketing strategies during the period 2021 to 2024. For… Read full abstract & cite →

TikTok algorithm consumer behavior marketing

Abstract doi.org/10.14569/IJACSA.2025.0160215 PDF

16

Data Mart Design to Increase Transactional Flow of Debit and Credit Card in Peruvian Bodegas

Author 1: Juan Carlos Morales-Arevalo Author 2: Erick Manuel Aquise-Gonzales Author 3: William Yohani Carpio-Ore Author 4: Emmanuel Victor Mendoza Sáenz Author 5: Carlos Javier Mazzarri-Rodriguez Author 6: Erick Enrique Remotti-Becerra Author 7: Edison Humberto Medina-La Plata Author 8: Luis F. Luque-Vega

The objective of this research is to design a Data Mart to identify tactical actions and increase the use of POS (points of sale) in the bodega business sector of Lima, Peru. A quantitative approach, using transaction history data, is applied using the Kimball methodology. This involves the ETL (Extract… Read full abstract & cite →

Business intelligence Extract Transform Load (ETL) dashboard data mart Point of Sale (POS)

Abstract doi.org/10.14569/IJACSA.2025.0160216 PDF

17

Evaluation of Convolutional Neural Network Architectures for Detecting Drowsiness in Drivers

Author 1: Bryan Hurtado Delgado Author 2: Marycielo Xiomara Oscco Guillen Author 3: Mario Aquino Cruz

Drowsiness in drivers is a condition that can manifest itself at any time, representing a constant challenge for road safety, especially in a context where artificial intelligence technologies are increasingly present in driver assistance systems. This paper presents a comparative evaluation of convolutional neural network (CNN) architectures for drowsiness detection… Read full abstract & cite →

Architectures detection drowsiness neural networks

Abstract doi.org/10.14569/IJACSA.2025.0160217 PDF

18

Integrating Deep Learning in Art and Design: Computational Techniques for Enhancing Creative Expression

Author 1: Yanjie Deng Author 2: Qibing Zhai

Deep learning and art design are being integrated, which is an innovative process that has the potential to reframe the way the human imagination is defined. This paper is an exploration of a broad field that showcases how AI enhances the experience of artist practice, especially content deep learning. This… Read full abstract & cite →

Deep learning art design creative expression computational techniques

Abstract doi.org/10.14569/IJACSA.2025.0160218 PDF

19

Scallop Segmentation Using Aquatic Images with Deep Learning Applied to Aquaculture

Author 1: Wilder Nina Author 2: Nadia L. Quispe Author 3: Liz S. Bernedo-Flores Author 4: Marx S. Garcia Author 5: Cesar Valdivia Author 6: Eber Huanca

This study evaluates the performance of deep learning-based segmentation models applied to underwater images for scallop aquaculture in Sechura Bay, Peru. Four models were analyzed: SUIM-Net, YOLOv8, DETECTRON2, and CenterMask2. These models were trained and tested using two custom datasets: SEG SDS GOPRO and SEG SDS SF, which represent diverse… Read full abstract & cite →

Image segmentation object detection deep learning computer vision aquaculture scallop segmentation aquatic images

Abstract doi.org/10.14569/IJACSA.2025.0160219 PDF

20

Performance Optimization with Span<T> and Memory<T> in C# When Handling HTTP Requests: Real-World Examples and Approaches

Author 1: Daniel Damyanov Author 2: Ivaylo Donchev

Optimization of application performance is a critical aspect of software development, especially when dealing with high-throughput operations such as handling HTTP requests. In modern C#, structures Span<T> and Memory<T> provide powerful tools for working with memory more efficiently, reducing heap allocations, and improving overall performance. This paper explores the practical… Read full abstract & cite →

NET C# optimization memory optimization span memory development HTTP requests data structures

Abstract doi.org/10.14569/IJACSA.2025.0160220 PDF

21

A Systematic Review of the Benefits and Challenges of Data Analytics in Organizational Decision Making

Author 1: Juan Carlos Morales-Arevalo Author 2: Ciro Rodríguez

Data analytics has been relied heavily in organizational decision-making, which allows accuracy, timeliness, and data-driven processes in a wide range of industries. These factors are influential as the figure and complexity of data are on the rise, along with problems like authentication, integration, and organizational resistance. The current study seeks… Read full abstract & cite →

Data analytics decision-making data-driven processes big data analytics systematic review

Abstract doi.org/10.14569/IJACSA.2025.0160221 PDF

22

DyGAN: Generative Adversarial Network for Reproducing Handwriting Affected by Dyspraxia

Author 1: Jes´us Jaime Moreno Escobar Author 2: Hugo Quintana Espinosa Author 3: Erika Yolanda Aguilar del Villar

Dyspraxia primarily affects coordination and is categorized into two forms: 1) Motor, and 2) Verbal ororal. This study focuses on motor dyspraxia, which influences individuals in learning movement-related tasks. Consequently, the DyGAN initiative employs deep convolutional aversarial generation networks, using deep learning to create characters resembling human handwriting. The methodology… Read full abstract & cite →

Children with neurodevelopmental disorders dyspraxia generative adversarial network deep learning deep convo-lutional neural network human handwriting

Abstract doi.org/10.14569/IJACSA.2025.0160222 PDF

23

Enhanced Cyber Threat Detection System Leveraging Machine Learning Using Data Augmentation

Author 1: Umar Iftikhar Author 2: Syed Abbas Ali

In the modern era of cyber security, cyber-attacks are continuously evolving in terms of complexity and frequency. In this context, organizations need to enhance Network Intrusion Detection Systems (NIDS) for anomaly detection. Although the existing Machine Learning models are in place to cater to the situations but new challenges emerge… Read full abstract & cite →

Anomaly detection cyber threat intelligence generative adversarial networks data augmentation Wasserstein GAN with gradient penalty

Abstract doi.org/10.14569/IJACSA.2025.0160223 PDF

24

Data Analytics for Product Segmentation and Demand Forecasting of a Local Retail Store Using Python

Author 1: Arun Kumar Mishra Author 2: Megha Sinha

In today's competitive business environment, understanding customers' expectations and choices is a necessity for the successful operations of a retail store. Forecasting demand also plays an important role in maintaining inventory at an optimum level. The work utilises data analytics for product segmentation and demand forecasting in a local retail… Read full abstract & cite →

Data analytics product segmentation demand forecasting multicriteria ABC classification seasonality

Abstract doi.org/10.14569/IJACSA.2025.0160224 PDF

25

YOLOv7-b: An Enhanced Object Detection Model for Multi-Scale and Dense Target Recognition in Remote Sensing Images

Author 1: Yulong Song Author 2: Hao Yang Author 3: Lijun Huang Author 4: Song Huang

To address the challenges of dense object distribution, scale variability, and complex shapes in remote sensing images, this paper proposes an improved YOLOv7-b model to enhance multi-scale target detection accuracy and robustness. First, deformable convolution (DCNv2) is introduced into the YOLOv7 backbone to replace the standard convolutions in the last… Read full abstract & cite →

YOLOv7-b remote sensing images object detection deformable convolution bi-level routing attention multi-scale

Abstract doi.org/10.14569/IJACSA.2025.0160225 PDF

26

Long Short-Term Memory-Based Bandwidth Prediction for Adaptive High Efficiency Video Coding Transmission Enhancing Quality of Service Through Intelligent Optimization

Author 1: Hajar Hardi Author 2: Imade Fahd Eddine Fatani

With the growing demand for high-quality video streaming, the necessity for efficient techniques to balance video quality and bandwidth has become increasingly critical to ensure a seamless user experience. Existing traditional adaptive streaming methods only react to network fluctuations, which often leads to delays, quality degradation, and buffering. This paper… Read full abstract & cite →

HEVC adaptive streaming LSTM networks quality of service proactive encoding adjustments High Efficiency Video Coding

Abstract doi.org/10.14569/IJACSA.2025.0160226 PDF

27

Detection of Stopwords in Classical Chinese Poetry

Author 1: Lei Peng Author 2: Xiaodong Ma Author 3: Zheng Teng

In this research, we address the problem of stopword detection in Classical Chinese Poetry, an area that has not been explored previously. Stopword detection is crucial in text mining tasks, as identifying and removing stopwords is essential for improving the performance of various natural language processing models. Inspired by the… Read full abstract & cite →

TF-IDF stopwords Chinese poetry frequency

Abstract doi.org/10.14569/IJACSA.2025.0160227 PDF

28

IoT CCTV Video Security Optimization Using Selective Encryption and Compression

Author 1: Kawalpreet Kaur Author 2: Amanpreet Kaur Author 3: Yonis Gulzar Author 4: Vidhyotma Gandhi Author 5: Mohammad Shuaib Mir Author 6: Arjumand Bano Soomro

Data security and privacy are critical concerns when integrating Closed-Circuit Television (CCTV) cameras with the Internet of Things (IoT). To enhance security, IoT data must be encrypted before transmission and storage. However, to minimize overheads related to storage space, computational time, and transmission energy, data can be compressed prior to… Read full abstract & cite →

Closed-Circuit Television (CCTV) decryption encryption internet of things (IoT) security

Abstract doi.org/10.14569/IJACSA.2025.0160228 PDF

29

RETRACTED: Integrating Artificial Intelligence to Automate Pattern Making for Personalized Garment Design

Author 1: Muyan Han

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IJACSA`s Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this… Read full abstract & cite →

Machine learning models pattern generation AI-assisted pattern construction data augmentation techniques CAD flattening

Abstract doi.org/10.14569/IJACSA.2025.0160229 PDF

30

Enhancing Recurrent Neural Network Efficacy in Online Sales Predictions with Exploratory Data Analysis

Author 1: Erni Widiastuti Author 2: Jani Kusanti Author 3: Herwin Sulistyowati

Online sales forecasting has become an essential aspect of effective business planning in the digital era. The widespread adoption of digital transformation has enabled companies to collect substantial datasets related to consumer behavior, market trends, and sales drivers. This study attempts to uncover patterns and predict sales growth by utilizing… Read full abstract & cite →

Exploratory data analysis recurrent neural networks online sales prediction sequential data trend patterns

Abstract doi.org/10.14569/IJACSA.2025.0160230 PDF

31

A Rapid Drift Modeling Method Based on Portable LiDAR Scanner

Author 1: Zhao Huijun Author 2: Liu Chao Author 3: Qi Yunpu Author 4: Song Zhanglun Author 5: Xia Xu

Traditional measurement methods in underground mining tunnels have faced inefficiencies, limited accuracy, and operational challenges, consuming significant time and labor in complex environments. These limitations severely restrict the efficiency and quality of mine management and engineering design. To enhance the efficiency and accuracy of 3D modeling in underground tunnels, this… Read full abstract & cite →

Underground mining 3D modeling portable 3D laser scanning simultaneous localization and mapping (SLAM) mine surveying inertial measurement unit (IMU)

Abstract doi.org/10.14569/IJACSA.2025.0160231 PDF

32

Dialogue-Based Disease Diagnosis Using Hierarchical Reinforcement Learning with Multi-Expert Feedback

Author 1: Shi Li Author 2: Xueyao Sun

In order to minimize the stochasticity of agents used in disease diagnosis within the dialogue system, and to enable them to interact with users based on the inherent connections between symptoms and diseases, while simultaneously addressing the issue of limited medical data, we propose the Hierarchical Reinforcement Learning with Multi-expert… Read full abstract & cite →

Disease diagnosis dialogue system large language model reinforcement learning reward model adversarial network dialogue agent

Abstract doi.org/10.14569/IJACSA.2025.0160232 PDF

33

BlockMed: AI Driven HL7-FHIR Translation with Blockchain-Based Security

Author 1: Yonis Gulzar Author 2: Faheem Ahmad Reegu Author 3: Abdoh Jabbari Author 4: Rahul Ganpatrao Sonkamble Author 5: Mohammad Shuaib Mir Author 6: Arjumand Bano Soomro

Blockchain is a peer-to-peer (P2P) network that distributes information and protects data integrity, security, and privacy. Constant simplification is required for information exchange. This comprehensive assessment seamlessly integrates Electronic Health Record (EHRs) with blockchain technology. EHRs are represented with different standards mainly HL7 and FHIR. EHR should be interpreted to… Read full abstract & cite →

Blockchain health care electronic health records (EHRs) interoperability and healthcare system

Abstract doi.org/10.14569/IJACSA.2025.0160233 PDF

34

Improving Air Quality Prediction Models for Banting: A Performance Evaluation of Lasso, mRMR, and ReliefF

Author 1: Siti Khadijah Arafin Author 2: Suvodeep Mazumdar Author 3: Nurain Ibrahim

This study explores the effectiveness of various feature selection methods in forecasting next-day PM2.5 levels in Banting, Malaysia. The accurate prediction of PM2.5 concentrations is crucial for public health, enabling authorities to take timely actions to mitigate exposure to harmful pollutants. This study compares three feature selection methods: Lasso, mRMR… Read full abstract & cite →

PM2.5 concentration feature selection Lasso mRmR RBFNN ReliefF

Abstract doi.org/10.14569/IJACSA.2025.0160234 PDF

35

Lightweight CA-YOLOv7-Based Badminton Stroke Recognition: A Real-Time and Accurate Behavior Analysis Method

Author 1: Yuchuan Lin

With the rapid development of sports technology, accurate and real-time recognition of badminton stroke postures has become essential for athlete training and match analysis. This study presents an improved YOLOv7-based method for badminton stroke posture recognition, addressing limitations in accuracy, real-time performance, and automation. To optimize the model, pruning techniques… Read full abstract & cite →

Badminton shot pose recognition YOLO V7 size adaptive input model pruning attention mechanism

Abstract doi.org/10.14569/IJACSA.2025.0160235 PDF

36

Fuzzy Evaluation of Teaching Quality in "Smart Classroom" with Application of Entropy Weight Coupled TOPSIS

Author 1: Yajuan SONG

This research aims to investigate the scientific assessment methodology for the teaching quality of smart classrooms and to develop a multi-dimensional evaluation system utilizing a combination of the entropy weight technique and the TOPSIS approach. To comprehensively assess the pedagogical proficiency of educators, this paper selects the dimensions of teaching… Read full abstract & cite →

Smart classroom entropy weight method TOPSIS method teaching quality optimization and improvement

Abstract doi.org/10.14569/IJACSA.2025.0160236 PDF

37

Long-Term Recommendation Model for Online Education Systems: A Deep Reinforcement Learning Approach

Author 1: Wei Wang

Intelligent tutoring systems serve as tools capable of providing personalized learning experiences, with their efficacy significantly contingent upon the performance of recommendation models. For long-term instructional plans, these systems necessitate the provision of highly accurate, enduring recommendations. However, numerous existing recommendation models adopt a static perspective, disregarding the sequential decision-making… Read full abstract & cite →

Deep reinforcement learning long-term recommendation intelligent tutoring system Markov Decision Process recurrent neural network

Abstract doi.org/10.14569/IJACSA.2025.0160237 PDF

38

Advanced Football Match Winning Probability Prediction: A CNN-BiLSTM_Att Model with Player Compatibility and Dynamic Lineup Analysis

Author 1: Tao Quan Author 2: Yingling Luo

In recent years, with the continuous expansion of the football market, the prediction of football match-winning probabilities has become increasingly important, attracting numerous professionals and institutions to engage in the field of football big data analysis. Pre-match data analysis is crucial for predicting match outcomes and formulating tactical strategies, and… Read full abstract & cite →

Football big data match prediction feature vector tactical understanding match analysis

Abstract doi.org/10.14569/IJACSA.2025.0160238 PDF

39

Effectiveness of Immersive Contextual English Teaching Based on Fuzzy Evaluation

Author 1: Mei Niu

Investigating the real-world impact of immersive contextual instruction on English language education, verifying its contribution to the enhancement of linguistic skills and the improvement of learning attitudes, and evaluating the practicality and worth of fuzzy evaluation in gauging teaching efficacy. A fuzzy complete assessment model was built utilizing the language… Read full abstract & cite →

Fuzzy evaluation immersion contextual English teaching teaching effectiveness teaching assessment

Abstract doi.org/10.14569/IJACSA.2025.0160239 PDF

40

Multi-Classification Convolution Neural Network Models for Chest Disease Classification

Author 1: Noha Ayman Author 2: Mahmoud E. A. Gadallah Author 3: Mary Monir Saeid

Chest diseases significantly affect public health, causing more than one million hospital admissions and approximately 50,000 deaths annually in the United States. Chest X-ray imaging technology, which is a critically important imaging technique, helps in examining, diagnosing, and managing chest conditions by providing essential insights about the presence and severity… Read full abstract & cite →

Convolution neural network classification chest X-ray image preprocessing U-Net deep learning

Abstract doi.org/10.14569/IJACSA.2025.0160240 PDF

41

Deep Learning-Based Attention Mechanism Algorithm for Blockchain Credit Default Prediction

Author 1: Wangke Lin Author 2: Yue Liu

With the rise of internet finance and the increasing demand for personal credit risk management, accurate credit default prediction has become essential for financial institutions. Traditional models face limitations in handling complex and large-scale data, especially in the blockchain domain, which has emerged as a crucial technology for securing and… Read full abstract & cite →

Deep learning attention mechanism blockchain credit default prediction special forces algorithm

Abstract doi.org/10.14569/IJACSA.2025.0160241 PDF

42

Modeling Cloud Computing Adoption and its Impact on the Performance of IT Personnel in the Public Sector

Author 1: Noorbaiti Mahusin Author 2: Hasimi Sallehudin Author 3: Nurhizam Safie Mohd Satar Author 4: Azana Hafizah Mohd Aman Author 5: Farashazillah Yahya

This study investigates the factors influencing cloud computing adoption in the public sector, emphasizing the performance of IT personnel. Through qualitative interviews with five IT management professionals in the public sector, we identify key challenges in integrating cloud computing systems. The primary issues include technical complexity, skill and knowledge deficits… Read full abstract & cite →

Cloud computing cloud integration model introduction performance of IT personnel public sector system integration

Abstract doi.org/10.14569/IJACSA.2025.0160242 PDF

43

TPGR-YOLO: Improving the Traffic Police Gesture Recognition Method of YOLOv11

Author 1: Xuxing Qi Author 2: Cheng Xu Author 3: Yuxuan Liu Author 4: Nan Ma Author 5: Hongzhe Liu

In open traffic scenarios, gesture recognition for traffic police faces significant challenges due to the small scale of the traffic police and the complex background. To address this, this paper proposes a gesture recognition network based on an improved YOLOv11. This method enhances feature extraction and multi-scale information retention by… Read full abstract & cite →

Traffic police gesture recognition loss function YOLO algorithm multi-scale feature fusion

Abstract doi.org/10.14569/IJACSA.2025.0160243 PDF

44

A Hybrid SETO-GBDT Model for Efficient Information Literacy System Evaluation

Author 1: Jiali Dai Author 2: Hanifah Jambari Author 3: Mohd Hizwan Mohd Hisham

Information literacy (IL) is essential for vocational education talents to thrive in the modern information age. Traditional assessment methods often lack quantitative precision and systematic evaluation models, making it difficult to accurately measure IL levels. This paper aims to develop a robust, data-driven model to assess information literacy in vocational… Read full abstract & cite →

Vocational education talent information literacy system building educational evaluation gradient augmentation decision tree

Abstract doi.org/10.14569/IJACSA.2025.0160244 PDF

45

Bridging the Gap Between Industry 4.0 Readiness and Maturity Assessment Models: An Ontology-Based Approach

Author 1: ABADI Asmae Author 2: ABADI Chaimae Author 3: ABADI Mohammed

The rapid evolution of Industry 4.0 technologies has created a complex and interconnected landscape of readiness and maturity assessment models. However, these models often fail to address the full spectrum of organizational readiness across strategic, technological, operational, and cultural dimensions, while also not accounting for emerging paradigms such as Industry… Read full abstract & cite →

Industry 4.0 readiness assessment maturity assessment digital transformation ontology development conceptual model knowledge engineering

Abstract doi.org/10.14569/IJACSA.2025.0160245 PDF

46

Eco-Efficiency Measurement and Regional Optimization Strategy of Green Buildings in China Based on Three-Stage Super-Efficiency SBM-DEA Model

Author 1: Xianhong Qin Author 2: Yaou Lv Author 3: Yunfang Wang Author 4: Jian Pi Author 5: Ze Xu

With the increasing attention of society to sustainable development, green building as an important sustainable building form has attracted much attention. However, the comprehensive assessment of eco-efficiency of green buildings faces many challenges, including the insufficient comprehensive analysis of all stages of the building life cycle and the oversimplification of… Read full abstract & cite →

Three stages data envelopment analysis super efficiency model green buildings ecological efficiency

Abstract doi.org/10.14569/IJACSA.2025.0160246 PDF

47

Watermelon Rootstock Seedling Detection Based on Improved YOLOv8 Image Segmentation

Author 1: Qingcang Yu Author 2: Zihao Xu Author 3: Yi Zhu

Automated grafting is an important means for modern agriculture to improve production efficiency and graft seedling quality, among which the use of visual systems to quickly segment target rootstock seedlings is the key technology to achieve automated grafting. This study aims to solve the problems of inaccurate image segmentation and… Read full abstract & cite →

Image segmentation YOLOv8s-seg lightweight deep learning

Abstract doi.org/10.14569/IJACSA.2025.0160247 PDF

48

Object Recognition IoT-Based for People with Disabilities: A Review

Author 1: Andriana Author 2: Elli Ruslina Author 3: Zulkarnain Author 4: Fajar Arrazaq Author 5: Sutisna Abdul Rahman Author 6: Tjahjo Adiprabowo Author 7: Puput Dani Prasetyo Adi Author 8: Yudi Yuliyus Maulana

This research focuses on a literature study on developing a Mini Smart Camera (MSC) system that utilizes Internet of Things (IoT) technology to help people with disabilities interact with their environment. The MSC serves as an assistive device, which integrates object recognition and speech recognition technologies along with an internet-based… Read full abstract & cite →

Internet of Things mini smart camera object recognition speech recognition assistive technology

Abstract doi.org/10.14569/IJACSA.2025.0160248 PDF

49

Transfer Learning for Named Entity Recognition in Setswana Language Using CNN-BiLSTM Model

Author 1: Shumile Chabalala Author 2: Sunday O. Ojo Author 3: Pius A. Owolawi

This research proposes a hybrid approach for Named-Entity Recognition (NER) for Setswana, a low-resource language, that combines a bidirectional long short-term memory (BiLSTM) with a transfer learning model and a convolutional neural network (CNN). Among the 11 official languages of South Africa, Setswana is a morphologically rich language that is… Read full abstract & cite →

Natural language processing named entity recognition convolutional neural network bidirectional long short-term memory Setswana

Abstract doi.org/10.14569/IJACSA.2025.0160249 PDF

50

Planning and Design of Elderly Care Space Combining PER and Dueling DQN

Author 1: Di Wang Author 2: Hui Ma Author 3: Yu Chen

With the continuous development of the aging phenomenon in society, people's attention to the planning of elderly care spaces is increasing. Currently, many scholars have used various spatial planning models to plan and design elderly care spaces. However, the resource utilization rate and comfort of the elderly care spaces designed… Read full abstract & cite →

Elderly care space planning and design prioritized experience replay dueling deep q-network algorithm spatial planning

Abstract doi.org/10.14569/IJACSA.2025.0160250 PDF

51

All Element Selection Method in Classroom Social Networks and Analysis of Structural Characteristics

Author 1: Zhaoyu Shou Author 2: Zhe Zhang Author 3: Jingquan Chen Author 4: Hua Yuan Author 5: Jianwen Mo

To deeply investigate the complex relationship between learners' structural characteristics in classroom social networks and the dynamics of learning emotions in smart teaching environments, an innovatively improved RP-GA. All Element Selection Method based on genetic algorithm is proposed. The method calculates the importance of factors based on the random forest… Read full abstract & cite →

Genetic algorithms element selection random forest partial least squares classroom network

Abstract doi.org/10.14569/IJACSA.2025.0160251 PDF

52

An NLP-Enabled Approach to Semantic Grouping for Improved Requirements Modularity and Traceability

Author 1: Rahat Izhar Author 2: Shahid Nazir Bhatti Author 3: Sultan A. Alharthi

The escalating complexity of modern software systems has rendered the management of requirements increasingly arduous, often plagued by redundancy, inconsistency, and inefficiency. Traditional manual methods prove inadequate for addressing the intricacies of dynamic, large-scale datasets. In response, this research introduces SQUIRE (Semantic Quick Requirements Engineering), a cutting-edge automated framework leveraging… Read full abstract & cite →

Requirements Engineering (RE) semantic clustering sentence-BERT natural language processing (NLP)

Abstract doi.org/10.14569/IJACSA.2025.0160252 PDF

53

Data-Driven Technology Augmented Reality Digitisation in Cultural Communication Design

Author 1: Na YIN

The digitalisation of intangible cultural heritage and big data technology provide great potential for the development of intangible cultural heritage in low-carbon reform tourism, which not only increases the accuracy of AR digital design, but also contributes to the management and protection of intangible cultural heritage based on tourism. Aiming… Read full abstract & cite →

Intangible cultural heritage AR digitization low-carbon tourism design test analysis dragonfly algorithm restricted Boltzmann machine model

Abstract doi.org/10.14569/IJACSA.2025.0160253 PDF

54

Spatial Attention-Based Adaptive CNN Model for Differentiating Dementia with Lewy Bodies and Alzheimer's Disease

Author 1: K Sravani Author 2: V RaviSankar

Differentiation of Alzheimer's Disease (AD) and Dementia with Lewy Bodies (DLB) utilizing brain perfusion Single Photon Emission Tomography (SPECT) is crucial and it might be difficult to distinguish between the two illnesses. The most recently discovered characteristic of DLB for a possible diagnosis is the Cingulate Island Sign (CIS). This… Read full abstract & cite →

Alzheimer's disease and dementia with lewy bodies differentiation spatial attention-based adaptive convolution neural network cingulate island sign improved random function-based birds foraging search dilated residual-long short-term memory

Abstract doi.org/10.14569/IJACSA.2025.0160254 PDF

55

Energy-Balance-Based Out-of-Distribution Detection of Skin Lesions

Author 1: Jiahui Sun Author 2: Guan Yang Author 3: Yishuo Chen Author 4: Hongyan Wu Author 5: Xiaoming Liu

Skin lesion detection plays a crucial role in the diagnosis and treatment of skin diseases. Due to the wide variety of skin lesion types, especially when dealing with unknown or rare lesions, models tend to exhibit overconfidence. Out-of-distribution (OOD) detection techniques are capable of identifying lesion types that were not… Read full abstract & cite →

Balanced energy regularization loss skin lesions out-of-distribution detection convolutional neural networks

Abstract doi.org/10.14569/IJACSA.2025.0160255 PDF

56

Using Fuzzy Matter-Element Extension Method to Cultural Tourism Resources Data Mining and Evaluation

Author 1: Fei Liu

This study explores the mining and evaluation of cultural and tourism resources based on fuzzy matter-element extension in the context of cultural and tourism integration. Through fieldwork and analysis of cultural and tourism resources, it is found that the fuzzy matter-element extension theory can be effectively applied to the mining… Read full abstract & cite →

Cultural and tourism integration fuzzy object meta-theory development organic integration

Abstract doi.org/10.14569/IJACSA.2025.0160256 PDF

57

Arabic Sentiment Analysis Using Optuna Hyperparameter Optimization and Metaheuristics Feature Selection to Improve Performance of LightGBM

Author 1: Mostafa Medhat Nazier Author 2: Mamdouh M. Gomaa Author 3: Mohamed M. Abdallah Author 4: Awny Sayed

Sentiment Analysis (SA) effectively examines big data, such as customer reviews, market research, social media posts, online discussions, and customer feedback evaluation. Arabic Language is a complex and rich language. The main reason for the need to enhance Arabic resources is the existence of numerous dialects alongside the standard version… Read full abstract & cite →

Arabic Sentiment Analysis (ASA) big data Light Gradient Boosting Machine (LightGBM) Optuna hyperparameter optimization metaheuristics feature selection machine learning

Abstract doi.org/10.14569/IJACSA.2025.0160257 PDF

58

Flexible Framework for Lung and Colon Cancer Automated Analysis Across Multiple Diagnosis Scenarios

Author 1: Marwen SAKLI Author 2: Chaker ESSID Author 3: Bassem BEN SALAH Author 4: Hedi SAKLI

Among humans, lung and colon cancers are regarded as primary contributors to mortality and morbidity. They may grow simultaneously in organs, having a harmful influence on the lives of people. If tumor is not diagnosed early, it is likely to spread to both of those organs. This research presents a… Read full abstract & cite →

Lung and colon cancers histopathological images LC25000 dataset lightweight convolutional neural networks multiple diagnosis scenarios

Abstract doi.org/10.14569/IJACSA.2025.0160258 PDF

59

Machine Learning-Based Denoising Techniques for Monte Carlo Rendering: A Literature Review

Author 1: Liew Wen Yen Author 2: Rajermani Thinakaran Author 3: J. Somasekar

Monte Carlo (MC) rendering is a powerful technique for achieving photorealistic images by simulating complex light interactions. However, the inherent noise introduced by MC rendering necessitates effective denoising techniques to enhance image quality. This paper presents a comprehensive review and comparative analysis of various machine learning (ML) methods for denoising… Read full abstract & cite →

Convolutional neural network Monte Carlo rendering generative adversarial network deep learning machine learning denoising techniques

Abstract doi.org/10.14569/IJACSA.2025.0160259 PDF

60

Optimization Technology of Civil Aircraft Stand Assignment Based on MSCOEA Model

Author 1: Qiao Xue Author 2: Yaqiong Wang Author 3: Hui Hui

The Chinese aviation transportation industry is constantly developing towards multiple objectives and constraints. The conventional optimization method for stand assignment of civil aviation aircraft has low efficiency and can no longer meet practical needs. Based on this, the paper firstly focuses on the problem of convergence and uniformity in multi-objective… Read full abstract & cite →

Collaborative evolution quantum algorithm stand assignment multi-objective optimization population

Abstract doi.org/10.14569/IJACSA.2025.0160260 PDF

61

Enhancing Urban Mapping in Indonesia with YOLOv11

Author 1: Muhammad Emir Kusputra Author 2: Alesandra Zhegita Helga Prabowo Author 3: Kamel Author 4: Hady Pranoto

Object recognition in urban and residential settings has become more vital for urban planning, real estate evaluation, and geographic mapping applications. This study presents an innovative methodology for house detection with YOLOv11, an advanced deep-learning object detection model. YOLO is based on a Convolutional Neutral Network (CNN), a type of… Read full abstract & cite →

YOLOv11 object detection house detection house counting computer vision deep learning urban mapping

Abstract doi.org/10.14569/IJACSA.2025.0160261 PDF

62

A Supervised Learning-Based Classification Technique for Precise Identification of Monkeypox Using Skin Imaging

Author 1: Vandana Author 2: Chetna Sharma Author 3: Yonis Gulzar Author 4: Mohammad Shuaib Mir

The monkeypox epidemic has spread to nearly every nation. Governments implement several strict policies, to stop the virus that causes monkeypox. For effective handling and treatment, early identification and diagnosis of monkeypox using digital skin lesion images is critical, and this work employed deep learning architectures to achieve this goal… Read full abstract & cite →

Deep learning monkeypox medical image processing image classification cross validation

Abstract doi.org/10.14569/IJACSA.2025.0160262 PDF

63

Classifying Weed Development Stages Using Deep Learning Methods

Author 1: Yasin ÇIÇEK Author 2: Eyyüp GÜLBANDILAR Author 3: Kadir ÇIRAY Author 4: Ahmet ULUDAG

The control of harmful weeds holds a significant place in the cultivation of agricultural products. A crucial criterion in this control process is identifying the development stages of the weeds. The technique to be used is determined based on the weed's growth stage. This study addresses the application of deep… Read full abstract & cite →

Deep learning weed development stages classification DenseNET Xception SqueezeNET GoogleNET EfficientNET ROI

Abstract doi.org/10.14569/IJACSA.2025.0160263 PDF

64

Target Detection of Leakage Bubbles in Stainless Steel Welded Pipe Gas Airtightness Experiments Based on YOLOv8-BGA

Author 1: Huaishu Hou Author 2: Zikang Chen Author 3: Chaofei Jiao

Gas-tightness experiment is an effective means to detect leakage of stainless steel welded pipe, and the vision-based bubble recognition algorithm can effectively improve the efficiency of gas-tightness detection. This study proposed a new detection network of YOLOv8-BGA using the YOLOv8 model as a baseline, which can achieve effective identification of… Read full abstract & cite →

Image processing stainless steel welded pipe non-destructive testing YOLOv8 attention mechanism loss function

Abstract doi.org/10.14569/IJACSA.2025.0160264 PDF

65

Broccoli Grading Based on Improved Convolutional Neural Network Using Ensemble Deep Learning

Author 1: Zaki Imaduddin Author 2: Yohanes Aris Purwanto Author 3: Sony Hartono Wijaya Author 4: Shelvie Nidya Neyman

The demand for broccoli in Indonesia has been increasing significantly, with an annual growth of approximately 15% to 20%. However, the supply availability remains insufficient, and its quality is often inconsistent. Therefore, a grading process is needed to classify broccoli into grades A, B, and C based on color, size… Read full abstract & cite →

Grading convolution neural network ensemble deep learning voting

Abstract doi.org/10.14569/IJACSA.2025.0160265 PDF

66

A Custom Deep Learning Approach for Traffic Flow Prediction in Port Environments: Integrating RCNN for Spatial and Temporal Analysis

Author 1: Abdul Basit Ali Shah Author 2: Xinglu Xu Author 3: Zheng Yongren Author 4: Zijian Guo

Port congestion poses a significant challenge to maritime logistics, especially for industries dealing with perishable goods like seafood. This study presents a custom deep learning model using Transformer architecture to predict real-time traffic flow at the Port of Virginia, with a focus on optimizing the movement of fish trucks. The… Read full abstract & cite →

Traffic flow prediction transformer model port congestion deep learning

Abstract doi.org/10.14569/IJACSA.2025.0160266 PDF

67

Enhanced Virtual Machine Resource Optimization in Cloud Computing Using Real-Time Monitoring and Predictive Modeling

Author 1: Rim Doukha Author 2: Abderrahmane Ez-zahout

Effective resource estimation is essential in cloud computing to minimize operational costs, optimize performance, and enhance user satisfaction. This study proposes a comprehensive framework for virtual machine optimization in cloud environments, focusing on predictive resource management to improve resource efficiency and system performance. The framework integrates real-time monitoring, advanced resource… Read full abstract & cite →

Cloud computing virtual machine optimization resource allocation machine learning

Abstract doi.org/10.14569/IJACSA.2025.0160267 PDF

68

Traffic Safety in Mixed Environments by Predicting Lane Merging and Adaptive Control

Author 1: Aigerim Amantay Author 2: Shyryn Akan Author 3: Nurlybek Kenes Author 4: Amandyk Kartbayev

Autonomous driving technology is primarily developed to enhance traffic safety through advancements in motion prediction and adaptive control mechanisms. Highway lane merging remains a high-risk scenario, accounting for approximately 7% of highway collisions globally due to misjudged vehicle interactions, according to international statistics. This paper proposes a two-stage deep learning… Read full abstract & cite →

Autonomous driving lane merging traffic safety trajectory prediction deep learning LiDAR LSTM

Abstract doi.org/10.14569/IJACSA.2025.0160268 PDF

69

Modular Analysis of Complex Products Based on Hybrid Genetic Ant Colony Optimization in the Context of Industry 4.0

Author 1: Yichun Shi Author 2: Qinhe Shi

With the development of science and technology, industrial construction has entered the era of 4.0 intelligent construction, and various algorithms have been widely applied in the modularization of production products. This study focuses on the modular optimization problem of complex products and establishes a hybrid genetic algorithm based on the… Read full abstract & cite →

Industry 4.0 genetic algorithm ant colony complex products modularization production efficiency

Abstract doi.org/10.14569/IJACSA.2025.0160269 PDF

70

Detection and Prediction of Polycystic Ovary Syndrome Using Attention-Based CNN-RNN Classification Model

Author 1: Siji Jose Pulluparambil Author 2: Subrahmanya Bhat B

Polycystic Ovary Syndrome (PCOS) has many challenges when it comes to its diagnosis and treatment due to the diversity of presentation and potential long-term consequences for health. For this reason, sophisticated data pre-processing and classification methods are implemented to enhance the accuracy of PCOS diagnosis. A number of innovative techniques… Read full abstract & cite →

Polycystic ovary syndrome contrast limited adaptive histogram equalization particle swarm optimization k- means clustering algorithm convolutional neural network recurrent neural network

Abstract doi.org/10.14569/IJACSA.2025.0160270 PDF

71

A Review of AI and IoT Implementation in a Museum’s Ecosystem: Benefits, Challenges, and a Novel Conceptual Model

Author 1: Shinta Puspasari Author 2: Indah Agustien Siradjuddin Author 3: Rachmansyah

The museums need to transform into modern museums by developing a digital ecosystem that integrates all elements in the museum to optimize organizational outcomes and impact people's welfare in the era of Society 5.0. This paper aims to conduct a review of the museum's digital ecosystem based on the implementation… Read full abstract & cite →

AI IoT digital museum digital ecosystem

Abstract doi.org/10.14569/IJACSA.2025.0160271 PDF

72

Optimizing the GRU-LSTM Hybrid Model for Air Temperature Prediction in Degraded Wetlands and Climate Change Implications

Author 1: Yuslena Sari Author 2: Yudi Firmanul Arifin Author 3: Novitasari Novitasari Author 4: Samingun Handoyo Author 5: Andreyan Rizky Baskara Author 6: Nurul Fathanah Musatamin Author 7: Muhammad Tommy Maulidyanto Author 8: Siti Viona Indah Swari Author 9: Erika Maulidiya

Accurate air temperature prediction is critical, particularly for micro air temperatures. The temperature of micro air changes quickly. Micro and macro air temperatures vary, particularly in degraded wetlands. By predicting air temperature, climate change in a degraded wetland environment can be predicted earlier. Furthermore, micro and macro air temperatures are… Read full abstract & cite →

Predictions temperature Gated Recurrent Unit (GRU) Long Short-Term Memory (LSTM) performance indicators

Abstract doi.org/10.14569/IJACSA.2025.0160272 PDF

73

Lightweight Parabola Chaotic Keyed Hash Using SRAM-PUF for IoT Authentication

Author 1: Nattagit Jiteurtragool Author 2: Jirayu Samkunta Author 3: Patinya Ketthong

This paper introduces a lightweight and efficient keyed hash function tailored for resource-constrained Internet of Things (IoT) environments, leveraging the chaotic properties of the Parabola Chaotic Map. By combining the inherent unpredictability of chaotic systems with a streamlined cryptographic design, the proposed hash function ensures robust security and low computational… Read full abstract & cite →

SRAM PUF PUF key generation chaotic keyed hash device authentication discrete-time chaotic

Abstract doi.org/10.14569/IJACSA.2025.0160273 PDF

74

A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition: Applications, Trends, and Challenges

Author 1: John Deutero Kisoi Author 2: Norfadzlan Yusup Author 3: Syahrul Nizam Junaini

Metaheuristic algorithms have emerged as promising techniques for optimizing human activity recognition (HAR) systems. This systematic review examines the application of these algorithms in HAR by analyzing relevant literature published between 2019 and 2024. A comprehensive search across multiple databases yielded 27 studies that met the inclusion criteria. The analysis… Read full abstract & cite →

Metaheuristic algorithm human activity recognition systematic review application trend challenge literature

Abstract doi.org/10.14569/IJACSA.2025.0160274 PDF

75

Bridging Data and Clinical Insight: Explainable AI for ICU Mortality Risk Prediction

Author 1: Ali H. Hassan Author 2: Riza bin Sulaiman Author 3: Mansoor Abdulhak Author 4: Hasan Kahtan

Despite advancements in machine learning within healthcare, the majority of predictive models for ICU mortality lack interpretability, a crucial factor for clinical application. The complexity inherent in high-dimensional healthcare data and models poses a significant barrier to achieving accurate and transparent results, which are vital in fostering trust and enabling… Read full abstract & cite →

Explainable AI healthcare machine learning predictive model

Abstract doi.org/10.14569/IJACSA.2025.0160275 PDF

76

Comparative Analysis of Undersampling, Oversampling, and SMOTE Techniques for Addressing Class Imbalance in Phishing Website Detection

Author 1: Kamal Omari Author 2: Chaimae Taoussi Author 3: Ayoub Oukhatar

Since this is one of the most challenging tasks in cyber security, many of them are affected by class imbalance when it comes to the performance of machine learning. This paper evaluates various strategies using a number of resampling-based approaches: ROS, RUS, and SMOTE-based methods in conjunction with XGBoost classifier… Read full abstract & cite →

Phishing website detection class imbalance XGBoost SMOTE-NC

Abstract doi.org/10.14569/IJACSA.2025.0160276 PDF

77

Deep Learning-Driven Detection of Terrorism Threats from Tweets Using DistilBERT and DNN

Author 1: Divya S Author 2: B Ben Sujitha

As globalization accelerates, the threat of terrorist attacks poses serious challenges to national security and public safety. Traditional detection methods rely heavily on manual monitoring and rule-based surveillance, which lack scalability, adaptability, and efficiency in handling large volumes of real-time social media data. These approaches often struggle with identifying evolving… Read full abstract & cite →

Terrorism global safety terrorist attacks data mining artificial intelligence natural language processing DistilBERT deep neural network

Abstract doi.org/10.14569/IJACSA.2025.0160277 PDF

78

Utilizing NLP to Optimize Municipal Services Delivery Using a Novel Municipal Arabic Dataset

Author 1: Homod Hamed Alaloye Author 2: Ahmad B. Alkhodre Author 3: Emad Nabil

The natural language processing paradigm has emerged as a vital tool for addressing complex business challenges, mainly due to advancements in machine learning (ML), deep learning (DL), and Generative AI. Advanced NLP models have significantly enhanced the efficiency and effectiveness of NLP applications, enabling the seamless integration of various business… Read full abstract & cite →

Arabic text classification machine learning (ML) deep learning (DL) hyperparameter optimization municipal services

Abstract doi.org/10.14569/IJACSA.2025.0160278 PDF

79

A Novel Hybrid Model Based on CEEMDAN and Bayesian Optimized LSTM for Financial Trend Prediction

Author 1: Yu Sun Author 2: Sofianita Mutalib Author 3: Liwei Tian

Financial time series prediction is inherently complex due to its nonlinear, nonstationary, and highly volatile nature. This study introduces a novel CEEMDAN-BO-LSTM model within a decomposition-optimization-prediction-integration framework to address these challenges. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm decomposes the original series into high-frequency, medium-frequency, low-frequency… Read full abstract & cite →

LSTM Bayesian optimization CEEMDAN financial time series time window selection

Abstract doi.org/10.14569/IJACSA.2025.0160279 PDF

80

Improving Performance with Big Data: Smart Supply Chain and Market Orientation in SMEs

Author 1: Miftakul Huda Author 2: Agus Rahayu Author 3: Chairul Furqon Author 4: Mokh Adib Sultan Author 5: Nani Hartati Author 6: Neng Susi Susilawati Sugiana

This study aims to explore the impact of big data-driven supply chain management, web analytics, and market orientation on corporate performance in medium-sized enterprises (MSEs) in Indonesia. By integrating these contemporary elements, the research seeks to provide insights into how digital technologies and strategic market practices can enhance organizational effectiveness… Read full abstract & cite →

Big data supply chain management web analytics corporate performance market orientation

Abstract doi.org/10.14569/IJACSA.2025.0160280 PDF

81

Color Multi-Focus Image Fusion Method Based on Contourlet Transform

Author 1: Zhifang Cai

Color Multi-Focus Image Fusion (MFIF) technology finds extensive use in areas such as microscopy, astronomy, and multi-scene photography where high-quality and detailed images are vital. This paper presents the Contourlet Transform alongside its enhanced version, the Non-Subsampled Contourlet Transform (NSCT), aimed at improving the outcomes of image fusion, with the… Read full abstract & cite →

Contourlet transform image fusing NSCT Laplacian Pyramid color multi-focus

Abstract doi.org/10.14569/IJACSA.2025.0160281 PDF

82

Enhanced Colon Cancer Prediction Using Capsule Networks and Autoencoder-Based Feature Selection in Histopathological Images

Author 1: Janjhyam Venkata Naga Ramesh Author 2: F. Sheeja Mary Author 3: S. Balaji Author 4: Divya Nimma Author 5: Elangovan Muniyandy Author 6: A. Smitha Kranthi Author 7: Yousef A. Baker El-Ebiary

The malignant development of cells in the colon or rectum is known as colon cancer, and because of its high incidence and possibility for death, it is a serious health problem. Because the disease frequently advances without symptoms in its early stages, early identification is essential. Improved survival rates and… Read full abstract & cite →

Colon cancer prediction capsule network autoencoder histopathological images early cancer detection

Abstract doi.org/10.14569/IJACSA.2025.0160282 PDF

83

Revolutionizing AI Governance: Addressing Bias and Ensuring Accountability Through the Holistic AI Governance Framework

Author 1: Ibrahim Atoum

Artificial intelligence (AI) possesses the capacity to transform numerous facets of our existence; however, it concomitantly engenders considerable risks associated with bias and discrimination. This article explores emerging technologies like Explainable AI (XAI), Fairness Metrics (FMs), and Adversarial Learning (AL) for bias mitigation while emphasizing the critical role of transparency… Read full abstract & cite →

Artificial intelligence framework bias discrimination governance key performance indicators

Abstract doi.org/10.14569/IJACSA.2025.0160283 PDF

84

Enhanced Early Detection of Diabetic Nephropathy Using a Hybrid Autoencoder-LSTM Model for Clinical Prediction

Author 1: U. Sudha Rani Author 2: C. Subhas

Early detection and precise prediction are essential in medical diagnosis, particularly for diseases such as diabetic nephropathy (DN), which tends to go undiagnosed at its early stages. Conventional diagnostic techniques may not be sensitive and timely, and hence, early intervention might be difficult. This research delves into the application of… Read full abstract & cite →

Autoencoder-LSTM Diabetic nephropathy early disease detection machine learning clinical data analysis hybrid models

Abstract doi.org/10.14569/IJACSA.2025.0160284 PDF

85

A Review of Cybersecurity Challenges and Solutions for Autonomous Vehicles

Author 1: Lasseni Coulibaly Author 2: Damien Hanyurwimfura Author 3: Evariste Twahirwa Author 4: Abubakar Diwani

With the continuously increasing demand for new technologies, many concepts have emerged in recent decades and the Internet of Things is one of the most popular. IoT is revolutionizing several aspects of human life with a large range of applications including the transportation sector. Based on IoT technologies and Artificial… Read full abstract & cite →

Internet of Things smart transportation autonomous vehicles cybersecurity

Abstract doi.org/10.14569/IJACSA.2025.0160285 PDF

86

Handling Imbalanced Data in Medical Records Using Entropy with Minkowski Distance

Author 1: Lastri Widya Astuti Author 2: Ermatita Author 3: Dian Palupi Rini

Medical records are essential for disease detection to help establish a diagnosis. Many issues with imbalanced classification are discovered in many cases of early disease detection and diagnosis using machine learning methods, resulting in decreased accuracy values due to imbalanced data distribution caused by the number of positive patients with… Read full abstract & cite →

Medical record imbalanced data classification distance entropy

Abstract doi.org/10.14569/IJACSA.2025.0160286 PDF

87

IoMT-Enabled Noninvasive Lungs Disease Detection and Classification Using Deep Learning-Based Analysis of Lungs Sounds

Author 1: Muhammad Sajid Author 2: Wareesa Sharif Author 3: Ghulam Gilanie Author 4: Maryam Mazher Author 5: Khurshid Iqbal Author 6: Muhammad Afzaal Akhtar Author 7: Muhammad Muddassar Author 8: Abdul Rehman

Noninvasive and accurate methods for diagnosing respiratory diseases are essential to improving healthcare consequences. The Internet of Medical Things (IoMT) is critical in driving developments in this field. This work presents an IoMT-enabled approach for lung disease detection and classification, using deep learning techniques to analyze lung sounds. The proposed… Read full abstract & cite →

Deep learning respiratory sound coronahack respiratory sound and coswara sound IoMT

Abstract doi.org/10.14569/IJACSA.2025.0160287 PDF

88

Readmission Risk Prediction After Total Hip Arthroplasty Using Machine Learning and Hyperparameter Optimized with Bayesian Optimization

Author 1: Intan Yuniar Purbasari Author 2: Athanasius Priharyoto Bayuseno Author 3: R. Rizal Isnanto Author 4: Tri Indah Winarni

Machine learning techniques are increasingly used in orthopaedic surgery to assess risks such as length of stay, complications, infections, and mortality, offering an alternative to traditional methods. However, model performance varies depending on private institutional data, and optimizing hyperparameters for better predictions remains a challenge. This study incorporates automatic hyperparameter… Read full abstract & cite →

Total hip arthroplasty orthopaedic surgery Bayesian Optimization machine learning algorithm hyperparameter optimization

Abstract doi.org/10.14569/IJACSA.2025.0160288 PDF

89

Forecasting Models for Predicting Global Supply Chain Disruptions in Trade Economics

Author 1: Limei Fu

Global supply chain disruptions have evolved into a critical challenge for trade economics, and have caused them to reach across industries and economies around the globe. The ability to foresee these disruptions is crucial for policymakers, businesses, and supply chain managers who want to develop actionable strategies for stability. The… Read full abstract & cite →

Supply chain disruptions forecasting models trade economics predictive analytics global resilience

Abstract doi.org/10.14569/IJACSA.2025.0160289 PDF

90

Developing an IoT Testing Framework for Autonomous Ground Vehicles

Author 1: Murat Tashkyn Author 2: Amanzhol Temirbolat Author 3: Nurlybek Kenes Author 4: Amandyk Kartbayev

Autonomous ground vehicles play a crucial role in the Internet of Things, offering transformative potential for applications such as urban transportation and delivery services. These vehicles can operate autonomously in uncertain environments, making reliable testing essential. This study develops and analyzes a testing framework for autonomous ground vehicles, focusing on… Read full abstract & cite →

Autonomous vehicle testing system IoT functional testing electronic modules delivery automation

Abstract doi.org/10.14569/IJACSA.2025.0160290 PDF

91

AI-Powered Intelligent Speech Processing: Evolution, Applications and Future Directions

Author 1: Ziqing Zhang

This paper provides an overview of the historical evolution of speech recognition, synthesis, and processing technologies, highlighting the transition from statistical models to deep learning-based models. Firstly, the paper reviews the early development of speech processing, tracing it from the rule-based and statistical models of the 1960s to the deep… Read full abstract & cite →

Intelligent speech recognition AI speech synthesis speech processing AI technology

Abstract doi.org/10.14569/IJACSA.2025.0160291 PDF

92

An Enhanced Whale Optimization Algorithm Based on Fibonacci Search Principle for Service Composition in the Internet of Things

Author 1: Yun CUI

Service composition in the Internet of Things (IoT) poses significant challenges owing to the dynamics in IoT ecosystems and the exponential increase in service candidates. This paper proposes an Enhanced Whale Optimization Algorithm (EWOA) by introducing the Fibonacci search principle for service composition optimization to overcome certain shortcomings of conventional… Read full abstract & cite →

Service composition Internet of Things quality of service whale optimization Fibonacci search

Abstract doi.org/10.14569/IJACSA.2025.0160292 PDF

93

SQRCD: Building Sustainable and Customer Centric DFIS for the Industry 5.0 Era

Author 1: Ruchira Rawat Author 2: Himanshu Rai Goyal Author 3: Sachin Sharma Author 4: Bina Kotiyal

Artificial Intelligence (AI) is considered a big turning point for the financial industry. Introducing Artificial General Intelligence (AGI) enhances the capability of all the areas where AI shows its power. The development of AGI is directly proportional to the need for more advanced automation by enhancing the features quick responsive… Read full abstract & cite →

Artificial General Intelligence (AGI) Digital Financial Inclusion System (DFIS) industry 5.0 customer-centric sustainability

Abstract doi.org/10.14569/IJACSA.2025.0160293 PDF

94

Efficient Personalized Federated Learning Method with Adaptive Differential Privacy and Similarity Model Aggregation

Author 1: Shiqi Mao Author 2: Fangfang Shan Author 3: Shuaifeng Li Author 4: Yanlong Lu Author 5: Xiaojia Wu

In recent years, personalized federated learning (PFL) has garnered significant attention due to its potential for safeguarding data privacy while addressing data heterogeneity across clients. However, existing PFL approaches remain vulnerable to privacy breaches, particularly under adversarial inference and client-side data reconstruction attacks. To address these concerns, we propose DP-FedSim… Read full abstract & cite →

Federated learning differential privacy gradient clipping model aggregation

Abstract doi.org/10.14569/IJACSA.2025.0160294 PDF

95

Smart Night-Vision Glasses with AI and Sensor Technology for Night Blindness and Retinitis Pigmentosa

Author 1: Shaheer Hussain Qazi Author 2: M. Batumalay

This paper presents the conceptualization of Smart Night-Vision Glasses, an innovative assistive device aimed at individuals with night blindness and Retinitis Pigmentosa (RP). These conditions, characterized by significant difficulty in seeing in low-light or dark environments, currently have no effective medical solution. The proposed glasses utilize advanced sensor technologies such… Read full abstract & cite →

Night-vision glasses night blindness Retinitis Pigmentosa (RP) IoT assistive technology sensor technology AI data processing low-light navigation wearable devices process innovation

Abstract doi.org/10.14569/IJACSA.2025.0160295 PDF

96

Comparative Analysis of Cardiac Disease Classification Using a Deep Learning Model Embedded with a Bio-Inspired Algorithm

Author 1: Nandakumar Pandiyan Author 2: Subhashini Narayan

Cardiac disease classification is a crucial task in healthcare aimed at early diagnosis and prevention of cardiovascular complications. Traditional methods such as machine learning models often face challenges in handling high-dimensional and noisy datasets, as well as in optimizing model performance. In this study, we propose and compare a novel… Read full abstract & cite →

Cardiac disease heart disease bio-inspired machine learning deep learning prediction classification

Abstract doi.org/10.14569/IJACSA.2025.0160296 PDF

97

Quantum Swarm Intelligence and Fuzzy Logic: A Framework for Evaluating English Translation

Author 1: Pei Yang

This study introduces the Quantum Swarm-Driven Fuzzy Evaluation Framework (QSI-Fuzzy) for assessing English translation software across multiple domains and criteria. The principal aim is to develop a scalable, adaptive, and interpretable evaluation framework that optimizes dynamic weight assignments while managing linguistic uncertainties. A major challenge in translation software evaluation lies… Read full abstract & cite →

English translation software quantum swarm intelligence fuzzy logic multi-domain evaluation optimization linguistic performance analysis

Abstract doi.org/10.14569/IJACSA.2025.0160297 PDF

98

Optimizing Athlete Workload Monitoring with Supervised Machine Learning for Running Surface Classification Using Inertial Sensors

Author 1: WenBin Zhu Author 2: QianWei Zhang Author 3: SongYan Ni

Monitoring athlete movement is important to improve performance, reduce fatigue, and decrease the likelihood of injury. Advanced technologies, including computer vision and inertial sensors, have been widely explored in classifying sport-specific movements. Combining automated sports action labeling with athlete-monitoring data provides an effective approach to enhance workload analysis. Recent studies… Read full abstract & cite →

Athlete monitoring machine learning models running surface classification Inertial Measurement Units (IMU) neural networks Support Vector Machines (SVM) Principal Component Analysis (PCA)

Abstract doi.org/10.14569/IJACSA.2025.0160298 PDF

99

LDA-Based Topic Mining for Unveiling the Outstanding Universal Value of Solo Keroncong Music as an Intangible Cultural Heritage of UNESCO

Author 1: Denik Iswardani Witarti Author 2: Danis Sugiyanto Author 3: Atik Ariesta Author 4: Pipin Farida Ariyani Author 5: Rusdah

Outstanding Universal Value (OUV) is an essential value of culture and nature. It is so extraordinary that it transcends national boundaries and becomes generally crucial for all humanity's current and future generations. A culture with this value needs permanent protection because it is considered a critical heritage for the world… Read full abstract & cite →

LDA OUV Solo keroncong text mining topic modeling

Abstract doi.org/10.14569/IJACSA.2025.0160299 PDF

100

Enhancing Chronic Kidney Disease Prediction with Deep Separable Convolutional Neural Networks

Author 1: Janjhyam Venkata Naga Ramesh Author 2: P N S Lakshmi Author 3: Thalakola Syamsundararao Author 4: Elangovan Muniyandy Author 5: Linginedi Ushasree Author 6: Yousef A. Baker El-Ebiary Author 7: David Neels Ponkumar Devadhas

Chronic Kidney Disease (CKD) is a chronic disease that progressively impairs kidney function to the point of wasting filtration, electrolyte imbalance, and blood pressure control. Early and precise prediction becomes necessary for successful disease management. This research demonstrates a new method involving Deep Separable Convolutional Neural Networks (DS-CNNs) in improving… Read full abstract & cite →

Chronic kidney disease deep separable convolutional neural networks learning rate warm-up with cosine annealing predictive accuracy optimization techniques

Abstract doi.org/10.14569/IJACSA.2025.01602100 PDF

101

Hybrid Artificial Bee Colony and Bat Algorithm for Efficient Resource Allocation in Edge-Cloud Systems

Author 1: Jiao GE Author 2: Bolin ZHOU Author 3: Na LIU

Integrating edge and cloud computing systems builds up a powerhouse, a framework for realizing real-time data processing and conducting large-scale computation tasks. However, efficient resource allocation and task scheduling are outstanding challenges in these dynamic, heterogeneous environments. This paper proposes an innovative hybrid algorithm that amalgamates the features of the… Read full abstract & cite →

Cloud computing edge computing resource allocation optimization task scheduling

Abstract doi.org/10.14569/IJACSA.2025.01602101 PDF

102

Pneumonia Detection Using Transfer Learning: A Systematic Literature Review

Author 1: Mohammed A M Abueed Author 2: Danial Md Nor Author 3: Nabilah Ibrahim Author 4: Jean-Marc Ogier

Deep learning models have significantly improved pneumonia detection using X-ray image analysis in the field of AI-driven healthcare, showing a major advancement in the effectiveness of medical decision systems. In this paper, we have conducted a systematic literature review of pneumonia detection techniques that applied transfer learning combined with other… Read full abstract & cite →

Pneumonia machine learning COVID-19: deep learning

Abstract doi.org/10.14569/IJACSA.2025.01602102 PDF

103

Adaptive and Scalable Cloud Data Sharing Framework with Quantum-Resistant Security, Decentralized Auditing, and Machine Learning-Based Threat Detection

Author 1: P Raja Sekhar Reddy Author 2: Pulipati Srilatha Author 3: Kanhaiya Sharma Author 4: Sudipta Banerjee Author 5: Shailaja Salagrama Author 6: Manjusha Tomar Author 7: Ashwin Tomar

The increasing prevalence of cloud environments makes it important to ensure secure and efficient data sharing between dynamic teams, especially in terms of user access and termination based on proxy re-encryption and hybrid authentication management schemes aimed at increasing scalability, flexibility, and adaptability and exploring a multi-proxy server architecture to… Read full abstract & cite →

Blockchain audit data security and privacy machine learning proxy re-encryption quantum-resistant cryptography

Abstract doi.org/10.14569/IJACSA.2025.01602103 PDF

104

ALE Model: Air Cushion Impact Characteristics of Seaplane Landing Application

Author 1: Yunsong Zhang Author 2: Ruiyou Li Shi Author 3: Bo Gao Author 4: Changxun Song Author 5: Zhengzhou Zhang

Seaplane landing is a strong nonlinear gas-liquid-solid multiphase coupling problem, and the coupling impact characteristics of air cushion are very complicated, and it is difficult to maintain the stability of the air-frame. In this paper, The ALE method is used to study the landing of seaplane at different initial attitude… Read full abstract & cite →

Seaplane ALE method multiphase coupling air cushion

Abstract doi.org/10.14569/IJACSA.2025.01602104 PDF

105

Self-Organizing Neural Networks Integrated with Artificial Fish Swarm Algorithm for Energy-Efficient Cloud Resource Management

Author 1: A. Z. Khan Author 2: B. Manikyala Rao Author 3: Janjhyam Venkata Naga Ramesh Author 4: Elangovan Muniyandy Author 5: Eda Bhagyalakshmi Author 6: Yousef A. Baker El-Ebiary Author 7: David Neels Ponkumar Devadhas

Cloud computing's exponential expansion requires better resource management methods to solve the existing struggle between system performance and energy efficiency and functional scalability. Traditional resource management practices frequently lead systems in large-scale cloud environments to produce suboptimal results. This research presents a brand-new computational framework that unites Self-Organizing Neural Networks… Read full abstract & cite →

Energy-efficient cloud resource management Self-Organizing Neural Networks (SONN) Artificial Fish Swarm Algorithm (AFSA) cloud optimization swarm intelligence resource utilization task scheduling

Abstract doi.org/10.14569/IJACSA.2025.01602105 PDF

106

Depression Detection in Social Media Using NLP and Hybrid Deep Learning Models

Author 1: S M Padmaja Author 2: Sanjiv Rao Godla Author 3: Janjhyam Venkata Naga Ramesh Author 4: Elangovan Muniyandy Author 5: Pothumarthi Sridevi Author 6: Yousef A.Baker El-Ebiary Author 7: David Neels Ponkumar Devadhas

One type of feeling that possesses a detrimental effect on people's day-to-day lives is depression. Globally, the number of persons experiencing long-term sentiments is rising annually. Many psychiatrists find it difficult to recognize mental disease or unpleasant emotions in patients before it's too late to improve treatment. Finding depression in… Read full abstract & cite →

Depression detection RoBERTa BiLSTM social media analysis deep learning

Abstract doi.org/10.14569/IJACSA.2025.01602106 PDF

107

Detecting Chinese Sexism Text in Social Media Using Hybrid Deep Learning Model with Sarcasm Masking

Author 1: Lei Wang Author 2: Nur Atiqah Sia Abdullah Author 3: Syaripah Ruzaini Syed Aris

Sexist content is prevalent in social media, which seriously affects the online environment and occasionally leads to offline disputes. For this reason, many scholars have researched how to automatically detect sexist content in social media. However, the presence of sarcasm complicates this task. Thus, recognizing sarcasm to improve the accuracy… Read full abstract & cite →

Sexism chinese deep learning sarcasm masking

Abstract doi.org/10.14569/IJACSA.2025.01602107 PDF

108

Machine Learning-Enabled Personalization of Programming Learning Feedback

Author 1: Mohammad T. Alshammari

Acquiring programming skills is daunting for most learners and is even more challenging in heavily attended courses. This complexity also makes it difficult to offer personalized feedback within the time constraints of instructors. This study offers an approach to predict programming weaknesses in each learner to provide appropriate learning resources… Read full abstract & cite →

Machine learning programming learning outcome feedback personalization

Abstract doi.org/10.14569/IJACSA.2025.01602108 PDF

109

Improving English Writing Skills Through NLP-Driven Error Detection and Correction Systems

Author 1: Purnachandra Rao Alapati Author 2: A. Swathi Author 3: Jillellamoodi Naga Madhuri Author 4: Vijay Kumar Burugari Author 5: Bhuvaneswari Pagidipati Author 6: Yousef A.Baker El-Ebiary Author 7: Prema S

Error detection and correction is an important activity that ensures the quality of written communication, especially in education, business, and legal documentation. State-of-the-art NLP approaches have several issues, including overcorrection, poor handling of multilingual texts, and poor adaptability to domain-specific errors. Traditional methods, based on rule-based approaches or single-task models… Read full abstract & cite →

Natural Language Processing (NLP) error detection writing skills improvement language models AI-Driven writing tools

Abstract doi.org/10.14569/IJACSA.2025.01602109 PDF

110

Hybrid Attention-Based Transformers-CNN Model for Seizure Prediction Through Electronic Health Records

Author 1: Janjhyam Venkata Naga Ramesh Author 2: M. Misba Author 3: S. Balaji Author 4: K. Kiran Kumar Author 5: Elangovan Muniyandy Author 6: Yousef A. Baker El-Ebiary Author 7: B Kiran Bala Author 8: Radwan Abdulhadi .M. Elbasir

Seizures are a serious neurological disease, and proper prognosis by electroencephalography (EEG) dramatically enhances patient outcomes. Current seizure prediction methods fail to deal with big data and usually need intensive preprocessing. Recent breakthroughs in deep learning can automatically extract features and detect seizures. This work suggests a CNN-Transformer model for… Read full abstract & cite →

Epileptic seizure prediction EEG signal analysis CNN-Transformer model deep learning in healthcare spatiotemporal feature extraction neural network optimization

Abstract doi.org/10.14569/IJACSA.2025.01602110 PDF

111

AI-Driven Transformer Frameworks for Real-Time Anomaly Detection in Network Systems

Author 1: Santosh Reddy P Author 2: Tarunika Chaudhari Author 3: Sanjiv Rao Godla Author 4: Janjhyam Venkata Naga Ramesh Author 5: Elangovan Muniyandy Author 6: A. Smitha Kranthi Author 7: Yousef A.Baker El-Ebiary

The detection of evolving cyber threats proves challenging for traditional anomaly detection because signature-based models do not identify new or zero-day attacks. This research develops an AI Transformer-based system with Bidirectional Encoder Representations from Transformers (BERT) technology with Zero-Shot Learning (ZSL) for real-time network system anomaly detection while solving these… Read full abstract & cite →

Anomaly detection network security transformer framework bidirectional encoder representations from transformers zero-shot learning

Abstract doi.org/10.14569/IJACSA.2025.01602111 PDF

112

Optimizing Social Media Marketing Strategies Through Sentiment Analysis and Firefly Algorithm Techniques

Author 1: Sudhir Anakal Author 2: P N S Lakshmi Author 3: Nishant Fofaria Author 4: Janjhyam Venkata Naga Ramesh Author 5: Elangovan Muniyandy Author 6: Shaik Sanjeera Author 7: Yousef A.Baker El-Ebiary Author 8: Ritesh Patel

The dramatic expansion of social media platforms reshaped business-to-customer interactions so organizations need to refine their marketing strategies toward maximizing both user engagement and marketing return on investment (ROI). Present-day social media marketing methods struggle to embrace user emotions fully while responding to market variations thus demonstrating the necessity for… Read full abstract & cite →

Sentiment analysis firefly algorithm social media marketing optimization user engagement marketing strategies

Abstract doi.org/10.14569/IJACSA.2025.01602112 PDF

113

Accurate AI Assistance in Contract Law Using Retrieval-Augmented Generation to Advance Legal Technology

Author 1: Youssra Amazou Author 2: Faouzi Tayalati Author 3: Houssam Mensouri Author 4: Abdellah Azmani Author 5: Monir Azmani

Understanding legal documentation is a complex task due to its inherent subtleties and constant changes. This article explores the use of artificial intelligence-driven chatbots, enhanced by retrieval-augmented generation (RAG) techniques, to address these challenges. RAG integrates external knowledge into generative models, enabling the delivery of accurate and contextually relevant legal… Read full abstract & cite →

AI Lawyer contract law legal technology Retrieval-Augmented Generation (RAG) Large Language Models (LLMs) GPT chatbots

Abstract doi.org/10.14569/IJACSA.2025.01602113 PDF

114

Fourth Party Logistics Routing Optimization Problem Based on Conditional Value-at-Risk Under Uncertain Environment

Author 1: Guihua Bo Author 2: Qiang Liu Author 3: Huiyuan Shi Author 4: Xin Liu Author 5: Chen Yang Author 6: Liyan Wang

In order to improve the level of logistics service and considering the impact of uncertainties such as bad weather and highway collapse on fourth party logistics routing optimization problem, this paper adopts Conditional Value-at-Risk (CVaR) to measure the tardiness risk, which is caused by the uncertainties, and proposes a nonlinear… Read full abstract & cite →

Logistics service routing optimization tardiness risk conditional value-at-risk improved Q-learning algorithm

Abstract doi.org/10.14569/IJACSA.2025.01602114 PDF

115

Optimized Dynamic Graph-Based Framework for Skin Lesion Classification in Dermoscopic Images

Author 1: J. Deepa Author 2: P. Madhavan

Early and accurate classification of skin lesions is critical for effective skin cancer diagnosis and treatment. However, the visual similarity of lesions in their early stages often leads to misdiagnoses and delayed interventions. This lack of transparency makes it challenging for dermatologists to interpret with validate decisions made by such… Read full abstract & cite →

Confidence partitioning sampling filtering dynamic graph convolutional recurrent imputation network ISIC-2019 skin disease dataset red billed blue magpie optimization algorithm hybrid dual attention-guided efficient transformer and UNet 3+

Abstract doi.org/10.14569/IJACSA.2025.01602115 PDF

116

Optimized Wavelet Scattering Network and CNN for ECG Heartbeat Classification from MIT–BIH Arrhythmia Database

Author 1: Mohamed Elmehdi AIT BOURKHA Author 2: Anas HATIM Author 3: Dounia NASIR Author 4: Said EL BEID

Early detection of cardiovascular diseases is vital, especially considering the alarming number of deaths worldwide caused by heart attacks, as highlighted by the world health organization. This emphasizes the urgent need to develop automated systems that can ensure timely and accurate identification of cardiovascular conditions, potentially saving countless lives. This… Read full abstract & cite →

Electrocardiogram (ECG) Convolutional Neural Network (CNN) Arrhythmia Rhythm (ARR) Maximum Relevance Minimum Redundancy (MRMR) Wavelet Scattering Network (WSN)

Abstract doi.org/10.14569/IJACSA.2025.01602116 PDF

117

Personalized Motion Scheme Generation System Design for Motion Software Based on Cloud Computing

Author 1: Jinkai Duan

The increase of national attention has also promoted the growth of the scale of sports health industry. However, for ordinary people who lack professional knowledge, intuitive data cannot make correct sports planning. Therefore, aiming at the problem that it is difficult for ordinary people to make correct exercise plan according… Read full abstract & cite →

Cloud computing sports random forest algorithm personaliza-tion system

Abstract doi.org/10.14569/IJACSA.2025.01602117 PDF

118

Enhancing Emotion Prediction in Multimedia Content Through Multi-Task Learning

Author 1: Wan Fan

This study presents a robust multimodal emotion analysis model aimed at improving emotion prediction in film and television communication. Addressing challenges in modal fusion and data association, the model integrates a Transformer-based framework with multi-task learning to capture emotional associations and temporal features across various modalities. It overcomes the limitations… Read full abstract & cite →

Multi task learning multimodal emotion analysis timing transformer attention

Abstract doi.org/10.14569/IJACSA.2025.01602118 PDF

119

Validation of an Adaptive Decision Support System Framework for Outcome-Based Blended Learning

Author 1: Rahimah Abd Halim Author 2: Rosmayati Mohemad Author 3: Noraida Hj Ali Author 4: Anuar Abu Bakar Author 5: Hamimah Ujir

The Adaptive Decision Support System Learning Framework (A-DSS-LF) was developed to address diverse learner needs in blended learning environments by integrating learning styles, cognitive levels, practical skills, and value practices. This study validates the framework using the Fuzzy Delphi Method (FDM), a consensus-building tool that synthesizes expert opinions and addresses… Read full abstract & cite →

Learner needs adaptive learning blended learning fuzzy delphi method decision support system

Abstract doi.org/10.14569/IJACSA.2025.01602119 PDF

120

Towards Two-Step Fine-Tuned Abstractive Summarization for Low-Resource Language Using Transformer T5

Author 1: Salhazan Nasution Author 2: Ridi Ferdiana Author 3: Rudy Hartanto

This study explores the potential of two-step fine-tuning for abstractive summarization in a low-resource language, focusing on Indonesian. Leveraging the Transformer-T5 model, the research investigates the impact of transfer learning across two tasks: machine translation and text summarization. Four configurations were evaluated, ranging from zero-shot to two-step fine-tuned models. The… Read full abstract & cite →

Abstractive summarization low-resource language Transformer T5 transfer learning

Abstract doi.org/10.14569/IJACSA.2025.01602120 PDF

121

AI-Driven Construction and Application of Gardens: Optimizing Design and Sustainability with Machine Learning

Author 1: Jingyi Wang Author 2: Yan Song Author 3: Haozhong Yang Author 4: Han Li Author 5: Minglan Zou

Artificial intelligence (AI) integration into environ-mental analysis has revolutionized various fields. Including the construction and application of gardens, by enabling precise classification and decision-making for sustainable practices. This paper presents a strong AI-driven framework uses convolutional neural network (CNN) and pretrained models like VGG16 and InceptionV3 to classify eight distinct… Read full abstract & cite →

Artificial intelligence machine learning construction and application of garden design convolutional neural network VGG16 InceptionV3

Abstract doi.org/10.14569/IJACSA.2025.01602121 PDF

122

Multi-Objective Osprey Optimization Algorithm-Based Resource Allocation in Fog-IoT

Author 1: Nagarjun E Author 2: Dharamendra Chouhan Author 3: Dilip Kumar S M

Fog Computing (FC) paradigm offers significant potential for hosting diverse delay-sensitive Internet of Things (IoT) applications. However, the limited resources of fog devices pose significant challenges for deploying multiple applications, particularly in heterogeneous and dynamic IoT scenarios, due to the absence of effective mechanisms for resource estimation and discovery. An… Read full abstract & cite →

Fog computing IoT resource allocation and real-location task allocation

Abstract doi.org/10.14569/IJACSA.2025.01602122 PDF

123

Leveraging Deep Semantics for Sparse Recommender Systems (LDS-SRS)

Author 1: Adel Alkhalil

RS (Recommender Systems) provide personalized suggestions to the user(s) by filtering through vast amounts of similar data, including media content, e-commerce platforms, and social networks. Traditional recommendation system (RS) methods encounter significant challenges. Collaborative Filtering (CF) is hindered by the lack of sufficient user-product engagement data, while CBF (Content Based… Read full abstract & cite →

LDA-2-Vec technique content representation topic-based modeling probabilistic matrix decomposition

Abstract doi.org/10.14569/IJACSA.2025.01602123 PDF

124

A Deep Learning Approach for Nepali Image Captioning and Speech Generation

Author 1: Sagar Sharma Author 2: Samikshya Chapagain Author 3: Sachin Acharya Author 4: Sanjeeb Prasad Panday

This article introduces a novel approach for Image-to-Speech generation that aims in converting images into textual captions along with spoken descriptions in Nepali Language using deep learning techniques. By leveraging computer vision and natural language processing, the system analyzes images, extracts features, generates human-readable captions, and produces intelligible speech output… Read full abstract & cite →

Image captioning speech generation image-to-speech generation deep learning BLEU score HiFiGaN TTS

Abstract doi.org/10.14569/IJACSA.2025.01602124 PDF

125

Knowledge Graph Path-Enhanced RAG for Intelligent Residency Q&A

Author 1: Jian Zhu Author 2: Huajun Zhang Author 3: Jianpeng Da Author 4: Hanbing Huang Author 5: Chongxin Luo Author 6: Xu Peng

As the demand for efficient information retrieval in specialized domains continues to rise, vertical domain question-answering systems play an increasingly important role in addressing domain-specific knowledge needs. This paper proposes a retrieval-augmented generation method that integrates path search in knowledge graphs to enhance intelligent question-answering systems for professional information retrieval… Read full abstract & cite →

Retrieval-augmented generation path search knowledge graph household registration policy vertical field

Abstract doi.org/10.14569/IJACSA.2025.01602125 PDF

126

Leveraging Machine-Aided Learning in College English Education: Computational Approaches for Enhancing Student Outcomes and Pedagogical Efficiency

Author 1: Danxia Zhu

The integration of machine-aided learning into college English education offers transformative potential for enhancing teaching and learning outcomes. This paper investigates the application of computational models, including machine learning algorithms and natural language processing tools, to optimize pedagogical practices and improve student performance. A series of experiments were conducted to… Read full abstract & cite →

Machine learning natural language processing computational intelligence data analytics pedagogy

Abstract doi.org/10.14569/IJACSA.2025.01602126 PDF

127

A Novel Hybrid Attentive Convolutional Autoencoder (HACA) Framework for Enhanced Epileptic Seizure Detection

Author 1: Venkata Narayana Vaddi Author 2: Madhu Babu Sikha Author 3: Prakash Kodali

Epilepsy, a prevalent neurological disorder, requires accurate and efficient seizure detection for timely intervention. This study presents a Hybrid Attentive Convolutional Autoen-coder (HACA) framework designed to address challenges in EEG signal processing for seizure detection. The proposed method integrates signal reconstruction, innovative feature extraction, and attention mechanisms to focus on… Read full abstract & cite →

Epileptic seizure detection EEG hybrid attentive convolutional autoencoder attention mechanism deep learning

Abstract doi.org/10.14569/IJACSA.2025.01602127 PDF

128

Deep Learning in Heart Murmur Detection: Analyzing the Potential of FCNN vs. Traditional Machine Learning Models

Author 1: Hajer Sayed Hussein Author 2: Hussein AlBazar Author 3: Roxane Elias Mallouhy Author 4: Fatima Al-Hebshi

This research investigates the performance of machine learning and deep learning models in detecting heart murmurs from audio recordings. Using the PhysioNet Challenge 2016 dataset, we compare several traditional machine learning models—Support Vector Machine, Random Forest, AdaBoost, and Decision Tree—with a Fully Convolutional Neural Network (FCNN). The findings indicate that… Read full abstract & cite →

Heart murmur detection machine learning deep learning cardiovascular diagnostics artificial intelligence phys-ioNet dataset

Abstract doi.org/10.14569/IJACSA.2025.01602128 PDF

129

Securing Internet of Medical Things: An Advanced Federated Learning Approach

Author 1: Anass Misbah Author 2: Anass Sebbar Author 3: Imad Hafidi

The Internet of Medical Things (IoMT) is transforming healthcare through extensive automation, data collection, and real-time communication among interconnected devices. However, this rapid expansion introduces significant security vulnerabilities that traditional centralized solutions or device-level protections often fail to adequately address due to challenges related to latency, scalability, and resource constraints… Read full abstract & cite →

Internet of Medical Things (IoMT) federated learning machine learning security intrusion detection systems decentralized framework

Abstract doi.org/10.14569/IJACSA.2025.01602129 PDF

130

Chinese Relation Extraction with External Knowledge-Enhanced Semantic Understanding

Author 1: Shulin Lv Author 2: Xiaoyao Ding

Relation extraction is the foundation of constructing knowledge graphs, and Chinese relation extraction is a particularly challenging aspect of this task. Most existing methods for Chinese relation extraction rely either on character-based or word-based features. However, the former struggles to capture contextual information between characters, while the latter is constrained… Read full abstract & cite →

Chinese relation extraction knowledge graph external knowledge semantic understanding attention mechanism

Abstract doi.org/10.14569/IJACSA.2025.01602130 PDF

131

Temperature Prediction for Photovoltaic Inverters Using Particle Swarm Optimization-Based Symbolic Regression: A Comparative Study

Author 1: Fabian Alonso Lara-Vargas Author 2: Jesus Aguila-Leon Author 3: Carlos Vargas-Salgado Author 4: Oscar J. Suarez

Accurate temperature modeling is crucial for maintaining the efficiency and reliability of solar inverters. This paper presents an innovative application of symbolic regression based on particle swarm optimization (PSO) for predicting the temperature of photovoltaic inverters, offering a novel approach that balances accuracy and computational efficiency. The study evaluates the… Read full abstract & cite →

Particle swarm optimization photovoltaic inverters multiple linear regression symbolic regression temperature pre-diction

Abstract doi.org/10.14569/IJACSA.2025.01602131 PDF

132

Towards Effective Anomaly Detection: Machine Learning Solutions in Cloud Computing

Author 1: Hussain Almajed Author 2: Abdulrahman Alsaqer Author 3: Abdullah Albuali

Cloud computing has transformed modern Information Technology (IT) infrastructures with its scalability and cost-effectiveness but introduces significant security risks. More-over, existing anomaly detection techniques are not well equipped to deal with the complexities of dynamic cloud environments. This systematic literature review shows the advancements in Machine Learning (ML) solutions for… Read full abstract & cite →

Anomaly cloud machine learning detection

Abstract doi.org/10.14569/IJACSA.2025.01602132 PDF

133

Enhanced Fuzzy Deep Learning for Plant Disease Detection to Boost the Agricultural Economic Growth

Author 1: Mohammad Abrar

Plant disease detection is a crucial technology to ensure agricultural productivity and sustainability. However, traditional methods tend to fail as they do not address imprecise and uncertain data in a satisfactory way. We propose the Enhanced Fuzzy Deep Neural Network (EFDNN) which integrate the fuzzy logic with deep neural networks… Read full abstract & cite →

Deep learning plant disease fuzzy deep learning agricultural production

Abstract doi.org/10.14569/IJACSA.2025.01602133 PDF

134

Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models

Author 1: Zahra Khalila Author 2: Arbi Haza Nasution Author 3: Winda Monika Author 4: Aytug Onan Author 5: Yohei Murakami Author 6: Yasir Bin Ismail Radi Author 7: Noor Mohammad Osmani

Accurate and contextually faithful responses are critical when applying large language models (LLMs) to sensitive and domain-specific tasks, such as answering queries related to quranic studies. General-purpose LLMs often struggle with hallucinations, where generated responses deviate from authoritative sources, raising concerns about their reliability in religious contexts. This challenge highlights… Read full abstract & cite →

Large-language-models retrieval-augmented generation question answering Quranic studies Islamic teachings

Abstract doi.org/10.14569/IJACSA.2025.01602134 PDF

135

Advanced Optimization of RPL-IoT Protocol Using ML Algorithms

Author 1: Mansour Lmkaiti Author 2: Ibtissam Larhlimi Author 3: Maryem Lachgar Author 4: Houda Moudni Author 5: Hicham Mouncif

This study explores the transformative potential of machine learning (ML) algorithms in optimizing the Routing Protocol for Low-Power and Lossy Networks (RPL), addressing critical challenges in Internet of Things (IoT) networks, such as Expected Transmission Count (ETX), latency, and energy consumption. The research evaluates the performance of Random Forest, Gradient… Read full abstract & cite →

IoT RPL machine learning routing efficiency energy consumption expected transmission count network optimization Artificial Intelligence (AI)

Abstract doi.org/10.14569/IJACSA.2025.01602135 PDF