IJACSA Vol. 16 Issue 6 (2025)

1

Detecting and Preventing Money Laundering Using Deep Learning and Graph Analysis

Author 1: Mamunur R Raja Author 2: Md Anwar Hosen Author 3: Md Farhad Kabir Author 4: Sharmin Sultana Author 5: Shah Ahammadullah Ashraf Author 6: Rakibul Islam

Money laundering is a major worldwide issue facing financial organizations, with its increasingly complicated and changing methods. Conventional rule-based anti-money laundering (AML) systems can fail to identify advanced fraudulent activity. This study shows a new hybrid model to detect suspicious transaction patterns precisely by efficiently combining GraphSAGE, a graph-based Machine… Read full abstract & cite →

Anti-money laundering (AML) deep learning (DL) LSTM GraphSAGE graph analysis transaction monitoring hybrid fusion model

Abstract doi.org/10.14569/IJACSA.2025.0160601 PDF

2

Transforming the Working Style of Call Center Agents Through Generative AI

Author 1: Satya Karteek Gudipati

As Generative Artificial Intelligence (Gen AI) is evolving rapidly, there is a significant change in the approach by the contact center industry with respect to work culture. Historically, customer service agents working in a contact center used to depend significantly on static scripts and fragmented information systems, which thereby resulted… Read full abstract & cite →

Generative AI call center transformation agent augmentation LLMs sentiment analysis hyper-personalization conversational AI AI ethics

Abstract doi.org/10.14569/IJACSA.2025.0160602 PDF

3

AI-Driven Education: Integrating Machine Learning and NLP to Transform Child Learning Systems

Author 1: Masuma Akter Semi Author 2: Md Borhan Uddin Author 3: Sharmin Sultana Author 4: Motmainna Tamanna Author 5: Azim Uddin Author 6: Khandakar Rabbi Ahmed

An Artificial Intelligence-driven child learning system with a Machine Learning and Natural Language Processing-based approach to dynamically personalize educational experiences for children is proposed in this study. Using a Sentence-BERT model to encode student queries for the computation of semantic similarity and knowledge domains to be retrieved. A T5-based transformer… Read full abstract & cite →

Artificial intelligence machine learning natural language processing adaptive learning systems sentencebert gradient boosting machine personalized feedback

Abstract doi.org/10.14569/IJACSA.2025.0160603 PDF

4

Application of Blockchain Frameworks for Decentralized Identity and Access Management of IoT Devices

Author 1: Sushil Khairnar

The growth in IoT devices means an ongoing risk of data vulnerability. The transition from centralized ecosystems to decentralized ecosystems is of paramount importance due to security, privacy, and data use concerns. Since the majority of IoT devices will be used by consumers in peer-to-peer applications, a centralized approach raises… Read full abstract & cite →

Blockchain decentralization identity and access management ethereum hyperledger

Abstract doi.org/10.14569/IJACSA.2025.0160604 PDF

5

Location Based Augmented Reality Navigation Application

Author 1: Samridhi Sanjay Pramanik Author 2: Aishwary Pramanik

This paper presents a novel Augmented Reality (AR) navigation system to overcome limitations of conventional 2D map-based applications in advanced real-world environments. Current AR navigation systems solutions often lack dynamic adaptation to user behavior and fail to deliver context-aware, personalized guidance. Addressing these gaps, we present a markerless, location-based AR… Read full abstract & cite →

Augmented reality location based application markerless AR GPS based application real world augmentation unity ARCore navigation system user interaction

Abstract doi.org/10.14569/IJACSA.2025.0160605 PDF

6

Metabolite Screening for Heart Disease Using Support Vector Machine-Based AI

Author 1: Edward L. Boone Author 2: Ryad A. Ghanam Author 3: Faten S. Alamri Author 4: Elizabeth B. Amona

Algorithms for feature selection are growing in interest among researchers aiming to connect specific features in a dataset with specific classifications. Recent developments in machine learning, particularly Support Vector Machine-based artificial intelligence algorithms have demonstrated excellent classification performance in highly nonlinear data. However, identifying which features contribute most to classification… Read full abstract & cite →

Machine learning genetic algorithm support vector machines classification heart disease metabolites

Abstract doi.org/10.14569/IJACSA.2025.0160606 PDF

7

Enhancing Deepfake Content Detection Through Blockchain Technology

Author 1: Qurat-ul-Ain Mastoi Author 2: Muhammad Faisal Memon Author 3: Salman Jan Author 4: Atif Jamil Author 5: Muhammad Faique Author 6: Zeeshan Ali Author 7: Abdullah Lakhan Author 8: Toqeer Ali Syed

Deepfake technology poses a growing threat to the authenticity and trustworthiness of digital media, necessitating the development of advanced detection mechanisms. While AI-based methods have shown promise, they generally face limitations in terms of generalization and scalability. We present a blockchain-enabled watermarking technique, characterized by its immutable, transparent, and decentralized… Read full abstract & cite →

Blockchain deep fake convolutional neural network (CNN) long short-term memory (LSTM) RNN (recurrent neural network) video and image

Abstract doi.org/10.14569/IJACSA.2025.0160607 PDF

8

Advancements in Deep Learning for Malaria Detection: A Comprehensive Overview

Author 1: Kiswendsida Kisito Kabore Author 2: Desire Guel Author 3: Flavien Herve Somda

Malaria remains a critical global health issue, with millions of cases reported annually, particularly in resource-limited regions. Timely and accurate diagnosis is vital to ensure effective treatment, reduce complications, and control transmission. Conventional diagnostic methods, including microscopy and Rapid Diagnostic Tests (RDTs), face considerable limitations such as dependency on skilled… Read full abstract & cite →

Malaria detection deep learning Convolutional Neural Networks (CNNs) medical imaging automated diagnostics

Abstract doi.org/10.14569/IJACSA.2025.0160608 PDF

9

Application of Deep Learning-Based Image Compression Restoration Technology in Power System Unstructured Data Management

Author 1: Junjie Zha Author 2: Aiguo Teng Author 3: Xinwen Shan Author 4: Hao Tang Author 5: Zihan Liu

In power-system unstructured-data management, a large volume of images from inspection drones, substation cameras, and smart meters is heavily compressed due to bandwidth and storage constraints, resulting in lower resolution that hinders defect detection and maintenance decisions. Although deep-learning super-resolution (SR) techniques have made significant advances, real-world deployments still require… Read full abstract & cite →

Image compression attention mechanism multimodal fusion unstructured data in the power industry image data

Abstract doi.org/10.14569/IJACSA.2025.0160609 PDF

10

Impact of Auxiliary Information in Generative Artificial Intelligence Models for Cross-Domain Recommender Systems

Author 1: Matthew O. Ayemowa Author 2: Roliana Ibrahim Author 3: Noor Hidayah Zakaria Author 4: Yunusa Adamu Bena

Recommender systems (RSs) are significant in enhancing the experiences of users across different online platforms. One of the major problems faced by the conventional RSs is difficulties in getting precise preferences for users, mostly for the users that has limited previous interaction data, and this eventually affects the performance of… Read full abstract & cite →

Generative adversarial networks auxiliary information cross-domain recommender systems data sparsity knowledge transfer

Abstract doi.org/10.14569/IJACSA.2025.0160610 PDF

11

The Anomaly Detection Algorithm Based on Random Matrix Theory and Machine Learning

Author 1: Yongming Lu

This study focuses on anomaly detection algorithms. Aiming at the limitations of traditional methods in complex data processing, an innovative algorithm that integrates random matrix theory and machine learning is proposed. First, different types of data, such as numerical values, texts, and images, are preprocessed, and random matrices are constructed… Read full abstract & cite →

Random matrix theory machine learning anomaly detection experimental simulation

Abstract doi.org/10.14569/IJACSA.2025.0160611 PDF

12

SkinDiseaseXAI: XAI-Driven Neural Networks for Skin Disease Detection

Author 1: Ammar Nasser Alqarni Author 2: Abdullah Sheikh

Accurate classification of skin diseases is an important step toward early diagnosis and therapy. However, deep learning models are frequently used in therapeutic contexts without transparency, reducing confidence and acceptance. This study introduces SkinDiseaseXAI, a unique convolutional neural network (CNN) that uses Grad-CAM++ to classify ten different types of skin… Read full abstract & cite →

XAI skin disease Grad-CAM++ convolutional neural networks clinical interpretability melanoma eczema atopic dermatitis fungal infections

Abstract doi.org/10.14569/IJACSA.2025.0160612 PDF

13

Emotion Recognition Algorithm Based on Multi-Modal Physiological Signal Feature Fusion Using Artificial Intelligence and Deep Learning

Author 1: Yue Pan

Emotion recognition technology that utilizes physiological signals has become highly important because of its diverse purposes in healthcare fields and human-computer interaction and affective computing, which require emotional state understanding for enhanced user experience and mental health management. Support Vector Machines (SVM) and Random Forest (RF) serve as traditional machine… Read full abstract & cite →

Emotion recognition physiological signals attention-based CNN-BILSTM-transformer multimodal fusion deep learning

Abstract doi.org/10.14569/IJACSA.2025.0160613 PDF

14

Dataset Development for Classifying Kick Types for Martial Arts Athletes Using IMU Devices

Author 1: Rudy Gunawan Author 2: Suhardi Author 3: Widyawardana Adiprawita Author 4: Tommy Apriantono

This study aims to establish a dataset of kicks in Kempo martial arts to categorize athletes' kick types based on their movement patterns. The real problem addressed in this research is the lack of an accurate, efficient, and portable system to automatically recognize and classify kick types in martial arts… Read full abstract & cite →

Classification dataset machine learning inertial sensors IMU martial art sport motion capture

Abstract doi.org/10.14569/IJACSA.2025.0160614 PDF

15

Comparative Analysis of Deep Learning Techniques for Passive Underwater Acoustic Target Recognition: Overview, Challenges, and Future Directions

Author 1: Song Yifei Author 2: Mohamad Farhan Mohamad Mohsin

Passive underwater acoustic target recognition (UATR) involves analyzing acoustic waves captured by passive sonar to extract valuable information about submerged targets. The underwater acoustics community has increasingly turned its attention to deep learning techniques, owing to their remarkable success in image recognition tasks. This study presents a comprehensive overview of… Read full abstract & cite →

Underwater acoustic target recognition deep learning deep network architecture classifier

Abstract doi.org/10.14569/IJACSA.2025.0160615 PDF

16

Method for Tea Leaf Plucking Timing Prediction with High Resolution of Images Based on YOLO11

Author 1: Kohei Arai Author 2: Yoho Kawaguchi

As a method for estimating the time when tea leaves reach their peak quality (amino acid content) (optimum picking time), our previous study revealed that the optimum picking time is when the accumulated temperature from the detection of germination of new buds reaches 600°C. However, the accuracy of this germination… Read full abstract & cite →

Tealeaf plucking YOLO budding detection spatial resolution optical image annotation germination rate

Abstract doi.org/10.14569/IJACSA.2025.0160616 PDF

17

The Factors Influencing Internet of Things Adoption in Public Hospitals: A Pilot Study

Author 1: Mutasem Zrekat Author 2: Othman Bin Ibrahim

Incorporating Internet of Things (IoT) technologies in healthcare represents a significant leap forward, capable of transforming the delivery and management of medical services. As healthcare systems across the globe increasingly seek innovative solutions to improve efficiency, enhance patient outcomes, and reduce operational costs, IoT emerges as a key enabler of… Read full abstract & cite →

Internet of Things adoption Jordan TOE

Abstract doi.org/10.14569/IJACSA.2025.0160617 PDF

18

Research on Network Flow Based on Statistical Analysis Methods

Author 1: Maruf Juraev Author 2: Inomjon Yarashov Author 3: Adilbay Kudaybergenov Author 4: Alimdzhan Babadzhanov Author 5: Zilolaxon Mamatova

To solve the problem of detecting unexpected situations in network traffic, it is proposed to determine the normal, unexpected states and behavior of the system using statistical methods. The statistical analysis methods used are the mean, variance, asymmetry coefficient, kurtosis coefficient, opposition coefficient and entropy coefficient. All statistical analysis methods… Read full abstract & cite →

Unexpected situation mean variance asymmetry coefficient kurtosis coefficient opposition coefficient entropy coefficient packet analysis markov chain packet features

Abstract doi.org/10.14569/IJACSA.2025.0160618 PDF

19

A Steel Surface Defect Detection Method Based on Lightweight Convolution Optimization

Author 1: Cong Chen Author 2: Ming Chen Author 3: Hoileong Lee Author 4: Yan Li Author 5: Jiyang YU

Surface defect detection of steel, especially the recognition of multi-scale defects, has always been a major challenge in industrial manufacturing. Steel surfaces not only have defects of various sizes and shapes, which limit the accuracy of traditional image processing and detection methods in complex environments. However, traditional defect detection methods… Read full abstract & cite →

YOLOv9s steel surface defect detection C3Ghost module SCConv module CARAFE upsampling operator

Abstract doi.org/10.14569/IJACSA.2025.0160619 PDF

20

Landslide Detection Method Based on Lightweight Convolution and Attention Mechanisms

Author 1: Cong Chen Author 2: Chengyang Zhang Author 3: Ran Chen Author 4: Jiyang YU

Landslide monitoring is a crucial component of geological disaster early warning systems. Traditional landslide detection methods often suffer from insufficient accuracy or low efficiency. To address these issues, this study proposes an improved landslide detection algorithm based on YOLOv11n, aiming to enhance both detection accuracy and efficiency by optimizing the… Read full abstract & cite →

YOLOv11n landslide detection GhostConv module C3K2-SCConv module SimAM attention mechanism

Abstract doi.org/10.14569/IJACSA.2025.0160620 PDF

21

Integration of 2D-CNN and LSTM Networks for Enhanced Image Processing and Prediction in Alzheimer’s Disease

Author 1: Aya Mohamed Abd El-Hamed Author 2: Mohamed Aborizka

The early diagnosis of Alzheimer’s disease remains a major challenge due to the complexity of magnetic resonance image interpretation and the limitations of existing diagnostic models. The slow memory loss associated with the gradual loss of thinking abilities, known as Alzheimer's disease, is the most common element of the illness… Read full abstract & cite →

Alzheimer’s disease magnetic resonance imaging two-dimensional convolutional neural network long short-term memory deep learning early detection

Abstract doi.org/10.14569/IJACSA.2025.0160621 PDF

22

Audio-Visual Multimodal Deepfake Detection Leveraging Emotional Recognition

Author 1: Alaa Alsaeedi Author 2: Amal AlMansour Author 3: Amani Jamal

Recently, there has been a significant reliance on the Internet. This creates a fertile environment for various risks, including fraud, privacy violations, and theft. The most common and dangerous risks at present are known as deepfakes. The development of deepfake technologies relies on advancements in artificial intelligence. Deepfake content can… Read full abstract & cite →

Machine learning deepfake multimodal sentiment of speech emotion recognition

Abstract doi.org/10.14569/IJACSA.2025.0160622 PDF

23

A Novel Multi-Modal Deep Learning Approach for Real-Time Live Event Detection Using Video and Audio Signals

Author 1: Pavadareni R Author 2: A. Prasina Author 3: Samuthira Pandi V Author 4: Ibrahim Mohammad Khrais Author 5: Alok Jain Author 6: Karthikeyan

Recent developments in live event detection have primarily focused on single-modal systems, where most applications are based on audio signals. Such methods normally rely on classification approaches involving the Mel-spectrogram. Single-modal systems, though effective in some applications, suffer from severe disadvantages in capturing the complexities of a real-world event, which… Read full abstract & cite →

Multi-modality feature fusion early fusion concatenation audio-video signals convolutional neural network (CNN) Long Short-Term Memory (LSTM) Mel Frequency Cepstral Coefficients (MFCC) ResNet (Residual Network)

Abstract doi.org/10.14569/IJACSA.2025.0160623 PDF

24

False News Recognition Model Based on Attention Mechanism and Multiple Features

Author 1: Qiongyao Suo Author 2: Hongzhen Chang

As the prevalence of social media continues to grow, the rapid and wide dissemination of false news has become a critical societal challenge, undermining public trust, creating social unrest, and distorting political discourse. Traditional fake news detection methods often rely solely on linguistic cues or shallow semantic analysis, which leads… Read full abstract & cite →

Fake news attention mechanism multiple features bidirectional gated recurrent unit

Abstract doi.org/10.14569/IJACSA.2025.0160624 PDF

25

A Reading-Aware Fusion Fact Reasoning Network for Explainable Fake News Detection

Author 1: Bofan Wang Author 2: Shenwu Zhang

The current growth of information exhibits an exponential trend, with fake news becoming a focal issue for both the public and governments. Existing fact-checking-based fake news detec-tion methods face two challenges: a heavy reliance on fact-checking reports, a lack of explanatory evidence related to the original reports, and a shallow… Read full abstract & cite →

Explainable fake news detection fact reasoning feature fusion

Abstract doi.org/10.14569/IJACSA.2025.0160625 PDF

26

Solar-Net: Adaptive Fusion of Spatial-Temporal Features for Resilient Solar Power Generation Forecasting

Author 1: Wenqian Su Author 2: Jason See Toh Seong Kuan Author 3: Xiangyu Shi Author 4: Yuchen Zhang

Solar power generation forecasting faces significant challenges due to intermittency and volatility, particularly under extreme weather conditions. This study proposes Solar-Net, a novel solar power generation prediction model based on a CNN+Transformer hybrid parallel architecture with an adaptive attention fusion mechanism. The CNN branch extracts spatial features from the power… Read full abstract & cite →

Solar power generation forecasting hybrid deep learning adaptive attention fusion CNN+Transformer extreme weather adaptability sustainable development goal 7

Abstract doi.org/10.14569/IJACSA.2025.0160626 PDF

27

Framework for Child Healthcare System Using Random Forest

Author 1: Mahesh Ashok Mahant Author 2: P. Vidyullatha

Proactive and customized approaches are necessary when it comes to the medical care of expectant mothers and children. Even if early and accurate disease prediction is based on readily available symptom information, it can significantly improve outcomes by promoting timely therapies. Extensive testing and specialist visits are common components of… Read full abstract & cite →

Child healthcare system random forest machine learning registration process medicine

Abstract doi.org/10.14569/IJACSA.2025.0160627 PDF

28

Enhancing Organizational Threat Profiling by Employing Deep Learning with Physical Security Systems and Human Behavior Analysis

Author 1: D. H. Senevirathna Author 2: W. M. M. Gunasekara Author 3: K. P. A. T. Gunawardhana Author 4: M. F. F. Ashra Author 5: Harinda Fernando Author 6: Kavinga Yapa Abeywardena

Organizations need a comprehensive threat profiling system that uses cybersecurity methods together with physical security methods because advanced cyber-threats have become more complex. The objective of this study is to implement deep learning models to boost organizational threat identification via human behavior assessment and continuous surveillance activities. Our method for… Read full abstract & cite →

Deep learning physical security human behavior analysis security operation centers threat profiling

Abstract doi.org/10.14569/IJACSA.2025.0160628 PDF

29

LaObese: A Serious Game Powered by Analytic Hierarchy Process for Culturally Tailored Childhood Obesity Prevention in Oman

Author 1: Nurul Akhmal Mohd Zulkefli Author 2: Mukesh Madanan Author 3: Zainab Mohammed Al-Nahdi Author 4: Jayasree Radhamaniamma

Childhood obesity is a growing public health concern in Oman, yet culturally appropriate digital tools for early prevention remain scarce. This study introduces LaObese, a mobile application and serious game designed to prevent obesity in Omani preschool children. The name LaObese derives from the Arabic word “La” (meaning “no”) and… Read full abstract & cite →

Serious games childhood obesity gamification analytic hierarchy process (AHP) preschool nutrition

Abstract doi.org/10.14569/IJACSA.2025.0160629 PDF

30

Anomaly Study of Computer Networks Based on Weighted Dynamic Network Representation Learning

Author 1: Xin Wei

One of the foremost significant challenges in the continuously increasing technological environment is the requirement to secure the authenticity of data. Network security is a primary method for securing the confidentiality of data throughout communication, one of several types of data security assurance. To secure networks against additional cyberattacks, trustworthy… Read full abstract & cite →

Network security attacks weighted dynamic network anomaly detection deep learning LSTM

Abstract doi.org/10.14569/IJACSA.2025.0160630 PDF

31

Deepfake Audio Detection Using Feature-Based and Deep Learning Approaches: ANN vs ResNet50

Author 1: Reham Mohamed Abdulhamied Author 2: Sarah Naiem Author 3: Mona M. Nasr Author 4: Farid Ali Moussa

The proliferation of algorithms and commercial tools for generating synthetic audio has sparked a surge in mis- information, especially on social media platforms. Consequently, significant attention has been devoted to detect such misleading content in recent years. However, effectively addressing this challenge remains elusive, given the increasing naturalness of fake… Read full abstract & cite →

Audio classification automatic speech recognition machine learning deep learning DEEP-VOICE

Abstract doi.org/10.14569/IJACSA.2025.0160631 PDF

32

Fine-Tuning OpenAI GPT Chatbot in Western Saudi Dialect: A Case Study of Taibah University

Author 1: Maimounah Alhujaili Author 2: Ruqayya Abdulrahman

The current era is characterized by technological advancement and innovation, which affect various sectors. Numerous remarkable and alluring computer programs and applications have surfaced, including ones that aim to replicate human behavior. A chatbot is an example of an Artificial Intelligence (AI) computer program that uses natural language to mimic… Read full abstract & cite →

Artificial intelligence (AI) large language model (LLM) generative pre-trained transformer (GPT) Modern Standard Arabic (MSA) Western Saudi dialect

Abstract doi.org/10.14569/IJACSA.2025.0160632 PDF

33

Automatic Detection of Natural Disasters Using Faster R-CNN with ResNet50 Backbone

Author 1: Shereen Essam Elbohy Author 2: Mona M. Nasr Author 3: Farid Ali Mousa

Natural disasters pose significant threats to human life and infrastructure. Timely detection and assessment of these events are crucial for effective disaster management. This study proposes an automatic detection system for natural disasters using aerial imagery. Accurate and timely detection of natural disasters is critical for minimizing their impact and… Read full abstract & cite →

Natural disasters detection satellite imagery convolutional neural networks (CNN) transformers deep learning ResNet50 proactive monitoring faster R-CNN disaster prevention computer vision

Abstract doi.org/10.14569/IJACSA.2025.0160633 PDF

34

Fusion of CNN and Transformer Architectures for Proactive Wildfire Detection in Satellite Imagery

Author 1: Shereen Essam Elbohy Author 2: Mona M. Nasr Author 3: Farid Ali Mousa

Wildfires pose a significant threat to ecosystems, human settlements, and air quality, necessitating advanced detection and mitigation strategies. Traditional wildfire detection methods often rely on manual observation and conventional machine learning approaches, which may lack efficiency and accuracy. This study proposes a novel deep learning model based on the ConvNeXt-Small… Read full abstract & cite →

Wildfire detection satellite imagery convolutional neural networks (CNN) transformers deep learning hybrid model proactive monitoring remote sensing disaster prevention computer vision

Abstract doi.org/10.14569/IJACSA.2025.0160634 PDF

35

Analyzing the Impact of Robotic Process Automation (RPA) on Productivity and Firm Performance in the Service Sector

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

Robotic Process Automation (RPA) has emerged as a transformative technology in the service sector, enabling organizations to automate repetitive and rule-based tasks with minimal human intervention. This study investigates the impact of RPA implementation on productivity and overall firm performance within service-oriented businesses. Using a mixed-method approach, quantitative data were… Read full abstract & cite →

Robotic process automation productivity improvement firm performance service sector digital transformation operational efficiency

Abstract doi.org/10.14569/IJACSA.2025.0160635 PDF

36

Systematic Literature Review on Artificial Intelligence-Driven Personalized Learning

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

Artificial Intelligence (AI) is widely used in various contexts, including education at different levels, such as K-12 (kindergarten through 12th grade) and higher learning. The impact of AI in education is becoming increasingly significant, making the academic sphere more effective, personalized, global, context-intensive, and asynchronous. Despite the publication of several… Read full abstract & cite →

Personalized learning model framework approach technique systematic literature review personalized learning components artificial intelligence

Abstract doi.org/10.14569/IJACSA.2025.0160636 PDF

37

Mobile Application Using Convolutional Neural Networks for Preliminary Diagnosis of Rosacea

Author 1: Angie Fiorella Sapaico-Alberto Author 2: Rosalynn Ornella Flores-Castañeda

Rosacea is a chronic skin disease affecting millions of people worldwide, characterized by redness and inflammatory lesions on the face. Given the need to improve early detection, this research aims to develop a mobile application using convolutional neural networks to improve the preliminary diagnosis of rosacea. For this purpose, increases… Read full abstract & cite →

Convolutional neural networks mobile application rosacea preliminary diagnosis sensitivity specificity

Abstract doi.org/10.14569/IJACSA.2025.0160637 PDF

38

Hybrid PSO-ACO Optimization for Rice Leaf Disease Classification Using Random Forest and Support Vector Machines

Author 1: Avip Kurniawan Author 2: Tri Retnaningsih Soeprobowati Author 3: Budi Warsito

This study proposes a hybrid machine learning framework for rice leaf disease detection by combining handcrafted feature extraction with metaheuristic optimization and classical classifiers. Using a dataset of 6,000 rice leaf images across seven classes, features including color, texture, shape, and edge were extracted and optimized using Spider Monkey Optimization… Read full abstract & cite →

Rice leaf disease particle swarm optimization (PSO) support vector machine (SVM) feature extraction precision agriculture

Abstract doi.org/10.14569/IJACSA.2025.0160638 PDF

39

Reinforcement Learning for Real-Time Scheduling in Dynamic Reconfigurable Manufacturing Systems

Author 1: Salah Hammedi Author 2: Abdallah Namoun Author 3: Mohamed Shili

This study presents a novel application of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) for scheduling optimization in Reconfigurable Manufacturing Systems (RMFS). The performance of these approaches is quantitatively evaluated and compared with traditional scheduling methods, specifically Shortest Processing Time (SPT) and Earliest Due Date (EDD), across several key… Read full abstract & cite →

Adaptability deep reinforcement learning (DRL) makespan manufacturing systems reinforcement learning (RL) resource utilization scheduling optimization shortest processing time (SPT) tardiness traditional scheduling methods

Abstract doi.org/10.14569/IJACSA.2025.0160639 PDF

40

A Proposed Framework for Loan Default Prediction Using Machine Learning Techniques

Author 1: Mona Aly SharafEldin Author 2: Amira M. Idrees Author 3: Shimaa Ouf

The accurate prediction of loan defaults is critical for the risk management strategies of financial institutions. Traditional credit assessment approaches have often relied on subjective judgment, leading to inconsistent decisions and heightened financial risk. This study investigates the application of machine learning techniques—namely Random Forest, Decision Tree, and Gradient Boosting—to… Read full abstract & cite →

Random forest decision trees gradient boosting machines feature selection feature importance loan default

Abstract doi.org/10.14569/IJACSA.2025.0160640 PDF

41

Advanced AI-Driven Safety Compliance Monitoring in Dynamic Construction Environment

Author 1: Aisha Hassan Author 2: Ali H. Hassan Author 3: Yasmin Christensen Author 4: Hussain Alsadiq

Construction safety is a critical global concern due to the high-risk environment faced by workers, with accidents often leading to serious injuries and fatalities. To enhance construction management, this study proposes a scalable deep-learning model for real-time compliance monitoring of safety regulations. The research gap addressed is the lack of… Read full abstract & cite →

YOLOv11n personal protection equipment (PPE) construction safety real-time object detection deep learning AI-driving compliance systems

Abstract doi.org/10.14569/IJACSA.2025.0160641 PDF

42

Reinforcement Learning Improves SVM-Driven Algorithms for Classifying Multi-Sensor Data for Medical Monitoring

Author 1: Zhiwei Xuan Author 2: Yajie Liu

Multi-sensor data in medical monitoring includes waveform changes in physiological signals and time-series characteristics of disease progression. These features typically exhibit high-dimensionality, large-scale, and time-varying characteristics. Nonlinear relationships exist between these features, increasing the difficulty of data processing and feature extraction, thereby reducing the classification capabilities of related algorithms. This… Read full abstract & cite →

Reinforcement learning improved SVM medical monitoring multi-sensor data classification processing

Abstract doi.org/10.14569/IJACSA.2025.0160642 PDF

43

Developing an Ontology-Driven and Governance-Integrated Method for Information Dashboard Design

Author 1: Ahadi Haji Mohd Nasir Author 2: Nik Habibullah Nik Mohd Nizam Author 3: Mohd Khairul Maswan Mohd Redzuan Author 4: Mohammad Nazir Ahmad

Despite the increasing reliance on information dashboards across industries, dashboard design practices remain fragmented, lacking standardized methodologies, ontological formalization, and governance integration. Addressing these gaps, this study develops a method to guide dashboard design by embedding ontological modeling and Information Governance (IG) principles. Two complementary artifacts are proposed: the Information… Read full abstract & cite →

Information dashboard ontological modelling information dashboard design ontology (IDDO) information dashboard design method (IDDM) canvas information governance (IG) unified ontological approach (UOA) design science research methodology (DSRM)

Abstract doi.org/10.14569/IJACSA.2025.0160643 PDF

44

Machine Learning and 5G Edge Computing for Intelligent Traffic Management

Author 1: Talbi Chaymae Author 2: Rahmouni M'hamed Author 3: Ziti Soumia

The integration of fifth-generation (5G) communication technology and Artificial Intelligence (AI) is reshaping urban mobility by enabling intelligent transportation systems and smarter cities. This synergy allows real-time traffic management, predictive maintenance, and enhanced autonomous driving, supported by high-speed, low-latency networks and advanced data analytics. By leveraging 5G’s strong connectivity, AI… Read full abstract & cite →

5G Edge computing traffic management dynamic routing smart cities machine learning

Abstract doi.org/10.14569/IJACSA.2025.0160644 PDF

45

A Rule-Based Framework for Clothing Fit Recommendation from 3D Body Reconstruction

Author 1: Hamid Ouhnni Author 2: Acim Btissam Author 3: Belhiah Meryam Author 4: Benachir Rigalma Author 5: Soumia Zit

This research presents a comprehensive framework for body size estimation that accurately derives anthropometric measurements—specifically, the circumferences of the waist and hips—from a singular image by utilizing OpenPose for joint localization and SMPLify-X for precise 3D body modeling. The proposed methodology involves projecting the generated three-dimensional model onto a horizontal… Read full abstract & cite →

Body size estimation SMPLify-X OpenPose 3D body modeling clothing size prediction e-commerce sizing human pose estimation

Abstract doi.org/10.14569/IJACSA.2025.0160645 PDF

46

TL-MC-ShuffleNetV2: A Lightweight and Transferable Framework for Elevator Guideway Fault Diagnosis

Author 1: Zhiwei Zhou Author 2: Xianghong Deng Author 3: Xuwen Zheng Author 4: Chonlatee Photong

This study presents TL-MC-ShuffleNetV2, a lightweight and transferable fault diagnosis framework designed for elevator guideway vibration analysis. To tackle challenges such as limited labeled data and the constraints of real-time deployment, the approach integrates Variational Mode Decomposition (VMD) for multi-scale signal separation and employs a customized 1D ShuffleNetV2 backbone with… Read full abstract & cite →

Transfer learning elevator guideway vibration signal analysis fault diagnosis lightweight deep neural network squeeze-and-excitation attention smart maintenance

Abstract doi.org/10.14569/IJACSA.2025.0160646 PDF

47

AI-Driven Intrusion Detection Systems for Securing IoT Healthcare Networks

Author 1: Muhammad Sajid Nawaz Author 2: Muhammad Ahsan Raza Author 3: Binish Raza Author 4: Manal Ahmad Author 5: Farial Syed

The integration of IoT in healthcare has remained very dynamic, with a lot of improvement in the health of patients and the running of operations. Integration also comes with new risks and threats, raising IoT healthcare networks as cyber victims with great potential. This study explores an AI-based solution to… Read full abstract & cite →

IoT intrusion detection system (IDS) convolutional neural network (CNN) recurrent neural network (RNN) cybersecurity

Abstract doi.org/10.14569/IJACSA.2025.0160647 PDF

48

Leveraged Cognitive Data Analytics and Artificial Intelligence to Enhance Sustainable Agricultural Practices: A Systematic Review

Author 1: Wongpanya S. Nuankaew Author 2: Patchara Nasa-Ngium Author 3: Pratya Nuankaew

This systematic review examines the transformative role of Cognitive Data Analytics (CDA) and Artificial Intelligence (AI) in advancing sustainable agricultural practices, with a primary objective to evaluate their applications in Precision Agriculture (PA), Internet of Things (IoT), smart irrigation, and Geographic Information Systems (GIS) from 2020 to 2025. Key findings… Read full abstract & cite →

Cognitive data analytics sustainable agriculture harnessing AI for agriculture precision agriculture environmental sustainability food security

Abstract doi.org/10.14569/IJACSA.2025.0160648 PDF

49

A Novel Approach for Enhancing Advanced Encryption Standard Performance and Cryptographic Resilience

Author 1: Muthu Meenakshi Ganesan Author 2: Sabeen Selvaraj

Advanced Encryption Standard (AES) encrypts data in blocks of sixteen bytes to secure confidential data stored in the cloud. For cloud-based systems, enhancements in existing encryption techniques are necessary as the nature of cyber threats evolves and computational speed becomes increasingly critical. This study presents an enhanced design of AES… Read full abstract & cite →

Cryptography NIST AES block cipher key expansion symmetric encryption galois field statistical techniques cryptanalysis

Abstract doi.org/10.14569/IJACSA.2025.0160649 PDF

50

An Enhanced LSTM Model Based on Feature Attention Mechanism and Emotional Intelligence for Advanced Sentiment Analysis

Author 1: Muhammad Naeem Aftab Author 2: Dost Muhammad Khan Author 3: Muhammad Zulqarnain Author 4: Muhammad Rizwan Akram

Sentiment analysis, a crucial yet complex task in natural language processing (NLP), is extensively employed to identify sentiment polarity within user-generated content. Traditional deep learning methods for textual sentiment analysis often overlook the influence of emotional modulation on extracting sentiment features. At the same time, their attention mechanisms primarily operate… Read full abstract & cite →

Sentiment analysis emotional intelligence attention mechanism two-state LSTM long-term dependencies

Abstract doi.org/10.14569/IJACSA.2025.0160650 PDF

51

Image Quality Assessment Based on Feature Fusion and Local Adaptation

Author 1: Minjuan GAO Author 2: Yankang LI Author 3: Xuande ZHANG

No-reference image quality assessment (NR-IQA) aims to evaluate the perceptual quality of images without access to corresponding reference images and has broad applications in real-world image processing scenarios. However, existing NR-IQA methods often suffer from limited accuracy and generalization, especially under complex and diverse distortion types. To address this challenge… Read full abstract & cite →

No-reference image quality assessment deep learning multi-scale feature fusion local adaptation

Abstract doi.org/10.14569/IJACSA.2025.0160651 PDF

52

Advancing Aerodynamic Coefficient Prediction: A Hybrid Model Integrating Deep Learning and Optimization Techniques

Author 1: Jad Zerouaoui Author 2: Rachid Ed-daoudi Author 3: Badia Ettaki Author 4: El Mahjoub Chakir

The aerospace industry increasingly relies on predictive models for aerodynamic coefficients to enhance design, performance, and optimization. While traditional methods like Computational Fluid Dynamics (CFD) and wind tunnel simulations offer accurate predictions, they are computationally intensive and time-consuming. This study explores a novel approach that fuses advanced Deep Learning (DL)… Read full abstract & cite →

Aerodynamic coefficients computational fluid dynamics deep learning convolutional neural networks optimization techniques evolutionary algorithms gradient-based optimization aerospace design

Abstract doi.org/10.14569/IJACSA.2025.0160652 PDF

53

AI-Powered Assessment of Resistance to Change in the Context of Digital Transformation

Author 1: Bachira Abou El Karam Author 2: Tarik Fissaa Author 3: Rabia Marghoubi

Digital transformation is a key driver of business evolution, but it comes with significant challenges, particularly employee resistance to change. This resistance can manifest in various forms, ranging from explicit opposition to more subtle hesitation toward new practices. Its underlying causes are diverse, including fear of the unknown, loss of… Read full abstract & cite →

Resistance to change digital transformation zero-shot LLMs prompt engineering allies strategy

Abstract doi.org/10.14569/IJACSA.2025.0160653 PDF

54

Self-Supervised Method for Risky Situation Detection in Road Traffic Sequences Using Video Masked Autoencoder

Author 1: Abdelhafid Berroukham Author 2: Mohammed Lahraichi Author 3: Khalid Housni

Road traffic accidents are a significant public health issue, particularly in developing nations, where infrastructure and traffic monitoring systems may be limited. Risky situations including sudden stopping, lane switching, and near-misses can lead to accidents. In this study, we present an original approach for recognizing risky situations in road traffic… Read full abstract & cite →

Video processing risk detection VideoMAE vision transformer deep learning computer vision

Abstract doi.org/10.14569/IJACSA.2025.0160654 PDF

55

Vision-Based Vehicle Classification Using Deep Learning Model

Author 1: Ahsiah Ismail Author 2: Amelia Ritahani Ismail Author 3: Muhammad Afiq Mohd Ara Author 4: Asmarani Ahmad Puzi Author 5: Suryanti Awang

Vehicle classification offers intelligent solutions for road traffic monitoring by enabling future prediction planning and decision making. Predictive analytics can be used to predict traffic congestion based on the types of vehicles on the road. In this research, the reliability of deep learning based models for vision-based vehicle classification is… Read full abstract & cite →

YOLO vehicle classification deep learning traffic monitoring

Abstract doi.org/10.14569/IJACSA.2025.0160655 PDF

56

Comparative Analysis of Rank and Roulette Wheel Selection Strategies in Genetic Algorithms for Spatial Layout Optimization

Author 1: Najihah Ibrahim Author 2: Fadratul Hafinaz Hassan Author 3: Sharifah Mashita Syed-Mohamad Author 4: Rosmayati Mohemad Author 5: Ahmad Shukri Mohd Noor

Autonomous urban planning, facility layout design, and interior design are critical and meticulous tasks that require the optimization of space arrangement. One of the main purposes of space arrangement is to achieve high space utilization with a non-complex arrangement for emergency assistance, particularly to enhance pedestrian safety in panic situations… Read full abstract & cite →

Genetic algorithm optimization spatial layout arrangement space utilization urban planning facility layout design rank selection roulette wheel selection

Abstract doi.org/10.14569/IJACSA.2025.0160656 PDF

57

AutiSim: A Virtual Reality Simulation Game Based on the Autism Spectrum Disorder

Author 1: Muhammad Aliff Muhd Farid Arfian Author 2: Ikmal Faiq Albakri Mustafa Albakri Author 3: Faaizah Shahbodin Author 4: Mohd Khalid Mokhtar Author 5: Asniyani Nur Haidar Abdullah Author 6: Norhaida Mohd Suaib Author 7: Muhammad Nur Affendy Nor'a Author 8: Abdul Hasib Jahidin

Technologies with altering reality like virtual reality (VR) have become more relevant to the public for their capabilities in the entertainment and healthcare field, as well as affordable for everyone. However, the emphasis on mental health-related simulation is often ignored due to technical complexities and wrong representation. Therefore, this study… Read full abstract & cite →

Virtual reality simulation game autism artificial intelligence

Abstract doi.org/10.14569/IJACSA.2025.0160657 PDF

58

Critical Success Factors for Knowledge Transfer in Enterprise System Projects: A Theoretical and Empirical Investigation

Author 1: Jamal M. Hussien Author 2: Riza bin Sulaiman Author 3: Ali H Hassan Author 4: Mansoor Abdulhak Author 5: Hasan Kahtan

Enterprise System Projects (ESPs) are fundamental enablers of digital transformation across organizations, yet they consistently suffer from high failure rates, often attributed to ineffective Knowledge Transfer (KT) practices. Despite the critical role of KT in ensuring project sustainability and long-term organizational learning, limited scholarly attention has been given to identifying… Read full abstract & cite →

Enterprise system projects (ESPs) knowledge transfer (KT) critical success factors (CSFs) digital transformation knowledge-sharing culture management support information systems implementation

Abstract doi.org/10.14569/IJACSA.2025.0160658 PDF

59

Content Validity Assessment Using Aiken’s V: Knowledge Integration Model for Blockchain in Higher Learning Institutions

Author 1: Nur Ilyana Ismarau Tajuddin Author 2: Ummu-Hani Abas Author 3: Khairi Azhar Aziz Author 4: Rozi Nor Haizan Nor Author 5: Nor Aziyatul Izni Author 6: Muhammad Nuruddin Sudin Author 7: Nur Aqilah Hazirah Mohd Anim Author 8: Noorashikin Md Noor

The integration of blockchain technology into higher learning institutions (HLIs) holds the potential to revolutionize data management, enhance transparency, and improve trust in academic systems. However, the effective adoption of blockchain requires a comprehensive and valid model that addresses the specific needs and contexts of HLIs. This study aims to… Read full abstract & cite →

Blockchain content validity aiken’s v higher learning institutions knowledge integration model

Abstract doi.org/10.14569/IJACSA.2025.0160659 PDF

60

An Interpretable Transformer-Based Approach for Context-Aware and Stylistically Aligned Academic Paraphrasing

Author 1: A. Z. Khan Author 2: Ritu Sharma Author 3: K. Kiran Kumar Author 4: Elangovan Muniyandy Author 5: Raman Kumar Author 6: Yousef A. Baker El-Ebiary Author 7: Prema S Author 8: Osama R. Shahin

Academic paraphrasing, particularly when aiming at contextual competence, coherence, and stylistic consistency, poses a significant challenge to non-native English speakers and novice researchers. This research seeks to create an interpretable transformer model specifically designed for paraphrasing academic texts that guarantees semantic correctness, contextual relevance, and scholarly style. Existing paraphrasing models… Read full abstract & cite →

Academic writing attention visualization context-aware paraphrasing reinforcement learning T5-transformer model

Abstract doi.org/10.14569/IJACSA.2025.0160660 PDF

61

Leveraging LSTM-Driven Predictive Analytics for Resource Allocation and Cost Efficiency Optimization in Project Management

Author 1: G. Gokul Kumari Author 2: Shokhjakhon Abdufattokhov Author 3: Sanjit Singh Author 4: Guru Basava Aradhya S Author 5: T L Deepika Roy Author 6: Yousef A.Baker El-Ebiary Author 7: Elangovan Muniyandy Author 8: B Kiran Bala

Resource planning and cost optimization are essential elements of effective project management. Conventional models are weak in changing environments because they cannot keep pace with intricate task interdependencies and changing project constraints. To overcome such weaknesses, this research envisions an LSTM-based predictive analytics model that deploys temporal trends and past… Read full abstract & cite →

Resource optimization project management long short-term memory predictive analytics task scheduling

Abstract doi.org/10.14569/IJACSA.2025.0160661 PDF

62

AccuLandNet: Enhancing Land Cover Detection with Deep Integrated Learning

Author 1: Geetha Guthikonda Author 2: M. Senthil Kumaran

Now-a-days, population growth is increasing more and more in all the places of the world. Specifically, this increase is in urban development based on economic and industrial improvement. It shows the massive impact on Land Use/Land Cover (LULC) and may change many times. The most popular use of land cover… Read full abstract & cite →

Land Use/Land Cover (LULC) U-Net Multi-Sensor Data Fusion (MSDF) Maximum Likelihood Classification (MLC) Support Vector Machines (SVM)

Abstract doi.org/10.14569/IJACSA.2025.0160662 PDF

63

Graph Neural Networks with Attention Mechanisms for Accurate Dengue Severity Prediction

Author 1: Monali G. Dhote Author 2: Puneet Thapar Author 3: Yousef A. Baker El-Ebiary Author 4: G. Indra Navaroj Author 5: R. Aroul Canessane Author 6: B. V. Suresh Reddy Author 7: Elangovan Muniyandy Author 8: Kapil Joshi

Dengue fever continues to be a significant public health issue across the globe because it can lead to life-threatening complications. Severity prediction in a timely and precise manner is imperative for proper clinical management and effective resource utilization. Conventional models fail to identify intricate relationships between heterogeneous clinical, demographic, and… Read full abstract & cite →

Attention mechanism dengue severity prediction Graph Neural Network healthcare analytics machine learning

Abstract doi.org/10.14569/IJACSA.2025.0160663 PDF

64

Metaheuristic-Driven Feature Selection for IoT Intrusion Detection: A Hierarchical Arithmetic Optimization Approach

Author 1: Jing GUO Author 2: Dejun ZHU Author 3: Qing XU

The increasing sophistication of cyberattacks in Internet of Things (IoT) networks requires strong Intrusion Detection Systems (IDS) with optimal feature selection mechanisms. High-dimensional data, computational complexity, and suboptimal detection accuracy hinder conventional IDS mechanisms. To overcome these limitations, in this study, the Hierarchical Self-Adaptive Arithmetic Optimization Algorithm (HSAOA) is introduced… Read full abstract & cite →

Intrusion detection internet of things feature selection hierarchical arithmetic optimization cybersecurity

Abstract doi.org/10.14569/IJACSA.2025.0160664 PDF

65

Enhancing SVM and KNN Performance Through Preprocessing Pipelines for Interactive mHealth Applications

Author 1: Btissam Elaziz Author 2: Charaf Eddine AIT ZAOUIAT Author 3: Mohamed Eddabbah Author 4: Yassin LAAZIZ

Mobile health (mHealth) applications are increasingly relying on artificial intelligence (AI) to provide accurate and real-time decision support for healthcare delivery. However, achieving the optimal balance between processing time and accuracy remains challenging, especially for interactive applications that rely on cloud computing for scalability and performance. This study investigates the… Read full abstract & cite →

Mobile health cloud computing machine learning SVM KNN data preprocessing

Abstract doi.org/10.14569/IJACSA.2025.0160665 PDF

66

Deep Reinforcement Learning Based Robotic Arm Control Simulation to Execute Object Reaching Task for Industrial Application

Author 1: John Mark Correa Author 2: Rudolph Joshua Candare Author 3: Junrie B. Matias

This study presents a deep reinforcement learning (DRL) approach to train a robotic arm for object reaching tasks in industrial settings, eliminating the need for traditional task-specific programming. Leveraging the Proximal Policy Optimization (PPO) algorithm for its stability in continuous control, the system learns optimal behaviors through autonomous trial-and-error. Central… Read full abstract & cite →

Reinforcement learning deep reinforcement learning reward shaping techniques robotic arm robot simulation

Abstract doi.org/10.14569/IJACSA.2025.0160666 PDF

67

Explainable Deep Temporal Modeling for Stroke Risk Assessment Using Attention-Based LSTM Networks

Author 1: P. Selvaperumal Author 2: F. Sheeja Mary Author 3: Pratik Gite Author 4: T L Deepika Roy Author 5: Yousef A. Baker El-Ebiary Author 6: Gowrisankar Kalakoti Author 7: Sandeep Kumar Mathariya

Stroke continues to be a major cause of mortality and disability globally, and precise risk prediction models are needed. Current models do not effectively incorporate temporal patient information, restricting the quality of prediction and clinical interpretability. This research introduces a new LSTM-based deep learning model enriched with an attention mechanism… Read full abstract & cite →

Attention mechanism deep learning imbalanced data LSTM networks SMOTE resampling stroke prediction

Abstract doi.org/10.14569/IJACSA.2025.0160667 PDF

68

Improving Cross-Lingual Fake News Detection in Indonesia with a Hybrid Model by Enhancing the Embedding Process

Author 1: Jihan Nabilah Hakim Author 2: Yuliant Sibaroni

In the digital age, the spread of false information across languages in digital form threatens the authenticity and credibility of information. This study aims to develop an efficient hybrid deep learning model for detecting cross-lingual fake news, particularly in resource-constrained environments, by enhancing the embedding process. It proposes a lightweight… Read full abstract & cite →

Cross-lingual fake news detection hybrid learning MUSE embeddings digital misinformation

Abstract doi.org/10.14569/IJACSA.2025.0160668 PDF

69

A Preliminary Study on Songket: A Preservation of Intangible Cultural Heritage

Author 1: Nik Siti Fatima Nik Mat Author 2: Syadiah Nor Wan Shamsuddin Author 3: Syarilla Iryani Ahmad Saany Author 4: Norkhairani Abdul Rawi Author 5: Julaily Aida Jusoh Author 6: Wan Malini Wan Isa Author 7: Addy Putra Md Zulkifli Author 8: Shahrul Anuwar Mohamed Yusof

Songket is a traditional Malaysian woven fabric, opulent, and symbolizes luxurious classical textiles of the old craft in Malaysia. Songket is part of the intangible cultural heritage to this day. However, cultural heritage preservation arises as a critical endeavor, especially for younger generations. There is a growing concern about the… Read full abstract & cite →

Songket weaving textiles aesthetic intangible cultural heritage processing songket cultural heritage Terengganu Malaysia

Abstract doi.org/10.14569/IJACSA.2025.0160669 PDF

70

Utilizing Machine Learning to Identify High-Risk Groups in Sickle Cell Anemia

Author 1: Haneen Banjar Author 2: Nofe Alganmi Author 3: Hajar Alharbi Author 4: Ahmed Barefah Author 5: Hatem Alahwal Author 6: Salwa Alnajjar Author 7: Abdulrahman Alboog Author 8: Salem Bahashwan Author 9: Galila Zaher

Sickle Cell Anemia (SCA) is a hereditary condition causing abnormal red blood cells, leading to severe health complications. Traditional treatment approaches for SCD often involve reactive management, which can delay appropriate interventions and worsen patient outcomes. The aim of this study is to leverage machine learning (ML) algorithms, including Logistic… Read full abstract & cite →

Sickle cells anemia feature selection predicting complication machine learning

Abstract doi.org/10.14569/IJACSA.2025.0160670 PDF

71

Real Time Accident Detection and Emergency Response Using Drones, Machine Learning and LoRa Communication

Author 1: Bandara H. M Author 2: Maduhansa H. K. T. P Author 3: Jayasinghe S. S Author 4: Samararathna A. K. S. R Author 5: Harinda Fernando Author 6: Shashika Lokuliyana

Road accidents and delayed emergency responses remain a major concern in urban environments, contributing to over 1.4 million fatalities globally each year. With rapid urbanization and increasing vehicle density, timely detection and efficient traffic management are critical to reducing the impact of such events. This study proposes a real time… Read full abstract & cite →

Accident detection machine learning IoT drones traffic management LoRa communication

Abstract doi.org/10.14569/IJACSA.2025.0160671 PDF

72

Cybersecurity and the NIST Framework: A Systematic Review of its Implementation and Effectiveness Against Cyber Threats

Author 1: Juan Luis Salas-Riega Author 2: Yasmina Riega-Virú Author 3: Mario Ninaquispe-Soto Author 4: José Miguel Salas-Riega

This systematic review evaluates the adoption and effectiveness of the NIST Cybersecurity Framework (CSF) in mitigating cyber threats across diverse sectors. Following PRISMA guidelines, we analyzed studies published between 2015 and 2024 from major academic databases, focusing on the framework's five core functions: Identify, Protect, Detect, Respond, and Recover. Results… Read full abstract & cite →

Cyberattacks small and medium enterprises risk management organizational resilience cyberthreats

Abstract doi.org/10.14569/IJACSA.2025.0160672 PDF

73

Hybrid Detection Framework Using Natural Language Processing (NLP) and Reinforcement Learning (RL) for Cross-Site Scripting (XSS) Attacks

Author 1: Carlo Jude P. Abuda

Cross-site scripting (XSS) attacks remained among the most persistent threats in web-based systems, often bypassing traditional input validation techniques through obfuscated or embedded scripting payloads. Existing detection models typically relied on static rules or shallow learning techniques, limiting their ability to adapt to evolving attack vectors. This research addressed that… Read full abstract & cite →

Cross-site scripting attacks deep neural network reinforcement learning natural language processing

Abstract doi.org/10.14569/IJACSA.2025.0160673 PDF

74

Evaluation Index System for Environmental Restoration Effectiveness Based on Landscape Pattern and Ecological Low-Carbon Construction

Author 1: Jingyuan Mao

Traditional green view rate (GVR) methods, which rely on two-dimensional planar images, have several limitations. They fail to capture the three-dimensional spatial characteristics of urban greenery, are frequently dependent on subjective parameters such as camera angles and lighting, and require labor-intensive manual analysis. These factors limit the accuracy and scalability… Read full abstract & cite →

Panoramic green perception rate deep learning urban green space vegetation recognition landscape assessment

Abstract doi.org/10.14569/IJACSA.2025.0160674 PDF

75

Application Analysis and Research of Text Model Based on Improved CNN-LSTM in the Financial Field

Author 1: Jing Chen Author 2: Chensha Li

With the continuous development of information technology, public opinion analysis based on open-source texts and financial situation awareness has become a research hotspot. This study focuses on financial news and commentary information. First, a topic crawler classification model combining the advantages of CNN and LSTM is proposed to improve the… Read full abstract & cite →

Financial information mining CNN-LSTM model stock price prediction sentiment analysis BiLSTM

Abstract doi.org/10.14569/IJACSA.2025.0160675 PDF

76

Ensuring Consistency in Group Decision Making: A Systematic Review of the FWZIC Method

Author 1: Ghazala Bilquise Author 2: Samar Ibrahim

Subjective opinions in decision-making processes are often vague, ambiguous, and imprecise due to the inherent subjectivity and variability in individual perspectives. This systematic study examines the Fuzzy Weighted Zero Inconsistency (FWZIC) method, which addresses these challenges by achieving consistency in group consensus and effectively managing uncertainties associated with subjective human… Read full abstract & cite →

FWZIC MCDM fuzzy sets subjective judgment group decision making

Abstract doi.org/10.14569/IJACSA.2025.0160676 PDF

77

Design and Implementation of Low-Cost Hybrid-Controlled Smart Wheelchair Based on PID Control Integrated with Vital Signs Monitoring

Author 1: M. Sayed Author 2: MG Mousa Author 3: Ali A. S Author 4: T. Mansour

According to international organizations’ statistics, the percentage of disabled people is considered not just a small percentage of the world's population. Improving the quality of life for people by using new technologies is one of the essential topics today. Although the wheelchair is the most common way of mobility for… Read full abstract & cite →

Smart wheelchair speech recognition PID healthcare mobile robot

Abstract doi.org/10.14569/IJACSA.2025.0160677 PDF

78

Design and Implementation of an Intelligent Laboratory Management System Based on UWB Technology

Author 1: Heng Sun Author 2: Qiang Gao

In recent years, the rapid development of educational informatization and the widespread adoption of Internet of Things (IoT) technologies have accelerated the transformation of university laboratories toward intelligent management. However, traditional laboratory management systems still suffer from limited automation, insufficient safety mechanisms, and poor real-time responsiveness. To address these issues… Read full abstract & cite →

UWB intelligent management laboratory management system IoT

Abstract doi.org/10.14569/IJACSA.2025.0160678 PDF

79

RFID Integration with Internet of Things: Data Processing Algorithm Based on Convolutional Neural Network

Author 1: Liang Wang

Radio Frequency Identification is a fast and reliable communication module that performs automatic data capture to identify and track individual objects and people. Frequency-coded tags employ resonant networks to decode their unique code. A multi-scatterer or multi-resonant method encodes the data. Research primarily related to the current investigation predicted that… Read full abstract & cite →

RFID chipless coding threshold data transmission error correction security authentication

Abstract doi.org/10.14569/IJACSA.2025.0160679 PDF

80

A Technique to Support Incremental Construction and Verification in Component-Based Software Development

Author 1: Faranak Nejati Author 2: Ng Keng Yap Author 3: Abdul Azim Abd Ghani

Technological advancements in recent decades have significantly increased the scale and complexity of software systems, which poses challenges to their development and reliability. Component-based software development (CBSD) offers a promising solution by enabling modular and efficient software construction. However, CBSD alone cannot fully address challenges such as ensuring reliability and… Read full abstract & cite →

Component-based software development incremental software construction software verification

Abstract doi.org/10.14569/IJACSA.2025.0160680 PDF

81

Real-Time Video Captioning on CPU and GPU: A Comparative Study of Classical and Transformer Models

Author 1: Othmane Sebban Author 2: Ahmed Azough Author 3: Mohamed Lamrini

This study proposes a scalable and hardware-adaptable approach to automatic video caption generation by comparing two architectures: a traditional encoder–decoder framework combining InceptionResNetV2 with GRU and a transformer-based model integrating TimeSformer with GPT-2. The system supports CPU and GPU deployment through a unified pipeline built on FFmpeg and ImageMagick for… Read full abstract & cite →

Video captioning transformer timesformer GPT-2 real-time inference spatiotemporal attention multimedia accessibility CPU and GPU deployment

Abstract doi.org/10.14569/IJACSA.2025.0160681 PDF

82

Endometriosis Lesion Classification Using Deep Transfer Learning Techniques

Author 1: Shujaat Ali Zaidi Author 2: Varin Chouvatut Author 3: Chailert Phongnarisorn

In resource-limited settings, assisting physicians with disease identification can significantly improve patient outcomes. Early diagnosis is crucial, as many patients could remain healthy with timely intervention. Recent advancements in deep learning models for medical image processing have enabled algorithms to achieve diagnostic accuracy comparable to that of healthcare professionals. This… Read full abstract & cite →

Endometriosis classification lesion detection medical image classification deep learning transfer learning DCGAN

Abstract doi.org/10.14569/IJACSA.2025.0160682 PDF

83

LASSO-Based Feature Extraction with Adaptive Windowing via DTW for Fault Diagnosis in Rotating Machinery

Author 1: Jirayu Samkunta Author 2: Patinya Ketthong Author 3: Nghia Thi Mai Author 4: Md Abdus Samad Kamal Author 5: Iwanori Murakami Author 6: Kou Yamada Author 7: Nattagit Jiteurtragool

In real-world engineering environments, faults in rotating machines typically occur for concise periods, which leads to poor stability and low accuracy in fault diagnosis. The traditional fault diagnosis of rotating machinery relies on analyzing time-series data to detect system degradation and faulty components. However, the complexity of rotating machinery and… Read full abstract & cite →

Rotating machinery fault analysis feature extraction LASSO regression

Abstract doi.org/10.14569/IJACSA.2025.0160683 PDF

84

The Influence of Familiarity with Traffic Regulations on Road Safety: A Simulated Study on Roundabouts and Intersections

Author 1: Raghda Alqurashi Author 2: Hasan J. Alyamani Author 3: Nesreen Alharbi Author 4: Hasan Sagga

International drivers who come from keep-right countries and drive in keep-left countries are frequently involved in road accidents due to unfamiliarity with keep-left traffic regulations. Due to unfamiliarity of the traffic regulation, the driver’s performance and behavior are subject to change. The objective of this study was to explore the… Read full abstract & cite →

Driving behavior driving performance familiarity with traffic regulations road intersections roundabouts

Abstract doi.org/10.14569/IJACSA.2025.0160684 PDF

85

Predicting Jobs, Shaping Economies: Bibliometric Insights into AI and Big Data in Workforce Demand Analysis

Author 1: EL Massi Fouad Author 2: ELouadi Abdelmajid

The integration of Big Data and Artificial Intelligence (AI) is fundamentally transforming how labor markets are analyzed, predicted and managed. Despite significant advances in using these technologies for workforce analytics, the field suffers from several critical limitations: existing approaches predominantly rely on data from online job portals that may not… Read full abstract & cite →

Big data Artificial Intelligence predictive modeling bibliometric analysis natural language processing labor market analytics

Abstract doi.org/10.14569/IJACSA.2025.0160685 PDF

86

Dynamic Polygon-Based Reverse Driving Detection Technique for Enhanced Road Safety

Author 1: Tara Kit Author 2: Youngsun Han Author 3: Anand Nayyar Author 4: Tae-Kyung Kim

Reverse driving and lane collapse pose serious risks to road safety, especially on complex infrastructures such as multi-lane highways, intersections, and roundabouts. Existing detection systems often depend on rigid lane configurations and struggle to adapt to varied road geometries and environmental conditions. Prior works are typically limited to straight, multi-lane… Read full abstract & cite →

Reverse driving detection lane collapse detection polygon zones object detection YOLOv8

Abstract doi.org/10.14569/IJACSA.2025.0160686 PDF

87

Comparative Analysis of Machine Learning Frameworks for Robust Ovarian Cancer Detection Using Feature Selection and Data Balancing

Author 1: DSS LakshmiKumari P Author 2: Maragathavalli P

One of the most serious malignancies that affects women’s health worldwide is ovarian cancer. As a result, prompt accurate diagnosis and treatment are necessary. This study’s primary objective is to determine whether or not OC is present within the body of a person by using a range of characteristics gleaned… Read full abstract & cite →

Ovarian cancer detection machine learning frame-work data balancing feature selection

Abstract doi.org/10.14569/IJACSA.2025.0160687 PDF

88

Comparative Study of Prenatal and Postnatal Images for Detecting Down Syndrome in Children

Author 1: Labanti Singha Author 2: Iqbal Ahmed

Down syndrome is a genetic disorder caused by the presence of an extra copy of chromosome 21, affecting both neurological development and physical features. Early and accurate diagnosis is critical for ensuring timely medical intervention and support. This study presents a comparative analysis of prenatal (ultrasound) and postnatal (facial) imaging… Read full abstract & cite →

Down syndrome prenatal ultrasound postnatal facial recognition CNN vision transformer ensemble learning

Abstract doi.org/10.14569/IJACSA.2025.0160688 PDF

89

Sign3DNet: An Enhanced 3D CNN Architecture for Bengali Word-Level Sign Language Recognition

Author 1: Safi Ullah Chowdhury Author 2: Nasima Begum Author 3: Tanjina Helaly Author 4: Rashik Rahman

Automated recognition of sign languages has been playing an important role in breaking barriers to communication and inclusion for the deaf and mute community. Several studies have been conducted on Bengali Sign Language (BdSL). However, Bengali Word-Level Sign Language (BdWLSL) remains unexplored due to the lack of large annotated datasets… Read full abstract & cite →

Bengali sign word recognition computer vision deep learning convolutional neural network spatial-temporal dynamics video data

Abstract doi.org/10.14569/IJACSA.2025.0160689 PDF

90

Integrating Blockchain and Smart Card Technologies for Secure Healthcare Data Management

Author 1: Zayneb Gaouzi Author 2: Imad Bourian Author 3: Khalid Chougdali

In recent years, the healthcare sector has faced growing challenges in managing patient data securely and efficiently, especially when it comes to data privacy and the way information is shared across healthcare providers. A number of digital solutions have been proposed over time, but more recently, blockchain has started to… Read full abstract & cite →

Healthcare security blockchain smart contracts

Abstract doi.org/10.14569/IJACSA.2025.0160690 PDF

91

Enhanced Feature Extraction for Accurate Human Action Recognition

Author 1: Tarek Elgaml Author 2: Ali Saudi Author 3: Mohamed Taha

This paper tackles the challenge of achieving accurate and computationally efficient human activity recognition (HAR) in videos. Existing methods often fail to effectively balance spatial details (e.g. body poses) with long-term temporal dynamics (e.g. motion patterns), particularly in real-world scenarios characterized by cluttered backgrounds and viewpoint variations. We propose a… Read full abstract & cite →

Human activity recognition human-computer inter-action spatial features temporal features SMART frame selection hierarchical fusion network HMDB51 dataset

Abstract doi.org/10.14569/IJACSA.2025.0160691 PDF

92

Deep Learning in Cephalometric Analysis: A Scoping Review of Automated Landmark Detection

Author 1: Idriss Tafala Author 2: Fatima-Ezzahraa Ben-Bouazza Author 3: Aymane Edder Author 4: Oumaima Manchadi Author 5: Bassma Jioudi

Cephalometric landmark identification is funda-mental for accurate cephalometric analysis, serving as a corner-stone in orthodontic diagnosis and treatment planning. However, manual tracing is a labor-intensive process prone to inter-observer variability and human error, highlighting the need for automated methods to improve precision and efficiency. Recent advances in Deep Learning have… Read full abstract & cite →

Artificial Intelligence deep learning cephalometric analysis landmark detection

Abstract doi.org/10.14569/IJACSA.2025.0160692 PDF

93

Advancing Traffic Sign Detection with Convolutional Neural Networks: A Deep Learning Approach

Author 1: OUAHBI Younesse Author 2: ZITI Soumia

Traffic sign detection is a key task in intelligent transportation systems, supporting road safety and traffic flow. This study introduces RoadNet, a lightweight Convolutional Neural Network (CNN) designed for real-time detection and classification of traffic signs in Moroccan road environments. The system addresses challenges such as occlusion, illumination variability, and… Read full abstract & cite →

Traffic sign detection convolutional neural net-works deep learning road safety intelligent transportation systems real-time detection artificial intelligence transportation efficiency

Abstract doi.org/10.14569/IJACSA.2025.0160693 PDF

94

MITG-CU: Multimodal Interaction Temporal Graphs Approach for Conversational Emotion Recognition

Author 1: Qian Xing Author 2: Yaqin Qiu Author 3: Minglu Chi Author 4: Xuewei Li Author 5: Changyi Gao

In the emotion recognition of conversations, the complementary relationship between the context information and multimodal data cannot be fully exploited. This results in insufficient comprehensiveness and accuracy in emotion recognition. To address these challenges, this paper proposed a Multimodal Interactive Temporal Graph Conversation Understanding model (MITG-CU) composed of textual, audio… Read full abstract & cite →

Emotion recognition multimodal interaction relational temporal graph cross-modal interaction feature fusion

Abstract doi.org/10.14569/IJACSA.2025.0160694 PDF

95

Cross-Domain Evaluation of Large Language Models for Abstractive Text Summarization: An Empirical Perspective

Author 1: Walid Mohamed Aly Author 2: Taysir Hassan A. Soliman Author 3: Amr Mohamed AbdelAziz

Large Language Models (LLMs) have demon-strated remarkable capabilities in generating human-like text; however, their effectiveness in abstractive summarization across diverse domains remains underexplored. This study conducts a comprehensive evaluation of six open source LLMs across four datasets: CNN / Daily Mail and NewsRoom (news), SAMSum (dialogue) and ArXiv (scientific) using… Read full abstract & cite →

Large language models natural language processing automatic text summarization prompt engineering summarization evaluation

Abstract doi.org/10.14569/IJACSA.2025.0160695 PDF

96

Foreign Key Constraints to Maintain Referential Integrity in Distributed Database in Microservices Architecture

Author 1: Shamsa Kanwal Author 2: Nauman Riaz Chaudhry Author 3: Reema Choudhary Author 4: Younus Ahamad Shaik Author 5: Pankaj Yadav Author 6: Ayesha Rashid

In the world of modern software development, microservices architecture has become increasingly popular due to its ability to help developers to build large and complex applications that are more agile, faster and more scalable. In large scale applications (such as e-commerce, healthcare, finance, social media, inventory management, travel booking, content… Read full abstract & cite →

Foreign key constraints relational mapping referential integrity saga pattern event driven architecture APIs microservice distributed database

Abstract doi.org/10.14569/IJACSA.2025.0160696 PDF

97

Integrating cGAN-Enhanced Prediction with Hybrid Intervention Recommendations Systems for Student Dropout Prevention

Author 1: Hassan Silkhi Author 2: Brahim Bakkas Author 3: Khalid Housni

Early-warning dashboards in higher education typically stop at tagging students as “at-risk,” offering no concrete guidance for remedial action; this limitation contributes to the loss of thousands of learners each year. Approach. We propose an integrated framework that (i) uses a class-balanced Conditional GAN to augment sparse attrition data, and… Read full abstract & cite →

Student dropout prediction machine learning in education personalized intervention systems Conditional Generative Adversarial Networks(cGAN) Large Language Models (LLMs) hybrid recommendation systems

Abstract doi.org/10.14569/IJACSA.2025.0160697 PDF

98

Habitat Intelligence: How Machine Learning Reveals Species Preferences for Ecological Planning and Conservation

Author 1: Meryem Ennakri Author 2: Soumia Ziti Author 3: Mohamed Dakki

The emerging confluence between artificial intelligence and ecology has generated a new research frontier, which we refer to as habitat intelligence, aiming to unveil species environment relationships through data-driven approaches. This SLR aims to summarise the pass to the current year (2025) of the research on the use of ML… Read full abstract & cite →

Artificial Intelligence machine learning deep learning species preferences habitat suitability modeling species distribution models (SDMs) ecological niche modeling conservation planning environmental monitoring explainable AI (xAI) habitat intelligence biodiversity management

Abstract doi.org/10.14569/IJACSA.2025.0160698 PDF

99

Deep Learning-Based Bone Age Growth Disease Detection (BAGDD) Using RSNA Radiographs

Author 1: Muhammad Ali Author 2: Muhammad Faheem Mushtaq Author 3: Saima Noreen Khosa Author 4: Naila Kiran Author 5: Humaira Arshad Author 6: Urooj Akram

Radiological bone age assessment is essential for diagnosing pediatric growth and developmental disorders. The conventional Greulich-Pyle Atlas, though widely used, is manual, time-intensive, and prone to inter-observer variability. While deep learning methods such as Convolutional Neural Networks (CNNs) offer automation potential, most existing models rely on transfer learning from natural… Read full abstract & cite →

Bone age estimation pediatric healthcare convolutional neural networks transfer learning YOLOv3 medical imaging

Abstract doi.org/10.14569/IJACSA.2025.0160699 PDF

100

Intelligent Agents in Disaster Risk Management: A Systematic Review of Advances and Challenges

Author 1: Hssaine Hamid Author 2: ELouadi Abedlmajid

Artificial Intelligence (AI) has emerged as a trans-formative technology in the domain of Disaster Risk Management (DRM), offering new possibilities for forecasting, preparedness, and rapid response in the face of increasingly frequent and complex natural disasters. This systematic literature review synthesizes the state-of-the-art advances in AI-driven intelligent agents applied to… Read full abstract & cite →

Intelligent agents artificial intelligence disaster risk management predictive analytics resilience early warning systems geospatial AI disaster response ethical challenges ma-chine learning climate change adaptation

Abstract doi.org/10.14569/IJACSA.2025.01606100 PDF

101

Artificial Intelligence in Disaster Risk Management: A Scientometric Mapping of Evolution, Collaboration, and Emerging Trends (2003–2025)

Author 1: Hssaine Hamid Author 2: ELouadi Abedlmajid

Recent years have seen a dramatic increase in the number of and severity of natural disasters, driven in part by climate change and urbanization. Artificial Intelligence (AI) appears to be a promising new technology that can transform disaster risk management (DRM) and provide new opportunities for prediction, monitoring, response, and… Read full abstract & cite →

Artificial Intelligence disaster risk management machine learning deep learning remote sensing bibliometric analysis natural disasters geospatial AI early warning systems

Abstract doi.org/10.14569/IJACSA.2025.01606101 PDF

102

XPathia: A Deep Learning Approach for Translating Natural Language into XPath Queries for Non-Technical Users

Author 1: Karam Ahkouk Author 2: Mustapha Machkour

XPath is a widely used language for navigating and extracting data from XML documents due to its simple syntax and powerful querying capabilities. However, non-technical users often struggle to retrieve the needed information from XML files, as they lack knowledge of XML structures and query languages like XPath. To address… Read full abstract & cite →

Deep learning XML databases neural networks text-to-XPATH natural language processing

Abstract doi.org/10.14569/IJACSA.2025.01606102 PDF

103

Random Forest Model Based on Machine Learning for Early Detection of Diabetes

Author 1: Inooc Rubio Paucar Author 2: Cesar Yactayo-Arias Author 3: Laberiano Andrade-Arenas

Diabetes mellitus presents a growing prevalence at the global level, representing a significant public health challenge. Despite the availability of specific treatments, it is imperative to develop innovative strategies that optimize early detection and management of the disease. The research aims to develop a model that allows for the early… Read full abstract & cite →

Data mining decision tree diabetes mellitus machine learning random forest

Abstract doi.org/10.14569/IJACSA.2025.01606103 PDF

104

Phishing Simulation as a Proactive Defense: A Customizable Platform for Training and Behavioral Analysis

Author 1: Abdulrahman Alsaqer Author 2: Hussain Almajed Author 3: Khalid Alarfaj Author 4: Mounir Frikha

Phishing is one of the most persistent threats, but a lot of awareness programs still use generic, static training. This paper fills in the gap identified above by existing studies through the introduction of a phishing simulation platform that provides personalized, role-based simulation with real-time behavioral tracking. It is a… Read full abstract & cite →

Phishing simulation awareness analytics cyber-security

Abstract doi.org/10.14569/IJACSA.2025.01606104 PDF

105

Reducing Computational Complexity in CNNs: A Focus on VGG19 Pruning and Quantization

Author 1: Md. Mijanur Rahman Author 2: Anik Datta Author 3: Md. Sabiruzzaman Author 4: Md Samim Ahmed Bin Hossain

The Convolutional Neural Network (CNN) models are effective in computer vision strategies and have gained popularity due to their strong performance in visual tasks. Nevertheless, models with architectures such as VGG19 are expensive in terms of computational resources and require huge memory, which limits their usage on low-end devices. The… Read full abstract & cite →

VGG19 Model optimization model compression pruning quantization structured pruning unstructured pruning memory management quantization-aware training 8-bit 4-bit

Abstract doi.org/10.14569/IJACSA.2025.01606105 PDF

106

Fake News Detection on Kashmir Issue Using Machine Learning Techniques

Author 1: Misbah Kazmi Author 2: Sadia Nauman Author 3: Sadaf Abdul Rauf Author 4: S. Ali Author 5: Ali Daud Author 6: Bader Alshemaimri

Focusing events are sudden, impactful occurrences that spark widespread discussions. Analyzing fake news during such events is challenging due to limited and short-lived datasets. Online fact checkers are slow in identifying fake news, and internet communities and forums become the primary source of news, allowing unchecked dissemination. This study proposes… Read full abstract & cite →

Classification algorithm fake news Kashmir issue machine learning techniques

Abstract doi.org/10.14569/IJACSA.2025.01606106 PDF

107

Text Classification Using Enhanced Binary Wind Driven Optimization Algorithm

Author 1: Jaffar Atwan Author 2: Mohammad Wedyan Author 3: Ahmad Hamadeen Author 4: Qusay Bsoul Author 5: Ayat Alrosan Author 6: Ryan Alturki

Document classification using supervised machine learning is now widely used on the internet and in digital libraries. Several studies have focused on English-language document classification. However, Arabic text includes high variation in its morphology, which leads to high extracted features and increases the dimensionality of the classification task. Towards reducing… Read full abstract & cite →

Text classification; Arabic documents; wind driven optimization algorithm; simulating annealing; feature selection

Abstract doi.org/10.14569/IJACSA.2025.01606107 PDF

108

Comparison of Conventional Techniques for House Electricity Consumption Forecasting

Author 1: Sandra Pajares Centeno Author 2: Hugo Alatrista-Salas

Electricity consumption monitoring is the auto-mated process of recording, processing, and analyzing electricity usage in real time to make informed decisions. This research aims to implement an artificial intelligence- and deep learning-based methodology to forecast monthly electricity consumption in Tacna, Peru, and generate decision-making indicators. To this end, we used… Read full abstract & cite →

Electricity consumption forecasting recurrent neural networks deep learning

Abstract doi.org/10.14569/IJACSA.2025.01606108 PDF