IJACSA Vol. 17 Issue 1 (2026)

1

A Multi-Model Adaptive Q-Learning Framework for Robust Portfolio Management in Stochastic Markets

Author 1: Sharmin Sultana Author 2: Md Borhan Uddin Author 3: Masuma Akter Semi Author 4: Shahanaj Akther Author 5: Urmi Chakraborty Author 6: Khandakar Rabbi Ahmed

This study presents TAQLA, a new Tabular Adaptive Q-Learning Agent for portfolio management in stochastic financial markets. TAQLA rests on a multi-model reinforcement learning (RL) architecture that integrates parameter-adaptive Q-Learning mechanisms into softmax-based exploration to reconcile short-term profit maximization with long-term capital preservation. The method is contrasted with vanilla Q-Learning… Read full abstract & cite →

Reinforcement learning Q-Learning tabular reinforcement learning portfolio management dynamic asset allocation

Abstract doi.org/10.14569/IJACSA.2026.0170101 PDF

2

AI-Driven Anomaly Prediction in Encrypted Network Traffic

Author 1: Sina Ahmadi

The rapid growth of computer networks has increased demand for more sophisticated tools for network traffic analysis and monitoring. The increasing reliance on networks has amplified the need for robust security and intrusion detection mechanisms. Numerous studies have sought to develop efficient methods for fast and accurate intrusion detection, each… Read full abstract & cite →

Machine learning deep learning recurrent neural networks intrusion detection

Abstract doi.org/10.14569/IJACSA.2026.0170102 PDF

3

An Intelligent Knowledge-Based Chatbot to Mitigate Travel Anxiety

Author 1: Jieyu Wang Author 2: Hungchih Yu Author 3: Dingfang Kang

With the emergence of intelligent chatbots, AI-driven conversational agents are increasingly being used to help tourists manage travel challenges and obtain effective solutions. Travel anxiety constitutes a significant impediment to tourism, substantially influencing travelers' future intentions. Given its multifaceted nature, spanning from subjective experiences to complex logistical arrangements, this study… Read full abstract & cite →

Tourist-centered design user-centered evaluation knowledge base context-aware chatbot travel anxiety

Abstract doi.org/10.14569/IJACSA.2026.0170103 PDF

4

Towards Robust Intrusion Detection: Exploring Feature Selection, Balancing Strategies, and Deep Learning for Minority Class Optimization

Author 1: Khalid LABHALLA Author 2: Amal BATTOU

The increasing connectivity of systems and the rapid growth of the Internet have intensified cybersecurity threats. It has been demonstrated that conventional signature-based intrusion detection methods are deficient, especially against Zero-Day attacks. An alternative approach involves the deployment of Intrusion Detection Systems (IDS) that are based on deep learning algorithms… Read full abstract & cite →

Network intrusion detection system imbalanced data minority class detection deep learning feature selection balancing techniques

Abstract doi.org/10.14569/IJACSA.2026.0170104 PDF

5

DriveRight: An Embedded AI-Based Multi-Hazard Detection and Alert System for Safe and Sustainable Driving

Author 1: Jamil Abedalrahim Jamil Alsayaydeh Author 2: Rex Bacarra Author 3: Ahamed Fayeez Bin Tuani Ibrahim Author 4: Mazen Farid Author 5: Aqeel Al-Hilali Author 6: Safarudin Gazali Herawan

Recent advances in Artificial Intelligence (AI) and Computer Vision have significantly enhanced the potential of Advanced Driver Assistance Systems (ADAS). However, existing solutions remain limited by high computational cost, single-function design, and dependence on expensive sensors such as radar and LiDAR. This study presents DriveRight, an embedded AI-based driver-assistance system… Read full abstract & cite →

Embedded AI computer vision intelligent transportation IoT-based ADAS deep learning real-time object detection Raspberry Pi

Abstract doi.org/10.14569/IJACSA.2026.0170105 PDF

6

Towards an AI-Powered Cyber Resilience Model: A Systematic Evaluation of Frameworks Against Emerging Threats

Author 1: Chhaya Jahajeeah-Suntoo Author 2: Sheeba Armoogum

This study presents a Systematic Literature Review of cyber resilience frameworks against emerging threats, published between 2010 and 2025. While numerous frameworks exist, their ability to anticipate, withstand, and evolve in the face of sophisticated attacks remains uncertain. The study maps frameworks across nine resilience goals, namely Identify, Protect, Detect… Read full abstract & cite →

Cyber resilience cybersecurity framework Artificial Intelligence emerging threats Zero Trust systematic literature review

Abstract doi.org/10.14569/IJACSA.2026.0170106 PDF

7

Dual Cognitive Pathway Architecture for Robust and Dialect-Aware English Reading Comprehension

Author 1: Jillellamoodi Naga Madhuri Author 2: Tonmoyee Doley Author 3: Purnachandra Rao Alapati Author 4: Vijaya Kumar P Author 5: Linginedi Ushasree Author 6: Marvin D. Mayormente Author 7: Aseel Smerat

Reading comprehension models frequently struggle to accommodate linguistic diversity, especially dialectal variations within the English language that disrupt semantic alignment and fairness. In order to overcome these drawbacks, this study proposes the Dual Cognitive Pathway-Based Dialect-Aware Cognitive Twin Framework (NeuroTwin-DialectaLearn) as a new framework that combines the principles of cognitive… Read full abstract & cite →

Cognitive Twin Dialectal Transfer Adaptive Tasking English reading comprehension personalized learning

Abstract doi.org/10.14569/IJACSA.2026.0170107 PDF

8

Proposed Technological Solution to Predict the Need for Health Professionals in Health Centers Using Random Forest

Author 1: Fiorella Patricia Mirano Surquislla Author 2: Gianfranco Henry Ore Paredes Author 3: Aguilar-Alonso Igor

The objective of this research is to develop a technological solution based on the Random Forest algorithm to predict healthcare workforce requirements in public healthcare centers in Peru, addressing staff shortages and unequal workforce distribution. A national dataset from the Peruvian Ministry of Health (MINSA) covering the period 2017–2024, segmented… Read full abstract & cite →

Technological solution Random Forest healthcare sector healthcare professional prediction human resource management

Abstract doi.org/10.14569/IJACSA.2026.0170108 PDF

9

A Readability-Driven Prompting Framework for Accurate Grade-Specific EFL Narrative Creation

Author 1: Ronald William Marbun Author 2: Makoto Shishido

The integration of Artificial Intelligence (AI) into English as a Foreign Language (EFL) education offers new opportunities for developing adaptive and engaging learning materials. Narrative-based content is central to improving reading comprehension, vocabulary acquisition, and learner motivation. However, maintaining grade-appropriate readability in AI-generated narratives remains a major challenge. This study… Read full abstract & cite →

Artificial Intelligence (AI) English as a Foreign Language (EFL) large language models (LLMs) readability metrics narrative generation prompt engineering educational technology

Abstract doi.org/10.14569/IJACSA.2026.0170109 PDF

10

HELM-BRCA: Hybrid Embedding and Learning Model for BRCA Methylation Classification

Author 1: Hemalatha D Author 2: N Gomathi

Breast cancer remains a highly heterogeneous disease for which it demands advanced computational techniques that can reveal significant biological patterns in high-dimensional epigenomic data. DNA methylation profiles generated by the Illumina HumanMethylation450 platform yield rich, clinically relevant signals but introduce significant analytical challenges due to their high dimensionality, sparsity, and… Read full abstract & cite →

Breast cancer classification DNA methylation TCGA-BRCA Truncated SVD autoencoder ensemble learning deep learning epigenomic biomarkers hybrid model machine learning pipeline high-dimensional data

Abstract doi.org/10.14569/IJACSA.2026.0170110 PDF

11

Strengthening Indonesia’s Unmanned Aerial Vehicle Manufacturing Industry: A Technology-Focused Strategic Analysis

Author 1: Satrio Utomo Author 2: Gani Soehadi Author 3: Sarjono Sarjono Author 4: Yanuar Iman Dwiananto Author 5: Adi Akhmadi Pamungkas Author 6: Ellia Kristiningrum Author 7: Budi Setiadi Sadikin Author 8: Hardono Hardono Author 9: Rizki Arizal Purnama Author 10: Helen Fifianny

The development of national Unmanned Aerial Vehicle (UAV) technology represents a strategic imperative that requires immediate implementation. This study provides strategic recommendations to strengthen Indonesia’s UAV industry by employing SWOT analysis, the Analytical Hierarchy Process (AHP), and the Quantitative Strategic Planning Matrix (QSPM). Sixteen key internal and external factors were… Read full abstract & cite →

Unmanned Aerial Vehicle technology adoption strategic development SWOT AHP QSPM

Abstract doi.org/10.14569/IJACSA.2026.0170111 PDF

12

AI Mathematical: Solving Math Challenges Using Artificial Intelligence Models

Author 1: Trinh Quang Minh Author 2: Ngo Thi Lan Author 3: Bui Xuan Tung Author 4: Phan Thanh Tuyen

Artificial Intelligence (AI) has emerged as a transformative tool for solving mathematical challenges across diverse domains, ranging from algebra and geometry to calculus and number theory. This study investigates the role of AI in mathematics by analyzing three representative platforms—MathGPT.org, Math-GPT.ai, and StudyX.ai—and by proposing ten Python-based problem-solving models tailored… Read full abstract & cite →

AI math solvers Artificial Intelligence STEM education MathGPT.org Math-GPT.ai StudyX.ai Python models Olympiad problems automated reasoning Kaggle AIMO Progress Prize

Abstract doi.org/10.14569/IJACSA.2026.0170112 PDF

13

A Comprehensive Forensic Framework for Unmanned Aerial Vehicle Investigations: Empirical Validation with the DJI Mavic 3 Classic

Author 1: Nidhiba Parmar Author 2: Naveen Kumar Chaudhary

The rapid proliferation of unmanned aerial vehicles has introduced significant challenges for digital forensics, particularly due to their increasing involvement in criminal, surveillance, and security-related incidents. The heterogeneous hardware architectures, proprietary data formats, encryption mechanisms, and volatile storage characteristics of modern drones complicate reliable evidence recovery and analysis. This study… Read full abstract & cite →

Drone forensics unmanned aerial vehicle DJI Mavic 3 Classic digital forensics metadata analysis digital twin anomaly detection evidence recovery

Abstract doi.org/10.14569/IJACSA.2026.0170113 PDF

14

Correlation Characteristics-Based Channel Estimation Method for GFDM Systems

Author 1: Xiaotian Li Author 2: Xiaoqing Yan Author 3: Zitian Zhao Author 4: Jiameng Pei

Generalized Frequency Division Multiplexing (GFDM) has broad application prospects due to its flexible subcarrier structure and low out-of-band leakage. Traditional channel estimation methods for GFDM systems rely on inserting a large number of pilot sequences, which reduces the data transmission rate. To address this problem, a channel estimation method for… Read full abstract & cite →

GFDM channel estimation correlation pilot

Abstract doi.org/10.14569/IJACSA.2026.0170114 PDF

15

ForenVoice-Secure: Robust and Privacy-Aware Audio Data Mining for Forensic Speaker Identification

Author 1: Mubarak Albathan

Speech is now routine evidence in criminal investigations, but forensic audio rarely matches the clean assumptions of standard speaker recognition. Clips are short, noisy, codec-compressed, and channel-mismatched, and they are increasingly exposed to replay and synthetic speech manipulation. Therefore, the cast criminal voice identification is forensic audio data mining, aiming… Read full abstract & cite →

Forensic audio data mining forensic voice analytics voice biometrics criminal identification speaker recognition anti-spoofing deepfake and replay detection convolutional neural networks long short-term memory federated learning privacy-preserving biometrics law enforcement intelligence systems

Abstract doi.org/10.14569/IJACSA.2026.0170115 PDF

16

GAN-Based Generation of Pre Disaster SAR for Earthquake Interferometry

Author 1: Kohei Arai Author 2: Kengo Ohiwane Author 3: Hiroshi Okumura

This study proposes an earthquake disaster detection method based on interferometric synthetic aperture radar (InSAR) using synthetic pre‑disaster SAR data generated from optical satellite images. Conventional InSAR analysis requires pre‑ and post‑disaster SAR image pairs acquired under strict orbital and observation constraints, which makes it difficult to obtain suitable pre‑disaster… Read full abstract & cite →

GAN SAR earthquake disaster DEM pix2pixHD CycleGAN interferometric SAR

Abstract doi.org/10.14569/IJACSA.2026.0170116 PDF

17

A Competitive Co-Evolutionary Approach for the Nurse Scheduling Problem

Author 1: Maizatul Farhana Mohamad Nazri Author 2: Zeratul Izzah Mohd Yusoh Author 3: Halizah Basiron Author 4: Azlina Daud

The Nurse Scheduling Problem (NSP) is a constrained combinatorial optimisation problem that plays a critical role in healthcare scheduling and constraint optimisation. Traditional evolutionary approaches often rely on static fitness evaluation, which struggles to balance feasibility and solution quality under complex real-world constraints. This study proposes a competitive co-evolutionary algorithm… Read full abstract & cite →

Nurse Scheduling Problem competitive co-evolution evolutionary algorithms healthcare scheduling constraint optimisation adversarial evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170117 PDF

18

Metaphorical Meaning Integration in Poetry Based on Online Discourse Data: Analysis from a Cognitive Linguistics Perspective

Author 1: Ying LIU Author 2: Jiting XUE

As an emerging literary form, online poetry has garnered significant attention due to its rapid dissemination, diverse styles, and complex metaphorical expressions. However, the process of metaphorical meaning integration in poetry is difficult to quantify, necessitating support from Artificial Intelligence technologies. This study integrates cognitive linguistics theory with AI algorithms… Read full abstract & cite →

Online discourse data poetic metaphor cognitive linguistics Artificial Intelligence semantic fusion

Abstract doi.org/10.14569/IJACSA.2026.0170118 PDF

19

Dynamic Decision Model for Tunnel Cross-Passage Layout Based on Multi-Source Sensor Data Fusion

Author 1: Xuejun Di Author 2: Musha Ruzi Author 3: Angang Liu

The layout of tunnel cross-passages is a critical aspect of tunnel construction and operational safety. Traditional methods, primarily based on static design, struggle to adapt to complex and variable geological and construction environments. This study proposes a dynamic decision model for cross-passage layout based on multi-source sensor data fusion to… Read full abstract & cite →

Multi-source sensor data fusion dynamic cross-passage deployment decision-making for tunnels Kalman filter reinforcement learning non-dominated sorting genetic algorithm II

Abstract doi.org/10.14569/IJACSA.2026.0170119 PDF

20

An AI-Driven VR Learning Framework Using RL-Optimized Transformer Models for Personalized English Proficiency Assessment

Author 1: A. Sri Lakshmi Author 2: E. S. Sharmila Sigamany Author 3: Revati Ramrao Rautrao Author 4: K. Ezhilmathi Author 5: Dr. Bhuvaneswari Pagidipati Author 6: Elangovan Muniyandy Author 7: Dr. Adlin Sheeba

Effective English language learning demands adaptive, interactive, and flexible instructional support, which traditional e-learning systems and existing AI tutors struggle to provide due to limited immersion, static feedback mechanisms, isolated task structures, and the absence of robust reward-driven learning strategies. Although prior studies on VR-based learning environments and Natural Language… Read full abstract & cite →

AI-driven learning Virtual Reality English language education reinforcement learning Natural Language Processing

Abstract doi.org/10.14569/IJACSA.2026.0170120 PDF

21

Advanced Multimodal AI for Resilient Healthcare: Enhancing Early Risk Assessment in Critical Care

Author 1: Shih-Wei Wu Author 2: Chengcheng Li Author 3: Te-Nien Chien Author 4: Yao-Yu Zhang

This study develops an advanced multimodal AI framework to strengthen early risk assessment in critical care and support resilient healthcare delivery. Utilizing the MIMIC-III database, this research extracted structured variables and clinical notes from 26,829 adult patients. A text mining approach based on the BERTopic model was employed to generate… Read full abstract & cite →

Resilient healthcare multimodal AI early risk assessment critical care clinical text mining

Abstract doi.org/10.14569/IJACSA.2026.0170121 PDF

22

Improved Sparrow Search Algorithm-Based Recurrent Neural Network for Short-Term Generation Load Forecasting of Hydropower Stations

Author 1: Liyuan Sun Author 2: Yilun Dong Author 3: Junwei Yang

To address the challenges of low accuracy and high randomness in short-term hydroelectric load forecasting within Multi-energy Coupled Virtual Power Plants (MC-VPPs), this study proposes a hybrid model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and an Improved Sparrow Search Algorithm (ISSA). Traditional methods, such as exponential… Read full abstract & cite →

Power plant load forecasting Mode Decomposition Long Short-Term Memory Sparrow Search Algorithm

Abstract doi.org/10.14569/IJACSA.2026.0170122 PDF

23

Integrating Augmented Reality Learning Objects in Intelligent Tutoring Systems: A Conceptual Model for Engaging Learning Experiences

Author 1: Hind Tahir Author 2: Najoua Hrich Author 3: Salma El Boujnani Author 4: Mohamed Khaldi

Despite the progress achieved by E-learning platforms, several limitations remain in sustaining learner engagement over time. With the rapid evolution of information and communication technologies, augmented reality has emerged as a powerful medium for designing pedagogical objects that are interactive, immersive, and adaptable to diverse learning contexts. The integration of… Read full abstract & cite →

Augmented reality intelligent tutoring systems E-learning adaptation engagement personalized learning learning objects

Abstract doi.org/10.14569/IJACSA.2026.0170123 PDF

24

Comparative Evaluation of CNN Architectures for Corn Leaf Diseases Classification

Author 1: M. Abdallah Author 2: M. F. Abu-Elyazeed

Corn has particular importance in the global food industry. Many diseases attack the corn crops, which affects the crop yield. Early classification and detection of these diseases are pivotal to preventing damage and achieving high crop productivity. Although deep learning, especially convolutional neural networks, has accomplished remarkable results in image… Read full abstract & cite →

Artificial intelligence Convolutional Neural Network deep learning image processing corn diseases classification

Abstract doi.org/10.14569/IJACSA.2026.0170124 PDF

25

Attention-Enhanced Hierarchical Transformer for Multimodal Integration of Mammograms and Clinical Data

Author 1: N. Kannaiya Raja Author 2: V S Krushnasamy Author 3: Nurilla Mahamatov Author 4: Prasad Devarasetty Author 5: S.T. Gopukumar Author 6: Sanjiv Rao Godla Author 7: Vuda Sreenivasa Rao

Breast cancer has been listed as one of the leading causes of death amongst women all over the world, and the current diagnostic techniques, which are founded on the manual examination of mammograms or individual clinical presentations, are often subjective, neither being consistent nor generalizable. The existing computer-aided diagnosis (CAD)… Read full abstract & cite →

Breast cancer diagnosis multimodal deep learning Graph Attention Network Bayesian uncertainty estimation explainable AI

Abstract doi.org/10.14569/IJACSA.2026.0170125 PDF

26

Health Information Exchange in Malaysia: Leveraging Interoperability on International Standards for Health Data Exchange

Author 1: Mohd Noor A. M. N. L Author 2: M. Batumalay Author 3: Balasubramaniam Muniandy Author 4: Lakshmi D Author 5: Vinoth Kumar P

This research proposal investigates the implementation of Health Information Exchange (HIE) in Malaysia, focusing on understanding the technological, policy, and financial challenges and opportunities associated with its adoption and effectiveness. Through a comprehensive survey approach targeting healthcare practitioners, policymakers, IT professionals, and patients, the study aims to elucidate the current… Read full abstract & cite →

Health data integration interoperability international health data standards implementation policy reforms process innovation

Abstract doi.org/10.14569/IJACSA.2026.0170126 PDF

27

Privacy-Preserving Adaptive Biometric Framework with Reinforcement Learning and Blockchain-Enabled Multi-Factor Authentication

Author 1: P. Selvaperumal Author 2: Sakshi Malik Author 3: Asfar H Siddiqui Author 4: Dekhkonov Burkhon Author 5: Elangovan Muniyandy Author 6: Garigipati Rama Krishna Author 7: P N V Syamala Rao M

Ensuring secure and privacy-preserving authentication in web applications remains a critical challenge due to the limitations of conventional single-factor approaches, which are vulnerable to attacks and fail to account for dynamic user behaviors. Existing multi-factor authentication (MFA) methods often rely on static rules, exposing users to unnecessary friction or weak… Read full abstract & cite →

Privacy-preserving authentication multi-factor authentication reinforcement learning biometric verification blockchain-enabled logging

Abstract doi.org/10.14569/IJACSA.2026.0170127 PDF

28

Intelligent Fruit-Picking Robot Using Convolutional Vision and Kinematic Control for Automated Harvesting

Author 1: Nurbibi Sairamkyzy Imanbayeva Author 2: Bekzat Ondasynuly Amanov Author 3: Aigerim Bakatkaliyevna Altayeva Author 4: Dana Kairatovna Ashimova

This study presents the design, development, and evaluation of an intelligent fruit-picking robot that integrates convolutional vision, adaptive gripping mechanisms, and kinematic control to enable automated harvesting in diverse orchard environments. The proposed system combines a dual-manipulator platform with an extendable scissor-lift mechanism to achieve wide workspace coverage, allowing efficient… Read full abstract & cite →

Fruit-picking robot automated harvesting computer vision deep learning kinematic control Mixed Fruit Dataset adaptive gripper transformer model agricultural robotics orchard automation

Abstract doi.org/10.14569/IJACSA.2026.0170128 PDF

29

Ghost-Vanilla Feature Maps: A Novel Hybrid Architecture for Efficient Fine-Grained Songket Motif Classification

Author 1: Yohannes Author 2: Muhammad Ezar Al Rivan Author 3: Siska Devella Author 4: Tinaliah

South Sumatra songket motifs present a challenging fine-grained classification task due to high inter-class similarity and substantial intra-class variability. This study proposes the Ghost-Vanilla Feature Map, a novel hybrid architecture that integrates low-cost ghost-generated features with the lightweight structural stability of VanillaNet to enhance discriminative feature learning while reducing computational… Read full abstract & cite →

Ghost Module fine-grained classification lightweight deep learning songket motif classification VanillaNet

Abstract doi.org/10.14569/IJACSA.2026.0170129 PDF

30

Forecast of Guangzhou Port Logistics Demand Based on Back Propagation Neural Network

Author 1: Xiu Chen Author 2: Lianhua Liu Author 3: Lifen Zheng

In recent years, the port economy of our country has developed rapidly. Guangzhou Port is an important node of the maritime transportation of the Belt and Road, connecting the hinterland economy of our country with the countries along the Belt and Road, which is of great significance in promoting the… Read full abstract & cite →

BP neural network GM(1 1) combination model port logistics demand

Abstract doi.org/10.14569/IJACSA.2026.0170130 PDF

31

Explainable AI Techniques for Interpretable Breast Cancer Classification

Author 1: Tony K. Hariadi Author 2: Qodri Aziz Author 3: Slamet Riyadi Author 4: Kamarul Hawari Ghazali Author 5: Khairunnisa Binti Hasikin Author 6: Tri Andi

Breast cancer is still a major health risk for women all over the world, and thus finding it early is very important for the patient's survival. Digital Breast Tomosynthesis (DBT) offers enhanced imaging capabilities relative to conventional mammography; yet, its quasi-3D characteristics provide distinct interpretability issues, often rendering deep learning… Read full abstract & cite →

Breast cancer DBT Grad-CAM ResNet-50 XAI

Abstract doi.org/10.14569/IJACSA.2026.0170131 PDF

32

Multi-Omics Integration Methods for AI-Based Breast Cancer Molecular Subtypes Classification

Author 1: Sajid Shah Author 2: Azurah A Samah Author 3: Siti Zaiton Mohd Hashim Author 4: Sarahani Harun Author 5: Zuraini Binti Ali Shah Author 6: Farkhana Binti Muchtar Author 7: Syed Hamid Hussain Madni

Breast cancer is one of the most life-threatening and heterogeneous diseases. It contains various molecular subtypes, each subtypes have different characteristics, treatment outcomes, and prognosis. The proper integration of multi-omics data, including genomics, epigenomics, transcriptomics, and proteomics, is very important for enhancing the breast cancer molecular subtypes classification accuracy. Despite… Read full abstract & cite →

Breast cancer classification integration methods molecular subtypes multi-omics

Abstract doi.org/10.14569/IJACSA.2026.0170132 PDF

33

Environmental Assessment of Chemicals: Artificial Intelligence for Predicting Persistence, Bioaccumulation, and Toxicity Properties

Author 1: Ayoub Belaidi Author 2: Rachid El Ayachi Author 3: Mohamed Biniz Author 4: Mohamed Oubezza Author 5: Youssef Youssefi

Early assessment of the persistence, bioaccumulation, and toxicity (PBT) of chemicals is a major challenge for environmental protection and international regulatory frameworks. The objective of this study is to compare the effectiveness of three graph-based deep learning architectures—a graph neural network (GNN), a message passing network (MPNN), and a graph… Read full abstract & cite →

PBT prediction persistence bioaccumulation toxicity QSAR models cheminformatics environmental risk assessment

Abstract doi.org/10.14569/IJACSA.2026.0170133 PDF

34

A Review on Intrusion Detection Models in Internet of Medical Things (IoMT)

Author 1: Aljorey Alqahtani Author 2: Monir Abdullah

The Internet of Medical Things (IoMT) environment is highly sensitive due to the nature of medical data and its direct connection to patient health, making it a prime target for sophisticated cyberattacks. This study explores the key security challenges within IoMT, discusses how Machine Learning (ML) can enhance threat detection… Read full abstract & cite →

Internet of Medical Things (IoMT) IDS Explainable Artificial Intelligence (XAI)

Abstract doi.org/10.14569/IJACSA.2026.0170134 PDF

35

A Blockchain-Based Privacy-Preserving Scheme for Integrity Verification and Fair Payment in Cloud Data Storage

Author 1: Li Zhenxiang Author 2: Jin Yuanrong Author 3: Mohammad Nazir Ahmad

Ensuring the integrity of outsourced data in cloud storage remains a critical challenge, especially when existing auditing schemes rely on centralized third-party auditors (TPAs), which introduce single points of failure, privacy leakage risks, and a lack of economic fairness. Current blockchain-based approaches improve transparency but still fail to simultaneously achieve… Read full abstract & cite →

Cloud storage blockchain integrity verification smart contract privacy-preserving audit fair payment

Abstract doi.org/10.14569/IJACSA.2026.0170135 PDF

36

EEG-Based Imagined-Speech Decoding: A Review

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

Non-invasive neural speech interfaces aim to reconstruct intended words from brain activity, offering critical communication options for individuals with severe dysarthria or locked-in syndrome. Among the available recording modalities, electroencephalography (EEG) remains the most accessible and cost-effective choice for long-term brain–computer interface (BCI) applications. Decoding imagined speech from EEG, however… Read full abstract & cite →

Electroencephalography (EEG) Imagined speech Brain–Computer Interfaces (BCIs) neural speech decoding deep learning transfer learning time–frequency analysis evaluation protocols

Abstract doi.org/10.14569/IJACSA.2026.0170136 PDF

37

Evaluating Field Flexibility Approaches in Relational Databases: A Performance Study of JSON and Column-Oriented Models in Library Systems

Author 1: Rizal Fathoni Aji Author 2: Nilamsari Putri Utami

This study examines two approaches for achieving field flexibility in library systems using relational databases: column-oriented tables and JSON data types. To evaluate the performance and practicality of flexible schema strategies, a dataset of 41,000 library records was implemented using both column-oriented and JSONB-based schemas in PostgreSQL. Five representative queries… Read full abstract & cite →

Field flexibility RDBMS column-oriented model JSON library systems

Abstract doi.org/10.14569/IJACSA.2026.0170137 PDF

38

Developing a Robotic-Integrated Leagility Adaptation Model Through Green Supply Chain Intelligence and Supply Chain Ambidexterity

Author 1: Miftakul Huda Author 2: Mohammad Hatta Fahamsyah Author 3: Agung Nugroho Author 4: Arie Indra Gunawan Author 5: Pepen Komarudin Author 6: Andrean Bagus Saputra

This study develops a Robotic-Integrated Leagility Adaptation Model by combining Green Supply Chain Intelligence (GSCI) and Supply Chain Ambidexterity (SCA) to enhance sustainable performance in the manufacturing sector. The rapid evolution of robotics, cyber-physical systems, and AI-enabled decision technologies has transformed supply chain dynamics, necessitating an adaptive model that balances… Read full abstract & cite →

Robotic integration Green Supply Chain Intelligence Supply Chain Ambidexterity leagility adaptation sustainable manufacturing performance

Abstract doi.org/10.14569/IJACSA.2026.0170138 PDF

39

A Privacy-Conscious Federated Reinforcement Learning Framework for Affect-Aware English Listening

Author 1: N. Sreedevi Author 2: V. Saranya Author 3: Kama Ramudu Author 4: M. Madhusudhan Rao Author 5: Sakshi Malik Author 6: Elangovan Muniyandy Author 7: Ahmed I. Taloba

The rapid growth of digital English listening platforms has intensified the need for intelligent personalization mechanisms that adapt to learner progression while preserving data privacy. Existing adaptive systems primarily rely on static difficulty scaling or centralized learning architectures, often neglecting learner engagement dynamics and raising concerns about sensitive data exposure… Read full abstract & cite →

Adaptive listening learning federated reinforcement learning affective proxy modeling privacy-preserving AI HuBERT speech representation English language learning

Abstract doi.org/10.14569/IJACSA.2026.0170139 PDF

40

Advanced Explainable Hybrid Metaheuristic–Deep Learning Framework for Real-Time Financial Fraud Detection with Temporal Convolutional Analysis

Author 1: Madhu Kumar Reddy P Author 2: M. N. V Kiranbabu

The increasing digitization in banking and related financial services has resulted in spurring the level of transactions with fraudulent patterns and thus demands detection solutions not only efficient but also interpretable and replicable. The earlier machine learning approaches, like K-Nearest Neighbors, Decision Trees, and Random Forests, are not efficient in… Read full abstract & cite →

Banking business intelligence convolutional neural network fraud detection Moth Flame Optimization

Abstract doi.org/10.14569/IJACSA.2026.0170140 PDF

41

A Few-Shot Semantic Meta-Learning Framework with CRF for Skill Entity Recognition in Open Innovation Ecosystems

Author 1: Nurchim Author 2: Muljono Author 3: Edi Noersasongko Author 4: Ahmad Zainul Fanani Author 5: Deshinta Arrova Dewi

The accelerating pace of digital transformation is reshaping labour-market dynamics, driving the emergence of new competencies, and intensifying the need for scalable skill-intelligence systems within open innovation ecosystems. Yet, research on Indonesian Named Entity Recognition (NER) remains limited for skill-extraction tasks, especially in low-resource contexts where annotated data are scarce… Read full abstract & cite →

Few-shot Named Entity Recognition skill intelligence process innovation open innovation Natural Language Processing

Abstract doi.org/10.14569/IJACSA.2026.0170141 PDF

42

Attention-Guided Lightweight MobileNetV2 for Real-Time Driver Drowsiness Classification on Edge-IoT Systems

Author 1: Yo Ceng Giap Author 2: Muljono Author 3: Affandy Author 4: Ruri Suko Basuki Author 5: Harun Al Azies Author 6: R. Rizal Isnanto Author 7: Deshinta Arrova Dewi

Driver drowsiness is a major cause of traffic accidents, so Edge-IoT platforms with limited resources need to be able to accurately and quickly detect when drivers are drowsy. This study examines attention-guided lightweight CNN design predicated on MobileNetV2 for real-time driver drowsiness detection. The authors compare a SE-enhanced MobileNetV2 to… Read full abstract & cite →

Driver drowsiness detection Edge-IoT deployment lightweight convolutional neural networks process innovation MobileNetV2 optimization squeeze-and-excitation attention

Abstract doi.org/10.14569/IJACSA.2026.0170142 PDF

43

Quality of Service and Customer Satisfaction: A Case Study of Call Center Services

Author 1: Jhoanna Iveth Santiago Rufasto Author 2: Sebastián Ramos-Cosi Author 3: Haslyd Claydiana Ramos Jara Author 4: Ana Huamani-Huaracca

Quality of service and customer satisfaction have become priority aspects for call center services, especially in a context where their use is becoming more and more frequent. In the district of Los Olivos, Lima, Peru, 60% of users who receive telephone service consider the quality of the service to be… Read full abstract & cite →

Quality of service customer satisfaction call center

Abstract doi.org/10.14569/IJACSA.2026.0170143 PDF

44

Deep Learning for Endometrium Segmentation in Transvaginal Ultrasound: A Systematic Review Towards Receptivity Assessment

Author 1: Asma Amirah Nazarudin Author 2: Siti Salasiah Mokri Author 3: Noraishikin Zulkarnain Author 4: Aqilah Baseri Huddin Author 5: Mohd Faizal Ahmad Author 6: Ashrani Aizzuddin Abd Rani Author 7: Seri Mastura Mustaza Author 8: Huiwen Lim

Deep learning (DL) has become a transformative approach in medical image analysis, offering superior accuracy and automation in image segmentation tasks. In reproductive imaging, transvaginal ultrasound (TVUS) serves as a crucial modality for evaluating the endometrial condition, which plays a critical role in assessing ovarian health. Although many studies have… Read full abstract & cite →

Endometrium segmentation deep learning image segmentation image processing endometrium receptivity assessment ovarian health

Abstract doi.org/10.14569/IJACSA.2026.0170144 PDF

45

Advances in Deep Learning for Affective Intelligence: Language Models, Multimodal Trends, and Research Frontiers

Author 1: Diego Andres Andrade-Segarra Author 2: Juan Carlos Santillán-Lima Author 3: Miguel Duque-Vaca Author 4: Fernando Tiverio Molina-Granja

The accelerated growth of digital content and the increasing presence of emotional expressions, polarized opinions, and toxic behaviors in social media have driven the development of advanced Affective Analysis techniques. This study presents a broad and up-to-date review of recent studies covering Sentiment Analysis, Emotion Recognition, Hate Speech Detection, cyberbullying… Read full abstract & cite →

Sentiment Analysis Emotion Recognition Hate Speech Detection cyberbullying deep learning Transformer-based Models Multimodal Analysis multilingual NLP Explainable AI (XAI) social media

Abstract doi.org/10.14569/IJACSA.2026.0170145 PDF

46

Architecture of an Intelligent Predictive Analytics System for Gas Environment Monitoring Based on Sensor-Series IoT Devices

Author 1: Anuar Kussainov Author 2: Gulnaz Zhomartkyzy Author 3: Rajermani Thinakaran

Industrial facilities operating with toxic and explosive gases require continuous monitoring systems capable not only of detecting threshold exceedances but also of anticipating hazardous trends. Conventional IoT-based gas monitoring solutions are primarily limited to real-time data acquisition and alarm triggering, which restricts their ability to prevent incidents proactively. This study… Read full abstract & cite →

Intelligent system predictive analytics IoT gas analyzer LoRaWAN industrial safety machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170146 PDF

47

A Systematic Literature Review on Organizational Readiness for Artificial Intelligence Adoption Based on the TOE Framework

Author 1: Sulistyo Aris Hirtranusi Author 2: Benny Ranti Author 3: Widijanto Satyo Nugroho Author 4: Wisnu Jatmiko

Artificial intelligence (AI) is increasingly being integrated into organizational processes, reshaping how organizations operate, compete, and make decisions. However, despite growing interest, many organizations face challenges in adopting AI effectively due to insufficient readiness. Prior research on organizational AI readiness has produced diverse and sometimes inconsistent conceptualizations, particularly with respect… Read full abstract & cite →

Artificial intelligence readiness organizational readiness TOE framework systematic literature review AI adoption

Abstract doi.org/10.14569/IJACSA.2026.0170147 PDF

48

Volumetric Feature Learning for High-Fidelity Two-Dimensional Dental Cast Image Reconstruction Using Generative Adversarial Networks (GANs)

Author 1: Eman Ahmed Eldaoushy Author 2: Manal A. Abdel-Fattah Author 3: Nermeen Ahmed Hassan Author 4: Mai M. El defrawi

Dentistry is a medical branch that diagnoses and treats oral diseases, helps maintain oral function, and improves oral aes-thetics. Dental casts are three-dimensional models of a patient’s oral tissues that can be used to study oral anatomy, assess oc-clusal relationships, and determine tooth alignment. Traditional-ly, they were made of gypsum… Read full abstract & cite →

Dental image reconstruction generative adversarial networks latent space representation two-dimensional to three-dimensional mapping volumetric deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170148 PDF

49

Selfdom Enhanced CatBoost Model for Remote Paddy Growth Monitoring and Fertilizer Recommendation in Precision Agriculture

Author 1: Shanmuga Priya S Author 2: V. Dhilip Kumar

Precision agriculture enables data-driven crop monitoring and improved resource utilization. Paddy cultivation requires continuous surveillance and timely fertilizer application because it is sensitive to soil nutrient dynamics, water availability, and climatic conditions. Conventional practices such as manual field inspection and heuristic fertilizer advisory methods are often labor-intensive and subjective, which… Read full abstract & cite →

Paddy growth monitoring fertilizer recommendation Selfdom Enhanced CatBoost Model Osprey Optimization Algorithm oppositional function

Abstract doi.org/10.14569/IJACSA.2026.0170149 PDF

50

A Robust Real-Time Multimodal Polynomial Fusion Framework for Sensor-Based Sign Language Recognition Using Flex–IMU Smart Gloves

Author 1: Dadang Iskandar Mulyana Author 2: Edi Noersasongko Author 3: Guruh Fajar Shidik Author 4: Pujiono

Sign language recognition is a critical component of assistive technologies for individuals with hearing and speech impairments. While vision-based approaches have shown promising performance, their reliability is often affected by illumination variations, occlusions, and background complexity. Wearable sensor–based solutions, particularly smart gloves integrating flex sensors and inertial measurement units (IMUs)… Read full abstract & cite →

Sign language recognition Hijaiyah sign language wearable sensors smart glove multimodal fusion polynomial temporal smoothing real-time recognition

Abstract doi.org/10.14569/IJACSA.2026.0170150 PDF

51

EnGMHE: Enhanced Geometric Mean Histogram Equalization for Low-Light Image Enhancement

Author 1: Rawan Zaghloul Author 2: Hazem Hiary

Low-light image enhancement has been extensively studied, with numerous methods proposed to address this challenge. Among these, Geometric Mean Histogram Equalization (GMHE) emerged as a histogram-based technique specifically designed for enhancing low-light images. Despite its effectiveness, GMHE has notable limitations: it often oversaturates results under specific conditions and amplifies noise… Read full abstract & cite →

Histogram Equalization image enhancement low-light enhancement denoising deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170151 PDF

52

Immersive Learning Environment Design of Outdoor Education Space Using Artificial Intelligence Augmented Reality Technology

Author 1: Chenguang Liu

This study presents a technically grounded design and implementation of an AI- and AR-enabled immersive learning environment for outdoor education. Moving beyond conceptual descriptions, the study develops an executable system framework that integrates adaptive navigation and positioning, context-aware virtual tours, task-driven scenario simulation, and real-time feedback mechanisms. Each functional module… Read full abstract & cite →

Artificial intelligence augmented reality outdoor education space immersive learning environment system-level evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170152 PDF

53

A Contextualized Learner-Profiling Transformer Architecture for Adaptive Grammar Error Diagnosis and Instruction

Author 1: Bukka Shobharani Author 2: M Vijaya Lakshmi Author 3: Kama Ramudu Author 4: Jasgurpreet Singh Chohan Author 5: S. Farhad Author 6: Elangovan Muniyandy Author 7: Gulnaz Fatma Author 8: Ahmed I. Taloba

Grammatical accuracy is a critical component of English as a Second Language (ESL) learning; however, many learners continue to struggle with recurring errors despite the availability of automated grammar correction tools. Although recent transformer-based models such as BERT, GPT, and T5 have demonstrated strong benchmark performance, existing grammar error correction… Read full abstract & cite →

Grammar error correction adaptive feedback Multi-task Learning transformer models ESL learning personalized language instruction

Abstract doi.org/10.14569/IJACSA.2026.0170153 PDF

54

Text-Driven Early Warning of Supply Chain Risks: A Hybrid Machine- and Deep-Learning Framework for the New Energy Vehicle (NEV) Industry

Author 1: Ma Chaoke Author 2: S. Sarifah Radiah Shariff Author 3: Noryanti Nasir Author 4: Gao Ying

The rapid expansion of New Energy Vehicles (NEVs) has increased the global NEV supply chains' exposure to diverse and interconnected risks. Distributed production networks frequently face disruptions driven by raw material volatility, evolving environmental regulations, customs clearance uncertainty, and geopolitical instability, underscoring the need for effective early-warning systems. To address… Read full abstract & cite →

New Energy Vehicle (NEV) supply chain risk natural language processing (NLP) text classification early warning system BERT

Abstract doi.org/10.14569/IJACSA.2026.0170154 PDF

55

Explainable CNN-Based Multiclass Household Waste Classification Using Grad-CAM for Smart Waste Management

Author 1: Fuzy Yustika Manik Author 2: Pauzi Ibrahim Nainggolan Author 3: T. H. F Harumy Author 4: Dewi Sartika Br Ginting Author 5: Aini Maharani Author 6: Hafizha Ramadayanti Author 7: Jessica Almalia Author 8: Muhammad Putra Harifin

Automated waste classification using computer vision has become essential for improving environmental sustainability and reducing manual sorting effort. This study presents an enhanced waste image classification model based on EfficientNet-B0, trained using a two-stage transfer learning strategy that combines feature extraction and fine-tuning. The proposed approach aims to enhance classification… Read full abstract & cite →

EfficientNet-B0 explainable AI Grad-CAM transfer learning waste classification

Abstract doi.org/10.14569/IJACSA.2026.0170155 PDF

56

RoBERTa-Enhanced Actor–Critic Reinforcement Learning for Adaptive and Personalized ESL Instruction

Author 1: Angalakuduru Aravind Author 2: A. Swathi Author 3: Jillellamoodi Naga Madhuri Author 4: R. Aroul Canessane Author 5: K. Lalitha Vanisree Author 6: Elangovan Muniyandy Author 7: Rasha M. Abd El-Aziz

Technology-Assisted Language Learning (TALL) has developed and has greatly transformed the way English as a Second Language (ESL) is taught. The current digital resources and smart solutions have enabled more interactive and accessible learning, providing learners with an opportunity to train their skills at any time and place. Nevertheless, most… Read full abstract & cite →

English as a Second Language adaptive learning reinforcement learning RoBERTa intelligent tutoring systems

Abstract doi.org/10.14569/IJACSA.2026.0170156 PDF

57

Exploring Cyber Trends and Threats Towards V2X Connected Vehicles in Malaysia: A Systematic Literature Review

Author 1: A’in Hazwani Ahmad Rizal Author 2: Noor Afiza Mat Razali Author 3: Sakinah Ali Pitchay Author 4: Taqiyuddin Anas

The rapid expansion of 5G enabled Vehicle to Everything (V2X) communication has evolved into an intelligent transportation system by supporting applications such as autonomous driving, real-time traffic optimization, and road safety management. However, the growing connectivity and diverse communication protocols also create major cybersecurity challenges, especially in the network tier… Read full abstract & cite →

5G V2X connected vehicles cybersecurity intrusion detection systems anomaly detection spoofing DoS network tier machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170157 PDF

58

Benchmarking Large Language Models for Dental Clinical Decision Support: A BERT Score Analysis of Claude Opus 4.5

Author 1: Achmad Zam Zam Aghasy Author 2: Muhammad Lutfan Lazuardi Author 3: Hari Kusnanto Josef

The integration of Large Language Models (LLMs) into clinical decision support systems represents a significant advancement in healthcare informatics. This study presents a comprehensive evaluation framework for benchmarking LLM-generated dental treatment recommendations using BERT Score as the primary semantic similarity metric. We evaluated Claude Opus 4.5 as a Clinical Decision… Read full abstract & cite →

BERT Score Large Language Models clinical decision support system semantic similarity Claude Opus 4.5

Abstract doi.org/10.14569/IJACSA.2026.0170158 PDF

59

Understanding IT Product Purchasing Behavior of MSMEs Using Sequential Pattern Mining Approaches

Author 1: Rendro Kasworo Author 2: R. Rizal Isnanto Author 3: Budi Warsito

Sequential pattern mining is a crucial analytical method for understanding purchasing behavior and uncovering hidden patterns in transactional data. Unlike most prior studies that apply sequential pattern mining primarily in consumer-oriented retail settings or evaluate algorithms in isolation, this study investigates IT product purchasing behavior among Small and Medium Enterprises… Read full abstract & cite →

Sequential pattern mining Apriori PrefixSpan CloSpan SMEs digital transformation

Abstract doi.org/10.14569/IJACSA.2026.0170159 PDF

60

Collaborative Dual-Framework Defense: CTI and LLM-Based Enhanced Smishing Detection

Author 1: Li Guangliang Author 2: Kalaivani Selvaraj Author 3: Mahinderjit Singh

Smishing has become a severe cybersecurity threat. Attackers now use AI and social engineering to craft more sophisticated campaigns. To address this challenge, this study proposes a dual-layer detection framework. It combines cyber threat intelligence (CTI), machine learning, and a large language model (LLM). The framework uses 22 features built… Read full abstract & cite →

Smishing detection cyber threat intelligence XGBoost semantic verification large language model

Abstract doi.org/10.14569/IJACSA.2026.0170160 PDF

61

Development of Image Processing Filters for Improving Visibility of Fine Dentoalveolar Structures in Dental Cone-Beam Computed Tomography Images

Author 1: Muhannad Almutiry Author 2: Asma’a Al-Ekrish Author 3: Saleh Alshebeili

Cone-beam computed tomography (CBCT) imaging in dentistry requires post-reconstruction image processing to enhance diagnostic quality while minimizing radiation exposure. Visualization of fine dentomaxillofacial structures, particularly the inferior alveolar canal (IAC) and dental pulp canals, presents significant diagnostic challenges in low-dose CBCT imaging. This study investigates the application of Wiener and… Read full abstract & cite →

Cone-beam computed tomography CBCT image processing Wiener filter LLMMSE filter dental imaging noise reduction low-dose imaging endodontics pulp canal visualization

Abstract doi.org/10.14569/IJACSA.2026.0170161 PDF

62

Blockchain-Based Audit Trails: Improving Transparency and Fraud Detection in Digital Accounting Systems

Author 1: Neni Maryani Author 2: Munawar Muchlish Author 3: Roza Mulyadi Author 4: Nurhayati Solehah

Blockchain technology has emerged as a transformative innovation in digital accounting, offering robust mechanisms to enhance auditability, data integrity, and fraud prevention. This study examines how blockchain-based audit trails can improve transparency and strengthen fraud detection within modern accounting information systems. Adopting a conceptual–analytical research design supported by secondary empirical… Read full abstract & cite →

Blockchain audit trails fraud detection digital accounting transparency data mining

Abstract doi.org/10.14569/IJACSA.2026.0170162 PDF

63

Optimizing the Accuracy of Alzheimer's Detection Using Machine Learning and Intelligent Feature Selection Strategies

Author 1: Suci Mutiara Author 2: Siti Nur Laila Author 3: Deppi Linda Author 4: Sri Lestari Author 5: Jean Antoni Author 6: Christian Petrus Silalahi

Alzheimer’s disease is a progressive neurodegenerative disorder for which early detection remains a significant challenge due to the complexity of clinical features and the high dimensionality of medical data. This study aims to improve the accuracy and reliability of Alzheimer’s disease detection by evaluating the performance of multiple machine learning… Read full abstract & cite →

Machine learning Alzheimer Random Forest (RF) logistic regression deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170163 PDF

64

Natural Human-Machine Interaction Using Static Hand Gestures for a Gestural Calculator System with DNN

Author 1: H. Abdelmoumene Author 2: L. Meddeber Author 3: O. Ghali Author 4: Y. Amellal

Hand gesture recognition (HGR) represents a real challenge for natural human-computer interaction, which aims to revolutionize the naturalness of traditional interfaces, allowing intuitive control of various devices without using a keyboard or mouse. Despite the availability of frameworks such as MediaPipe, which enable better detection and tracking, the major challenge… Read full abstract & cite →

Hand gesture recognition gestural calculator interface DNN MediaPipe natural human-computer interaction

Abstract doi.org/10.14569/IJACSA.2026.0170164 PDF

65

Community-Aware Influence Maximization for Suppressing Cryptocurrency Scam Misinformation

Author 1: Naglaa Mostafa Author 2: Hatem Abdelkader Author 3: Asmaa H.Ali

Cryptocurrency fraud campaigns often rely on large-scale social-media diffusion to recruit victims, normalize false claims, and coordinate multi-level marketing behavior. This study examines the dynamics of the One Coin scam. It proposes an influence-maximization (IM)-driven workflow for identifying high-impact accounts whose intervention can reduce future misinformation diffusion. A directed Twitter… Read full abstract & cite →

Influence maximization misinformation suppression cryptocurrency scams OneCoin diffusion models Leiden community-aware seeding

Abstract doi.org/10.14569/IJACSA.2026.0170165 PDF

66

Enhancing Business Cybersecurity Through Integrated Defense and Incident Response: A Comparative Decision Framework

Author 1: Jurgen Mecaj

Business operations increasingly depend on digital workflows, hybrid infrastructures, and third-party ecosystems, making cybersecurity incidents a direct business continuity and governance problem rather than solely a technical concern. This paper proposes an integrated cyber defense and defense-to-response decision framework for organizations seeking to reduce exposure to external attacks and unauthorized… Read full abstract & cite →

Cyber defense incident response business continuity NIST CSF 2.0 SP 800-61r3 ISO/IEC 27001:2022 CIS Controls v8.1 MITRE ATT&CK Zero Trust EDR SOAR MCDA

Abstract doi.org/10.14569/IJACSA.2026.0170166 PDF

67

An Efficient Skin Cancer Stage Diagnostic Approach Using Customized Inception V3 Deep Learning Model

Author 1: Adnan Afroz Author 2: Shaheena Noor Author 3: Muhammad Umar Khan Author 4: Shakil Ahmed Bashir

Among all stages of skin cancer, Melanoma, Basel Cell Carcinoma (BCC), and Squamous Cell Carcinoma (SCC) have a significant impact on world health. Although deep learning offers promising potential for dermatological categorization, only limited disease groups have benefited, since most studies focus on particular illnesses rather than covering comprehensive human… Read full abstract & cite →

Melanoma Basal Cell Carcinoma (BCC) Squamous Cell Carcinoma (SCC) Inceptionv3 Bayesian tuning skin cancer classification

Abstract doi.org/10.14569/IJACSA.2026.0170167 PDF

68

Exploring the Impact of Gamified Artificial Intelligence–Driven English Vocabulary Learning Systems on Learner Retention and Motivation

Author 1: Gogineni Aswini Author 2: Madhu Munagala Author 3: V. Saranya Author 4: Keerthana R Author 5: Aseel Smerat Author 6: Vinisha Sumra Author 7: Ahmed I. Taloba

The growing development of digital learning platforms has posed an increased demand on gamified and artificial intelligence-based methods of enhancing English vocabulary learning. However, existing studies often treat gamification and AI as loosely pair components, relying on static game mechanics or post-hoc analytics that limit personalization, adaptability, and long-term learning… Read full abstract & cite →

AI-driven gamified learning adaptive educational systems English vocabulary acquisition learner motivation and engagement vocabulary retention

Abstract doi.org/10.14569/IJACSA.2026.0170168 PDF

69

Enhancing Misinformation Detection on Twitter with a Content-Based Multi-Lingual Bert Model

Author 1: Krishna Kumar Author 2: Akila Venkatesan

The rapid spread of misinformation during global crises like COVID-19 has severely impacted public health, governance, and social trust. Social media platforms such as Twitter have amplified this issue, underscoring the urgent need for multilingual, real-time misinformation detection. The proposed Content-based Attention Multi-lingual BERT (CA-BERT) model addresses this challenge by… Read full abstract & cite →

Component misinformation detection multi-lingual BERT content-based attention mechanism syntactic-semantic similarity explainable AI LIME interpretability COVID-19 misinformation cross-lingual generalization twitter adversarial robustness

Abstract doi.org/10.14569/IJACSA.2026.0170169 PDF

70

MQTT Broker Congestion Mitigation Using Huffman Deep Compression

Author 1: Ammar Nasif Author 2: Zulaiha Ali Othman Author 3: Nor Samsiah Sani Author 4: Yousra Abudaqqa

This study presents an improved MQTT protocol designed to address broker congestion and connection overflow in large-scale IoT networks. The proposed method integrates Huffman Deep Compression (HDC) at the publisher side to mitigate network traffic and latency. Unlike standard MQTT, which suffers from broker overload, our approach applies efficient data… Read full abstract & cite →

Compression network congestion connection overflow deep learning IoT broker congestion IoT network sensor latency reduction publishers broker MQTT power consumption

Abstract doi.org/10.14569/IJACSA.2026.0170170 PDF

71

Advancing Blood Supply Chain Prediction Based on a Novel Hybrid Machine Learning

Author 1: Chaimae Mouncif Author 2: Mohamed Amine Ben RABIA Author 3: Adil Bellabdaoui

Blood supply chains constitute a critical yet often overlooked component of modern public health systems, as they coordinate donors, collection centers, hospitals, and patients. One of the major operational challenges lies in planning the deployment of mobile blood collection units under highly variable and uncertain spatio-temporal demand. In this context… Read full abstract & cite →

Blood supply chain mobile blood collection units spatio-temporal prediction hybrid machine learning decision support

Abstract doi.org/10.14569/IJACSA.2026.0170171 PDF

72

An Integrated Carbon Footprint Calculation System Model for Net Zero Emission in the Manufacturing Industry Based on GHG Protocol and DEFRA

Author 1: Dinar Rahayu

Manufacturing industries play a critical role in achieving Net Zero emission targets due to their significant contribution to greenhouse gas emissions. However, existing carbon footprint calculation practices often apply the GHG Protocol and emission factor standards independently, resulting in fragmented methodologies and limited decision-support capabilities. This study develops a carbon… Read full abstract & cite →

Carbon footprint net zero emission GHG protocol DEFRA decision support system

Abstract doi.org/10.14569/IJACSA.2026.0170172 PDF

73

AI-Based Parkinson’s Disease Diagnosis and Prediction with Therapeutic Game Design for Engagement

Author 1: Marwah Muwafaq Almozani Author 2: Hüseyin Demirel

Parkinson’s disease (PD) is a progressive neurodegenerative disease that impacts motor and cognitive functions, and early diagnosis and management are essential to enhance patient outcomes. The study assumes the implementation of Artificial Intelligence (AI)-based diagnostic and predictive algorithms, along with therapeutic game design, to assist patients in improving the management… Read full abstract & cite →

Parkinson’s disease Artificial Intelligence (AI) mobile application early diagnosis machine learning algorithms

Abstract doi.org/10.14569/IJACSA.2026.0170173 PDF

74

A Systematic Review and Taxonomy of Privacy-Preserving Blockchain Consensus Mechanisms

Author 1: Sahnius Usman Author 2: Sharifah Khairun Nisa Habib Elias Author 3: Suriayati Chuprat Author 4: Ahmad Akmaluddin Bin Mazlan

Blockchain systems rely on consensus mechanisms to validate transactions and coordinate distributed participants, making consensus a critical layer that shapes security, trust, and privacy. Although blockchain is increasingly applied in privacy-sensitive domains such as healthcare, smart cities, and the Internet of Things, existing review studies primarily examine security or performance… Read full abstract & cite →

Blockchain Byzantine Fault Tolerance consensus mechanisms privacy-aware consensus privacy preserving systematic literature review

Abstract doi.org/10.14569/IJACSA.2026.0170174 PDF

75

Experimental Validation of Contextual Parameters and Comparative Analysis with State-of-the-Art in CARS Recommendation Systems in Ubiquitous Computing

Author 1: Pranali Gajanan Chavhan Author 2: Ritesh Vamanrao Patil

The most important role of Consumer Behavior prediction plays in e-commerce, various ways of marketing, and Context-aware Recommendation Systems (CARS). From an Amazon consumer dataset, we conduct a comparative analysis of different machine learning models to compare their performance or effectiveness in predicting consumer behavior based on an Amazon consumer… Read full abstract & cite →

Context-aware recommendation systems multi-modal recommendation transformer-based models experimental validation contextual parameter modeling user behavior modeling

Abstract doi.org/10.14569/IJACSA.2026.0170175 PDF

76

Edge Artificial Intelligence for Real-Time Fresh Produce Identification in Retail Weighing Systems

Author 1: Shi Han Teo Author 2: Jun Kit Chaw

Real-time recognition of loose fresh produce is a key requirement for intelligent retail weighing systems, enabling automated replacement of or assistance to manual PLU-based item selection. However, the deployment performance of recent YOLO architectures on embedded edge platforms such as the NVIDIA Jetson Xavier NX remains insufficiently studied in practical… Read full abstract & cite →

Real-Time object detection Edge Artificial Intelligence (Edge AI) transfer learning YOLO algorithm nvidia jetson fresh produce recognition TensorRT optimization model quantization

Abstract doi.org/10.14569/IJACSA.2026.0170176 PDF

77

Calibrated Residual Intelligence for Intra-Procedural CBCT–Based Collateral Grading in Ischemic Stroke

Author 1: Kazi Ashikur Rahman Author 2: Nur Hasanah Ali Author 3: Ahmad Sobri Muda Author 4: Nur Asyiqin Amir Hamzah Author 5: Noradzilah Ismail

Brain stroke occurs when the brain’s blood supply is disrupted, leading to oxygen deprivation and rapid neuronal death. Ischemic stroke, the focus of this study, accounts for most cases and is strongly influenced by collateral circulation, a network of alternative vessels that stabilize perfusion when a primary artery is obstructed… Read full abstract & cite →

Collateral circulation brain stroke ischemic stroke deep learning ResNet-18

Abstract doi.org/10.14569/IJACSA.2026.0170177 PDF

78

A Robust RT-DETR-Based Method for Complex Self-Service Buffet Scene Detection

Author 1: Zhengwang Xu Author 2: Hongyang Xiao Author 3: Zhou Huang

Object detection in buffet-style environments is highly challenging due to densely stacked tableware, frequent occlusions, strong illumination reflections, and substantial visual similarity across categories, all of which undermine the robustness of existing detectors. To address these issues, this paper proposes an improved real-time detection transformer–based model with a lightweight design… Read full abstract & cite →

RT-DETR lightweight object detection multi-scale feature fusion attention enhancement buffet-scene perception

Abstract doi.org/10.14569/IJACSA.2026.0170178 PDF

79

Predicting Students’ Cognitive Profiles Using Explainable Machine Learning

Author 1: Sonia Corraya Author 2: Fahmid Al Farid Author 3: M Shamim Kaiser Author 4: Shamim Al Mamun Author 5: Jia Uddin Author 6: Hezerul Abdul Karim

Conventional educational strategies fail to comprehend and leverage the diversity of learners’ cognitive strengths and overlook their innate intelligence, a fundamental driver of learning. To address this gap, this study proposes a machine learning (ML) framework to predict students’ overall innate intelligence scores, independent of subject domain or exam structure… Read full abstract & cite →

Explainable AI SHAP feature selection machine learning innate intelligence prediction cognitive profiles student diversity

Abstract doi.org/10.14569/IJACSA.2026.0170179 PDF

80

Inter-Robots Position Estimation Using UWB Positioning Devices for Distributed Cooperative Control of Multiple Robots

Author 1: Airi Kojima Author 2: Kohei Yamagishi Author 3: Tsuyoshi Suzuki

Ultra-wide band (UWB)-based positioning methods for static environments have been continuously improved; how-ever, many existing approaches rely on fixed reference nodes, and methods for directly computing relative positions among mobile units have not been sufficiently investigated. This paper presents a relative positioning approach for multi-robot systems using UWB wireless communication… Read full abstract & cite →

Mobile robots distributed cooperative control mutual positioning system relative position estimation UWB position-ing devices geometric structure

Abstract doi.org/10.14569/IJACSA.2026.0170180 PDF

81

DAMCSeg: Dynamic Adaptive Multi-Modal Collaborative Semantic Segmentation

Author 1: Qirui Liao Author 2: Zuohua Ding Author 3: Hongyun Huang

While current semantic segmentation models excel in controlled environments, they often struggle with key challenges such as dynamic multi-modal data, small target recognition, and computational efficiency for edge deployment. Motivated by these limitations, this study explores targeted solutions and presents DAMCSeg (Dynamic Adaptive Multi-modal Collaborative Semantic Segmentation), an innovative framework… Read full abstract & cite →

Dynamic Adaptive Multi-Modal Collaborative Dual-Stage Attention Fusion (DSAF) End-to-End Detection-Segmentation Joint Framework Lightweight Multi-Modal Fusion (LMMF)

Abstract doi.org/10.14569/IJACSA.2026.0170181 PDF

82

Comparison of Histogram Equalization and Multi-Scale Retinex Methods for Near-Infrared Image Enhancement in Drowsiness Detection

Author 1: Moh Hadi Subowo Author 2: Pulung Nurtantio Andono Author 3: Guruh Fajar Shidik Author 4: Heru Agus Santoso

Computer vision-based drowsiness detection faces significant challenges in low-light conditions, particularly when using near-infrared (NIR) sensors for driver monitoring systems. Appropriate image enhancement methods are crucial to improve detection accuracy. This study systematically evaluates five enhancement methods: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Brightness… Read full abstract & cite →

Image enhancement near-infrared drowsiness detection histogram equalization multi-scale retinex CLAHE no-reference quality metrics

Abstract doi.org/10.14569/IJACSA.2026.0170182 PDF

83

Smart Agriculture in Morocco: An Intelligent Deep Learning Framework for Crop Disease Diagnosis

Author 1: Hajar Krim Author 2: Abdelhadi Assir

The Moroccan agricultural sector is currently navigating a pivotal transformation driven by the “Generation Green 2020–2030” national strategy, which places a high priority on the digitalization of farming practices to bolster resilience against climate volatility and phytopathological risks. This study proposes a robust Smart Agriculture Framework engineered to automate crop… Read full abstract & cite →

Smart agriculture deep learning framework Morocco generation green crop disease PSO-CNN precision farming

Abstract doi.org/10.14569/IJACSA.2026.0170183 PDF

84

Evolution of Image Captioning Models: A Systematic PRISMA Review

Author 1: Abdelkrim SAOUABE Author 2: Khalid TIZRA Author 3: Doha BANOUI

This article presents a systematic review of image captioning approaches conducted according to the PRISMA methodology, ensuring a rigorous, transparent, and reproducible analysis of the literature. The study traces the evolution of image captioning methods, beginning with early machine learning–based techniques that rely on handcrafted visual features, object detection, and… Read full abstract & cite →

Image captioning vision-language models semantic-based models transformer models attention mechanism pre-trained models GPT-based models

Abstract doi.org/10.14569/IJACSA.2026.0170184 PDF

85

Interpretable Structural Stability Analysis for Long-Term Cognitive IoT Time-Series Data

Author 1: Basab Nath Author 2: Yonis Gulzar

Long-term heterogeneous time-series data generated by large-scale sensing and environmental monitoring systems exhibit complex temporal behavior that is not fully captured by prediction-driven learning models. While most existing approaches emphasize short-term forecasting accuracy, comparatively little attention has been given to the analysis of long-term structural stability inherent in such data… Read full abstract & cite →

Structural stability training-free framework total variation regularization rolling statistics stability index

Abstract doi.org/10.14569/IJACSA.2026.0170185 PDF

86

Interpreting Multimodal Fake News Detection Models: An Experimental Study of Performance Factors and Modality Contributions

Author 1: Noha A. Saad Eldien Author 2: Wael H. Gomaa Author 3: Khaled T. Wassif Author 4: Hanaa Bayomi

The widespread dissemination of multimodal mis-information requires models that can reason across textual and visual content while remaining interpretable. However, many existing multimodal fusion approaches implicitly assume uniform modality reliability, providing limited transparency into modality contributions. This study introduces TweFuse-W, a lightweight multimodal framework for fine-grained fake-news detection that reframes… Read full abstract & cite →

Multimodal fake news detection modality reliability modeling adaptive fusion interpretable fusion lightweight multi-modal models

Abstract doi.org/10.14569/IJACSA.2026.0170186 PDF

87

Enhancing SCADA Security in Critical Infrastructure: A Multi-Layered Architecture Using IoT-Based Monitoring and AI-Driven Anomaly Detection

Author 1: Mohammad Alqahtani Author 2: Abdulkarim Amin Author 3: Kyounggon Kim Author 4: Seokhee Lee

Supervisory Control and Data Acquisition (SCADA) systems are central to the efficient operation of critical infrastructure such as energy, water, and industrial networks. However, the increased digital integration of SCADA components, especially through Internet of Things (IoT) technologies, has simultaneously broadened their exposure to cyber threats. This project presents a… Read full abstract & cite →

SCADA security industrial IoT anomaly detection machine learning digital forensics wind turbine telemetry

Abstract doi.org/10.14569/IJACSA.2026.0170187 PDF

88

Lightweight Dual-YOLOv8 Instance-Aware Semantic Segmentation for Real-Time Autonomous Driving on Edge ARM/GPU Platforms

Author 1: Safa Teboulbi Author 2: Seifeddine Messaoud Author 3: Mohamed Ali Hajjaji Author 4: Mohamed Atri Author 5: Abdellatif Mtibaa

Semantic segmentation is a fundamental component of autonomous driving systems, enabling accurate scene understanding and object-level perception. However, achieving precise instance-level delineation while maintaining real-time performance on resource-constrained platforms remains a significant challenge, particularly for edge deployment scenarios. This paper proposes a lightweight dual-YOLOv8 fusion framework for instance-aware semantic segmentation… Read full abstract & cite →

Autonomous driving instance-aware semantic seg-mentation real-time instance segmentation YOLOv8 dual-model fusion edge deployment

Abstract doi.org/10.14569/IJACSA.2026.0170188 PDF

89

Lightweight Machine Learning for Real-Time Gear Change Prediction in Autonomous Parking

Author 1: Ahmed A. Kamel Author 2: Reda Alkhoribi Author 3: M. Shoman Author 4: Mohammed A. A. Refaey

Real-time motion planning for autonomous parking on embedded advanced driver-assistance system (ADAS) platforms faces a fundamental computational bottleneck: transformer-based approaches (e.g., Motion Planning Trans-former, Diffusion-based planners) achieve strong performance but incur prohibitive computational costs unsuitable for resource-constrained automotive systems. This work proposes a lightweight alternative machine learning approach using Random… Read full abstract & cite →

Autonomous parking direction change detection embedded systems machine learning motion planning random forest rapidly-exploring random trees rapidly-exploring random tree star

Abstract doi.org/10.14569/IJACSA.2026.0170189 PDF

90

Relative Position Estimation for Multi-Robots Based on Vertex Distance Between Regular Tetrahedral Units

Author 1: Airi Kojima Author 2: Kohei Yamagishi Author 3: Tsuyoshi Suzuki

Optimal decentralized cooperative control in multi-robot systems requires simultaneous local sensing and inter-agent communication. Ultra-wide band (UWB) wireless communication has been investigated as a self-contained positioning system capable of supporting both functions within a single device. Conventional UWB-based positioning methods estimate absolute positions using distance measurements relative to fixed anchors… Read full abstract & cite →

Multi-robot system distributed cooperative control relative position estimation self-contained positioning system inrter-vertex distance geometric structure

Abstract doi.org/10.14569/IJACSA.2026.0170190 PDF

91

Explainable AI for Enhancing Awareness of Academic Stress Among International University Students

Author 1: Ahmed Almathami Author 2: Richard Stone

Academic stress is a common challenge in higher education, especially for international university students who must adapt to new academic systems, expectations, and learning environments. In recent years, artificial intelligence has been increasingly used to analyze academic data and estimate student stress. However, most AI-based systems prioritize prediction accuracy over… Read full abstract & cite →

Explainable artificial intelligence academic stress student awareness international university students learning analytics Human–Computer Interaction

Abstract doi.org/10.14569/IJACSA.2026.0170191 PDF

92

TRI-GATE: A Tri-Modal Anti-Spoofing System for Gate Access Using Vehicle, License Plate, and Face Recognition

Author 1: Muhannad Alsultan Author 2: Thamer Alghonaim Author 3: Abdulaziz Alorf Author 4: Bandar Alwazzan Author 5: Faisal Alsakakir Author 6: Abdullah Alhassan Author 7: Yousif Hussain

Vehicle gate access, in general, still relies heavily on manual inspection of identification cards and visual verification by security guards, which is slow, tedious, and susceptible to spoofing. Single-modality, computerized systems that utilize license plates, vehicle appearance, and facial recognition can partially alleviate this difficulty. Still, they are prone to… Read full abstract & cite →

Tri-modal anti-spoofing vehicle recognition license plate recognition face recognition real-time gate access control multimodal biometrics

Abstract doi.org/10.14569/IJACSA.2026.0170192 PDF

93

Comparative Evaluation of Deep Learning Architectures and Hybrid Heuristics for Automated Gambling Content Detection

Author 1: Eros Anaya Sánchez Author 2: Chesney Taichi Marchena Tejada Author 3: Jose Alfredo Herrera Quispe

The exponential proliferation of online gambling content represents a multifaceted challenge for contemporary automated content moderation systems, primarily driven by the sophisticated visual obfuscation and semantic complexity characteristic of modern digital advertising. This study conducts a rigorous comparative evaluation of the efficacy of Deep Learning (DL) architectures against classical Machine… Read full abstract & cite →

Deep learning image classification gambling detection ResNet50 hybrid systems transfer learning Convolutional Neural Networks platform governance content moderation

Abstract doi.org/10.14569/IJACSA.2026.0170193 PDF

94

From Accuracy to Insight: Explainability in Review Rating Prediction with Transformers

Author 1: Dhefaf T. Radain Author 2: Dimah Alahmadi Author 3: Arwa M. Wali

Mobile application (app) reviews provide valuable information that facilitates understanding of users’ needs, leading to better design of developed products. They have abundant data that can be utilized by different models to explain the prediction results to stakeholders. This will lead mobile app developers to trust and rely on the… Read full abstract & cite →

Explainability LIME review rating prediction SHAP transformer-based models

Abstract doi.org/10.14569/IJACSA.2026.0170194 PDF

95

Machine Learning-Based Sentiment Analysis Pipeline for Evaluating Hajj Food Service Quality

Author 1: Amjad Enad Almutairi Author 2: Aisha Yaquob Alsobhi Author 3: Abdulrhman M Alshareef

Pilgrimage, also known as Hajj, brings together millions of people each year, creating significant challenges in managing, organizing, and maintaining the quality of various services. Among these essential services, food provision plays a vital role in shaping pilgrims’ overall experience and satisfaction. Despite its importance, research focusing on food services… Read full abstract & cite →

Sentiment analysis service quality machine learning Hajj food service

Abstract doi.org/10.14569/IJACSA.2026.0170195 PDF

96

DEEP: A Distributed Energy Efficient Routing Protocol for Internet of Nano-Things

Author 1: Saoucene Mahfoudh Author 2: Areej Omar Balghusoon

Nanotechnology offers transformative capabilities across healthcare, environmental monitoring, and industrial automation. When integrated with modern communication technologies, Wireless Nano Sensor Networks (WNSNs) form the Internet of Nano Things (IoNT), interconnecting nanoscale devices with conventional networks. Despite its potential, efficient routing in IoNT remains challenging due to severe energy constraints, limited… Read full abstract & cite →

Routing protocol Internet of Nano Things nano-sensor energy efficiency energy harvesting terahertz communication

Abstract doi.org/10.14569/IJACSA.2026.0170196 PDF

97

Predictive Data Mining Analysis of Ownership Structures and their Influence on Corporate Tax Avoidance

Author 1: Tuti Herawati Author 2: Helmi Yazid Author 3: Nurhayati Soleha Author 4: Munawar Muchlish

This study aims to examine the impact of corporate ownership structures on tax avoidance using a predictive data mining approach. The main challenge addressed is understanding how variations in ownership influence a firm’s strategic financial decisions, particularly its tendency to engage in tax minimization practices. By applying advanced predictive data… Read full abstract & cite →

Data mining big data tax avoidance tax burden corporate ownership structure

Abstract doi.org/10.14569/IJACSA.2026.0170197 PDF