IJACSA Vol. 17 Issue 8 (2026)

1

An Intelligent Ensemble Learning Framework for Visibility and CAVOK Prediction to Support Logistics Operations

Author 1: Omar Alghushairy Author 2: Raed Alsini Author 3: Ahmed Alamri Author 4: Saud Yonbawi Author 5: Ayman Yafoz Author 6: Xiaogang Ma

The supply chain operation is fully dependent on weather conditions due to the damage that produce may face during bad weather or accidents that may occur because of cloud cover, especially in Saudi Arabia, which experiences frequent cloud cover throughout the year. In this study, a new weather prediction model… Read full abstract & cite →

Weather forecasting sky CAVOK extra tree supply chain visibility distance Saudi Arabia

Abstract doi.org/10.14569/IJACSA.2026.0170801 PDF

2

AI-Mediated STEM Problem Solving: Activity Distribution and Functional Roles in a Systematic Review

Author 1: Mika Lim

Artificial intelligence (AI) is increasingly embedded in STEM problem-solving activities, yet existing reviews have generally organized the literature by technology type or learning outcome. This review shifts the analytic focus from what AI can do to how it is integrated into students' STEM problem solving. It examined where AI was… Read full abstract & cite →

Artificial intelligence STEM education AI-mediated learning human-AI interaction functional roles systematic review

Abstract doi.org/10.14569/IJACSA.2026.0170802 PDF

3

Five Whys as an Epistemic-Honesty Scaffold for Multi-Agent LLM Analysis of Industrial Time Series

Author 1: Yoshihiro Ochi Author 2: Yasunobu Uchiyama

Multi-agent large language model (LLM) pipelines increasingly analyse industrial time series. Where the true causes lie outside the recorded signal, they tend to report confident, plausible-sounding mechanisms not grounded in the data — a false root-cause handed to an engineer. We port Toyota’s Five Whys onto such a multi-agent system… Read full abstract & cite →

Five Whys multi-agent systems large language models root-cause analysis epistemic honesty causal abstention industrial time series argumentation framework human–AI collaboration

Abstract doi.org/10.14569/IJACSA.2026.0170803 PDF

4

A Reproducible Mobile-Cloud Framework for Preliminary Non-Invasive Anemia Screening from Palpebral Conjunctiva Images

Author 1: Enrique Lee Huamaní Author 2: Erasmo Montufar-Barrientos Author 3: Victor Romero-Alva Author 4: Linett Velasquez-Jimenez

Non-invasive anemia screening from palpebral conjunctiva images may support preliminary triage when access to laboratory hemoglobin testing is limited. This study presents and evaluates a reproducible mobile-cloud framework that integrates guided image capture, automated eye-region localization, region-of-interest extraction, color normalization, image-quality control, deep-learning inference, probability calibration, longitudinal storage, and user… Read full abstract & cite →

Anemia screening conjunctival image analysis mobile health deep learning EfficientNet mobile-cloud computing reproducible research

Abstract doi.org/10.14569/IJACSA.2026.0170804 PDF

5

Reversible Visual Obfuscation via Embedded Recovery Metadata: Combining Median‑Filter Irreversibility with Cryptographic Restoration

Author 1: Andranik Karakhanyan

Although cloud storage platforms are widely used to safeguard personal images, data leakage and unauthorized access remain persistent threats, exposing sensitive visual content. Linear obfuscation methods such as Gaussian blur can be reversed by deconvolution when kernel parameters are known, while non-linear median filtering permanently destroys details but also eliminates… Read full abstract & cite →

Sensitive information visual privacy image processing blur filter median filtering data obfuscation reversible concealment

Abstract doi.org/10.14569/IJACSA.2026.0170805 PDF

6

Automatic Generation and Optimization Algorithm for Brand Visual Identity System Based on Multimodal Generative Adversarial Network

Author 1: Yunfei Gong Author 2: Xiaoyue Cao

A Brand Visual Identity System (BVIS) is a crucial carrier for conveying a brand's core values, but traditional manual design struggles to meet the demands of efficient and personalized design in the digital age. Addressing the shortcomings of existing Multimodal Generative Adversarial Networks (MM-GANs) in BVIS design, such as semantic-visual… Read full abstract & cite →

Brand digitalization visual identity system multimodal generative adversarial network attention mechanism

Abstract doi.org/10.14569/IJACSA.2026.0170806 PDF

7

Integrated Health Belief and User Engagement Model for Physical Activity Apps

Author 1: Nur Farahin Mohd Johari Author 2: Nazlena Mohamad Ali Author 3: Mohamad Hidir Mhd Salim

Physical activity applications support monitoring, motivating, and sustaining healthy behavior, yet long-term engagement remains challenging. This study validates an integrated Health Belief Model and User Engagement Model for explaining behavior change among physical activity application users. Survey data from 423 users were analyzed using Partial Least Squares Structural Equation Modeling… Read full abstract & cite →

Mobile health physical activity applications health belief model user engagement model behavior change technology engagement

Abstract doi.org/10.14569/IJACSA.2026.0170807 PDF

8

Evaluating Government Website Readiness for Generative AI Search Engines: A Framework and Empirical Assessment

Author 1: Hussein Ali Bahadi

The rapid adoption of generative artificial intelligence (GenAI) search engines is changing how citizens obtain government information. Unlike conventional search engines, which return ranked links, GenAI systems synthesise answers directly from retrieved sources, positioning government websites as inputs to AI-mediated information ecosystems rather than as destinations. To our knowledge, no… Read full abstract & cite →

Generative AI government websites AI readiness E-government retrieval-augmented generation SmartPLS digital transformation evaluation framework

Abstract doi.org/10.14569/IJACSA.2026.0170808 PDF

9

A Hybrid Econometric and Machine Learning Framework for Identifying Nonlinear Dynamics Between Digital Economy and Carbon Emissions

Author 1: Jinghan Li

The rapid expansion of the digital economy has reshaped global production systems and energy consumption patterns, yet its environmental consequences remain theoretically ambiguous. This study investigates the nonlinear relationship between digital economy development and carbon emissions using a cross-country panel dataset covering 45 countries during 2012–2024. A hybrid econometric and… Read full abstract & cite →

Digital economy carbon emissions nonlinear relationship system GMM machine learning energy efficiency industrial upgrading

Abstract doi.org/10.14569/IJACSA.2026.0170809 PDF

10

A Multi-Layer Hyper-Personalization Pipeline for Predicting Next User Actions Across Web, App, Email, and Social Channels

Author 1: Kohei Arai

Hyper-personalization systems that unify customer behavior across Web, App, Email, and social channels are increasingly central to digital business strategy, yet published evaluations rarely isolate which modeling component—collaborative filtering, sequential prediction, or behavioral clustering—actually drives predictive value, and rarely validate a full pipeline end-to-end before committing to production data collection… Read full abstract & cite →

Hyper-personalization collaborative filtering sequential recommendation LSTM Markov chain customer segmentation multi-channel analytics ablation study

Abstract doi.org/10.14569/IJACSA.2026.0170810 PDF

11

Deep Learning Approaches for Apple Disease Classification and Detection: A PRISMA 2020 Systematic Literature Review

Author 1: Brahim Ouben Hssain Author 2: Khalil Ladrham Author 3: Noureddine El Barbri Author 4: Rachid El Ayachi

Apple diseases cause great losses in fruit yield and quality, while manual diagnosis is time-consuming, subjective, and difficult to scale across orchards. Deep learning has thus become a staple in image-based disease recognition, but evidence remains scattered across classification and object-detection paradigms, heterogeneous datasets, and inconsistent evaluation protocols. This systematic… Read full abstract & cite →

Apple disease recognition deep learning convolutional neural networks object detection PRISMA 2020 systematic literature review precision agriculture

Abstract doi.org/10.14569/IJACSA.2026.0170811 PDF

12

When Calibrated Detectors Meet New Attacks: Per-Category Reliability of Machine-Learning Intrusion Detection Under Distribution Shift

Author 1: Khalid Alalawi

Machine-learning intrusion detectors are usually reported with accuracy or F1 on a single train and test split, and their confidence scores are often read operationally as probabilities without an explicit calibration check. We test that assumption. We measure the reliability of the predicted probabilities of three classifiers, logistic regression, random… Read full abstract & cite →

Intrusion detection probability calibration distribution shift expected calibration error decision curve analysis class imbalance network security

Abstract doi.org/10.14569/IJACSA.2026.0170812 PDF

13

Explainable Machine Learning for Cardiovascular Disease Prediction Using BRFSS Health Indicators

Author 1: Sakchai Tangprasert Author 2: Suwit Chantasen Author 3: Nalinpat Bhumpenpein Author 4: Yuenyong Nilsiam Author 5: Siranee Nuchitprasitchai

Cardiovascular disease remains a major public health burden and is associated with demographic, behavioral, and chronic-health characteristics. This study evaluates explainable machine learning for cross-sectional classification of self-reported cardiovascular disease status using the 2020 CDC Behavioral Risk Factor Surveillance System (BRFSS)-derived Personal Key Indicators of Heart Disease dataset. After removing… Read full abstract & cite →

Cardiovascular disease machine learning explainable artificial intelligence cardiovascular disease status classification behavioral risk factor surveillance system cross-industry standard process for data mining (CRISP DM)

Abstract doi.org/10.14569/IJACSA.2026.0170813 PDF

14

Transformer Health Index Prediction Using Static and Temporal Learning Models

Author 1: Omar Ahmed Mohammed Hamood Al-Shaikh Author 2: Arfah Ahmad Author 3: Ahmad Jazlan Haja Mohideen Author 4: Muhammad Sharil Yahaya

Transformer health index (THI) prediction supports condition-based maintenance by mapping dissolved-gas, oil-quality, and furan indicators to an interpretable asset-condition score. This study develops a supervised benchmarking framework using 3,392 transformer oil diagnostic records from 510 transform-ers. Thirteen diagnostic features from dissolved gas analysis, oil quality analysis, and furan analysis are… Read full abstract & cite →

Transformer health index machine learning deep learning liq-uid neural networks oil diagnostic data condition-based mainte-nance smart grid asset management

Abstract doi.org/10.14569/IJACSA.2026.0170814 PDF

15

ROMBandAR: Development of an AR-Based Wrist ROM Assessment Prototype for Rehabilitation

Author 1: Nurul Aimi Johan Author 2: Wan Rizhan Author 3: Normala Rahim

Wrist range of motion (ROM) assessment is important in rehabilitation because wrist movement supports hand positioning and functional hand use. Limited wrist movement can be caused by a variety of conditions, including neurological conditions such as stroke and musculoskeletal or orthopedic injuries such as distal radius fractures or tendon repairs… Read full abstract & cite →

ROMBandAR augmented reality wrist range of motion rehabilitation pose estimation MediaPipe vuforia extrinsic feedback

Abstract doi.org/10.14569/IJACSA.2026.0170815 PDF

16

Intelligent Booking Systems for Natural Resources and Urban Development: A Geospatial SLR

Author 1: Stephanie Anak Nanta Author 2: Jack Febrian Rusdi Author 3: Louisa Suly Anak Nanta Author 4: Imtiaz Ali Brohi Author 5: Vicente Aquino Pitogo

Rapid urbanization and increasing pressure on natural resources demand efficient, transparent, and sustainable management systems. However, existing booking systems in government agencies often operate in silos and lack integration with geospatial data. This study conducts a Systematic Literature Review (SLR) to identify essential components of intelligent booking systems (IBS) and… Read full abstract & cite →

Intelligent booking systems natural resource management Geographic Information Systems (GIS) satellite remote sensing data mining Multi-Criteria Decision Making (MCDM)

Abstract doi.org/10.14569/IJACSA.2026.0170816 PDF

17

A Smart Transformer-Based Model to Classify Extremist Behavior on Social Media

Author 1: Tamer Salah Author 2: Hazem M. El-Bakry Author 3: Amira Rezk

In recent years, social media has been used as a fertile environment for spreading radical ideologies through extremist organizations and groups to recruit new individuals capable of implementing their schemes. This has led to an enormous amount of extremist data that can be used by research agencies to analyze and… Read full abstract & cite →

Social media transformer-based model word embedding Bi-LSTM network additive attention

Abstract doi.org/10.14569/IJACSA.2026.0170817 PDF

18

Benchmarking Bio-Inspired Metaheuristics for Load-Aware Task Offloading in Hierarchical IoT-Fog-Cloud Ecosystems

Author 1: Lamia Oualili Author 2: Mohamed EL Ghmary Author 3: Hassan Echoukairi

The multi-objective nature of task scheduling for novel latency-sensitive applications in IoT-Fog-Cloud hierarchies presents persistent challenges, where optimizing one QoS metric often degrades another. Although bio-inspired algorithms provide adaptive solutions, existing comparative studies are constrained by narrow algorithmic scopes, limited evaluation metrics, and a notable absence of statistical validation. To… Read full abstract & cite →

Load balancing IoT-Fog-cloud metaheuristic algorithms iFogSim QoS optimization wilcoxon signed-rank test task scheduling edge computing

Abstract doi.org/10.14569/IJACSA.2026.0170818 PDF

19

Emerging Technologies and Cybersecurity in Critical Information Infrastructure and Industrial Control Systems: An Integrated Cyber Risk Pathway Model

Author 1: Jennita Rao Appanah Appayya Author 2: Sandhya Armoogum Author 3: Kaleem Ahmed Usmani

The digital transformation of Critical Information Infrastructure (CII) and Industrial Control Systems (ICS) through Industry 4.0 technologies introduces significant cybersecurity challenges. While existing research examines technologies individually, little attention has been given to how their combined adoption reshapes the overall threat landscape. This study presents a Multivocal Literature Review, synthesising… Read full abstract & cite →

Critical Information Infrastructure (CII) Industrial Control Systems (ICS) emerging technologies industry 4.0 IT/OT convergence cyber resilience cyber-physical systems multivocal literature review integrated cyber risk pathway model

Abstract doi.org/10.14569/IJACSA.2026.0170819 PDF

20

MalBERT-Temporal: Transformer-Based Zero-Day Malware Detection in Windows Executable Binaries Under Strict Temporal Isolation

Author 1: Manar Alanazi Author 2: Israa Alsiyat

In current malware detection benchmarks, random train/test splits are commonly used, allowing temporal leakage to occur and obscuring performance degradation caused by concept drift. Moreover, traditional classifiers that operate on a one-dimensional PE feature vector do not explicitly model long-range interactions between structurally distant feature groups. This study introduces a… Read full abstract & cite →

Zero-day malware detection transformer self-attention portable executable temporal isolation concept drift BODMAS structural feature modeling

Abstract doi.org/10.14569/IJACSA.2026.0170820 PDF

21

Next-Generation Explainable Sensor-Fusion-Based Artificial Intelligence Framework for In-Home Diabetic Foot Ulcer Risk Prediction

Author 1: Jayashree J Author 2: Vijayashree J Author 3: Vijayarajan V Author 4: Saravanan S

Diabetic Foot Ulcers (DFUs) are one of the most serious but preventable complications of diabetes mellitus that often develop without clinical signs until the late stages. This means that neuropathic and vascular abnormalities should be diagnosed in time to be effectively prevented. This study presents the design and development of… Read full abstract & cite →

DFU risk assessment sensor-fusion-based monitoring system Explainable Artificial Intelligence (XAI) MLP-BiLSTM hybrid model home-based healthcare monitoring

Abstract doi.org/10.14569/IJACSA.2026.0170821 PDF

22

ReadTech: A Mobile Reading Application for Grade 7 Learners

Author 1: Mary Jane U. Quibilan

Reading comprehension remains a fundamental skill for academic success; however, many Grade 7 learners continue to experience difficulties in vocabulary development, text comprehension, and access to engaging instructional materials. This study developed and evaluated the ReadTech Mobile Reading Application using a Design and Development Research (DDR) methodology. The study integrated… Read full abstract & cite →

Android application digital learning educational technology mobile learning reading comprehension

Abstract doi.org/10.14569/IJACSA.2026.0170822 PDF

23

A Blockchain-Based Role-Based Access Control Gateway for Secure Electronic Health Record Access and Tamper-Evident Auditing

Author 1: Osamah Saleh Author 2: Shouki A. Ebad

Electronic Health Record (EHR) systems store sensitive patient information and require strong access control and reliable audit records. Traditional centralized Role-Based Access Control (RBAC) systems may be vulnerable to unauthorized access, privilege escalation, audit-log modification, and undetected changes to stored records. This study developed a lightweight blockchain-based RBAC gateway integrated… Read full abstract & cite →

Electronic health records role-based access control blockchain smart contracts OpenMRS audit logs access control cybersecurity

Abstract doi.org/10.14569/IJACSA.2026.0170823 PDF

24

A Cross-Sector Leadership Framework for Organizational Change and Sustainability: Testing the Impact of Intrinsic Leadership Values Using Structural Equation Modeling

Author 1: Sudirman Said Author 2: Aurik Gustomo Author 3: Yudo Anggoro

Purpose: Leading involves challenging the status quo. A critical function of leadership is to drive change, ensuring both the immediate performance and long-term sustainability of organizations. Design/methodology/approach: Research on leadership, changes, and sustainability has focused predominantly on individual sectors—public, private, or non-profit. Despite the increasing demand for cross-sectoral collaborations, studies… Read full abstract & cite →

Intrinsic leadership values organizational change sustainable change organizational sustainability leadership development cross-sector leadership public-private-nonprofit collaboration change management

Abstract doi.org/10.14569/IJACSA.2026.0170824 PDF

25

Research Trends in Human-Computer Interaction for Immersive Virtual Reality Applications: A Bibliometric Study (2010-2026)

Author 1: Nur Sauri Yahaya Author 2: Sobihatun Nur Abdul Salam Author 3: Hanis Salwani Othman

The rapid advancement of immersive technologies has significantly expanded the scope of Human-Computer Interaction (HCI) within the context of Immersive Virtual Reality (IVR). Despite the growing body of research, the field remains fragmented, with limited understanding of its intellectual and thematic development. This study conducts a comprehensive bibliometric analysis of… Read full abstract & cite →

Human-computer interaction (HCI) Immersive virtual reality (IVR) immersive technologies User experience (UX) bibliometric analysis science mapping

Abstract doi.org/10.14569/IJACSA.2026.0170825 PDF

26

Memory-Augmented Autoencoder for Industrial Anomaly Detection

Author 1: Hai Son Author 2: Duc Ngo

Unsupervised anomaly detection in industrial manufacturing requires identifying defective products utilizing only defect-free training images. Reconstruction-based methods using autoencoders are widely applied but suffer from limited discriminative power when defects occupy small image regions. This work introduces a Memory-Augmented Autoencoder (MAA) that combines a U-Net reconstruction component with a patch-level… Read full abstract & cite →

Anomaly detection autoencoder memory bank nearest-neighbor search MVTec AD unsupervised learning industrial inspection U-Net

Abstract doi.org/10.14569/IJACSA.2026.0170826 PDF

27

Understanding Player Choice in Digital Games: Qualitative Study Using Thematic Analysis

Author 1: Faris Haziq Sarif Author 2: Amelia Jati Robert Jupit Author 3: Jacey-Lynn Minoi Author 4: A. Imran Nordin

Player choice is a fundamental aspect of digital games, shaping how players interact with virtual worlds and narratives. In many games, player choices are reflected through mechanics such as morality systems, which record decisions as virtuous or harmful and shape how characters and the game world react. For game designers… Read full abstract & cite →

Player choice thematic analysis player engagement

Abstract doi.org/10.14569/IJACSA.2026.0170827 PDF

28

Metaheuristic-Based Test Case Selection for Regression Testing: A Systematic Literature Review

Author 1: Siti Hawa Mohamed Shareef Author 2: Rabatul Aduni Sulaiman Author 3: Nazri Mohd Nawi Author 4: Wan Noor Hamiza Wan Ali Author 5: Nurul N. Jamal Author 6: Fairuz Amalina

Regression testing plays a crucial role in ensuring that software modifications do not adversely affect existing functionality. However, test suites continue to grow, and the retest-all method has become increasingly impractical due to high execution costs and time constraints. Consequently, Test Case Selection (TCS) has emerged as an important optimization… Read full abstract & cite →

Regression testing test case selection metaheuristic algorithms search-based software testing

Abstract doi.org/10.14569/IJACSA.2026.0170828 PDF

29

TriMFB: A Tri-Feature Fusion Framework Integrating Modality-Specific and Cross-Modal Representations for Fine-Grained Fake News Detection

Author 1: Idza Aisara Norabid Author 2: Masita Jalil Author 3: Noor Hafhizah Abd Rahim

The rapid spread of multimodal misinformation on social media platforms has strengthened the need for effective fake news detection systems. While recent multimodal approaches integrate textual and visual information, most existing studies formulate the task as binary classification, thereby failing to fully exploit the rich label structures available in fine-grained… Read full abstract & cite →

Bilinear pooling early fusion fake news detection fine-grained multimodal

Abstract doi.org/10.14569/IJACSA.2026.0170829 PDF

30

An Intelligent On-Demand Laundry Logistics Platform with Real-Time Location Tracking and Machine Learning-Based Customer Support

Author 1: Muhammad Faris Rosli Author 2: Siti Zuraidah Ibrahim Author 3: Mohd Nazri A Karim Author 4: Mohd Hafizuddin Mat Author 5: Faridah Hanim Mohd Noh Author 6: Tanakorn Inthasuth

Traditional on-demand laundry operations suffer from inefficient manual coordination, a lack of real-time delivery visibility, and delayed customer support handling. To address these operational challenges, this study presents LaundrOTrack, an intelligent on-demand laundry logistics platform connecting customers, drivers, and administrators within a unified digital ecosystem. Powered by Flutter for cross-platform… Read full abstract & cite →

On-demand laundry logistics Location-Based Services (LBS) linear SVC intent classification flutter cloud firestore

Abstract doi.org/10.14569/IJACSA.2026.0170830 PDF

31

Learning to Schedule Machines and Operators: A Human-Aware Deep Q-Learning Framework for Dynamic Flexible Job Shops

Author 1: Taji Hajar Author 2: Ayad Ghassane Author 3: Zaki Abdelhamid Author 4: Khammal Adil

Most learning-based schedulers for job shop problems assume static workforce performance. However, dynamic dual-resource job shops must navigate stochastic disturbances and time-varying task durations due to human factors. This study proposes a human-centered Deep Reinforcement Learning framework, DD4LQN, for dynamic flexible job shop scheduling under operator learning and forgetting dynamics… Read full abstract & cite →

Deep reinforcement learning dynamic flexible job shop dual-resource scheduling human-centered manufacturing learning and forgetting

Abstract doi.org/10.14569/IJACSA.2026.0170831 PDF

32

Institutional Readiness for Generative AI-Enhanced Assessment and Feedback in Higher Education: Evidence from an Exploratory Study

Author 1: Dolantina Hyka Author 2: Elion Shabanaj Author 3: Jurgen Mecaj Author 4: Ardita Hykaj Author 5: Elton Skendaj

The emergence of Generative Artificial Intelligence (GenAI) technology is changing the ways of assessment and feedback procedures in higher education institutions by allowing for a more flexible and personalized process of learning. However, the effective use of GenAI requires pedagogic design, governance, and implementation strategies. The current study will discuss… Read full abstract & cite →

Generative AI assessment feedback higher education academic integrity formative assessment institutional readiness

Abstract doi.org/10.14569/IJACSA.2026.0170832 PDF

33

Earthquake Damage Risk Classification Using Machine Learning Models Based on Built-Up Area Indices from Satellite Imagery

Author 1: Gunawan Prayitno Author 2: Eko Sediyono Author 3: Irwan Sembiring Author 4: Sri Yulianto Joko Prasetyo

One of the challenges in assessing earthquake damage risk in areas with high seismic activity and limited data is the lack of a detailed building inventory and the absence of available data. Therefore, a remote sensing and machine learning framework is needed that can utilize the built-up area index with… Read full abstract & cite →

Seismic risk assessment vulnerability proxy remote sensing machine learning data scarcity

Abstract doi.org/10.14569/IJACSA.2026.0170833 PDF

34

Semantic Interoperability of Heterogeneous Information Systems: A Hybrid Framework for Machine Learning-Assisted Semantic Mapping Recommendation

Author 1: Aïcha KOULOU Author 2: Mohamed CHEKOUR Author 3: Norelislam EL HAMI Author 4: Nabil HMINA

Semantic interoperability is a major challenge in the integration of heterogeneous information systems, where differences in data structures, terminologies, and representations complicate the automatic identification of schema matches. Traditional schema matching approaches, primarily based on rules or lexical similarity measures, have limitations in the face of the increasing complexity of… Read full abstract & cite →

Semantic interoperability schema matching machine learning XGBoost information systems

Abstract doi.org/10.14569/IJACSA.2026.0170834 PDF

35

Can We Trust Concept-Based ECG Explanations? A TCAV Analysis of Confounding and Reproducibility in Atrial Fibrillation Detection

Author 1: El Hassane Jennah Author 2: Houda Indjaren Author 3: Lhoucine Ben Taleb Author 4: Hamid El Malali Author 5: Azeddine Mouhsen

AI explanation methods are typically validated in one of two ways: by confirming that an explanation is not an artifact of the underlying data (a confound check), or by confirming that it generalizes to unseen data. Fewer studies examine whether an explanation is stable across independently trained copies of the… Read full abstract & cite →

Explainable AI concept activation vectors TCAV atrial fibrillation reproducibility electrocardiogram

Abstract doi.org/10.14569/IJACSA.2026.0170835 PDF

36

HR-CD-Auth: Consent-Bound Cross-Domain Pseudonymous Authentication for Vocational Counseling and Employment Matching

Author 1: Haewon Byeon Author 2: Changmin Keum

Vocational counseling platforms move sensitive records across counseling centers, enterprise human-resource systems, public employment services, and automated matching engines. Direct reuse of cross-domain authentication leaves identifiers linkable, separates authentication from purpose and consent, and gives revocation no protocol-level effect on cached credentials. HR-CD-Auth combines domain-scoped pseudonyms, compartmented data keys, consent-bound… Read full abstract & cite →

Anonymous authentication consent management cross-domain authentication employment matching forward secrecy pseudonymization vocational counseling

Abstract doi.org/10.14569/IJACSA.2026.0170836 PDF

37

Popularity Bias and Category Diversity in Review-Based Rating Prediction for E-Commerce: An Empirical Study on Indonesian Marketplace Data

Author 1: Andy Supriyadi Author 2: Herman Dwi Surjono Author 3: Handaru Jati

Online marketplaces increasingly rely on user reviews to estimate product quality. However, most empirical comparisons of review-based prediction models report only aggregate accuracy and overlook how item popularity and category composition shape that accuracy. This study presents a controlled empirical analysis of review-based rating prediction on a self-collected, anonymized corpus… Read full abstract & cite →

Review-based rating prediction popularity bias category diversity product characteristics e-commerce Indonesian text classification

Abstract doi.org/10.14569/IJACSA.2026.0170837 PDF

38

Orientation-Aware Feature Fusion for Accurate Rotated Object Detection in Remote Sensing Images

Author 1: Jing Zhang Author 2: Mas Rina Mustaffa Author 3: Fatimah Khalid Author 4: Zainal Abdul Kahar

Conventional feature fusion mechanisms largely overlook orientation information, making it difficult to effectively represent objects with diverse rotational patterns. To address this issue, we propose YOLO-RSL, a lightweight rotated object detector that introduces orientation awareness into feature representation, feature fusion, and localization optimization. The proposed Rotation-Oriented Iterative Attentional Feature Fusion… Read full abstract & cite →

Rotated object detection oriented bounding box feature fusion remote sensing image

Abstract doi.org/10.14569/IJACSA.2026.0170838 PDF

39

Feasibility of Generative Artificial Intelligence and Large Language Model Adoption to Enhance Organisational Knowledge Management Practices

Author 1: Surya Sumarni Hussein Author 2: Nur Azaliah Abu Bakar Author 3: Saidatul Rahah Hamidi Author 4: Shuhaida Mohamed Shuhidan Author 5: Siti Salbiyah Abdul Geni Author 6: Anitawati Mohd Lokman

Generative artificial intelligence (Gen AI) and large language models (LLMs) offer substantial potential to improve how organisations capture, organise, retrieve and reuse knowledge. Existing knowledge management (KM) frameworks, however, seldom integrate Gen AI/LLM-specific processes, data governance, and ethical requirements in highly regulated public-sector settings, and few have been empirically evaluated… Read full abstract & cite →

Generative artificial intelligence (Gen AI) large language models (LLM) knowledge management diffusion of innovation (DOI) lokman's emotion and importance quadrant (LEIQ™) content validity

Abstract doi.org/10.14569/IJACSA.2026.0170839 PDF

40

Machine Learning for Distributed Acoustic and Fibre-Optic Sensing in Infrastructure Monitoring: A Systematic Review (2015-2025)

Author 1: Abd Rahim Saidin Author 2: Illani Mohd Nawi

Distributed acoustic sensing (DAS), together with broader distributed fibre-optic sensing (DFOS), has emerged over the past decade as a candidate technology for large-scale infrastructure monitoring, while machine learning (ML) is increasingly used to interpret its high-throughput data. This study presents a PRISMA 2020-guided bibliometric and systematic-mapping review of this convergence… Read full abstract & cite →

Bibliometric analysis convolutional neural network deep learning distributed acoustic sensing distributed fibre-optic sensing infrastructure monitoring machine learning phase-sensitive OTDR structural health monitoring systematic review

Abstract doi.org/10.14569/IJACSA.2026.0170840 PDF

41

CEM MoE: A Lightweight Sparse Mixture of Experts Framework for Empathetic Dialogue Generation

Author 1: Zhinan Gou Author 2: He Liu Author 3: Yufan Wang

Empathetic dialogue generation requires models to understand user emotions and produce fluent, relevant, and emotionally appropriate responses. Existing lightweight models offer favorable deployment efficiency, but shallow emotion prediction layers limit their ability to represent fine-grained and dynamically changing emotional states. To address this limitation, CEM-MoE, a lightweight sparse mixture of… Read full abstract & cite →

Empathetic dialogue generation sparse mixture of experts lightweight dialogue model emotion modeling expert routing

Abstract doi.org/10.14569/IJACSA.2026.0170841 PDF

42

From Personality to Performance: An Automated Grouping Tool for Collaborative Learning

Author 1: Dalila Raissouni Author 2: Ahmed El Oualkadi Author 3: Kamal Reklaoui

As part of pre-service teacher training, this study presents a new web-based tool that intelligently groups learners based on their personality profiles (PCM, or Process Communication Model) to enhance collaborative work in flipped classrooms. The software automatically groups users by personality based on the results of a personality test. The… Read full abstract & cite →

Personality-based grouping Process Communication Model (PCM) Computer-Supported Collaborative Learning (CSCL) team diversity educational technology teacher training flipped classroom collaborative learning

Abstract doi.org/10.14569/IJACSA.2026.0170842 PDF

43

Development of An Artificial Neural Network-Based System to Detect Lane and Roadside Traffic Signs

Author 1: Viraj Sonawane Author 2: Balasaheb Agarkar Author 3: Sachin Chaudhari

Ensuring road safety requires timely and accurate detection of lanes and roadside traffic signs, which are essential for assisting drivers and reducing accident risks. However, robust simultaneous detection remains challenging due to variations in illumination, occlusions, complex backgrounds, and diverse road conditions. This research introduces a novel vision-based system for… Read full abstract & cite →

Lane detection roadside traffic sign recognition contrast limited adaptive histogram equalization (CLAHE) self-artificial attentive gated neuro recurrent unit (SAAG-NRU) graphical user interface (GUI)

Abstract doi.org/10.14569/IJACSA.2026.0170843 PDF

44

HDST-CAF: Adaptive CNN Transformer Frequency Fusion for Deepfake Detection Under Visual Degradation

Author 1: Sanjitha S Laad Author 2: Smitha Shekar B

Digital media integrity is seriously threatened by deepfake technology, especially under real-world visual degradations such as poor resolution, noise, greyscale conversion, and compression artefacts. Unlike prior hybrid deepfake detectors that focus primarily on clean benchmark datasets, the proposed tri-stream framework is designed to improve robustness under multiple concurrent visual degradations… Read full abstract & cite →

Vision transformer frequency domain analysis convolutional neural networks feature pyramid network multi-condition dataset cross-attention fusion and deepfake detection

Abstract doi.org/10.14569/IJACSA.2026.0170844 PDF

45

A Pipeline for Integrating and Analyzing Public Business Data in Albania Using Web Scraping and Machine Learning

Author 1: Markela Muca Author 2: Klodiana Bani Author 3: Amarildo Alla

The increasing availability of fragmented public business and procurement data creates opportunities for company-level empirical analysis, but limited evidence exists on how integrated procurement and administrative records can be used to characterize heterogeneous economic operators in Albania. This study investigates the analytical value of integrating public procurement records from the… Read full abstract & cite →

Web Scraping machine learning data integration public data analysis public procurement benchmarking Albania

Abstract doi.org/10.14569/IJACSA.2026.0170845 PDF

46

Revitalizing Cultural Heritage Education Through a Flow Theory-Based Serious Game Model for Enhancing User Engagement

Author 1: Wan Malini Wan Isa Author 2: Syadiah Nor Wan Shamsuddin Author 3: Norkhairani Abdul Rawi Author 4: Maizan Mat Amin Author 5: Nor Hafidzah Abdullah Author 6: Wan Mohd Adzim Wan Mohd Zain

Cultural heritage education plays a vital role in preserving national identity and transmitting traditional knowledge across generations. However, younger learners often perceive learning about cultural heritage as passive and less engaging than contemporary digital experiences. Although serious games have emerged as promising tools for heritage education, many existing applications lack… Read full abstract & cite →

Cultural heritage education serious games flow theory user engagement songket learning educational technology

Abstract doi.org/10.14569/IJACSA.2026.0170846 PDF

47

A Recursive Prompt-Driven Reasoning Approach for Multi-Hop Arabic Fatwa Question Answering

Author 1: Manal Al-Qahtani Author 2: Bader Alkhamees Author 3: Mourad Ykhlef

Answering Arabic fatwa inquiries represents a highly critical yet challenging task in Natural Language Processing, requiring consideration of multiple jurisprudential conditions, interpretation of interconnected concepts, and synthesis of evidence distributed across multiple religious sources. Existing Arabic question answering systems, including conventional single-step question answering (QA) models and retrieval-augmented generation (RAG)… Read full abstract & cite →

Arabic question answering Arabic fatwa question answering Arabic natural language processing multi-hop reasoning retrieval-augmented generation hybrid retrieval large language models question decomposition

Abstract doi.org/10.14569/IJACSA.2026.0170847 PDF

48

Privacy-Aware Generative Augmentation for Imbalanced Intrusion Detection Using Differential Privacy

Author 1: Trung Ha Author 2: Tran Khanh Dang Author 3: Dinh Thi Hong Loan

Machine learning-based intrusion detection systems are often constrained by severe class imbalance, while generative augmentation may memorize distinctive network-flow records and expose membership information. This study evaluates privacy-aware augmentation for CICIDS2017 by integrating differentially private stochastic gradient descent (DP-SGD) into a conditional generative adversarial network and a variational autoencoder, yielding… Read full abstract & cite →

Intrusion detection system class imbalance synthetic data generation differential privacy DP-SGD

Abstract doi.org/10.14569/IJACSA.2026.0170848 PDF

49

Leveraging Machine Learning for Predicting Marginal Field Oil Well Production Using Electrical Submersible Pump Operations Data

Author 1: Mohamed Zafir Bin Mohamad Gazali Author 2: Rabeea Jaffari Author 3: Areej Fatemah Meghji Author 4: Tuan Mohammad Yusoff Shah Author 5: Syed Muhammad Noaman Ahmed Shah Author 6: Muhammad Hammad Rasool

Electrical Submersible Pumps (ESPs) are critical components of oil well production that can be evaluated for their longevity through daily performance predictions. Traditional predictive methodologies have failed to handle the complexity of dynamic multi-well systems, creating a demand for automated Machine Learning (ML) pipelines based on operational data. In this… Read full abstract & cite →

Oil production prediction Electrical Submersible Pump (ESP) machine learning forecasting

Abstract doi.org/10.14569/IJACSA.2026.0170849 PDF

50

Explainable AI for Trustworthy 6G Edge-Cloud Smart Cities: A Systematic Review

Author 1: Qaisar Abbas Author 2: Riyad Almakki

Smart cities are becoming more interconnected, data-driven, and increasingly autonomous, with 6th-generation communication, edge-cloud computing, Internet of Things infrastructures, digital twins, federated learning, and artificial intelligence supporting them. The same technology that enables low-latency mobility, adaptive energy management, public safety analytics, environmental analytics, and responsive digital governance is creating a… Read full abstract & cite →

Explainable artificial intelligence trustworthy artificial intelligence smart cities 6G networks edge intelligence cloud computing federated learning digital twins cybersecurity AI governance human-centered AI sustainable urban intelligence

Abstract doi.org/10.14569/IJACSA.2026.0170850 PDF

51

Feature Selection Versus Feature Extraction for IoT Intrusion Detection Under Temporal Evaluation

Author 1: Mahdia Abdessamad Author 2: El Bachir Tazi

Comparative studies of feature selection (FS) and feature extraction (FE) for IoT intrusion detection rely almost universally on random train/test splits, which let temporally adjacent, highly similar records appear on both sides of the partition. This study asks whether such conclusions survive when this evaluation leakage is removed. We reproduce… Read full abstract & cite →

Internet of things intrusion detection feature selection feature extraction data leakage temporal evaluation concept drift TON-IoT machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170851 PDF

52

A Coordinate Transformation Framework for Vision-Guided Robotic Positioning in Automated Vehicle Body Marking

Author 1: Olzhas Olzhayev Author 2: Azhar Tursynova Author 3: Sayat Ibrayev

Vision-guided robotic positioning plays a critical role in modern manufacturing by enabling robots to accurately adapt to variations in workpiece position. This study proposes a coordinate transformation framework for automated vehicle body marking that converts visually detected target coordinates into executable robot motions through a unified processing pipeline. The proposed… Read full abstract & cite →

Coordinate transformation vision-guided robotics robotic positioning vehicle body marking monocular vision tool center point (TCP) industrial robotics

Abstract doi.org/10.14569/IJACSA.2026.0170852 PDF

53

An Explainable Hierarchical Deep Representation Learning Framework for Multi-Class Host Rock Recognition from Mining Data

Author 1: Sami F Karali Author 2: Mohammed I Thanoon Author 3: Majed Nawas Author 4: Ahlem Fatnassi Author 5: Abed Saif Ahmed Alghawli Author 6: Zakhriya Alhassan Author 7: Yahia Said

This study aims to improve the automatic recognition of geological host rocks from mining data and to make the prediction process easier to understand for geologists, mining engineers, and data analysts. The study uses a mining dataset containing global mineral and rock information. The data are cleaned, missing values are… Read full abstract & cite →

Host rock recognition mining data feature selection explainable artificial intelligence geological feature selection mineral exploration multi-class rock recognition

Abstract doi.org/10.14569/IJACSA.2026.0170853 PDF

54

Effective Chunking for Retrieval-Augmented Generation over Structured Institutional Documents

Author 1: Siti Sarah Izhan Khalib Author 2: Abdul Hadi Abd Rahman Author 3: Lailatul Qadri Zakaria

Retrieval-Augmented Generation (RAG) has improved domain-specific question answering tasks by grounding responses in an external knowledge base. However, institutional documents differ from general corpora due to the presence of structured content such as tables, flowcharts, fee breakdowns and program codes. Existing RAG pipelines typically employ generic chunking strategies designed for… Read full abstract & cite →

Chunking retrieval-augmented generation structured text documents

Abstract doi.org/10.14569/IJACSA.2026.0170854 PDF

55

Explainable Neuro-Symbolic Learning for Mohs Hardness Prediction in Mining and Mineral Processing

Author 1: Mohammed I. Thanoon Author 2: Sami F. Karali Author 3: Mohamed H. Helal Author 4: Ahlem Fatnassi Author 5: Majed Nawas Author 6: Monir Abdullah Author 7: Asma A. Alhashmi Author 8: Abdulbasit A. Darem

Predicting Mohs hardness from mineralogical information is still a challenging task because of the complex and nonlinear relationship between mineral composition and hardness properties. Traditional machine learning (ML) and deep learning (DL) strategies have been used to address this study, exploiting mineralogical attributes for prediction. Conversely, these techniques are frequently… Read full abstract & cite →

Mohs hardness prediction mineral mining adabelief optimizer explainable artificial intelligence physicochemical property

Abstract doi.org/10.14569/IJACSA.2026.0170855 PDF

56

Applying Visual Servoing for Industrial Robots Through Interactive Segmentation and Robot-Computer Communication

Author 1: Zeinel Momynkulov Author 2: Moldir Kuatova Author 3: Amandyk Tuleshov

Industrial robots are increasingly required to operate in flexible manufacturing environments where conventional offline programming and manually defined working positions limit production adaptability. This study presents a visual servoing framework that integrates interactive image segmentation with industrial robot control through robot-computer communication. The proposed system enables an operator to specify… Read full abstract & cite →

Industrial robotics interactive segmentation human-robot interaction robot-computer communication industrial automation

Abstract doi.org/10.14569/IJACSA.2026.0170856 PDF

57

SHAP Guided Transformer-Based Deep Learning Model for Diamond Clarity Grading in Smart Mining and Resource Valuation Systems

Author 1: Ahlem Fatnassi Author 2: Shouki A. Ebad Author 3: Sami F Karali Author 4: Mohammed I Thanoon Author 5: Majed Nawas Author 6: Monir Abdullah Author 7: Asma A. Alhashmi Author 8: Abdulbasit A. Darem

Diamond clarity grading aims to automatically determine the quality of diamonds by analyzing visible inclusions and structural flaws using intelligent analysis methods. By integrating diamond cost prediction with clarity grading, a unified architecture can be employed for both quality examination and market value evaluation, where clarity attributes support the determination… Read full abstract & cite →

Diamond clarity grading quality assessment deep learning multi-head self-attention model optimization

Abstract doi.org/10.14569/IJACSA.2026.0170857 PDF

58

Classification of Lymph Node Tumor Images Using Adaptive Filtering in Convolutional Neural Networks

Author 1: Jamal M. Alothman Author 2: Nameer N. El. Emam Author 3: Issa Qabajeh Author 4: Mhammed Almakadmeh

Tumors of the lymphatic system can be either benign or malignant. However, because traditional diagnostic approaches rely on subjective assessment by individual pathologists and often lead to significant discrepancies and misclassifications, early and accurate diagnosis is important for successful treatment and improved scientific outcomes. We compare an adaptive convolutional neural… Read full abstract & cite →

Convolutional neural networks adaptive image filtering lymph node tumor images image classification feature extraction

Abstract doi.org/10.14569/IJACSA.2026.0170858 PDF

59

An Early Warning System for Proactive DDoS Attack Detection Using Ordinary Differential Equation-Based Scoring

Author 1: Radhakrishna Vangipuram Author 2: Rajput Eswar Sai Singh Author 3: Ramesh Kumar Aytha Author 4: Sravankiran Vangipuram Author 5: Sreenivasa Rao Annaluri

Distributed Denial of Service (DDoS) attacks are still a significant issue facing cybersecurity, flooding infrastructure with large packet torrents and disrupting networks across the globe. To mitigate against such volumetric attacks, the best approach is to intercept the threat long before any attack gets underway. However, conventional defense systems react… Read full abstract & cite →

DDoS early warning signals ordinary differential equation residual low threshold medium threshold high threshold

Abstract doi.org/10.14569/IJACSA.2026.0170859 PDF

60

Semantic-Driven Community Detection in Complex Networks Using Fuzzy Logic and Multi-Criteria Decision Making

Author 1: Mohamed El-Moussaoui Author 2: Mohamed Hanine Author 3: Sulieman S. Alshuhri Author 4: Amal Alomran Author 5: Amir Mohamed Talib

The study introduces a novel approach to community detection in complex networks by integrating fuzzy logic with multi-criteria decision-making techniques. Unlike traditional methods that rely primarily on topological metrics, the proposed approach incorporates semantic attributes to identify meaningful community structures. Fuzzy logic addresses the inherent uncertainty and ambiguity in processing… Read full abstract & cite →

Community detection fuzzy logic Multi-Criteria Decision Making (MCDM) social network analysis semantic communities

Abstract doi.org/10.14569/IJACSA.2026.0170860 PDF

61

Influence of Big Data Analytics Capability on Employee Creative Behavior: A Modified Mediation Model

Author 1: Mohammed Bassam Nassoura Author 2: Nabil Abudarwish Zarqa Author 3: Mohammad Musa Al-Momani Author 4: Bilal Alnassar Author 5: AbdelRahman Ismail Author 6: Ahmad Awadallah

The purpose of the present study was to assess the effect of Big Data Analytics capabilities on employees' creative behavior in the Jordanian service sector and to explore the mediating influence of employee curiosity and the moderating influence of top management support. The proposed model was developed using an integrative… Read full abstract & cite →

Big data analytics employee creative behavior curiosity management support the knowledge-based view self-determination theory

Abstract doi.org/10.14569/IJACSA.2026.0170861 PDF

62

GTA: Automated Semantic NLP-Based Generation of UML Activity Diagrams from Acceptance Criteria

Author 1: Samia Nasiri Author 2: Salim Bloundi Author 3: Mohammed Lahmer

In agile development, user stories express stakeholder needs, and the associated acceptance criteria (AC), written in the Given/When/Then (GWT) notation, specify the behaviour expected of the system under stated preconditions. Their manual translation into UML activity diagrams is laborious and remains sensitive to wording differences between authors. Existing automated approaches… Read full abstract & cite →

Behaviour-driven development GWT acceptance criteria UML activity diagrams sentence-BERT NLI natural language processing requirements engineering

Abstract doi.org/10.14569/IJACSA.2026.0170862 PDF

63

A Novel Multi-Objective Service Scheduling Framework for Fog-Driven Agricultural Internet of Things

Author 1: Mengya NIE Author 2: Fengmin JIAO

Timely, context-aware service recommendations are most significant for maximizing system efficiency and resource utilization in fog-enabled Internet of Things (IoT) for smart agriculture. Classical Grey Wolf Optimization (GWO) algorithms are prone to premature convergence and lack robustness for multi-objective and dynamic environments. This study presents an Enhanced Grey Wolf Optimization… Read full abstract & cite →

Grey wolf fog computing internet of things smart agriculture service recommendation

Abstract doi.org/10.14569/IJACSA.2026.0170863 PDF

64

CTU-Tutor: Contextual and User-Aware Language Understanding for Personalized Tutoring with Long-Form Text Generation

Author 1: Akanksha Bisht Author 2: Oshin Sharma

This study introduces CTU-Tutor, a contextual and user-aware intelligent tutoring system for generating personalized long-form text as learning content. The system combines BERT-based learner embeddings, K-means for proficiency clustering, Transformer-XL for long-context modelling, BiLSTM-CRF for concept extraction, and LDA-based knowledge graph construction. A retrieval-augmented Transformer-XL model produces personalized content, which… Read full abstract & cite →

Contextual and user-aware tutoring system bidirectional encoder representations of transformers transformer-XL bidirectional long short-term memory with conditional random fields explainable AI latent dirichlet allocation

Abstract doi.org/10.14569/IJACSA.2026.0170864 PDF

65

Cryptographic Attestation Against Integrity Attacks in Service Monitoring: A Threat Model and Verifiable Architecture

Author 1: M A Anusuya Author 2: Chayadevi M L Author 3: Shraddha C Author 4: Vani H Y

Service uptime monitoring infrastructure is a high-value target for data-integrity attacks: a single compromised or dishonest monitoring provider can fabricate availability records, retroactively suppress outage evidence, or silently alter historical data, and clients today have no cryptographic means of detecting such manipulation. This study develops a threat model for monitoring-data… Read full abstract & cite →

Cryptographic attestation threat modelling Byzantine fault tolerance Sybil resistance digital signatures data integrity tamper-evident logging adversarial security analysis non-repudiation

Abstract doi.org/10.14569/IJACSA.2026.0170865 PDF

66

Territorial Governance in Transition: Multi-Level Frameworks, Citizen Participation, and Digital Transformation (2018–2026)

Author 1: Abdelhakim Errakha Author 2: Sanaa DFOUF Author 3: Daafi Redouan Author 4: Fekkak HAMDI

From 2018 to 2025, the combination of the democratic deficit and growing digitalization caused significant changes to territorial governance. To understand these changes, this study assembles the data of more than thirty countries from five different continents and describes the following three interrelated phenomena: the restructuring of multi-level governance systems… Read full abstract & cite →

Territorial governance decentralisation multi-level governance citizen participation digital transformation regional development public policy

Abstract doi.org/10.14569/IJACSA.2026.0170866 PDF

67

A Feature Model-Based Configurable Framework for Reproducible Intervention Evaluation

Author 1: Carlos Murillo-Barrera Author 2: Miguel Vera Author 3: Nicolás Márquez Author 4: Cristian Vidal-Silva

Intervention evaluation frequently relies on fixed analytical workflows that provide limited support for methodological adaptability, reproducibility, and systematic management of analytical variability. This study proposes a Feature Model-based configurable framework that represents data preparation, feature construction, statistical inference, robustness validation, and decision-support generation as interdependent analytical features. Mandatory, optional, alternative… Read full abstract & cite →

Feature models variability management configurable systems reproducible analytics decision support systems intervention evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170867 PDF

68

A New Metadata-Aware Retrieval-Augmented Generation (RAG) Architecture for Trustworthy Legal Question Answering

Author 1: Alexandra V. Jove-Ticona Author 2: Luis J. Duarte-Coaquera Author 3: Israel N. Chaparro-Cruz Author 4: Silvana B. Cabana-Yupanqui Author 5: Americo Chaparro-Guerra

Large Language Models (LLMs) offer strong capabilities for Natural Language Processing, yet their inherent uncertainty often produces hallucinations, confident but incorrect statements, which is critical in domains requiring precise knowledge representation. Retrieval-Augmented Generation (RAG) reduces this risk through information retrieval, but standard pipelines still suffer from fragmented context and weak… Read full abstract & cite →

RAG Retrieval-Augmented Generation LLMs legal question answering

Abstract doi.org/10.14569/IJACSA.2026.0170868 PDF

69

AlphaOCR: Integrated Deep Learning and Optical Character Recognition for Receipt Extraction

Author 1: Muhammad Haikal Iman Osman Author 2: Nor Samsiah Sani Author 3: Luan Xiang Wei Liu Author 4: Zalinda Othman Author 5: Mohd Aliff Afira Sani Author 6: Ibrahim Zebiri

Receipts are vital documents that validate transactions by capturing key details such as dates, items purchased, prices, and seller information. However, accurately documenting receipts is often compromised by issues like blurring, which can result from poor image quality, physical wear, or suboptimal scanning conditions. While the Processing Key Information Extraction… Read full abstract & cite →

AlphaOCR optical character recognition information extraction receipt processing deep learning

Abstract doi.org/10.14569/IJACSA.2026.0170869 PDF

70

Federated Defense in the Medical Edge: A Reputation-Aware Secure Aggregation Protocol for IoMT Intrusion Detection

Author 1: Feras Fares AL-Mashakbah Author 2: Abdullah Alqammaz Author 3: Ala’ Khalifeh Author 4: Eman Fares Al Mashagbah Author 5: Essam Said Hanandeh

Smart hospitals deploy Internet of Medical Things (IoMT) sensors and MQTT brokers to stream clinical telemetry over resource-constrained edge gateways. Centralized network intrusion detection systems (NIDS) expose sensitive traffic traces and create single-point failures; federated learning (FL) avoids raw-data centralization but remains vulnerable to client-side model poisoning and server-side inspection… Read full abstract & cite →

Federated learning intrusion detection internet of medical things secure aggregation poisoning attacks reputation systems MQTT security

Abstract doi.org/10.14569/IJACSA.2026.0170870 PDF

71

An Initial Validation of a Pedagogical Simplification Gain Index for Arabic Technical Text Simplification

Author 1: Khalid Essaadani Author 2: Soumia Ziti Author 3: Karima Salah-Eddine

Simply calculating the readability score or the degree of lexical overlap for Arabic technical text simplification does not provide the full picture because an explanation that is shorter may still damage the technical meaning, weaken terminology consistency, or fail to reduce learner difficulty. This study introduces the Pedagogical Simplification Gain… Read full abstract & cite →

Arabic technical text simplification automatic text simplification pedagogical evaluation PSGI Auto human evaluation

Abstract doi.org/10.14569/IJACSA.2026.0170871 PDF

72

Sustainability-Centric Software Development: A Quantitative Framework for the SDLC

Author 1: Madhura G K Author 2: Piyush Kumar Pareek

Although software systems increasingly shape energy consumption, economic output, and societal welfare, most development methods still prioritise schedule, cost, and functionality. This paper presents an approach to software development that prioritises sustainability by embedding environmental, economic, and social objectives into the SDLC from the very beginning. The framework represents sustainability… Read full abstract & cite →

Sustainability-centric framework social impact score total cost of ownership global sustainability index software development lifecycle

Abstract doi.org/10.14569/IJACSA.2026.0170872 PDF

73

Definitional Label Leakage in Aquaculture Water Quality Anomaly Detection

Author 1: Rosida Vivin Nahari Author 2: Teguh Prasetyo Author 3: Riza Alfita

Semi-supervised anomaly detection is widely pro-posed for Internet of Things water quality monitoring in aquaculture. However, reported high scores often result from definitional label leakage. This occurs when an observation is labelled anomalous based on a physicochemical threshold, and that same variable is used as an input feature. The benchmark… Read full abstract & cite →

Anomaly detection aquaculture water quality label leakage one-class SVM liquid state machine autoencoder

Abstract doi.org/10.14569/IJACSA.2026.0170873 PDF

74

Hybrid Blockchain-Based Tokenization for Secure and Interoperable Electronic Payment Systems

Author 1: Omniah Abdullah Al Ibrahim Author 2: Suhair Alshehri

The rapid evolution of digital payment technologies has highlighted critical challenges in ensuring data security, transparency, and compliance across centralized tokenization systems. Traditional models such as those used by EMVCo and major card networks rely on Token Service Providers (TSPs) and token vaults, which, while effective at masking Primary Account… Read full abstract & cite →

Blockchain-based payment systems decentralized tokenization electronic payment security smart contracts hybrid payment model

Abstract doi.org/10.14569/IJACSA.2026.0170874 PDF

75

Effectiveness of Adaptive Shape Prediction Model for Cytological Cell Segmentation

Author 1: Afaf Omar Tareef

Cell segmentation continues to be a significant challenge in medical image processing, as traditional segmentation methods fail to provide precise and thorough segmentation of complex structures such as touching and overlapping cells. The geometric data points of these cells are inadequate for accurate contour estimation to yield a correct segmentation… Read full abstract & cite →

Cytology image shape prediction model shape priors overlapping cell segmentation

Abstract doi.org/10.14569/IJACSA.2026.0170875 PDF

76

Black-Box Cybersecurity Assessment of Software as a Medical Device (SaMD): A Case Study

Author 1: Abdulmajeed Alshammari Author 2: Shouki A. Ebad

Based on Sommerville’s robust software reliability theory and eight design principles based on best practices (DPG), this study conducted a comparative assessment of two Dexcom software platforms: the Dexcom Clarity web portal and the Dexcom ONE+ iPhone application. The first phase of the study employed a “black-box” assessment framework, combining… Read full abstract & cite →

SaMD DPG black-box testing dexcom clarity dexcom ONE+ input validation CSP XSS CSRF mHealth security

Abstract doi.org/10.14569/IJACSA.2026.0170876 PDF

77

LEAOT: Load-, Deadline-, Latency-, and Energy-Aware Task Offloading for Scalable IoT Edge–Cloud Systems

Author 1: Ayman Noor

Internet of Things (IoT) applications increasingly offload sensing and analytic tasks to edge and cloud resources. Cloud processing offers high computational capacity but can increase round-trip delay and wireless energy consumption, while edge processing reduces access delay but can suffer from queue buildup when many devices select the same nearby… Read full abstract & cite →

Internet of Things edge computing cloud computing task offloading latency energy efficiency deadline-aware scheduling

Abstract doi.org/10.14569/IJACSA.2026.0170877 PDF

78

Calendar and Session Representations in Mixed-Calendar Multivariate Forecasting

Author 1: Khudran M. Alzhrani

Multivariate forecasting with variables observed on different temporal calendars requires a choice about how their histories are represented in a common input sequence. Prior work has largely treated mixed-calendar alignment as a preprocessing step or focused on modeling irregular observations, leaving direct comparisons of alternative temporal representations under identical forecast… Read full abstract & cite →

Mixed-calendar forecasting temporal representation multivariate time-series forecasting temporal alignment irregular time series observation gaps

Abstract doi.org/10.14569/IJACSA.2026.0170878 PDF

79

Code Smell Severity in Predictive Modeling for Software Quality Risk and Maintenance Effort: A Systematic Literature Review

Author 1: Aamina Banu Author 2: Thenuri Hettiarachchi Author 3: Chaman Wijesiriwardana

Code smells are structural indicators of poor soft-ware design that have been empirically associated with higher defect rates and increased maintenance effort. Although research on smell detection, software quality risk, and maintenance effort estimation has grown significantly, these areas have largely been studied separately. In addition, the role of smell… Read full abstract & cite →

Code smells maintenance effort software quality risk smell severity software quality

Abstract doi.org/10.14569/IJACSA.2026.0170879 PDF

80

Indie at Scale: How Large Language Models Make Community-Centered Game Development Economically Viable

Author 1: Jusuf Qarkaxhija

Large language models are now widely used in software engineering, yet most evidence of their practical effect comes from corporate productivity studies or synthetic bench-marks. This study offers a complementary view from the solo Indie developer’s perspective: a reflective case study of two iOS games developed in continuous pair programming… Read full abstract & cite →

Large language models AI-assisted software development mobile accessibility spatial audio cognitive engagement human-AI collaboration Indie games

Abstract doi.org/10.14569/IJACSA.2026.0170880 PDF

81

Vocabulary Without Infrastructure: A Bibliometric Analysis of Explainability and Equity in AI-Based Personalised Learning

Author 1: Ahmed Echchoayeby Author 2: Wafae Abbaoui Author 3: Reida Ilal Author 4: Nassim Kharmoum Author 5: Soumia Ziti

Artificial intelligence has become a central paradigm in personalised learning. However, it remains unclear whether the two principles that responsible AI treats as foundational, explainability and equity, are integrated into the field’s intellectual architecture or are only loosely associated with it. This question is addressed through a bibliometric analysis of… Read full abstract & cite →

Bibliometric analysis AI in education personalised learning explainable AI educational equity responsible AI science mapping

Abstract doi.org/10.14569/IJACSA.2026.0170881 PDF

82

Improving Flight Delay Prediction Using Integrated Departure Disruption, Airline-Route Stability, and Schedule Intensity Features

Author 1: Huthaifa Aljawazneh

In the aviation industry, flight delays represent a major challenge due to their economic impact, operational disruptions, and adverse effects on passenger satisfaction and transportation efficiency. This study proposes an integrated feature engineering framework for flight delay prediction that combines three groups of engineered features, namely Departure Disruption Features (DDFs)… Read full abstract & cite →

Flight delay prediction feature engineering aviation analytics class imbalance machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170882 PDF

83

Deep Autoencoder and Deep Q-Network-Based Feature Learning with Multi-Head Residual Attention DNN for Cyberattack Classification

Author 1: Dharani Kanta Roy Author 2: Hemanta Kumar Kalita

As society’s digital infrastructure grows, so do the threats. We cannot deny internet access, so we need to safeguard ourselves against cyber threats. Every day, new threats emerge. The most conventional systems classify cyberattacks in a closed set; that is, training and testing use the same set of attack behaviors… Read full abstract & cite →

Cyberattack classification deep autoencoder feature selection deep q-network multi-head attention deep neural network SHapley additive exPlanations

Abstract doi.org/10.14569/IJACSA.2026.0170883 PDF

84

Hybrid Approach Combining Markov Chains, HMM, and Bidirectional GRU to Detect Blockages in Programming

Author 1: Grota Abdelkader Author 2: Mohammed Erritali Author 3: Patrick Etcheverry Author 4: Thierry Nodenot

A student stuck in a failing compile loop looks productive from across the room. The instructor sees keystrokes; the blockage is invisible until the student raises a hand or falls silent. Existing detection methods work at session level: they report how many compilations occurred, not what happened between them. Sub-minute… Read full abstract & cite →

Attention mechanism bidirectional GRU blockage detection early detection educational data mining

Abstract doi.org/10.14569/IJACSA.2026.0170884 PDF

85

A Database-Driven Architecture for Integrating and Serving 3D Condominium and Cadastral Information: The CondoMaps Platform

Author 1: Tarawut Boonlua Author 2: Kritsanu Palopakorn Author 3: Sontaya Ratanatip Author 4: Somporn Wongjampa Author 5: Praewpan Pasanam Author 6: Viparat Nupattaya Author 7: Ketut Tomy Suhari

Three-dimensional condominium information systems require more than visual building models: spatial objects must be constructed from authoritative plans and linked consistently to cadastral, registration, building, unit, parcel, and juristic-person records. This study presents CondoMaps, a database-driven architecture for constructing, organizing, and serving 3D condominium information in Thailand. The workflow transforms… Read full abstract & cite →

3D Condominium model spatial database 3D cadastre data integration web GIS service-oriented architecture CondoMaps

Abstract doi.org/10.14569/IJACSA.2026.0170885 PDF

86

Uncertainty Adaptive Shared Control with Multimodal Intent Fusion for Human-Guided Quadruped Locomotion on Uneven Terrain

Author 1: Likai Wu

Human-guided quadruped locomotion requires re-liable intent inference and safe authority allocation under un-certain interaction and uneven terrain. This study proposes an uncertainty-adaptive shared-control framework with multimodal intent fusion for a Unitree Go2 quadruped. Interaction force, relative human–robot motion, operator-motion cues, and terrain context are fused through a temporal model to… Read full abstract & cite →

Quadruped robot human–robot interaction shared autonomy intent recognition uncertainty estimation terrain-aware locomotion assistive robotics

Abstract doi.org/10.14569/IJACSA.2026.0170886 PDF

87

Predicting PM10 from PM2.5 and NO2 Levels in Metropolitan Lima, Peru: A Regularized Regression Approach

Author 1: Jorge Chavarri Centeno Author 2: Hilter Lanche Cerron Author 3: Joel Rivera Matias Author 4: Juan Jesús Soria-Quijaite Author 5: Nemias Saboya

Suspended particle pollution (PM10) represents a serious public health problem in Metropolitan Lima, since its high levels are related to an increase in respiratory and heart diseases. This work aims to anticipate PM10 concentrations in seven districts of Lima using a multiple linear regression (OLS) model with the Ridge and… Read full abstract & cite →

Multiple linear regression particulate matter of 10 micrometers or less (PM10) particulate matter of 2.5 micrometers or less (PM2.5) nitrogen dioxide (NO2) ridge regularization Lasso regularization

Abstract doi.org/10.14569/IJACSA.2026.0170887 PDF

88

Conformal, Explainable Machine Learning for Forensic Malware Triage and IoT Attribution

Author 1: Arafat Al-Dhaqm Author 2: Abdullah Alajmi

Machine learning can triage digital evidence at scale, but two obstacles limit its forensic adoption: opaque decisions, and point predictions without a valid statement of confidence. We present ForensiQ, a hierarchical and explain-able framework that pairs a two-stage detect-then-attribute pipeline with split-conformal prediction. Conformal calibration is distribution-free and turns classifier… Read full abstract & cite →

Digital forensics memory forensics malware attribution conformal prediction explainable artificial intelligence IoT security machine learning

Abstract doi.org/10.14569/IJACSA.2026.0170888 PDF

89

From IoT Vulnerabilities to Intrusion Detection: An Explainable Vulnerability-Aware Machine Learning Framework for Smart Home IoT Security

Author 1: Huda Aldawghan Author 2: Mounir Frikha

The swift deployment of IoT-based smart home appliances has increased the attack surface for the smart environment and exposed it to attacks like botnet command-and-control communications, brute force attacks, denial-of-service attacks, and web-based attacks. Even though the Intrusion Detection Systems (IDSs) that use Machine Learning (ML) algorithms achieve a very… Read full abstract & cite →

IoT security intrusion detection system machine learning XGBoost random forest vulnerability-aware security botnet detection smart home explainable AI network-flow analysis

Abstract doi.org/10.14569/IJACSA.2026.0170889 PDF

90

A Trust-Aware Federated Learning Framework Based on Zero Trust Architecture for Secure Cloud Intrusion Detection

Author 1: Eman M. Mohamed Author 2: Suzan S. Basloom

Cloud computing provides scalable infrastructure but introduces critical challenges to data privacy, trust management, and intrusion detection. To address these issues, we present a trust-aware framework that integrates a Federated Learning (FL)-based distributed ensemble with Zero Trust Architecture (ZTA) for secure cloud environments. The framework enables collaborative learning while keeping… Read full abstract & cite →

Cloud security federated learning zero trust architecture intrusion detection system trust filtering mechanism decentralized learning

Abstract doi.org/10.14569/IJACSA.2026.0170890 PDF

91

A Heterogeneity-Aware Federated Learning Framework for Graph Neural Networks in Multi-Hospital Medical Systems

Author 1: Chethana Prasad Kabgere Author 2: Shylaja S S

Medical institutions increasingly hold data that is relational rather than tabular: patients linked by medical history, diagnostics by dependency, and doctors by consultations. Graph neural networks (GNNs) are a natural fit for such data, but hospitals cannot pool it directly, since records remain confined by law to the collecting hospital… Read full abstract & cite →

Inter-hospital federated learning medical graphs geometric coherence message-passing operators structural heterogeneity

Abstract doi.org/10.14569/IJACSA.2026.0170891 PDF

92

DISR: Distributional Item Representations for Sequential Recommendation

Author 1: Abdelilah BAJJOU Author 2: El Habib NFAOUI

Sequential recommender systems predict the next item from a chronological interaction history, typically representing users and items as point embeddings scored by dot product. DISR (Distributional Item Representations for Sequential Recommendation) instead represents items and sequence queries as diagonal Gaussian distributions. Item means are initialized from frozen Sentence-BERT text representations… Read full abstract & cite →

Sequential recommendation distributional embedding Kullback–Leibler divergence cold-start recommendation text-initialized embeddings amazon reviews 2023

Abstract doi.org/10.14569/IJACSA.2026.0170892 PDF

93

Boosting Versus Foundation Models for Ordinal Home-Sharing Engagement Classification in Latin America

Author 1: Pedro Shiguihara Author 2: Nils Murrugarra

Review activity is the standard public proxy of guest engagement on peer-to-peer accommodation platforms, yet is modeled almost exclusively as count regression. This study presents a three-level engagement classification of peer-to-peer home-sharing in Latin America: 76,624 listings from Buenos Aires, Santiago, and Mexico City, with tercile labels from training folds… Read full abstract & cite →

Peer-to-peer home-sharing ordinal classification engagement prediction gradient boosting multilingual text embeddings hyperparameter optimization tabular foundation models TabPFN Latin America

Abstract doi.org/10.14569/IJACSA.2026.0170893 PDF

94

A User-Intent Context-Aware Recommendation Approach for Low-Resource Online Learning Environments (Mauritanian Context)

Author 1: Massra Sabeima

User experience in distance learning is often conceptualized through interactions between the learner and the system’s interface. These interactions can be understood as the dynamic interplay between user behavior and system behavior, each initiated or guided by distinct mechanisms. User behavior, inherently autonomous, cannot be directly dictated, whereas system behavior… Read full abstract & cite →

User intent modeling context-aware recommendation low-resource online environments

Abstract doi.org/10.14569/IJACSA.2026.0170894 PDF

95

Beyond Model-Centric AI: Embedding Governance as an Architectural Component with Measurable Efficiency

Author 1: Waleed Al Shehri

Production AI systems typically apply governance—compliance checking, auditing, and explanation—as an external control layer that observes outputs after they are produced. In this paper, embedded governance is proposed: a governance gate that forms part of the system architecture itself, positioned between inference and release, so that no output leaves the… Read full abstract & cite →

AI governance software architecture embedded governance governance efficiency ablation study failure semantics out-of-distribution detection MLOps

Abstract doi.org/10.14569/IJACSA.2026.0170895 PDF

96

Proportional Reward and Temporal Discounting for Monopoly-Free Heterogeneous Metaheuristic Portfolios

Author 1: Bilal Bataineh Author 2: Sofian Kassaymeh

Real-world optimization landscapes are typically dynamic, high-dimensional, and uncertain, and a single meta-heuristic with fixed control parameters rarely sustains strong performance across such environments, as formalized by the No Free Lunch theorem. Existing adaptive frameworks attempt to address this through online operator or algorithm selection, but they suffer from two… Read full abstract & cite →

Adaptive metaheuristic relative improvement metric sliding window hybrid optimization algorithmic selection dynamic adaptation

Abstract doi.org/10.14569/IJACSA.2026.0170896 PDF

97

Focal-Loss Transformer with Feedback-Validation Learning for Imbalanced Drug Interaction Extraction

Author 1: Hiba Chanaa Author 2: El Habib Nfaoui Author 3: Chafik Boulealam Author 4: Chakir Loqman

Drug-drug interactions (DDIs) are a leading cause of preventable adverse drug events, and the standard benchmark corpus for extracting them from biomedical text is severely im-balanced towards non-interacting pairs, making rare interaction types hard to learn. We asked whether combining focal loss, entity-aware transformer encoding, and feedback-guided feature fusion in… Read full abstract & cite →

Drug-drug interaction extraction biomedical natural language processing transformer focal loss class imbalance pharmacovigilance

Abstract doi.org/10.14569/IJACSA.2026.0170897 PDF

98

Deep Learning-Based System for Accurate Sleep Stage Classification and Early Detection of Sleep Disorders Using EEG and EOG Signals

Author 1: Ramesh G Author 2: Vaikunta Pai T Author 3: Ramona Birau Author 4: Virgil Popescu Author 5: Gabriela Ana Maria Lupu (Filip) Author 6: Roxana Mihaela Nioata (Chireac) Author 7: Cristina Sultanoiu (Patularu) Author 8: Md. Abul Kalam Azad

Sleep is essential for maintaining overall physical and mental health, yet analyzing sleep patterns manually is a com-plex and time-consuming process that requires expert knowledge and is often prone to subjectivity. In this study, an automated and efficient deep learning-based approach is developed to classify sleep stages using the Sleep-EDF… Read full abstract & cite →

Sleep stage classification EEG EOG deep learning CNN LSTM sleep disorders polysomnography

Abstract doi.org/10.14569/IJACSA.2026.0170898 PDF

99

The Agentic Enterprise Capability Framework (AECF): A Governance-First Architecture for Scalable AI Agent Deployments

Author 1: KHAMMAL Adil Author 2: HAMZANE Ibrahim Author 3: MARZAK Abdelaziz Author 4: Hajar Taji Author 5: Elmostafa Erraitab Author 6: Mohamed EL WAFIQ

Enterprise AI adoption has reached a structural inflection point: while a majority of organizations have deployed generative AI, few have established mature governance models for autonomous agents. This disparity reflects a fundamental architectural gap: Multi-Agent Systems, Enterprise Architecture, AI agent deployment, and software architecture have approached agent coordination from separate… Read full abstract & cite →

Enterprise AI agents agentic architecture design science research AI governance multi-agent systems policy-as-code

Abstract doi.org/10.14569/IJACSA.2026.0170899 PDF

100

Evaluating AI-Based Veterinary Symptom-Assessment Chatbots: A Review-Informed Framework for Safety, Triage, Usability, and Owner-Style Symptom Reporting

Author 1: Manisha Chawla Author 2: Abeer Alsadoon Author 3: Ahmed Fadhil Author 4: Mohammed Ameen Author 5: Arafat Abdulgader Mohammed Elhag Author 6: Areej Althubaity Author 7: Ahmed Hamza Osman

In the medical field, AI has made its mark, and almost every medical profession has experienced the development of a chatbot to check symptoms and provide instructions to the owners at an early stage. However, determining the safety and reliability of these tools will be challenging since pets are unable… Read full abstract & cite →

Veterinary chatbot artificial intelligence symptom assessment pet health scenarios triage behaviour safety communication usability expert benchmark

Abstract doi.org/10.14569/IJACSA.2026.01708100 PDF

101

A Real-Time IoT-Based Intelligent Health Monitoring Framework with Multi-Sensor Fusion and Automated Anomaly Detection for Continuous Cardiac Assessment

Author 1: J R Deepak Author 2: C Veerabathiran Author 3: Jamil Abedalrahim Jamil Alsayaydeh Author 4: S Padmanabhan Author 5: N Adhithan Author 6: Rex Bacarra

Continuous and real-time health monitoring is essential for the early detection of cardiovascular and physiological abnormalities in remote and resource-limited settings. This research aims to develop an intelligent IoT-based framework for multi-sensor health data acquisition, real-time processing, and automated anomaly detection for continuous cardiac and physiological assessment. The proposed system… Read full abstract & cite →

Internet of Things (IoT) wearable health monitoring device biomedical sensors cardiac monitoring vital sign measurements

Abstract doi.org/10.14569/IJACSA.2026.01708101 PDF

102

Dimensionality-Performance Trade-Off Analysis of the Hybrid Character-Word Embedding (HCWE) Architecture for Medical Text Classification Under Noise Conditions

Author 1: Mustazzihim Suhaidi Author 2: Rabiah Binti Abdul Kadir Author 3: Sabrina Tiun

Medical text classification is critical for applications such as automated triage and disease surveillance. However, real-world data is often corrupted by character-level noise, which substantially degrades the performance of traditional models. Although the Hybrid Character-Word Embedding (HCWE) architecture has demonstrated promising results, the optimal embedding dimensionality for balancing classification performance… Read full abstract & cite →

Dimensionality analysis medical text classification noise robustness hybrid embedding HCWE feature hashing statistical significance multi-objective optimization

Abstract doi.org/10.14569/IJACSA.2026.01708102 PDF

103

Adaptive Energy-Security Framework for IoT Devices Using Hybrid SPECK and Kyber Encryption

Author 1: Vegesna Siva Rama Krishnam Raju Author 2: Venkateswararao Pulipati

With the fast growth of the Internet of Things (IoT), the problem of secure and energy-efficient data exchange in resource-constrained settings has become even more challenging. The traditional cryptographic systems (RSA and ECC) are resource-consuming and susceptible to quantum attacks, and lightweight cyphers are not only less powerful but also… Read full abstract & cite →

Post-Quantum cryptography lightweight cryptography hybrid cryptography IoT security quantum-resistant networks energy-efficient encryption Kyber SPECK network lifetime quantum computing

Abstract doi.org/10.14569/IJACSA.2026.01708103 PDF

104

Factors Affecting the Adoption and Use of Big Data and The Moderating Effect of Information Quality

Author 1: Mohammed Bassam Nassoura Author 2: Nabil Abudarwish Zarqa Author 3: Mohammad Musa Al-Momani Author 4: Bilal Alnassar Author 5: AbdelRahman Ismail Author 6: Ahmad Awadallah

This research aims to explore the impact of various factors on the adoption and application of big data in small and medium-sized enterprises (SMEs) operating in the service sector in the Hashemite Kingdom of Jordan. The research employs a quantitative analytical methodology, grounded in the theory of technology, organization, and… Read full abstract & cite →

Big data information quality and theory of technology organization and environment

Abstract doi.org/10.14569/IJACSA.2026.01708104 PDF