Journal of Advances in Information Technology
type: channel
description: Journal of Advances in Information Technology is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology. Our aim is to publish experimental and theoretical results in strategy, infrastructure, human resources, system development and implementation, IT risk, IT management, data science, communications, technology development and futures, national policies and standards, software engineering, etc. In particular, we encourage a multidisciplinary/convergent approach based on the following broadly based branches of computer science for the application areas highlighted below: · Artificial Intelligence · Machine Learning · Data Science · Natural Language Processing · Cloud Computing · Internet of Things · Image Processing · Information Security · Networked Control Systems · Communication Systems Technologies · Digital Signal Processing in Communications · Neural Networks, Fuzzy Logic, Reasoning · Human-Computer Interaction (HCI) · Software Engineering · Non-technical but relevant topics such as Information Policy, Ethics and Legal issues as appropriate.
language: en
publication name: Journal of Advances in Information Technology
copyright: For articles published in JAIT, the copyright will be retained by the authors. Articles are licensed under the Creative Commons Attribution License (CC BY 4.0), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited. These conditions allow for maximum use and exposure of the work, while ensuring that the authors receive proper credit. In exceptional circumstances, articles may be licensed differently. If you have a specific condition (such as one linked to funding) that does not allow this license, please mention this to the editorial office of the journal at submission.
items: Countering Paraphrasing and Style Transfer in Educational Settings: A GAN-Based Approach to Identifying AI-Generated Academic Essays
items: AI-Driven Forecasting of Operating Room Quality Indicators Using LSTM Neural Networks: A Data-Driven Framework for Smart Surgical Management
items: Enhancing Genetic Algorithms Optimization Using Epigenetic-Inspired Mechanisms
items: LLM-Based Multi-agent Collaborative Spectrum Cognition Method
items: Integrating Whole Blood Gene Expression and Clinical Metadata into a Machine Learning Pipeline for Predictive Coronary Artery Disease Diagnosis
items: Towards Compliant and Private EHR Sharing: An Experimental Evaluation of ZKP-Blockchain Integration for Healthcare Data
items: Machine Learning Approaches for Imbalanced Post-Disaster Building Damage Classification: A Case Study of the 2022 Cianjur Earthquake
items: Region-Aware Attention with Hybrid Angular-Cosine Margin Loss for Prosopagnosia Face Recognition
items: A Novel Lightweight Multi-Octave Dilated Temporal Convolutional Network with Explainable AI and Meta-Learning Approach Based Digital Financial Fraud Detection
items: BFS-ZAT: Blockchain-Enabled Federated Security with Zero-Knowledge Adaptive Trust in Multi-Cloud Environments
items: Enhancing Heart Rate Estimation with POS-SSA Remote Photoplethysmography
items: Enhancing Aggregation Efficiency and Training Stability in Heterogeneous Federated Learning Using the Adaptive LoRA FL Framework
items: AgroExpense-OCR: Vision Transformer-Based Optical Character Recognition for Agricultural Receipt Digitization
items: Optimization of Task Offloading and Resource Allocation in the Internet of Everything: A Comprehensive Survey
items: Detection the Ripeness of Oil Palm Fresh Fruit Bunch Using Pretrained and Improved Models of YOLOv8
items: Secure and Intelligent DAWN-MANET Routing via AI and IoT Integration for Disaster Response
items: FedPrivEngine: A Federated Distributed Data Engineering Framework for Privacy-Preserving Analytics in Healthcare and Finance
items: A Stacking Ensemble Approach Integrating XGBoost and Deep Neural Networks with SMOTE for Financial Fraud Detection
items: Are Emergent Misaligned Models Self-Aware of their Misalignment? Latent Introspection in Activation Spaces of Niche Misaligned LLM
items: Explainable Deep Learning Models for Nutmeg Seed Image Quality Assessment
items: Graph-Driven Artificial Intelligence Architecture for Modelling Spatial, Temporal, and Environmental Interactions in Crop Yield Forecasting
items: Evaluating Machine Learning Models for DDoS Detection in SDNs
items: DeepARTS: A Graph-Transformer Continual Learning Framework for Adaptive Regression Test Sequence Recommendation
items: A Metrics-Based Framework for Model Selection in Object Detection Systems
items: Explainable Hybrid Modeling of Stock Market Turning Points: An Integrated ARIMA-LSTM-SHAP Approach
items: AI-Powered Detection of Advanced Persistent Threats (APTs): A Decision Tree Model for Intrusion Detection Using MITRE ATT&CK Behavioral Analysis
items: The Effect of Data Balancing Techniques in RMSProp and Adam Optimizers in Deep Learning Models for Wildlife Recognition: A Pathway to Image-Based Visual Servoing (IBVS)
items: Graph Neural Network Based Personalized Recommendation with Edge Enrichment Using Ratings and Review Sentiments
items: A Comparative Analysis of Hypertension Risk Detection Using Machine Learning and Deep Learning Techniques
items: Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation

Countering Paraphrasing and Style Transfer in Educational Settings: A GAN-Based Approach to Identifying AI-Generated Academic Essays
type: item
description: The generation of content by Artificial Intelligence (AI) in educational settings represents a growing consequence of the irresponsible use of generative AI that threatens academic integrity. Mitigating this phenomenon through AI-based detection remains a major challenge, given that generative AI tools, such as Large Language Models (LLMs), evolve rapidly. Classical detection tools such as anti-plagiarism software are no longer capable of tracking or curbing the spread of this phenomenon. Furthermore, the existing AI-based tools fail to detect the content generated, paraphrased, or humanized by bots that alter sentence structure. In this article, we propose a novel architecture based on Generative Adversarial Networks (GANs). This framework is a generative model composed of two main components trained through a minimax game. The first is the generator; it generates or reformulates text, while its opponent, the discriminator, tries to detect whether the text is human-generated or produced by the generator. We used DistilGPT-2 as the generator, trained simultaneously with DistilRoBERTa, the discriminator. Our framework was evaluated on 44,868 academic text samples and achieved 99% precision on unmodified texts. Moreover, our approach demonstrates strong reliability, maintaining a precision of 92% despite paraphrasing and humanization, as well as noise injection attacks that render other detection tools ineffective. The use of GANs enables our discriminator to more effectively identify deep semantic patterns and reformulated structures. Finally, we discuss future research directions to improve the adaptability and long-term relevance of the proposed approach in light of rapid advances in artificial intelligence.
subject: Regular paper
  topic: section
subject: Education
  topic: keywords
subject: Generative AI
  topic: keywords
subject: GAN
  topic: keywords
subject: Paraphrasing Attacks
  topic: keywords
subject: Academic Integrity
  topic: keywords
subject: Adversarial Training
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Learning
  topic: keywords
subject: AI
  topic: keywords
creator: Zohair Elmourabit
creator: Asmaâ Retbi
rights: Copyright (c) 2026 Zohair Elmourabit, Asmaâ Retbi https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10

AI-Driven Forecasting of Operating Room Quality Indicators Using LSTM Neural Networks: A Data-Driven Framework for Smart Surgical Management
type: item
description: Operating Room (OR) efficiency directly influences patient safety, resource allocation, and overall hospital performance. Despite the routine monitoring of operating room quality indicators, most analyses remain retrospective and provide limited support for forward-looking management. This study develops a forecasting framework based on Long Short-Term Memory (LSTM) neural networks and integrates the prediction results into a Business Intelligence (BI) visualization environment. Monthly specialty-level operational data from a medical center in Taiwan covering 2021 to 2024 were analyzed, including seven key operating room quality indicators. Different historical input windows ranging from 3 to 24 months were examined to evaluate their effects on predictive performance. To assess whether deep learning was necessary for this task, model performance was compared with traditional statistical approaches, including Moving Average, automated Autoregressive Integrated Moving Average (auto-ARIMA), and Naïve forecasting models, using a strictly chronological training and testing split. Indicators characterized by short-term operational variability, such as cancellation and scheduling delay rates, were more accurately predicted using shorter historical windows, whereas structurally stable metrics including utilization and occupancy rates showed improved performance with longer input sequences. While conventional statistical models performed competitively for some stable indicators, LSTM provided more consistent performance across heterogeneous metrics. The forecasting outputs were incorporated into an interactive dashboard to facilitate real-time monitoring and threshold-based management. Although the analysis was conducted using data from a single medical center, the results demonstrate the feasibility of integrating predictive analytics into routine surgical quality management.
subject: Selected Papers from Conferences
  topic: section
subject: Business Intelligence
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: Quality Indicators
  topic: keywords
creator: Wen-Shin Hsu
creator: Pei-Ju Wang
rights: Copyright (c) 2026 Wen-Shin Hsu, Pei-Ju Wang https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10

Enhancing Genetic Algorithms Optimization Using Epigenetic-Inspired Mechanisms
type: item
description: Genetic Algorithms (GA), developed several decades ago, are widely used to solve optimization problems. Since the invention of genetic algorithms, significant progress has been made in genetics, leading to the discovery of many new mechanisms of heredity that were not previously incorporated into genetic algorithms. One such group is epigenetic processes. The article presents two epigenetic-based operations used in genetic algorithms: cytosine methylation and allele inactivation (gene silencing). This article presents two original versions of genetic algorithms that use epigenetic processes. It presents the results of research on the impact of the introduced modifications on the genetic algorithm's performance. The study examined two optimization problems: the loading optimization problem and the outlier detection problem. The experiments examined the optimal probability of occurrence of the introduced modifications, the average number of iterations of the genetic algorithm with and without modifications required to obtain the optimal result, and compared execution times. The research confirmed that introducing new operations into genetic algorithms can improve their performance.
subject: Selected Papers from Conferences
  topic: section
subject: genetic algorithm
  topic: keywords
subject: epigenetic process
  topic: keywords
subject: optimization
  topic: keywords
creator: Kornel Chrominski
creator: Malgorzata Przybyla-Kasperek
creator: Rafal Skinderowicz
rights: Copyright (c) 2026 Kornel Chrominski, Malgorzata Przybyla-Kasperek, Rafal Skinderowicz https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10

LLM-Based Multi-agent Collaborative Spectrum Cognition Method
type: item
description: Current spectrum cognition methods based on mathematical models and deep learning excel at single tasks with clear inputs and outputs, such as signal detection and classification. However, they generally lack autonomous task decomposition, logical reasoning, and tool coordination capabilities. Consequently, it is difficult for them to directly transform raw signal data into valuable information in complex and dynamic wireless communication environments. Multi-Agent Systems (MAS), due to their autonomy, distributivity, and collaborative nature, show broad application prospects in interdisciplinary fields like communications and artificial intelligence. This paper proposes an MAS-based spectrum cognition method, implemented through a “master-slave” MAS architecture jointly driven by the Qwen3 and MiniCPM4 Large Language Models (LLMs). The system consists of a Master Agent, a Signal Perception Sub-Agent, a Knowledge Retrieval Sub-Agent, and a Cognitive Reasoning Sub-Agent, working together to accomplish collaborative spectrum cognition tasks. In experiments, focusing on Wi-Fi signal analysis, we constructed a signal sample database using Universal Software Radio Peripheral (USRP) platforms. The proposed method automated data analysis, feature extraction, historical data querying, and preliminary signal emitter direction estimation for the database. The results demonstrate that the proposed method achieves autonomous task decomposition, signal sample feature identification, and spectrum cognition for Wi-Fi signal emitters. It highlights the potential of MAS in communication signal analysis and offers a new technical approach for intelligent communication detection, automated signal monitoring, and smart emitter identification.
subject: Selected Papers from Conferences
  topic: section
subject: Multi-Agent System
  topic: keywords
subject: Communication Signal Analyzer
  topic: keywords
subject: Spectrum Cognition
  topic: keywords
subject: Large Language Model
  topic: keywords
creator: Lihao Liu
creator: Jianhui Wang
creator: Jingjing Li
creator: Nan Hu
creator: Wenlin Zhang
creator: Da Zhang
rights: Copyright (c) 2026 Lihao Liu, Jianhui Wang, Jingjing Li, Nan Hu, Wenlin Zhang, Da Zhang https://creativecommons.org/licenses/by/4.0
date: 2026-05-25
publication date: 2026-05-25

Integrating Whole Blood Gene Expression and Clinical Metadata into a Machine Learning Pipeline for Predictive Coronary Artery Disease Diagnosis
type: item
description: Timely and accurate identification of signs of Coronary Artery Disease (CAD) remains a central issue in modern preventive cardiology. Traditional diagnostic methods overlook the molecular alterations that drive disease occurrence and progression. This study proposes a multimodal machine learning framework integrating whole-blood gene expression profiles with clinical variables to support molecularly informed CAD diagnostics. We employed a Machine Learning framework which prioritizes robustness and interpretability over maximal predictive performance. Models based on the combined microarray and clinical data achieved the highest internal discrimination (AUC ≈ 0.76). The confounding analyses identified age as the dominant predictor, whereas gene expression features contributed an independent and biologically meaningful signal. The final selected gene set was enriched in immune and inflammatory pathways relevant to CAD pathophysiology. External validation revealed a decrease in performance, consistent with expected cross-platform domain shifts. The study underscores both the potential and the challenges of transcriptomic prediction of CAD, highlighting the importance of rigorous validation and data interpretation in high-dimensional biomedical modeling.
subject: Regular paper
  topic: section
subject: Coronary Artery Disease, Functional Enrichment, Gene Expression, Machine Learning, Random Forest, Statistical Analysis
  topic: keywords
creator: Zhilbert Tafa
creator: Bilgin Demir
creator: Djansel Bukovec
rights: Copyright (c) 2026 Zhilbert Tafa, Bilgin Demir, Djansel Bukovec https://creativecommons.org/licenses/by/4.0
date: 2026-05-25
publication date: 2026-05-25

Towards Compliant and Private EHR Sharing: An Experimental Evaluation of ZKP-Blockchain Integration for Healthcare Data
type: item
description: The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability. While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality—either exposing sensitive data or obscuring it beyond regulatory scrutiny. To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions. A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification. A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure. Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance. End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims. The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.
subject: Regular paper
  topic: section
subject: privacy-preserving;
  topic: keywords
subject: zero-knowledge proof
  topic: keywords
subject: auditability
  topic: keywords
subject: permissioned blockchain
  topic: keywords
subject: electronic health records
  topic: keywords
creator: Yan Watequlis Syaifudin
creator: Vipkas Al Hadid Firdaus
creator: Imam Fahrur Rozi
creator: Chandrasena Setiadi
creator: Suryani Dyah Astuti
creator: Nobuo Funabiki
creator: Maskur
creator: Kadek Suarjuna Batubulan
rights: Copyright (c) 2026 Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi, Suryani Dyah Astuti, Nobuo Funabiki, Maskur, Kadek Suarjuna Batubulan https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22

Machine Learning Approaches for Imbalanced Post-Disaster Building Damage Classification: A Case Study of the 2022 Cianjur Earthquake
type: item
description: Quick and accurate assessment of building damage after a disaster is essential for effective recovery and reconstruction planning. While traditional field surveys are reliable, they are often time-consuming, resource-intensive, and can be affected by human bias. This study proposes a comparative evaluation of eight machine learning algorithms: LightGBM, Extreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling Technique (SMOTE) + Random Forest (RF), Balanced Random Forest (BRF), Random Forest, Logistic Regression, Support Vector Machine (SVM) (Radial Basis Function (RBF)), and Naive Bayes, for multiclass classification of post-earthquake building damage utilizing real-world data on the 2022 Cianjur earthquake. The dataset comprises 3,063 records detailing geospatial and environmental characteristics, comprising epicentral distance, elevation, slope type, soil type, and vulnerability zone. To ensure reliable model performance on imbalanced data, a 10-fold cross-validation strategy was applied, evaluated across five metrics: accuracy, precision, recall, F1-Score, and Area Under the ROC Curve (AUC). The results indicate that ensemble-based models, particularly XGBoost (AUC = 0.860), LightGBM (AUC = 0.858), and BRF (AUC = 0.857), outperform traditional classifiers in both predictive accuracy and sensitivity toward severely damaged buildings. The outcomes underscore the effectiveness of gradient boosting and balanced ensemble methods in managing complex, imbalanced datasets, highlighting their potential for integration into automated post-disaster damage assessment systems. This research contributes to the development of intelligent, data-driven decision-support tools that can help accelerate recovery efforts in earthquake-prone regions.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: post-disaster damage assessment
  topic: keywords
subject: machine learning
  topic: keywords
subject: imbalanced classification
  topic: keywords
subject: XGBoost
  topic: keywords
subject: LightGBM
  topic: keywords
subject: Random Forest
  topic: keywords
subject: SVM
  topic: keywords
creator: Eka Rahmawati
creator: Catur Edi Widodo
creator: Sorja Koesuma
rights: Copyright (c) 2026 Eka Rahmawati, Catur Edi Widodo, Sorja Koesuma https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24

Region-Aware Attention with Hybrid Angular-Cosine Margin Loss for Prosopagnosia Face Recognition
type: item
description: Prosopagnosia diminishes an individual’s ability to recognize familiar faces, under pose variations, occlusions, and when faces appear visually similar. This study develops a deep learning framework, ResRAMACL, with ResNet-101, Region-Aware Attention Mechanism (RAAM), and a novel Hybrid Angular-Cosine Margin Loss (HACML), for supporting prosopagnosia patients to recognize human faces. Facial images are aligned using 2D facial landmark plotting, which separates key semantic regions from the images. ResNet-101 extracts feature maps for each region, which are weighted by RAAM to highlight cognitively crucial areas. These features are fused into a single embedding vector and trained using HACML, and the facial embeddings are stored in a Structured Query Language (SQL) database. The prediction phase generates an embedding using a trained model, checks the similarity with the stored embeddings, and predicts the final result. Compared to the existing loss functions, the proposed HACML achieved up to 2.16 units higher inter-class separation and up to 0.44 units lower intra-class variance. Ablation study proved higher performance compared with variations of the pipeline. The generalizability analysis proved minimal variation in performance when tested with other datasets. Overall, this proves the significance of the approach for integration with Augmented Reality (AR)-based or mobile cognitive assistance technologies, enabling prosopagnosia patients to recognize faces.
subject: Regular paper
  topic: section
creator: Bhavana Nagaraj
creator: Rajanna Muniswamy
rights: Copyright (c) 2026 Bhavana Nagaraj, Rajanna Muniswamy https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13

A Novel Lightweight Multi-Octave Dilated Temporal Convolutional Network with Explainable AI and Meta-Learning Approach Based Digital Financial Fraud Detection
type: item
description: The number of online banking and financial services through mobile apps is growing steadily. The number of customers using these apps for their monetary transactions is drastically increasing day by day. However, the increasing use of these apps on smart devices raises security concerns. Therefore, it is becoming a significant process to implement effective mechanisms to prevent fraud and protect personal data. In today’s financial world, the use of credit cards for online purchases has increased exponentially, and with it, the fraud that accompanies it. It is very difficult to detect fraudulent transactions in banking transactions. Therefore, the proposed work has introduced a novel deep learning framework based on a complexity-reduced Multi-Octave Dilated Temporal Convolutional Network (MO-DTCN) with Explainable Artificial Intelligence (XAI) approaches, including Lime and Shap. With these advancements, the proposed model can detect fraudulent financial transactions. To reduce the complexity of the architecture, the proposed work has utilized a Weighted Pruning and Quantization approach. In addition, meta-learning helps to fine-tune the performance model. Experimental evaluation on this dataset proves that the proposed MO-DTCN framework is effective, as it attains an accuracy of 99.13%, precision of 99.48%, recall of 99.35%, and F1-Score of 99.60% for an 80% training split with a very high Matthews Correlation Coefficient (MCC) of 0.9945, reflecting strong robustness on highly imbalanced data.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: Financial Fraud Detection, Explainable Artificial Intelligence , Meta-Learning,
  topic: keywords
creator: Mohd Abdul Rahim Khan
creator: Yasir Hashim Naif
creator: Salima Sarahan ALMughairi
rights: Copyright (c) 2026 Mohd Abdul Rahim Khan, Yasir Hashim Naif, Salima Sarahan ALMughairi https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24

BFS-ZAT: Blockchain-Enabled Federated Security with Zero-Knowledge Adaptive Trust in Multi-Cloud Environments
type: item
description: The proposed system introduces a blockchain-federated security system and a zero-knowledge adaptive trust system that would allow secure and auditable access control on multi-cloud platforms. There is a need to overcome the issues of heterogeneity of infrastructure, dynamic threats, and uninteroperable and privacy-sensitive trust management in securing access in a multi-cloud environment. The suggested model incorporates a federated identity management system, which combines biometric authentication and cryptographic identity that runs on blockchain to provide non-repudiable identity management. The system involves a blockchain trust authority that handles cryptographic keys and a dynamic on-chain trust score, which facilitates privacy-preserving authentication employing zero-knowledge proofs. Hybrid cryptography provides secure data access and sharing by combining fine-grained access control with homomorphic encryption, based on attribute-based encryption and homomorphic encryption, respectively. The combination of a blockchain and differential privacy allows executing queries in an auditable and privacy-guaranteed manner with clear data owner policies. To achieve cross-cloud interoperability, a trust-aware blockchain-aided proxy re-encryption scheme permits protected information sharing with adjustable trust levels, and trust re-assessment and blockchain-aided key refreshing schemes are resistant to dynamic threats. Experiments conducted on a federated multi-cloud testbed have shown that the proposed framework obtains the following security guarantee and efficiency, as compared to existing schemes authentication delay of 180 ms, a transaction throughput of 220 tps, and an accuracy of trust evaluation of 94%. Results of the experiment indicate that the proposed framework is scalable, privacy-protecting, and reliable in terms of access control to federated multi-cloud systems.
subject: Regular paper
  topic: section
subject: Multi-Cloud Environments
  topic: keywords
subject: Blockchain Trust Authority
  topic: keywords
subject: Cloud Provider
  topic: keywords
creator: Prabakaran K
rights: Copyright (c) 2026 Prabakaran K https://creativecommons.org/licenses/by/4.0
date: 2026-05-25
publication date: 2026-05-25

Enhancing Heart Rate Estimation with POS-SSA Remote Photoplethysmography
type: item
description: Remote Photoplethysmography (rPPG) enables non-contact heart rate monitoring from facial videos but it is highly susceptible to motion artefacts, illumination changes, and sensor noise. A framework combining the Plane Orthogonal-to-Skin (POS) method and Singular Spectrum Analysis (SSA) was proposed in this work to address these challenges by first projecting normalized Red Green Blue (RGB) signals onto a skin-tone–orthogonal subspace to suppress illumination and motion distortions and then decomposing the resulting signal into components that isolate physiologically meaningful oscillations. Evaluation on the PFF and UBFC-Phys dataset demonstrates that this approach consistently outperforms conventional single-channel, statistical, and chrominance-based methods by achieving a mean absolute error of 4.99 beats per minute (bpm) and correlation of 0.76 on PFF, and a mean absolute error of 4.11 bpm with correlation of 0.86 on UBFC-Phys. Furthermore, the comparison with results reported in the existing literature indicates that the proposed framework achieves competitive accuracy relative to popular learning-based rPPG approaches. These findings indicate that integrating chrominance projection with adaptive temporal decomposition significantly improves robustness and accuracy for contact-free heart rate estimation.
subject: Selected Papers from Conferences
  topic: section
subject: facial video-based rPPG
  topic: keywords
subject: POS (Plane Orthogonal-to-Skin)
  topic: keywords
subject: SSA (Singular Spectrum Analysis)
  topic: keywords
creator: Ruixuan Wang
creator: Wei Quan
creator: Bogdan Matuszewski
creator: Nick A Heywood
creator: Christopher Gaffney
creator: Katie Hoad
rights: Copyright (c) 2026 Ruixuan Wang, Wei Quan, Bogdan Matuszewski, Nick A Heywood, Christopher Gaffney, Katie Hoad https://creativecommons.org/licenses/by/4.0
date: 2026-04-24
publication date: 2026-04-24

Enhancing Aggregation Efficiency and Training Stability in Heterogeneous Federated Learning Using the Adaptive LoRA FL Framework
type: item
description: In recent years, Low-Rank Adaptation (LoRA) has emerged as a highly effective parameter-efficient fine-tuning technique for Transformer-based models. This paper extends the applicability of LoRA to traditional Deep Neural Networks (DNNs) by integrating it into a Federated Learning (FL) framework. The principal aim of this study is to facilitate data privacy protection while simultaneously minimizing weight transfer overhead in heterogeneous environments. To address the challenges of Non-Independent Identically Distribution (Non-IID) data, this study proposes the Adaptive LoRA FL framework, which incorporates feature mapping and category unification mechanisms to harmonize highly heterogeneous datasets. By integrating LoRA fine-tuning with feature mapping and category unification mechanisms, it effectively integrates highly heterogeneous datasets (e.g., CIC-IDS2017, Edge-IIoTset, and TON-IoT datasets). Furthermore, the performance of Adaptive LoRA FL framework is evaluated against state-of-the-art FL methods and demonstrate its generalizability across diverse application domains. Experimental results demonstrate that compared to full-weight aggregation, the Adaptive LoRA FL framework significantly reduces client communication costs while maintaining improved global accuracy. By integrating an early stopping mechanism, the training process terminates once convergence is reached, effectively minimizing redundant computation and communication overhead.
subject: Topic: Internet of Things (IoT) in Smart Systems and Applications
  topic: section
subject: Low-Rank Adaptation
  topic: keywords
subject: Heterogeneous Federated Learning
  topic: keywords
subject: Feature Mapping
  topic: keywords
creator: Lin-Huang Chang
creator: Yu-Sheng Hsieh
creator: Tsung-Han Lee
rights: Copyright (c) 2026 Lin-Huang Chang, Yu-Sheng Hsieh, Tsung-Han Lee https://creativecommons.org/licenses/by/4.0
date: 2026-04-16
publication date: 2026-04-16

AgroExpense-OCR: Vision Transformer-Based Optical Character Recognition for Agricultural Receipt Digitization
type: item
description: This study concentrates on the development of AgroExpense-OCR, an Optical Character Recognition (OCR) system for agricultural expense receipts, employing the Vision Transformer (ViT) architecture to improve the recognition of both printed and handwritten text. Utilizing a dataset of over 15,000 systematically prepared items, experimental results demonstrate that FastViT achieves the fastest convergence and lowest Character Error Rate (CER), despite transient instability during handwritten sample training. The best character-level performance is achieved by FastViT, with a test loss of 0.03144 and Certificate of Analysis (CER) of 0.0086 using greedy decoding and 0.0091 with beam search. Evaluation results demonstrated superior performance, with printed receipts attaining an accuracy of 96.80% and handwritten receipts 92.40%, surpassing traditional CNN-RNN methodologies. The AgroExpense-OCR application was created for both mobile and web platforms, including features such as receipt scanning, automatic expense categorization, cost analysis, and budget notifications. User satisfaction assessments indicated a high level of acceptance, thereby confirming the practical applicability of the system. This research makes a significant contribution to the advancement of digital agriculture and establishes a foundation for integration with future smart farm management systems.
subject: Regular paper
  topic: section
subject: Agricultural Receipts
  topic: keywords
subject: Digital Agriculture
  topic: keywords
subject: Optical Character Recognition
  topic: keywords
subject: Smart Farming
  topic: keywords
subject: Vision Transformer
  topic: keywords
creator: Wongpanya S. Nuankaew
creator: Chanapa Phikhason
creator: Watthana Kayowaen
creator: Tatsaneewan Yenwattana
creator: Pratya Nuankaew
rights: Copyright (c) 2026 Wongpanya S. Nuankaew, Chanapa Phikhason, Watthana Kayowaen, Tatsaneewan Yenwattana, Pratya Nuankaew https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13

Optimization of Task Offloading and Resource Allocation in the Internet of Everything: A Comprehensive Survey
type: item
description: The Internet of Everything (IoE) enables ubiquitous connectivity among heterogeneous devices but poses significant challenges for task offloading and resource allocation due to high dynamics and strict resource constraints. To address these complexities, optimization plays a pivotal role in efficiently orchestrating these resources. This survey comprehensively reviews optimization methodologies for IoE task offloading. Methodologically, this study relies on a comprehensive literature synthesis to distill evolutionary algorithmic trends and evaluate their applicability to specific IoE scenario constraints. The review is structured around six major paradigms: classic, Lyapunov-based, heuristic, game-theoretic, AI-native, and hybrid optimization. For each, representative techniques, assumptions, and performance characteristics are summarized. Comparative analysis highlights the evolution from model-based formulations toward intelligent, adaptive, and collaborative frameworks. Key challenges are identified, including scalability bottlenecks, learning instability in non-stationary environments, and complex multi-objective trade-offs. Finally, emerging research directions are outlined. This work bridges theoretical foundations with intelligent methodologies, offering a unified perspective for advancing cognitive and sustainable IoE ecosystems.
subject: Regular paper
  topic: section
subject: Adaptive scheduling, decision making, internet of everything, resource allocation, scheduling optimization, task offloading
  topic: keywords
creator: Wang Dayong
creator: Liping Lei
creator: Zhen Wang
creator: Cui Cui
rights: Copyright (c) 2026 Wang Dayong, Liping Lei, Zhen Wang, Cui Cui https://creativecommons.org/licenses/by/4.0
date: 2026-03-26
publication date: 2026-03-26

Detection the Ripeness of Oil Palm Fresh Fruit Bunch Using Pretrained and Improved Models of YOLOv8
type: item
description: The ripeness of oil palm fresh fruit bunches significantly impacts the quality and economic value of palm oil production. This study proposes a novel approach using deep learning, particularly You Only Look Once (YOLO)v8, to classify and detect the ripeness of oil palm fruit bunches. By leveraging pretrained models and fine-tuning techniques, this study aims to improve detection accuracy while reducing inference time. The methodology involves data preparation, model training, and evaluation, utilizing a dataset comprising 427 images of oil palm fruit bunches classified into raw, half-ripe, and ripe categories. Additionally, the images were augmented to increase the dataset size. The YOLOv8 architecture, known for its scalability and efficiency, is applied to improve the classification process. Results demonstrate that YOLOv8 provides a balance between accuracy and processing speed, making it a suitable tool for real-time applications in the palm oil industry. This study contributes to reducing the reliance on manual techniques, lowering operational costs, and increasing the overall efficiency of the oil palm industry.
subject: Regular paper
  topic: section
subject: Deep Learning
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Object Detection
  topic: keywords
subject: Oil Palm Fresh Fruit Bunch
  topic: keywords
subject: Computer Vision
  topic: keywords
creator: Pattanapong Saeleung
creator: Yutthapong Pianroj
creator: Teerasak Punvichai
creator: Piyanart Chotikawanid
creator: Saysunee Jumrat
creator: Doungrat Chitcharoen
creator: Jirapond Muangprathub
rights: Copyright (c) 2026 Pattanapong Saeleung, Yutthapong Pianroj, Teerasak Punvichai, Piyanart Chotikawanid, Saysunee Jumrat, Doungrat Chitcharoen, Jirapond Muangprathub https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10

Secure and Intelligent DAWN-MANET Routing via AI and IoT Integration for Disaster Response
type: item
description: The Disaster Area Wireless Mesh Mobile Ad Hoc Network (DAWN-MANETs) face many challenges such as: moving topology, lack of pre-determined infrastructure and vulnerable to a variety of attacks which make it quite difficult in establishing the reliable and secure communication. Although current Ad hoc On-Demand Distance Vector (Secure-AODV) and AI-based MANET schemes are designed for addressing the security or adaptability problems only and do not have a disaster-aware decision-making model in place. In this paper, we present an integrated Artificial Intelligence-Internet of Things (AI-IoT) secure routing framework for DAWN-MANETs. The proposed approach successfully integrates management of IoT-assisted situational awareness and AI-based decision-making engine along with secure AODV routing protocol. This approach assures adaptive and secured routing under dynamic and adverse disaster situations. As network and environmental data can be monitored and analyzed in real-time, the approach can dynamically calculate routing paths which best balancing between reliability and security under the stochastic environment. Experimental results demonstrate that the proposed framework significantly improves the important network performance parameters as compared to traditional AODV and Secure-AODV protocols. The robustness of packet delivery, communication delay and security against attack scenarios have been well improved. With these strengths, it is believed that the integration disaster awareness and intelligent decision-making into secure routing protocols can greatly enhance the robustness and fitness for DAWN-MANETs. Despite focusing on simulation-based validation in the present research, the framework provides a scalable basis for actual application in disaster responses.
subject: Regular paper
  topic: section
subject: Secure Routing
  topic: keywords
subject: DAWN-MANET
  topic: keywords
subject: Disaster Response
  topic: keywords
creator: Khanista Namee
rights: Copyright (c) 2026 Khanista Namee https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22

FedPrivEngine: A Federated Distributed Data Engineering Framework for Privacy-Preserving Analytics in Healthcare and Finance
type: item
description: Privacy-preserving analytics is essential in healthcare and financial domains where sensitive data must comply with regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI-DSS). Although Federated Learning (FL) enables collaborative model training without sharing raw data, existing frameworks often lack compliance-aware orchestration, efficient encrypted query execution, domain flexibility, and scalability under Homomorphic Encryption (HE). This paper presents FedPrivEngine, a federated and distributed data engineering framework designed for regulation-aware analytics across institutional silos. The framework integrates Homomorphic Encryption (HE), optional Differential Privacy (DP), Spark-based distributed orchestration, and a compliance-aware query planner to enforce policy constraints during federated execution. Two domain-specific models are implemented: HealthPrivNet, a Gated Recurrent Unit (GRU)-based model evaluated on the Open Safely healthcare dataset, and FinanceRiskNet, a Multi-Layer Perceptron (MLP)-based model assessed on the FICO HELOC credit risk dataset. Experimental results show that FedPrivEngine achieves high classification accuracy (93.8% in healthcare and 91.2% in finance), ensures zero policy violations, and supports encrypted query execution with success rates above 98%, while maintaining sublinear scalability and moderate system overhead. These results demonstrate the feasibility of secure, accurate, and regulation-aware federated analytics in policy-sensitive environments.
subject: Regular paper
  topic: section
creator: Srinivas Lakkireddy
rights: Copyright (c) 2026 Srinivas Lakkireddy https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22

A Stacking Ensemble Approach Integrating XGBoost and Deep Neural Networks with SMOTE for Financial Fraud Detection
type: item
description: The detection of financial fraud is one of the most serious challenges in modern financial systems and keeps on growing with increasingly complex and dynamic fraudulent activities. The paper proposes a robust hybrid model by integrating Extreme Gradient Boosting (XGBoost) and Deep Neural Networks within a stacking ensemble framework to improve the accuracy and scalability of fraud detection. It is evaluated on the pre-processed Credit Card Fraud Detection Dataset by balancing using the Synthetic Minority Over-sampling Technique (SMOTE) technique to handle the inherent class imbalance. The model achieved near-perfect performance, with a cross-validated average accuracy of 99.7% and consistently high precision, recall, and F1-scores across folds. The comparison done with previous works underlines how well the model overcomes some of the traditional challenges like data imbalance and evolving fraud patterns while scalability and adaptability for real-time applications are kept intact. Besides, the combination of advanced preprocessing techniques with ensemble learning ensures the robustness of the model in detecting fraudulent transactions. Though the model is promising, this study recognizes its limitations, such as computational complexity and dataset dependency, and proposes future research directions to optimize the model for diverse datasets and real-world constraints. Therefore, this study gives a great boost to financial fraud detection with its scalable, interpretable, and highly accurate solution and paves the path for further development in securing financial systems against fraud.
subject: Regular paper
  topic: section
subject: Financial Fraud
  topic: keywords
subject: Hybrid Machine Learning Model
  topic: keywords
subject: DNN
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Stacking Ensemble
  topic: keywords
subject: SMOTE
  topic: keywords
creator: Noor Ahmed
creator: Fadi Al-Turjman
rights: Copyright (c) 2026 Noor Ahmed, Fadi Al-Turjman https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13

Are Emergent Misaligned Models Self-Aware of their Misalignment? Latent Introspection in Activation Spaces of Niche Misaligned LLM
type: item
description: Large language models can exhibit emergent misalignment when finetuned on narrowly malicious tasks. Whilst this misalignment is widespread across models of all sizes, the question of whether it overrides the model’s safety training or suppresses it post-finetuning remains poorly understood. In this, we investigate this question for the Qwen2.5 32B model organism finetuned on risky financial advice, which shows broad misalignment. We evaluate our hypothesis that an emergent misaligned model is self-aware of its activation-space alignment by conducting four experiments using linear probing and causal tracing. Our results on linear probing suggest that diffuse “risk” representations exist across all layers. We also observe, through latent introspection analysis, a strong alignment between misaligned activations and base-model “refusal vectors” using causal tracing and activation patching. By further leveraging this idea, we conduct a more granular mechanistic interpretability analysis using mean ablation and direct logit attribution to identify which components L62H12 and L62H28 may contribute most to suppressing safety training. We validate our findings by evaluating the model’s attack success rate on the standard Jailbreak bench dataset before and after mean ablation of the suppression heads. Our findings underscore the importance of representational consistency of evaluations in misaligned models for assessing the role of misaligned finetuning in undermining the model’s safeguards. Broadly, our work shows that misaligned models exhibit a quantitative conflict: latent safety representations are computed across the network, but a few late-layer heads may override them.
subject: Selected Papers from Conferences
  topic: section
subject: emergent misalignment
  topic: keywords
subject: model organism
  topic: keywords
subject: causal tracing
  topic: keywords
subject: linear probing
  topic: keywords
subject: vector steering
  topic: keywords
subject: latent knowledge
  topic: keywords
subject: large language models
  topic: keywords
creator: Ajay Agarwal
creator: Tatsuhito Hasegawa
rights: Copyright (c) 2026 Ajay Agarwal, Tatsuhito Hasegawa https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13

Explainable Deep Learning Models for Nutmeg Seed Image Quality Assessment
type: item
description: Nutmeg seed quality plays a key role in determining the economic value and export potential of Indonesian spice products. Manual assessment of quality is often subjective, time-consuming, and depends on the evaluator’s experience. This study introduces an explainable deep learning method designed to automatically and transparently evaluate nutmeg image quality. The model employs the EfficientNet-B0 architecture trained on 300 nutmeg images categorized into three quality levels: good, moderate, and rotten. To enhance interpretability, Explainable Artificial Intelligence (XAI) techniques—specifically Gradient-Weighted Class Activation Mapping (Grad-CAM) and local Interpretable Model-Agnostic Explanations (LIME)—were applied to highlight the image regions most influential in the model’s decisions. Model performance was evaluated using accuracy, precision, recall, F1-score, and mean Average Precision (mAP). The EfficientNet-B0 model, coupled with XAI methods, achieved an overall accuracy of 78% and a mAP of 70.29%, with Grad-CAM and LIME visualizations consistently highlighting the key visual features that determine nutmeg quality. These findings demonstrate that integrating deep learning with XAI can produce an objective, efficient, and dependable quality assessment system, which offers potential applications to other agricultural products.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: Explainable Deep Learning, EfficientNet-B0, Grad-CAM, LIME, Nutmeg Seed Quality, Artificial Intelligence, Smart Agriculture
  topic: keywords
creator: Manuel Soares Dos Reis Pacheco
creator: Hadiyanto Hadiyanto
creator: Ridwan Sanjaya
rights: Copyright (c) 2026 Manuel Soares Dos Reis Pacheco, Hadiyanto Hadiyanto, Ridwan Sanjaya https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22

Graph-Driven Artificial Intelligence Architecture for Modelling Spatial, Temporal, and Environmental Interactions in Crop Yield Forecasting
type: item
description: This research investigates the development and evaluation of self-supervised learning models for groundnut yield forecasting, aiming to support precision agriculture through accurate, data-driven predictions. The study focuses on three model architectures within a self-supervised framework: a Convolutional Neural Network (CNN) for spatial data, a Recurrent Neural Network-Transformer (RNN-Transformer) hybrid for temporal data, and a Graph Neural Network (GNN) for relational agronomic data. Each model is trained using a tailored pretext task masked patch reconstruction for CNN, time-series forecasting for RNN-Transformer, and link prediction for GNN enabling the extraction of meaningful features from unlabeled datasets. The models were trained on multisource data, including Sentinel-2 satellite imagery, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset provided by the ECMWF and India Meteorological Department (IMD) weather data, and soil-agronomic records from ICRISAT and Soil Grids. Comparative results show that the GNN-based model achieved the best performance, with a Root Mean Square Error (RMSE) of 176.4 kg/ha, Mean Absolute Percentage Error (MAPE) of 6.3%, and R² of 0.93. In contrast, the CNN and RNN–Transformer models reported higher RMSE values (245.8 kg/ha and 218.3 kg/ha) and lower R² scores (0.82 and 0.87), confirming the superior predictive accuracy of the GNN approach. The GNN also demonstrated strong regional generalization, achieving an R² of 0.93 in the Southern Semi-Arid Zone, and showed superior pretext task accuracy at 93.6%. Additionally, it required only 58 min of training and converted into 22 epochs, offering a balanced profile of accuracy and efficiency. These findings confirm the effectiveness of graph-based, self-supervised learning in modeling complex agricultural systems and highlight its potential for scalable deployment in real-world precision agriculture applications.
subject: Regular paper
  topic: section
subject: Self-Supervised Learning, Yield Prediction, Precision Agriculture, Graph Neural Network (GNN), Remote Sensing, Deep Learning Architectures
  topic: keywords
creator: M Deepika N
creator: Dr. K. Sreeama murthy
rights: Copyright (c) 2026 M Deepika N, Dr. K. Sreeama murthy https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22

Evaluating Machine Learning Models for DDoS Detection in SDNs
type: item
description: With the increased usage of internet-enabled devices in the contemporary networked world, vulnerability to security attacks has significantly widened. Distributed Denial of Service (DDoS) attacks are particularly critical, as they impact service availability and diminish valuable computing and internet resources. The study aims to enhance fine-grained DDoS identification by evaluating and comparing the performance of machine learning algorithms over Software-Defined Networks (SDNs), to improve accuracy and reduce false positives. We assessed machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), Random Forest (RF), Naive Bayes (NB), and the Hidden Markov Model (HMM), against the labelled DDoS datasets, the LR-HR DDoS 2024 and InSDN. We employed a vaccine-based binary wolf grey optimization feature selection approach to rank data attributes by their respective levels of importance. The measures employed for the purpose included time delay, accuracy, false positive rate, and true positive rate. From the models compared, XGBoost showed the most effective detection, with the best accuracy and reduced false positive instances, especially when employed with an enhanced vaccine-based feature selection. Experimental results confirm that XGBoost, especially when combined with vaccine-based Binary Grey Wolf Optimization (BGWO) feature selection, gives a highly effective solution for detecting DDoS attacks in a Software Defined Network environment.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: Software Defined Networks
  topic: keywords
subject: Distributed Denial of service
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Özgür Tonkal
creator: Jeremia Anthony Mgungile
rights: Copyright (c) 2026 Özgür Tonkal, Jeremia Anthony Mgungile https://creativecommons.org/licenses/by/4.0
date: 2026-03-26
publication date: 2026-03-26

DeepARTS: A Graph-Transformer Continual Learning Framework for Adaptive Regression Test Sequence Recommendation
type: item
description: Continuous Integration (CI) pipelines require fast regression feedback under strict time budgets, making full regression suites impractical for large systems. Deep Adaptive Regression Test Sequencer (DeepARTS) generates an ordered regression test sequence per CI cycle under a budget K. The approach combines a Graph Attention Network (GAT) for code–test dependency encoding, a Transformer encoder for context-aware modeling of candidate tests, and a deep Q-network for sequential test selection using a detection-oriented reward with an execution mask. Continual learning based on entropy-guided replay and Maximum Mean Discrepancy (MMD) drift detection updates model parameters across cycles without full retraining. Evaluation uses Regression Test Prioritization Torrent (RTPTorrent), a dataset containing 20 open-source Java projects and more than 100,000 TravisCI build logs, with chronological per-project splits to reduce temporal leakage. Under identical candidate sets and budget \mathbit{K}, DeepARTS improves early fault detection compared with Linkage Learning-based Non-Dominated Sorting Genetic Algorithm (L2-NSGA), achieving Average Percentage of Faults Detected (APFD) = 0.94 with Precision@K = 99.2% and Recall@K = 98.7%, while maintaining per-cycle recommendation latency near 2.5 s on Central Processing Unit (CPU). Generalization beyond Java projects and TravisCI logs remains to be validated.
subject: Regular paper
  topic: section
subject: Adaptive Regression Test Sequence
  topic: keywords
subject: Optimum Regression Testing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Adaptive Test Sequence Recommendation System
  topic: keywords
creator: Srinivasa Rao Kongarana
creator: Ananda Rao A
creator: Radhika Raju P
rights: Copyright (c) 2026 Srinivasa Rao Kongarana, Ananda Rao A, Radhika Raju P https://creativecommons.org/licenses/by/4.0
date: 2026-03-26
publication date: 2026-03-26

A Metrics-Based Framework for Model Selection in Object Detection Systems
type: item
description: Object detection models, such as the You Only Look Once (YOLO) family, have been widely deployed in agriculture, surveillance, and industrial inspection. However, selecting an appropriate model remains challenging due to trade-offs between precision, recall, and efficiency. Conventional evaluation approaches often rely on a single metric such as an F1-score, which may obscure critical domain-specific requirements. This study proposes a metrics-based framework that integrates multiple evaluation indicators into a unified composite index using Multi-Criteria Decision-Making (MCDM) principles. Precision, recall, and F1-score were normalized and weighted according to use-case priorities across four experimental scenarios: indoor/non-molted, indoor/molted, outdoor/non-molted, and outdoor/molted. Composite scores and rankings were computed under three operational contexts: security/surveillance, real-time edge applications, and quality inspection. Results show that Scenario 3 (outdoor/non-molted) consistently achieved the highest composite performance across all contexts, while Scenario 2 (indoor/molted) and Scenario 4 (outdoor/molted) varied in ranking depending on use-case weights. Sensitivity analysis further confirmed the robustness of Scenario 3 under shifting recall weight assignments. Compared to single-metric evaluation, the proposed framework offered finer-grained differentiation, highlighting trade-offs that are critical for real-world deployment. The findings demonstrate the value of multi-metric integration for systematic model selection and provide practical guidance for applying object detection in diverse operational environments.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: YOLO; object detection; model selection; multi-criteria decision-making; precision–recall trade-off; automated object counting
  topic: keywords
creator: Sudasawan Ngammongkolwong
creator: Rungtiva Saosing
rights: Copyright (c) 2026 Sudasawan Ngammongkolwong, Rungtiva Saosing https://creativecommons.org/licenses/by/4.0
date: 2026-04-16
publication date: 2026-04-16

Explainable Hybrid Modeling of Stock Market Turning Points: An Integrated ARIMA-LSTM-SHAP Approach
type: item
description: In recent years, global shocks such as the COVID-19 pandemic, geopolitical conflicts, and trade tensions have substantially intensified volatility in financial markets, thereby increasing the frequency of price turning points. This study proposes a novel approach that addresses the growing demand for artificial intelligence models that are not only highly predictive but also interpretable. Traditional econometric models, such as Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), are effective at capturing linear dependencies but fail to adequately represent nonlinear market dynamics. Meanwhile, machine learning methods, despite their strong predictive power, often exhibit limited interpretability. To bridge this gap, this paper presents an explainable hybrid framework that integrates ARIMA, Long Short-Term Memory (LSTM), and Shapley Additive exPlanations (SHAP). Using daily data from ten major global stock indices from January 1, 2010 to September 30, 2025, the study employs a hybrid labeling approach that combines price extrema, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD) indicators. Forecasting accuracy is further enhanced by incorporating ARIMA residual–based feature fusion. The model’s performance is evaluated using both statistical (F1-Score, Precision-Recall Area Under the Curve (PR-AUC), Root Mean Squared Error (RMSE), and economic (Sharpe ratio) metrics within a walk-forward validation framework. The results show that the ARIMA-LSTM-SHAP model achieves an optimal balance between predictive accuracy and economic efficiency, achieving an F1-Score of 0.60 and a Sharpe ratio exceeding 1.5. The findings highlight the framework’s practical relevance for investors and policymakers by facilitating the early detection of turning cycles and supporting risk-informed decision-making.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: turning-point detection
  topic: keywords
subject: explainable artificial intelligence
  topic: keywords
subject: behavioral finance
  topic: keywords
subject: adaptive market hypothesis
  topic: keywords
subject: economic validation
  topic: keywords
creator: Tsolmon Sodnomdavaa
rights: Copyright (c) 2026 Tsolmon Sodnomdavaa https://creativecommons.org/licenses/by/4.0
date: 2026-03-26
publication date: 2026-03-26

AI-Powered Detection of Advanced Persistent Threats (APTs): A Decision Tree Model for Intrusion Detection Using MITRE ATT&CK Behavioral Analysis
type: item
description: Advanced Persistent Threats (APTs) demand intrusion detection systems that are not only highly accurate but also operationally transparent and aligned with analyst workflows. This paper presents Decision Tree–Based Intrusion Detection System (DTB-IDS), a decision tree–based intrusion detection system that performs multi-level classification of network flows into (i) benign versus malicious traffic, (ii) MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) tactics, and (iii) primary ATT&CK techniques. DTB-IDS is trained on a behaviorally enriched dataset obtained by carefully merging and harmonizing UWF-ZeekData24 and NF-UQ-NIDS-v2, yielding 628,415 Zeek-style flow records mapped to 13 tactics and 24 techniques. Using 33 engineered, semantically meaningful flow features and 3 specialized Classification and Regression Tree (CART) trees, DTB-IDS achieves strong performance on the merged dataset, with 99.2% binary accuracy, 99.1% tactic F1, and 99.4% technique micro-F1, together with a very low Hamming loss (≈10⁻³). Cross-dataset validation on Canadian Institute for Cybersecurity Intrusion Detection Systems 2017 (CICIDS 2017) confirms the robustness of the learned decision boundaries, with 99.9% binary accuracy and 99.2% tactic F1 without retraining. Temporal and community-based splits further demonstrate that performance is sustained under stricter generalization regimes. Compared with Random Forest, Support Vector Machine (SVM), Deep Neural Networks (DNNs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Explainable Boosting Machine baselines, validated by McNemar’s and paired t-tests. Feature-importance analysis and rule-path inspection show that a small set of interpretable, ATT&CK-aligned features and decision paths account for most predictions, making DTB-IDS a practical and transparent APT detection solution for security operations centers.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: Advanced Persistent Threat (APT), Intrusion Detection System (IDS), Decision Tree, MITRE ATT&CK, Interpretability.
  topic: keywords
creator: asem daoud
creator: Mohamed Hamdi
rights: Copyright (c) 2026 asem daoud, Prof.Mohamed Hamdi https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13

The Effect of Data Balancing Techniques in RMSProp and Adam Optimizers in Deep Learning Models for Wildlife Recognition: A Pathway to Image-Based Visual Servoing (IBVS)
type: item
description: Data collection in animal ecology has become more common, and extracting information can be done using deep learning methods. Challenges arise when using the dataset as the training input, including balancing the data quality and quantity. This research aims to investigate the effect of data balancing techniques on the optimizer—specifically, Root Mean Square Propagation (RMSProp) and Adam—in a deep learning image recognition model. Once recognition capability is obtained, it provides feedback for an Image-Based Visual Servoing (IBVS) system. The wildlife dataset is from Serengeti National Park and contains 41,762 images across 7 classes. Before applying data-balancing techniques, we determined that 1,100 images were needed, which we considered a balanced distribution across all classes since it was close to the original dataset. The next step was applying the balancing technique. We offered two balancing techniques: the first was to augment three classes with each class’s 1,100 images, while the other four classes retained their original number of images. The second involved reducing the number of larger classes to 1,100 images. The result showed that combining the deep learning model AlexNet with RMSProp achieved the highest Macro F1-Score of 0.8118, outperforming the other deep learning models with the Adam optimizer. The best balancing technique reduced the larger class to a fixed number of images in the smaller dataset, and AlexNet with RMSProp is a good choice for real-time visual servoing.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: data balancing techniques, wildlife animal recognition, image-based visual servoing, RMSProp, Adam, deep learning
  topic: keywords
creator: Iman Herwidiana Kartowisastro
creator: Cuk Tho
creator: Reina Setiawan
rights: Copyright (c) 2026 Iman Herwidiana Kartowisastro, Cuk Tho, Reina https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10

Graph Neural Network Based Personalized Recommendation with Edge Enrichment Using Ratings and Review Sentiments
type: item
description: Visual features and user-item interaction graphs enable recommendation systems to model complex relationships and improve accuracy. We propose Heterogeneous Graph Recommender Network with Bayesian Personalized Ranking for Fashion (HGRN-BPR-F), a Graph Neural Network that places multimodal edge features, explicit ratings and review sentiments, directly on user-item interactions. Unlike prior work aggregating features at nodes, our edge-centric design preserves interaction-specific context. The graph data is extracted from a real-world dataset of Amazon Fashion and exhaustive preprocessing is done to include relevant features for both users and products. Model training employs the Bayesian Personalized Ranking (BPR) loss function, which optimizes the ranking between positive and negative user–item pairs, with hard negatives used to enhance learning effectiveness. Our model achieves AUC of 0.885 ±0.010 evaluated over three seeds, demonstrating strong ranking performance with a 10.2% improvement over the rating-only baseline. Ablation experiments further demonstrate that combining ratings and sentiment as edge features substantially outperforms single-modality and node-feature-based configurations, thereby validating our edge-centric multimodal architecture.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: Bayesian Personalized Ranking (BPR) Loss
  topic: keywords
subject: Cosine similarity
  topic: keywords
subject: Graph Neural Network (GNN)
  topic: keywords
subject: Heterogeneous graphs
  topic: keywords
creator: Anu Mathews
creator: Sheba Selvam
rights: Copyright (c) 2026 Anu Mathews, Sheba Selvam https://creativecommons.org/licenses/by/4.0
date: 2026-03-26
publication date: 2026-03-26

A Comparative Analysis of Hypertension Risk Detection Using Machine Learning and Deep Learning Techniques
type: item
description: A major global health concern that raises health risks considerably is hypertension. Improving early diagnosis and management of hypertension and improving patient outcomes represent the 2 benefits of utilizing Machine Learning (ML) algorithms for early detection. A lot of attention has been paid to them. This work tackles the important issue of hypertension identification by utilizing a sizable dataset from Kaggle that contains a range of physiological and demographic features. To accurately evaluate this dataset, the suggested model incorporates a number of ML classifiers, including Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Random Forest (RF), Decision Tree Classifier (DT), Artificial Neural Network (ANN) and Covering Number 2 (CN2) rule inducer, to investigate their prediction potential. Performance criteria such as accuracy, precision, sensitivity, specificity, and F1-score were used to assess each classifier’s effectiveness. According to the results, the GB approach had the highest accuracy (99.2%), followed by AdaBoost (96.4%). The 2 algorithms that performed exceptionally well were RF and CN2 rule inducers, which had respective accuracy rates of 93.8% and 93.5%. These results demonstrate the possibility of accurately predicting hypertension with state-of-the-art ML techniques, offering useful information to support healthcare providers in making knowledgeable decisions about patient care.
subject: Topic: Machine Learning in Computer and Information Systems
  topic: section
subject: Artificial intelligence, classification, gradient boosting, hypertension., Kaggle, machine learning
  topic: keywords
creator: Areen arab
creator: Hamza Abu Owida
creator: MuneeraAltayeb Altayeb
rights: Copyright (c) 2026 Eng.Areen Arabiat, Dr.Hamza Abu Owida, Eng.MuneeraAltayeb https://creativecommons.org/licenses/by/4.0
date: 2026-05-13
publication date: 2026-05-13

Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation
type: item
description: Federated Learning (FL) has emerged as a transformative approach for distributed machine learning, particularly in edge computing environments where data privacy, low latency, and bandwidth efficiency are critical. This paper presents a systematic review and performance evaluation of FL techniques tailored for edge computing. It categorizes state-of-the-art methods into four dimensions: optimization strategies, communication efficiency, privacy-preserving mechanisms, and system architecture. Using benchmarking datasets such as MNIST, CIFAR-10, FEMNIST, and Shakespeare, it assesses five leading FL algorithms across key performance metrics including accuracy, convergence time, communication overhead, energy consumption, and robustness to non-Independent and Identically Distributed (IID) data. Results indicate that SCAFFOLD achieves the highest accuracy (0.90) and robustness, while Federated Averaging (FedAvg) excels in communication and energy efficiency. Visual insights are provided by a taxonomy diagram, dataset distribution chart, and a performance matrix. Problems including data heterogeneity, energy limitations, and repeatability still exist despite advancements. To enable the creation of more robust and scalable FL systems for edge-based intelligence, this analysis identifies existing gaps and provides an organized research agenda in the future.
subject: Regular paper
  topic: section
subject: edge computing
  topic: keywords
subject: federated learning
  topic: keywords
subject: machine learning
  topic: keywords
subject: non-IID data
  topic: keywords
subject: privacy reservation
  topic: keywords
creator: Sales Aribe Jr.
creator: Gil Nicholas Cagande
rights: Copyright (c) 2026 Sales Aribe Jr., Gil Nicholas Cagande https://creativecommons.org/licenses/by/4.0
date: 2026-02-23
publication date: 2026-02-23