IAES International Journal of Artificial Intelligence (IJ-AI)
IAES International Journal of Artificial Intelligence (IJ-AI), ISSN/e-ISSN 2089-4872/2252-8938 publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence (AI) and machine learning (ML) areas and their applications in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithms, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; supervised learning; unsupervised learning; deep learning; big data and AI approaches; reinforcement learning; and learning with generative adversarial networks; etc. This journal is indexed in Scopus and all published papers since 2018 issues were included in scopus.com. This journal has been included in SCImago Journal Rank (SJR) since 2020 (or since SJR 2019). The CiteScore for the IAES International Journal of Artificial Intelligence is 2.9 (Q2 CiteScore Best Quartile), with an SNIP of 0.429 and an SJR of 0.247. The journal is published by Institute of Advanced Engineering and Science (IAES). Submit your paper now through Online submission ONLY. Kindly please download the IJ-AI template in MS Word or LaTeX. Submission for publication in this journal presumes that the paper is not being considered for publication elsewhere and is not under review by any other publication. A submitted paper should be an original work, and should not have been presented or published previously.All papers undergo a thorough evaluation process while following all ethical guidelines to maintain the highest publishing standards. The IJ-AI Team strictly review suitability, originality of manuscripts for publication. Publication integrity is one of the principle responsibilities we take. Our Editors maintain transparency of research, assess manuscript for scientific value, ensure that content is free from any manifestations of discrimination, and evaluate information objectively without any conflict of interests. Papers are published in the bimonthly journal (Feb, Apr, Jun, Aug, Oct, and Dec). If you have any problems submitting your papers, please contact us at email: ijai@iaesjournal.com, cc: editor@iaesjournal.com.
Institute of Advanced Engineering and Science
Leveraging artificial intelligence in education a comprehensive review of the Arab literature
This descriptive analytical study relied on the systematic literature review (SLR) addressing the role of artificial intelligence (AI) in improving education management and delivery, empowering teaching and teachers, learner learning, and learning assessment. The SLR sample was determined in 125 articles from Almandumah and Scopus databases. The analysis results revealed: 39.2% of articles were related to the Saudi Arabia context, 34.4% were published in 2024, and 68.8% were quantitative. The most prominent AI applications used were: ChatGPT, Alexa, Grammarly, Duolingo, Chatbot, and Coursera. The results also showed that the most areas of the educational process in which AI applications can be implemented were: empowering teaching and teachers (62.4%), and improving learner learning (48.8%). The SLR also came up with some practices that can be applied to enhance the AI use in improving the educational process. An opinion poll was applied on 37 academic experts to identify their agreement on the potential of implementing these developed practices in the areas of the educational process. The results showed that the experts' agreement on the potential of implementing these practices in the areas of the educational process was very high. The study recommended some useful mechanisms for implementing these practices.
Copyright (c) 2026 Mohamed Abdelraouf Elsayed, Taleb Saleh Alattas http://creativecommons.org/licenses/by-sa/4.0
Strategic optimization of artificial intelligence digital learning in informatics engineering
This study examines the strategic optimization of artificial intelligence (AI)-based digital learning in the Department of Informatics Engineering by analyzing the interaction between internal and external factors that influence successful implementation. Employing a qualitative approach based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, this research utilizes the internal factor analysis summary (IFAS) and external factor analysis summary (EFAS) to assess institutional readiness, constraints, and strategic opportunities for AI integration in higher education. The results indicate that external factors exert a stronger strategic influence than internal factors, as reflected by a higher EFAS score (1.50) compared to the IFAS score (1.25). Key external opportunities include the increasing demand for AI competencies, supportive national policies, and opportunities for industry collaboration, while the main internal limitations are limited faculty expertise in AI and insufficient AI-specific learning resources. Based on these findings, this study formulates a SWOT-based strategic framework that converts analytical outcomes into practical recommendations for curriculum development, faculty capacity building, and the adoption of adaptive AI learning technologies. This research contributes a context-specific and empirically grounded strategic model that advances AI integration beyond descriptive analysis, supporting more effective, personalized, and sustainable digital learning, particularly within Informatics Engineering programs in Indonesian Islamic higher education institutions.
Copyright (c) 2026 Aan Ansori, Birru Muqdamien, Ahmad Tabrani, Reza Syafrijal, Sutanto, Eko Wahyu Wibowo, Syifa Amara Dhestiyani http://creativecommons.org/licenses/by-sa/4.0
Consciousness in artificial intelligence systems: an ontological approach
The question of whether artificial intelligence (AI) systems can attain consciousness remains central ontological challenge within interdisciplinary discourse spanning AI, philosophy of mind, and cognitive neuroscience. This study investigates the metaphysical status of AI systems by interrogating the explanatory clarity of their existence and the potential emergence of subjectivity from non-biological matter. Employing an integrative methodology that combines a comprehensive literature review of scholarly texts and recent empirical findings with philosophical critical analysis, the research explores the conditions under which AI systems might be considered conscious entities. Findings suggest that the expanding societal integration of AI necessitates a re-evaluation of consciousness and cognition beyond anthropocentric parameters. The study posits a novel interpretive framework for addressing the ontological problem of AI, one that implicates future legal, moral, and interactive structures surrounding conscious non-biological agents. This conceptual repositioning invites a deeper inquiry into the criteria for recognizing, engaging with, and regulating advanced AI systems. By grounding design principles in ontological clarity, the framework offers guidance for constructing AI systems capable of reflexive processing, minimal phenomenality, and ethically aligned behavior that bridges conceptual analysis with implementation pathways.
Copyright (c) 2026 Andreas Yumarma, Wan Sen http://creativecommons.org/licenses/by-sa/4.0
Long short-term memory based activity detection using skeleton joints data: a systematic review
In today's security and surveillance applications, recognizing abnormal activity is critical component of identifying possible hazardous or unusual human behaviors. There is need for new technologies that can detect abnormal human behaviors precisely. The present review investigates various aspects of detection process, focusing on skeleton joints input data. It then explores adoption of deep learning (DL) architectures, such as long short-term memory (LSTMs) and transformers, to improve accuracy and robustness of recognition models. The review explores synergistic integration of LSTMs and transformers to improve recognition of unusual activity. By integrating LSTMs' processing capabilities and attention mechanisms of transformers, enhanced models can accurately identify intricate patterns of activity. Despite the advancements that have been made in the field, the challenges that remain are still related to recognition of unusual activities. These include lack of scalability for large datasets, need for models that can recognize complex behaviors across diverse applications, and need to ensure that detection is performed in a low-latency manner. The paper explores future directions of developing LSTM-based models that can recognize unusual activity using skeleton joint data in a cloud-based environment. The review emphasizes the potential of such solutions that can take advantage of the processing power of graphics processing units (GPUs) and tensor processing units (TPUs) and enable real-time recognition of activity in large datasets.
Copyright (c) 2026 Bakkala Santha Kumar, R. Shankar http://creativecommons.org/licenses/by-sa/4.0
A bibliometric analysis of artificial intelligence applications in geospatial and geographic intelligence
This study presents a comprehensive bibliometric analysis of the application of artificial intelligence in geospatial research and geographic intelligence. Using the Scopus database and visualization tools such as VOSviewer and Microsoft Excel, this study systematically maps the scientific literature across the five analytical dimensions: publication trends, citation structure, co-citation patterns, bibliographic merging, and keyword co-occurrence. The findings indicate a significant increase in global research interest, especially after 2020, with major contributions from the United States, China, and the United Kingdom. The results also reveal thematic clusters ranging from remote sensing and disaster response to smart city planning and spatial prediction. Through science mapping and performance analysis, the study highlights the intellectual structure and conceptual evolution of the field. The study contributes to academic understanding by identifying research gaps, emerging themes, and collaboration patterns, which can guide future interdisciplinary research in the artificial intelligence and geospatial domain.
Copyright (c) 2026 Chatarina Muryani, Singgih Prihadi, Gentur Adi Tjahjono, Albertus Erico Jerry Krisna Nugroho, Fitria Dewi Kartika http://creativecommons.org/licenses/by-sa/4.0
Bridging gaps in health artificial intelligence: challenges in MDPI research articles
Technological advancements in artificial intelligence (AI) have transformed healthcare by improving early disease detection, personalized treatment, predictive analytics, and clinical decision support systems. However, AI adoption in healthcare faces critical challenges, including data privacy concerns, algorithmic bias, regulatory barriers, usability issues, and system interoperability. Addressing these issues requires standardized regulations, ethical frameworks, and interdisciplinary collaboration to ensure responsible AI integration. This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer. The review focuses on Multidisciplinary Digital Publishing Institute (MDPI) journal articles to identify key contributors, emerging trends, and research gaps in AI-driven healthcare. Findings highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics, while exposing persistent challenges such as a lack of standardized AI models, ethical concerns, and accessibility disparities. By mapping the research landscape, this study provides evidence-based insights and recommendations to address AI adoption barriers, improve transparency, and guide future research in healthcare AI. The results contribute to developing a more equitable, efficient, and trustworthy AI-driven healthcare system.
Copyright (c) 2026 Irwan Bastian, Aqilla Rahman Musyaffa, Lukman Nulhakim, Novia Putri Bahirah, Dewi Agushinta R. http://creativecommons.org/licenses/by-sa/4.0
Integration of artificial intelligence technology in learning evaluation using the ADDIE method
Artificial intelligence offers significant potential to enhance learning evaluation, yet implementing fair and objective assessments remains a challenge. This study developed an artificial intelligence-based learning evaluation system to improve assessment inclusivity and efficiency. Employing the research and development method with the analysis, design, development, implementation, and evaluation (ADDIE) model, the system features a virtual assistant chatbot and an automated grading tool artificial intelligence assessment. The product underwent validation by media and material experts, followed by field trials with 20 secondary school teachers. The results demonstrated high feasibility, with validation scores of 96% by user interface/user experience (UI/UX) and 91.2% by material experts, and an implementation feasibility score of 94.4%. The system successfully assisted teachers in reducing administrative burdens and providing personalized feedback based on student learning styles. Future developments should expand subject coverage and support diverse assessment formats.
Copyright (c) 2026 Adip Wahyudi, Slamet Arifin, Mukhammad Solikhin, Alfi Sahrina, Feri Fahrian Maulana, Mohammad Firzon Ainur Roziqin, Maria Emerita Indraningrum Shrestha http://creativecommons.org/licenses/by-sa/4.0
Advances in current state of diagnostic approaches towards investigating pervasive development disorder
Pervasive development disorder (PDD) is characterized by various types of neurodevelopment conditions influencing communication skill, behavioral, and social skills. Current state of screening methods mainly uses neurophysiological and clinical assessments that are reliable but suffer from challenges e.g., scalability, increased cost, and subjectivity. Artificial intelligence (AI) based solution is witnessed to have increased adoption in this regard also suffers from clinical integration, data bias, and explainability-oriented problems despite its potential contribution till date. Therefore, the proposed study presents a systematic review of recent literature with a closer emphasis on AI and non-AI-based methodologies adopted to diagnose PDD. The study outcome also exhibits similar facts. The novelty of this work resides in its comparative analysis, temporal mapping of current trends, and its integrative framework. It also contributes towards identifying certain underexplored areas, thereby facilitating practically viable insight towards the future direction of implementation. The idea is to understand its viability, which is not only clinically relevant but also offers sustainable diagnostics.
Copyright (c) 2026 Ambika Sriranga, Herur Rangaiah Ranganatha http://creativecommons.org/licenses/by-sa/4.0
Analyzing academic acceptance of artificial intelligence using extended technology acceptance model
This study aims to analyze the acceptance of artificial intelligence (AI) technology among Indonesian academics using extended technology acceptance model (TAM). The analysis involved general extended technology acceptance model for e-learning (GETAMEL) independent variables, which include subjective norm (SN), experience (EXP), enjoyment (ENJOY), computer anxiety (CA), and self-efficacy (SE), as well as mediator variables: perceived usefulness (PU) and perceived ease of use (PEOU). This study uses technology innovations (TI) as moderator variable and behavioral intention (BI) as a dependent variable. The analysis reveals that SN has a significant influence over PU but not PEOU. ENJOY and SE have a significant positive influence over both PU and PEOU, while EXP and CA don’t have a significant influence over both variables. PU and PEOU have a significant positive influence over BI. TI strengthens the relation between PU and BI, but weakens the relation between PEOU and BI. This finding provides an important outlook for developing a strategy to increase the acceptance of AI technology in the academic environment through an approach that takes into account the factors of pleasure, self-confidence, and TI.
Copyright (c) 2026 Nanang Suryadi, Abdurrahman Hakim, Adelia Shabrina Prameka, Wildan Syafitri, Muhammad Irfan Islami http://creativecommons.org/licenses/by-sa/4.0
Energy loss prediction using least absolute shrinkage and selection operator regression and SHAP explainability
Technical energy loss estimation in power distribution systems is essential for improving operational efficiency and cost-effectiveness. However, distribution-level datasets are often failed to cope with the nonlinear behavior and sparse, low-resolution data typical in modern grid environments. This study proposes an interpretable artificial intelligence (AI) framework based on least absolute shrinkage and selection operator (LASSO) regression integrated with Shapley additive explanations (SHAP) to estimate and explain technical energy losses at the feeder level. An exploratory multicollinearity assessment using correlation analysis and variance inflation factor (VIF) revealed severe redundancy among operational variables, justifying the adoption of L1-regularized regression. Hyperparameter tuning via LassoCV identified an optimal regularization parameter, resulting in strong predictive performance. Comparative evaluation with nonlinear models, including random forest and gradient boosting, demonstrated that LASSO achieves competitive or superior generalization performance while preserving interpretability. Feature importance analysis and SHAP-based explanations confirmed that operational loading variables particularly infeed energy, load factor, and maximum demand are the dominant drivers of technical losses. SHAP dependence and interaction analyses further revealed context-dependent behavior among correlated predictors, enriching interpretability beyond coefficient-based rankings. The results demonstrate that regularized linear modeling, when combined with explainable AI techniques, provides a robust, transparent, and practically deployable solution for technical loss estimation in distribution networks.
Copyright (c) 2026 Nur Diana Izzani Masdzarif, Siti Azirah Asmai, Yogan Jaya Kumar, Muhammad Hafidz Fazli Md Fauadi http://creativecommons.org/licenses/by-sa/4.0
Deep learning for lung cancer diagnosis: a comparative artificial intelligence study
Early and accurate lung cancer diagnosis (LCD) is critical, yet traditional imaging methods such as X-rays and computed tomography (CT) scans are costly, invasive, and heavily reliant on expert interpretation. This study investigates artificial intelligence (AI)-driven diagnostics by comparing deep learning models (convolutional neural network (CNN), residual network (ResNet), and visual geometry group 16 (VGG16)) with traditional machine learning algorithms (logistic regression (LR) and support vector machine (SVM)), using lung image database consortium and image database resource initiative (LIDC-IDRI) and Kaggle lung CT scan datasets. Performance was evaluated across multiple metrics: accuracy, sensitivity, specificity, and area under the curve-receiver operating characteristic (AUC-ROC). Among the models, ResNet achieved the highest performance, with an accuracy of 94%, sensitivity of 95%, specificity of 93%, and AUC-ROC close to 1. CNN and VGG16 also showed superior metrics compared to LR and SVM, highlighting the robustness of deep learning techniques. These results demonstrate that deep learning models not only achieve higher diagnostic accuracy but also significantly reduce false detections compared to traditional approaches. The findings support the potential of AI to automate and enhance LCD, thereby improving accessibility, consistency, and speed in clinical settings. Future work will emphasize clinical validation, address ethical challenges, and focus on integrating AI models into real-world healthcare workflows to improve patient outcomes.
Copyright (c) 2026 Phaneendra Varma Chintalapati, Prasanth Aruchamy, Alur Praneetha http://creativecommons.org/licenses/by-sa/4.0
Hybridization of hybrid-ARIMA-EM and XGBoost for enhanced price predictive modeling
Managing finance entails the art and science of distributing available and potential funds among various competing needs. Government expenditures fund programs that provide a wide range of services to different population segments. As a result, the demand for enhanced and additional services often surpasses the government's financial capacity. Firstly, the price forecasting procedures for the extreme gradient boosting (XGBoost), gated recurrent unit (GRU), and hybrid-ARIMA-EM models will be summarized. Secondly, the accuracy of the models will be assessed on two real datasets collected from Kaggle (Crude_Oil_Price and KL_apartment). This study then proposes combining the hybrid-ARIMA-EM model with XGBoost to enhance the price forecasting performance in terms of time series analysis. Experimental results show that the suggested combination outperforms other selected models in price forecasting accuracy.
Copyright (c) 2026 Isam Ahmed M. Yaqoob, Khairul Azhar Kasmiran, Teh Noranis Mohd Aris, Nor Azura Husin, Mohd Yunus Sharum http://creativecommons.org/licenses/by-sa/4.0
Social news factuality verification using large language models
Social media platforms have greatly accelerated the spread of news, but this rapid information flow also amplifies the risk of misinformation. Traditional automatic detection methods that rely solely on textual features often struggle with nuanced, emerging content. This paper present a novel pipeline that verifies the factuality of social news by clustering related articles into events and using large language models (LLMs) to extract and verify claims against trusted news sources. The approach groups social-media posts and mainstream reports on the same event, extracts atomic claims with a model like GPT-4, and checks each claim’s truthfulness by comparing it to the cluster’s reliable news. This pipeline was evaluated on a newly constructed Vietnamese news dataset of 1,765 articles (including 723 social-media items), manually annotating claims as true or false. The LLM-based method achieved high accuracy (≈88.9% F1-score on claim verification and 92.1% F1-score on overall news verification). These results demonstrate that carefully prompted LLMs, combined with event-level clustering of evidence, can outperform traditional methods (e.g., bidirectional encoder representations from transformers (BERT)-based classifiers) in verifying news. The paper discusses advantages of clustering over simple retrieval, scalability considerations for LLMs, and prospects for multilingual and knowledge-enhanced verification.
Copyright (c) 2026 Tran Duc Duong, Hai Hoan Do http://creativecommons.org/licenses/by-sa/4.0
Utilizing Poisson modeling and advanced machine learning techniques for enhanced detection of slowloris attacks
This paper presents a secure framework for detecting and mitigating slowloris attacks in software defined networks (SDN) using Poisson arrival modeling to generate attack traffic. Slowloris attacks imitate legitimate traffic patterns, making them difficult to identify. The framework includes three modules: a data generation module, a feature selection module based on the blended statistical and information gain (BSIG) approach, and a traffic classification module using machine learning and deep learning algorithms. Poisson arrival modeling captures the stochastic nature of attack traffic by introducing variability in packet inter-arrival times, thereby simulating realistic network conditions. Monte Carlo simulations establish anomaly thresholds, while the BSIG method identifies key features, such as inter-arrival variability and CPU utilization. Support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) classifiers achieve high precision in distinguishing legitimate traffic from attack traffic. The integration of Poisson modeling with advanced analytical methods improves detection capability while maintaining scalability for similar stealth attacks. The proposed methodology provides high detection accuracy and adapts to dynamic network environments, enhancing web server defenses against slowloris attacks in real-world scenarios. The framework also reduces false positives, supports efficient resource utilization, and strengthens proactive security management in modern SDN infrastructures under varying conditions.
Copyright (c) 2026 Prathima Mabel John, Mamatha Ram, Vani Kurugod Ashwathanarayana Reddy, Santhosh Babu, Rama Mohan Babu Kasturi Nagappasetty http://creativecommons.org/licenses/by-sa/4.0
Exploring cognitive patterns in children with autism spectrum disorder using correlation and cluster analysis
This paper investigates the relationships between age and five cognitive abilities in children diagnosed with autism spectrum disorder (ASD). Data from 210 children aged 6 to 12 years who completed measures of visual motor precision (VM), facial memory (FM), spatial vision (SV), drawing memory (DM), and orientation (OR) were analyzed. Data preparation, descriptive statistical analysis, correlation analysis, cluster analysis, and factor analysis were conducted. The results showed that age was not significantly associated with the cognitive measures. The clearest association was a moderate positive correlation between VM and FM (0.34). Cluster analysis identified three groups. However, the low silhouette score (0.1368) indicated weak separation and substantial overlap between cognitive profiles. Factor analysis suggested three latent dimensions: factor 1 was strongly associated with VM and FM; factor 2 was negatively related to DM and moderately positively related to OR; and factor 3 was primarily driven by SV. These findings suggest that cognitive abilities in children with ASD operate relatively independently, with some shared mechanisms between certain skills. It can be concluded that individualized approaches in assessment and intervention strategies are warranted.
Copyright (c) 2026 Hana Bezzih, Muna Darweesh, Amjad Gawanmeh http://creativecommons.org/licenses/by-sa/4.0
Design of intelligent embedded system for personal protective equipment detection and face recognition access control
This paper presents an artificial intelligence (AI)-powered automated access control system that aims to reduce delays and improve safety. The primary problem addressed is effective monitoring of compliance with personal protective equipment (PPE) and secure access control for personnel entering sites. This study represents the design and development of an access control system that includes accurate detection of essential PPE items (e.g., safety helmets, gloves, goggles, and gas detectors), integration of facial recognition for identity verification, real-time monitoring of video feeds, and an intuitive user interface for security personnel to manage access and compliance efficiently. The software part uses you only look once (YOLO) version 8 for real-time object detection, classification, and drawing the bounding boxes around the detected object in a single forward pass. The hardware platform consists of NVIDIA Jetson AGX Orin 64 GB as an edge computing device. The developed AI-based embedded system is tested and validated with real-world scenarios and achieved a mean average precision (mAP) of 98.4% for PPE detection and 99.38% accuracy for face recognition.
Copyright (c) 2026 Mariam Mesfer, Aoosh Matar, Reem Saif, Alyazy Saleh, Irfan Ahmed, Moath Awawdeh, Anees Bashir http://creativecommons.org/licenses/by-sa/4.0
Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences
This study proposes a method to identify conserved sequence positions in viral data, with the primary goal of facilitating their translation into proteins. The approach aims to support the early detection of sub-sequences that hold promise as vaccine candidates. The method involves five key steps. First, mutation analysis using the Kimura model: analyzed mutations in the viral data using the Kimura model to provide insights into sequence variations. Second, alignment method selection to align sequences effectively, the progressive alignment approach with the neighbor-joining algorithm was chosen. Third, hybrid algorithm for identifying unchanged sequences: a hybrid algorithm that combines progressive alignment and a logic learning machine (LLM) was employed to identify unchanged sequences. Fourth, determining conserved sequences: based on the longest unchanged sequence, conserved regions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data were identified. Fifth, immuno-bioinformatics analysis for vaccine candidate peptides: using immuno-bioinformatics, protein sequences were analyzed to identify potential vaccine candidates based on B cell epitopes. Experimental validation confirmed the algorithm’s ability to identify candidate proteins and generate vaccine-worthy peptides from conserved protein sequences. The streamlined approach focuses on conserved protein sequences, ensuring efficient and targeted identification of vaccine candidates. By leveraging these conserved regions, contributions can be made to the expedited development of vaccines.
Copyright (c) 2026 Felza Ridho, Mohammad Isa Irawan, Nurul Hidayat, Awik Puji Dyah Nurhayati http://creativecommons.org/licenses/by-sa/4.0
Deep transfer learning architecture for depression detection using PHQ-9 analysis in Bengaluru urban population
In the metropolitan cities in India, there is a scarcity of population-based statistics on depression prevalence. This study utilized the patient health questionnaire-9 (PHQ-9) instrument to look at the prevalence and risk factors for depression in major metropolitan cities. This study's population-based analysis identifies major demographic and socioeconomic characteristics associated with depression in an Indian urban context. This quantitative inquiry conducted a population-centric analysis of 987 people from a metropolitan city. Depression was measured and validated using the PHQ-9 questionnaire to calculate the prevalence rates and sociodemographic characteristics. A deep transfer learning framework integrating visual and textual features was proposed for automated classification of depression, to assess mental health patterns in urban populations. Depression symptoms were common, with many respondents reporting persistent symptoms like a persistent lack of interest, pervasive sorrow, sleep difficulties, and suicidal ideation. Bivariate studies found relationships between these symptoms and other demographic and socioeconomic characteristics, whereas multivariate analysis revealed significant predictors. In addition, the classification of people’s stress in metropolitan regions is analyzed through various pre-trained models to show better outcomes. The substantial link between sociodemographic characteristics and depression levels shows the importance of mental health interventions for this urban Indian population.
Copyright (c) 2026 U. Ananthanagu, Pooja Agarwal http://creativecommons.org/licenses/by-sa/4.0
Transforming correspondence learning through a smart assistant system to improve student outcomes and engagement
The insufficient feedback in business correspondence education at the higher education level necessitates innovation in adaptive and responsive learning resources. This study seeks to create and evaluate the efficacy of an artificial intelligence (AI)-driven correspondence learning assistant (CORLA) to enhance students' correspondence abilities. The study employs a research and development (R&D) methodology by utilizing the Dick and Carey model, which has ten phases of instructional development. The participants in the study were students enrolled in Digital Office Administration Study Program, Universitas Negeri Jakarta. Data were gathered by observation, interviews, questionnaires, and assessments (pre-test and post-test), and evaluated by three experts in materials, learning design, and media. The findings indicated that CORLA demonstrates a significant degree of feasibility, practicality, and efficacy. CORLA's advanced features encompass AI-driven automatic feedback, letter composition simulation, learning progress monitoring, discussion forums, and an intuitive user interface (UI). This study advances the creation of an effective and practical AI-based learning media model tailored to the requirements of teaching business correspondence abilities in higher education.
Copyright (c) 2026 Marsofiyati, Roni Faslah, Maulana Amirul Adha, Muhammad Ikhwan, Faerozh Bin Madli http://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning model for enhanced short-term gold price forecasting
Accurate short-term gold price forecasting is crucial for the financial decision-making. This paper introduces a short-term gold prediction network (STGP-Net), a novel hybrid deep learning model designed to enhance prediction accuracy by integrating one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM). STGP-Net leverages the 1D-CNN's ability to extract local temporal features and the LSTM capacity to model long-range dependencies within gold price time series. Various sliding window configurations are employed to generate input sequences for multi-step ahead prediction. Comprehensive experiments were conducted comparing STGP-Net against 1D-CNN + recurrent neural network (RNN) and 1D-CNN + bidirectional long short-term memory (BiLSTM) baseline models across three configurations using metrics like mean absolute error (MAE), root mean square error (RMSE), and determination (R²). The results demonstrated that STGP-Net consistently provided better performance and robustness, proving more effective for short-term gold price forecasting than the alternative hybrid models tested.
Copyright (c) 2026 Hoang Ha Nguyen, Minh Duc Nguyen, Cuong H. Nguyen-Dinh http://creativecommons.org/licenses/by-sa/4.0
Resource optimization via identification of articles with aspects of priority and high utility
Owing to the linear economic growth, the manufacturing of consumer products has increased manifold, products in diversified packaging and formats are now available in abundance, sometimes even with less or no requirement, resulting in losses. Addressing the gap that exists between productivity and requirements can minimize losses and unnecessary burden on manufacturing units. To minimize losses, a hybrid algorithm is proposed, using the advantages offered by classification and high utility patterns to identify the dominant aspects of articles with a high probability of sale. Articles are identified in a two-step process, identification of samples with context as prominent parameter by vanilla feedforward neural network (VNN) as a first step. The second step comprises the determination of utility pattern mining in articles using faster high-utility itemset miner (FHN), sentiment score obtained provides the utility pattern of the product. Proposed hybrid VNN + FHN, along with convolutional neural network (CNN), long short-term memory (LSTM), and transformer-based prediction model (TPM) were used for assessing the utility-driven product analysis. The hybrid VNN + FHN proposed displays the best adaptability by extracting 158 patterns with a utility of 162.88 units. The algorithm outperforms CNN in utility, LSTM and TPM in speed, and CNN in pattern count, making it a better choice for resource optimization.
Copyright (c) 2026 Garima Srivastava, Vaishali Singh, Sachin Kumar http://creativecommons.org/licenses/by-sa/4.0
Biometric authentication using dual-modal deep learning based-on electrocardiogram and ear features
Biometric authentication systems are essential for secure access control; however unimodal systems are vulnerable to spoofing and environmental variations. This study proposes a dual-modal biometric system combining electrocardiogram (ECG) signals and ear features to enhance security and accuracy. Deep learning architectures including VGG-verydeep16 and convolutional neural network 5 (CNN5) are evaluated for feature extraction and fusion. Experimental results show that hybrid model (VGG-verydeep16 + CNN5) achieves around 98% accuracy, outperforming individual models. The system adapts to dataset size, using CNN5 for large datasets and VGG-verydeep16 for smaller ones. This approach offers a robust, efficient, and scalable solution for real-world biometric authentication.
Copyright (c) 2026 Mohamed S. Khalaf, Said Fathy Al-Zoghdy, Mariana Barsoum, Ibrahim Omara http://creativecommons.org/licenses/by-sa/4.0
Systematic review of fraud detection using AI and ML with an emphasis on telecommunication industry
The telecommunications industry is one of the top industries affected by fraudulent activities. Given the financial impact, on top of confidentiality breaches, security concerns, and reduced service quality as well as consumer dissatisfaction, there is an immediate need to implement effective fraud detection approaches. While there have been different fraud detection systems implemented, technological advancements as well as the improved techniques of fraudsters have made the traditional approaches no longer efficient. This paper aims to further investigate the use of artificial intelligence (AI) and machine learning (ML) to create efficient and advanced fraud detection models based on the strategy used, models applied, accuracy of the system, as well as future research work suggested. A systematic review of 50 papers was conducted. The most prevalent strategy was supervised one, majority of papers used software instead of hardware, and the most common ML models were artificial neural network (ANN), support vector machines (SVM), and decision tree.
Copyright (c) 2026 Soly Mathew Biju, Sindi Rryta http://creativecommons.org/licenses/by-sa/4.0
Determining student scaffolding levels in geometry problem-solving: a fuzzy inference system using Mamdani method
This study aims to adopt a fuzzy logic inference system using Mamdani method to determine the appropriate scaffolding level for students based on errors in solving geometry problems. The fuzzy inference system (FIS) assessed the students' understanding and provided scaffolding to address specific learning needs by analyzing common mistakes in geometry problem-solving. This method optimized the educational process by offering personalized support, enhancing students' problem-solving skills, and reducing error rates. The data processing criteria using the FIS involved analyzing the scores of mathematics education students at a private university in Malang when solving geometry problems, based on Pólya's stages. Mamdani was used to provide recommendations based on students' cognitive data while solving geometry problems, in line with scaffolding components. The results showed that the system personalized scaffolding levels for students working on geometry problems. This contributed to the field of educational technology by presenting a new approach to adaptive learning and emphasized the importance of personalized pedagogical support. The approach was particularly beneficial for geometry teachers who provided individualized scaffolding based on students' errors, contributing to improved learning outcomes.
Copyright (c) 2026 Yuniar Ika Putri Pranyata , Susiswo, Tjang Daniel Chandra http://creativecommons.org/licenses/by-sa/4.0
Enhancing pedestrian detection in adverse conditions using YOLOv5s with adaptive weighted fusion efficient channel attention module
Pedestrian detection constitutes a critical task within advanced driver assistance systems (ADAS), where reliable identification of pedestrians is essential for ensuring vehicular safety. Although deep learning has substantially improved detection performance, existing state-of-the-art models continue to exhibit notable degradation in adverse weather conditions and low-light. To mitigate these challenges, this study introduces an enhanced pedestrian detection framework based on you only look one version 5 (YOLOv5s), retrained on an augmented common object in context (COCO) dataset focused on the person class. Additionally, a novel, lightweight, and adaptive attention mechanism called: the weighted fusion efficient channel attention (WF-ECA) module is incorporated into the detection architecture. The WF-ECA module selectively focusses on important features without compromising computational efficiency or inference speed. Comparative experiments demonstrate a 5% increase in mean average precision (mAP) in comparison to the baseline model, thereby demonstrating the efficacy of the proposed attention module in improving detection robustness under challenging environmental conditions. These findings highlight the potential of attention-based mechanisms to enhance pedestrian detection performance in real-world ADAS applications.
Copyright (c) 2026 Oumayma Rachidi, Badr Bououlid Idrissi, Chafik Ed-Dahmani http://creativecommons.org/licenses/by-sa/4.0
AI-driven hybrid neural network for electrocardiogram-based authentication and predictive health monitoring
The increasing adoption of digital healthcare systems demands secure and reliable patient authentication mechanisms. Traditional methods such as passwords and PINs are vulnerable to security breaches, motivating the use of biometric-based solutions. Among various biometrics, the electrocardiogram (ECG) signal is distinctive, stable, and non-invasive, making it suitable for secure authentication. This paper proposes CardioGuard, an artificial intelligence (AI)–based authentication framework that employs a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract discriminative features from ECG signals and classify users as genuine or impostors. In addition to access control, the system analyzes ECG patterns to support early detection of potential cardiovascular abnormalities. Experimental results demonstrate that CardioGuard achieves improved authentication accuracy and enhanced predictive health insights compared to conventional approaches, highlighting its effectiveness for secure and intelligent healthcare monitoring.
Copyright (c) 2026 Swati Lakshmi Boppana, Velicheti Anantha Lakshmi, Padala SriKavitha, Venkata Ashok Kalaga, Venkateswara Rao Naramala, Suneetha Thalluru, Gunturi S. Raghavendra http://creativecommons.org/licenses/by-sa/4.0
Android-based tomato leaf disease classification using a lightweight MobileNetV2 convolutional neural network
Early screening of tomato leaf diseases is important because foliar symptoms can reduce plant vigor and delay appropriate crop management. This study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture. The contribution of this work is the integration of model training, independent testing, and on-device Android deployment that supports camera and gallery inputs without relying on server-side computation. The dataset consisted of 1,200 balanced tomato leaf images from five classes: bacterial spot, late blight, target spot, tomato yellow leaf curl virus, and healthy leaf. Images were resized, normalized, augmented for training, and divided into training, validation, and independent testing subsets. The model obtained 94.12% training accuracy, 93.00% validation accuracy, and 89.00% independent test accuracy. The confusion matrix showed that tomato yellow leaf curl virus was classified without error, whereas bacterial spot, late blight, target spot, and healthy leaves produced several misclassifications because of similar lesion and discoloration patterns. The results show that MobileNetV2 is suitable for lightweight mobile disease screening, although larger field datasets, cross-validation, model comparison, and explainability analysis are still needed for broader deployment.
Copyright (c) 2026 Andi Riansyah, Irfan Eka Mahdy, Mochamad Abdul Basir, Noorminshah A. Iahad http://creativecommons.org/licenses/by-sa/4.0
A transfer learning-based approach for automatic monument detection
Architectural heritage connects us to the cultural achievements of past civilizations. Patan Durbar Square in Nepal is home to many such structures, yet identifying them remains a challenge for tourists. This paper presents an automated monument recognition system built on the backbone of convolutional neural networks (CNNs). A dataset of 1832 images of 9 important monuments from Patan was created, and build a detection system with MobileNetV2, a light-weight CNN, to detect monuments in Patan Durbar Square with a near-perfect F1 score of 98.94%. The approach utilizes transfer learning to adapt the model to local architectural styles. A detailed ablation study is performed to determine the optimal network design and augmentation strategies. Class-wise performance is further analyzed to verify robustness against visual occlusion and similarity. Finally, the model is deployed as a mobile application using Flutter and the FastAPI framework. This work demonstrates the viability of lightweight CNNs for real-time cultural heritage preservation.
Copyright (c) 2026 Santosh Giri, Jebish Purbey, Sunil Adhikari, Paarit Pokharel, Babu R. Dawadi, Bipun Man Pati, Atsushi Ito, Sushant Chalise, Sanjivan Satyal http://creativecommons.org/licenses/by-sa/4.0
Hybrid quantum-classical neural networks for brain computed tomography scan diagnosis
Medical image classification is considered as very important field of diagnosis and treatment of neurological disorders, which include stroke, tumors, and hemorrhages, it can used to facilitate timely medical intervention. A hybrid quantum-classical convolutional neural network (QCNN) is presented by combining quantum information processing with classical deep learning techniques for improved feature extraction and classification accuracy. The model combines convolutional neural network (CNN), which handles initial feature extraction with a PennyLane and TensorFlow based layer that employs quantum entanglement and superposition principles to enhance classification performance. The model is trained and tested over a computed tomography (CT) scan image dataset which has four classes (normal, stroke, tumor, and hemorrhage). Various learning rates are tested and employed a hybrid backpropagation method to improve efficiency. Confusion matrices, region of conversion receiver operating characteristic (ROC) curves, and plots for the training convergence that all pointed to promising classification accuracy were derived with a detailed analysis accordingly. Overall, the results indicate that combining quantum computing with deep learning architectures could improve classification performance at a lowest computational cost. The results suggest the viability of hybrid quantum-classical models for medical imaging applications and indicate that quantum computing is a promising direction for enhancing diagnostic accuracy in the area of radiology.
Copyright (c) 2026 Bilal R. Altamer, Muhamad Azhar Abdilatef Alobaidy, Aws Hazim Saber Anaz, Zahraa Tarik AlAli http://creativecommons.org/licenses/by-sa/4.0
Intelligent object sorting system using Dobot Magician and computer vision
This study aims to analyze the relationship between object color, shape, and count and the performance of a robotic object sorting system, measured by execution time and accuracy. The Dobot Magician robot was used for object manipulation, while computer vision based on object detection was implemented using Python and an open source computer vision library (OpenCV). Color and shape segmentation were performed using the hue, saturation, and value (HSV) color space and OpenCV library. Experimental results indicate that three shapes (circle, triangle, and square) and four colors (green, red, blue, and yellow) affect picking time and accuracy differently. Squares generally required the longest time to pick, while triangles often had the shortest times across various colors. Accuracy remained consistently high across all colors and shapes, with green and yellow objects showing slightly higher average accuracy. These findings provide valuable insights into the factors influencing the performance of automated robotic grasping systems, which can be applied to optimize robot design and improve the efficiency and precision of object sorting tasks.
Copyright (c) 2026 Tossapol Jangnoi, Thewin Sakunbunyong, Viroch Sukontanakarn, Tanawat Chalardsakul http://creativecommons.org/licenses/by-sa/4.0
WVisionBERT-VL: a multimodal model architecture for toxicity classification on social media platforms using large language models
The increasing prevalence of toxic content on social media, conveyed through text, images, and videos, poses significant challenges for automated content moderation systems. Although prior studies have reported promising results in unimodal and bimodal settings, they often fail to capture implicit and contextual toxicity emerging from interactions across multiple modalities, particularly in non-English environments. This paper proposes WVisionBERT-VL, an end-to-end multimodal framework for toxicity detection that integrates text, image, and video modalities within a unified architecture. The proposed model incorporates modality-specific encoders, bidirectional multi-head cross attention (BMHCA) for cross-modal synchronization, and an adaptive fusion gate to dynamically balance modality contributions. A balanced multimodal dataset is constructed from social media platforms, including X, Instagram, and TikTok, and refined using a model-based labeling strategy with limited human-in-the-loop validation. Experimental results on a custom Indonesian dataset demonstrate strong in-domain performance, achieving an accuracy of 94.12%, a macro-F1 of 0.9407, and a receiver operating characteristic - area under the curve (ROC-AUC) of 0.9721, with robustness further validated through five-fold cross-validation. Cross-dataset evaluation highlights challenges related to domain shift, underscoring the need for future research on robust and domain-adaptive multimodal toxicity detection.
Copyright (c) 2026 Witta Listiya Ningrum, Achmad Benny Mutiara, Diana Ikasari http://creativecommons.org/licenses/by-sa/4.0
Detection of illegal drug activities in Indonesia via social media X
In today's digital world, social media platforms such as X (formerly Twitter) are widely used for communication but also for illegal activities, such as selling illicit drugs. This study examines how posts from X related to illegal drug sales in Indonesia can be analyzed using machine learning techniques, with a focus on incorporating features related to personally identifiable information (PII) and web URLs. The main questions are how to use classification methods to detect these posts and which drugs are most often mentioned. A total of 11,777 posts from X were collected using web scraping. After cleaning and processing the data, five machine learning models were used. The support vector machine (SVM) model showed the best results with a weighted F1-score of 97.08%. The findings show that abortion pills are the most frequently mentioned drugs, followed by sexual enhancement and anesthetic drugs. This study also used visual tools to show the most common hashtags, posts distribution over time, and types of drugs discussed. This study underlines the importance of monitoring social media to protect public health and highlights how incorporating PII features can improve the detection of illegal activities. Future research should look at posts in other languages and from other platforms, considering PII features for enhanced analysis.
Copyright (c) 2026 Adi Hanif Sedar, Lukman Yudokusumo, Indra Budi, Amanah Ramadiah, Aris Budi Santoso, Prabu Kresna Putra http://creativecommons.org/licenses/by-sa/4.0
Hybrid optimization of dual-port converter for electric vehicles
Vehicle-to-grid (V2G) technology, which cuts peak loads, levels load, and modulates voltages but generates power system instability, accelerated by the growing popularity of electric-powered vehicles. This research suggests a unique three-level full-bridge non-isolated buck-boost bidirectional direct current (DC)-DC converter that integrates solar (photovoltaic (PV)) systems, vehicle batteries, and the power grid to charge plug-in electric vehicles (EVs). This converter is combined with hybrid Tasmanian-hawk optimization (HTHO). By enabling EV batteries to be charged concurrently from PV systems and the grid, the converter improves charging flexibility and efficiency. The exploration and exploitation phases of HTHO, a new method that combines elements of the Harris hawks and Tasmanian devil algorithms for optimization. Using a 69-node test system, the suggested methodology, which is implemented in MATLAB/Simulink, evaluates power quality improvements in controlled and bidirectional charging operations. At a total harmonic distortion (THD) value of 1.768%, which indicates minimal harmonic distortion in the signal and exceeds conventional optimization techniques, the suggested converter, which integrates with HTHO, enhances charging flexibility and efficiency and helps to preserve overall power system stability. The research strengthens EV charging infrastructure through the seamless integration of natural renewable resources, multi-method optimization, and efficient grid operation strategies.
Copyright (c) 2026 Vidhya Kuruvilla, Immanuel Selvakumar, Pandiyan Venkatesh Kumar http://creativecommons.org/licenses/by-sa/4.0
An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation
This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
Copyright (c) 2026 Upekkha Lau, Meditya Wasesa http://creativecommons.org/licenses/by-sa/4.0
SHAP-enhanced ensemble learning for yield prediction: insights from CY-Bench data on Indian wheat
Predicting crop yield accurately is essential for providing food security and improving agricultural practices. This study examines the use of ensemble machine learning models combined with Shapley additive explanations (SHAP) feature selection to improve wheat yield prediction in India. The study uses CY-Bench data, incorporating normalized difference vegetation index (NDVI), meteorological data, and soil moisture data for yield prediction. Various ensemble techniques, including voting, stacking, and boosting are evaluated. Boost m1 ensemble model consistently outperforms other models in the prediction. Additionally, the integration of SHAP-based feature selection with the best ensemble model significantly improves the model accuracy and interpretability by identifying the most influential features affecting yield. The results show the effectiveness of ensemble boosting model, in capturing the complex relationships within agricultural data particularly when combined with feature selection. This method improves the transparency and actionability of machine learning models for agronomists, policymakers, and farmers.
Copyright (c) 2026 Soma Gupta, Dayal Kumar Behera, Satarupa Mohanty, Subhra Swetanisha, Ritik Mallik, Namita Panda http://creativecommons.org/licenses/by-sa/4.0
Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling
Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Copyright (c) 2026 Iwan Ady Prabowo, Hendro Wijayanto, Teguh Susyanto http://creativecommons.org/licenses/by-sa/4.0
GWO-optimized sparse Bayesian least squares regression for direction-of-arrival estimation in MIMO networks
Accurate direction-of-arrival (DOA) estimation is a critical requirement for massive multiple-input multiple-output (MIMO) systems operating in fifth-generation (5G) and beyond (5G/B5G) wireless environments. Although sparse Bayesian learning (SBL)–based techniques have demonstrated improved robustness by exploiting signal sparsity, their performance is often limited by fixed hyperparameter selection, sensitivity to noise, and suboptimal residual error minimization. To address these challenges, this paper proposes an optimized sparse Bayesian least squares regression (SBLSR) framework in which grey wolf optimization (GWO) is employed to adaptively optimize Bayesian hyperparameters and regression coefficients. The proposed approach jointly enforces sparsity and minimizes estimation error, enabling robust DOA estimation under dynamic noise conditions and varying network density. Extensive simulations conducted in a massive MIMO environment demonstrate that the optimized SBLSR consistently outperforms conventional SBLSR and state-of-the-art benchmark techniques in terms of root mean square error (RMSE), closely approaching the Cramér–Rao lower bound (CRLB) across a wide range of signal-to-noise ratios, sensor configurations, and Monte Carlo trials. The findings validate that the suggested optimized SBLSR framework offers a noise-resilient solution for high-precision DOA estimation in practical massive MIMO and MIMO radar systems.
Copyright (c) 2026 Anne Gowda Aleri Byregowda, Babu Nallur Venkateshappa, Anughna Narayanaswamy http://creativecommons.org/licenses/by-sa/4.0
Machine learning techniques for rainfall prediction: a systematic literature review
There are numerous aspects of human life in which knowing how much rain to expect might be beneficial. Heavy rainfall events such as flash floods and landslides, as well as droughts, can be predicted with effective rainfall forecasting. Because of reliable weather forecasts, the infrastructure required to capture rainwater and cultivate crops may be planned ahead of time. A variety of machine learning (ML) and deep learning (DL) algorithms enable accurate weather forecasting. This work seeks to provide a full overview of the numerous ML algorithms used for rainfall prediction by focusing on the technique, input parameters, and several performance measures. The review consists of 51 works divided into three sections. It is found that long short-term memory (LSTM), one of the DL algorithms, is mostly used by researchers for developing the model, but in recent years, ensemble learning and hybrid learning have also gained popularity among researchers as they give more accurate results. These methods need to be explored further.
Copyright (c) 2026 Deepa Sharma, Anand Kumar Shukla, Punam Rattan http://creativecommons.org/licenses/by-sa/4.0
YOLOv11 optimization for tiny object in crowded scenes
Small object detection in crowded urban and aerial scenes remains a critical challenge due to limited pixel information and information loss in deep neural networks. This study introduces a novel optimization framework for YOLOv11, specifically engineered for tiny-scale targets by integrating convolutional block attention modules (CBAM), k-means anchor clustering, and an enhanced feature pyramid network (FPN). Evaluated on the TinyPerson and COCO-mini datasets, the YOLOv11-optimized model achieves significant performance breakthroughs, delivering a +7.3% gain in mean average precision (mAP) and a +10.5% increase in recall over the baseline. Notably, the model achieved a recall of 0.072 on the TinyPerson dataset, with double sensitivity of standard YOLOv11. With a high-speed inference rate of 27.3 FPS, this research demonstrates that strategic architectural refinements can drastically improve small object detection reliability without compromising real-time viability on edge devices.
Copyright (c) 2026 Husna Sarirah Husin, Howard Chong Yun Hao, Pan Yuan Fei, Mohsen Marjani, Suriana Ismail http://creativecommons.org/licenses/by-sa/4.0
Forecasting income inequality in Vietnam’s regions using machine learning
This study applies artificial neural networks (ANN) and support vector regression (SVR) to forecast the Gini coefficient across Vietnam’s seven socio-economic regions using limited data from 2014–2022 (N = 63). Despite the small dataset, robust techniques including repeated cross-validation, temporal backtesting, regularization, and bootstrapping were employed to mitigate overfitting. The results show a modest continued decline in income inequality through 2025, with faster improvements in urban regions (Red River Delta and Southeast) than in rural and highland areas. ANN outperformed SVR with an average cross-validation coefficient of determination (R²) of 0.93. The novelty lies in its granular regional forecasting and region-specific policy recommendations beyond national-level analyses. Key drivers—per capita income and simple housing—support targeted interventions such as housing credits in the Mekong River Delta and agricultural investments in the Central Highlands. The findings provide an evidence-based foundation for regionally tailored strategies to reduce income disparities in Vietnam.
Copyright (c) 2026 T. Thai-Phuong, L. Nguyen-Son http://creativecommons.org/licenses/by-sa/4.0
Comparison algorithms with optimization for clustering multi-criteria
In higher education, forecasting student graduation is crucial for early intervention development, policy formulation, and class planning. This categorizes the factors that affect student graduation according to both academic and non-academic traits. To compare various clustering techniques, such as K-means, fuzzy C-means (FCM), K-medoids, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, Gaussian mixture model (GMM), and deep learning (DL), as well as particle swarm optimization (PSO) and genetic algorithm (GA) optimization for K-means and FCM, this article applies the hybrid elbow + silhouette optimization prior to the clustering process. Two clusters were found in the pre-cluster optimization research results. K-means, FCM, GMM, and DL clustering all demonstrated more distinct centroid separation; K-means and GMM were the most visually stable and comprehensible. Inter-cluster separation is the main goal of post-cluster optimization, and the K-means + PSO method is the best option. The cross-dataset validation findings indicate that the clustering model exhibits moderate consistency on the new dataset, with an adjusted Rand index (ARI) of 0.478 and a normalized mutual information (NMI) of 0.433.
Copyright (c) 2026 Ida Mulyadi, Titik Khawa Abdul Rahman, Muhammad Faisal http://creativecommons.org/licenses/by-sa/4.0
Image-based estimation of surface roughness in Al-7075 drilling
Surface roughness is an important quality parameter that affects the performance, durability, and reliability of machined components. Measuring the internal surface roughness of drilled holes using conventional contact-based methods is often difficult. Accessibility of the internal surface is complicated due to the diameter of the drilled hole and the need to interrupt the machining process. To overcome these limitations, the proposed work offers a non-contact method for estimating the surface roughness of drilled Al-7075 using image-based analysis. Drilled surfaces are machined using different speeds and feed rates. High-resolution images of the drilled surface are captured using a custom-built image-capturing setup. Texture and frequency features were extracted using gray-level co-occurrence matrix (GLCM), discrete Fourier transform (DFT), and discrete wavelet transform (DWT) techniques. Surface arithmetic average roughness (Ra) was measured using these extracted features. The estimated roughness parameters were then validated by comparing them against roughness parameters obtained by means of the contact stylus technique. According to the experimental results, the accuracy of the wavelet technique is higher, with mean absolute errors of 0.32 μm compared to those obtained using GLCM and DFT techniques. The findings demonstrate that the proposed image-based framework is a reliable and practical solution for non destructive surface roughness prediction.
Copyright (c) 2026 Shilpa M. Karegoudra, Vamsidhar Yendapalli http://creativecommons.org/licenses/by-sa/4.0
Comparison of machine learning algorithms to identify muscular atrophy and muscular dystrophy
Muscular dystrophy is a genetic disorder characterized by progressive muscle weakness and degeneration, while muscular atrophy involves the loss of muscle tissue due to pathological conditions. Accurate differentiation between these neuromuscular disorders is critical for effective clinical diagnosis and treatment planning. This study presents a machine learning– based framework for classifying muscular dystrophy and muscular atrophy using surface electromyography (sEMG) signals. sEMG data were acquired and processed to extract informative features with low computational complexity. Several machine learning classifiers several techniques were used and assessed, such as random forest (RF), support vector machine (SVM), gradient boosting (GB), and extreme gradient boosting (XGBoost). To further enhance classification performance, a novel ensemble learning approach combining multiple machine learning models was proposed. Experimental results exhibit that the suggested ensemble model completes significantly higher grouping accurateness and robustness compared to individual classifiers and existing methods reported in the literature. The results of this investigation indicate that ensemble-based machine learning techniques integrated with sEMG analysis provide a reliable and non-invasive diagnostic aid, with strong potential for early detection and improved clinical management of neuromuscular disorders.
Copyright (c) 2026 Ganga Bhavani Billa, Venkateswara Rao Peddada http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of YOLOv11-based vehicle detection and tracking for urban intelligent transportation systems
This paper proposes and evaluates an integrated vehicle detection and tracking framework based on you only look once (YOLO)v11 for intelligent transportation systems (ITS). The combination of the deep simple online and real-time tracking (DeepSORT) algorithm helps maintain vehicle identity across consecutive frames, thereby enhancing the stability of the multi-object tracking system. Additionally, the slicing-aided hyper inference (SAHI) technique is integrated to improve the detection efficiency of small vehicles in remote sensing imagery and urban surveillance video data collected in Thai Nguyen, Vietnam. The system's performance is comprehensively evaluated through several key quantitative indicators, including mean average precision (mAP), multiple objects tracking accuracy (MOTA), and identification F1-score (IDF1), across realistic urban traffic scenarios. The results show that the framework significantly improves detection accuracy, tracking consistency, and small object recognition efficiency in real-world urban traffic scenarios. This paper provides useful insights for selecting appropriate detection and tracking configurations in ITS applications.
Copyright (c) 2026 Thang C. Vu, Dung T. Nguyen, Minh T. Nguyen, Long Q. Dinh, Mui D. Nguyen http://creativecommons.org/licenses/by-sa/4.0
A machine learning framework for predicting and optimizing return on investment across marketing channels
In the current fast-paced competitive marketing environment, firms require data-centric methods to maximize their investments in several avenues. This research work applies machine learning techniques to estimate the return on investment (ROI) for marketing costs, which helps organizations in budgeting more effectively. Four models including random forest, extreme gradient boosting (XGBoost), gradient boosting, and linear regression were utilized for their accuracy in making predictions. Results showed that the highest accuracy was achieved by linear regression at 99.39%, random forest at 99.07%, gradient boosting at 99.01%, and XGBoost at 98.81%. It was further noted that digital marketing avenues such as social media and online stores gave the highest ROI, indicating that companies should prioritize digital marketing more than traditional marketing. On a practical level, this approach helps marketing team for choosing high performing channels since it estimates expected returns from each marketing channels and make smarter budget allocation. Yet, the study is done by using Kaggle dataset. In order to improve its generality, future research may use larger real-world datasets and extensive visualization techniques.
Copyright (c) 2026 Chandra Chathura, Keerthan Saya, Sathishkumar Mani http://creativecommons.org/licenses/by-sa/4.0
E-TEXTLOC: efficient text localization and extraction in real-world video scenes
Localization of an accurate text is quite a complicated issue, especially when attempting to extract from a complex video. It is mainly due to the limitation of resources, dynamic background, and text variability. There is various artificial intelligence based methods adopting machine learning for addressing such issues encounter lower positional accuracy and incur maximized computational cost. Therefore, the proposed system introduces efficient text localization and extraction for comprehensive positional accuracy in complex videos (E-TEXTLOC). Different from conventional approaches, E-TEXTLOC facilitates sampling of video frames while MobileNetV2 is deployed towards faster localization of text. The outcome of recognized text is further refined by a verifier module, which provides a self-supervised response. Assessed on the YouTube video dataset, the proposed model accomplishes 98.2% accuracy with 29.6 ms towards generating analytical outcomes. It means the proposed model accomplishes 6-10% accuracy enhancement with a 40-50% reduction of speed in contrast to the existing system. The implications of the proposed study can be stated towards surveillance system, autonomous vehicles, and assistive devices that works in real-time.
Copyright (c) 2026 Dayananda Kodala Jayaram, Puttegowda Devegowda http://creativecommons.org/licenses/by-sa/4.0
A holistic energy expenditure tracking framework: fuelling wellness with holistic energy tracking
Effective public health management is essential for a country’s social and economic development. A strong public health system reduces the strain on healthcare infrastructure by encouraging preventive measures. With significant lifestyle changes in recent years, monitoring both energy intake (EI) (diet) and energy expenditure (EE) (physical activity (PA)) has become crucial to maintaining public health. Existing methods primarily track exercise-related EE using self-assessment tools or wearable devices, often neglecting occupation-related activities. This results in an underestimation of total EE. To address this limitation, we propose the holistic energy expenditure tracking (HEET) framework, which aims to provide a comprehensive estimate of daily EE by including occupational activities. The framework was applied to data from a dietitian who collected 180 data points from working professionals across three different occupations. Exploratory data analysis revealed a 41% increase in metabolic equivalent of task (MET) values and a 27% rise in calorific values when occupational EE was considered. A categorical scoring system was developed based on the new calorific values, enabling dietitians to offer more personalized dietary recommendations. The study highlights the need for a standardized framework that captures all daily activities, offering a more accurate and holistic approach to public health monitoring and intervention.
Copyright (c) 2026 Prachi Kadam, Gagandeep Kaur, Leena Manojkumar Panchal, Smita Nirkhi http://creativecommons.org/licenses/by-sa/4.0
A machine learning framework for skin cancer classification using texture descriptors
Skin cancer remains a critical health and economic concern worldwide. Timely and accurate diagnosis is crucial for improving mortality rate of patients. Although automated machine learning (ML) models assist doctors in clinical assessment of skin cancer from dermoscopic images, their performance often suffers from extreme class imbalance, as benign image samples greatly exceed malignant ones. This leads to false detection and delayed diagnosis of skin cancer. To overcome this issue, the study proposes an efficient and lightweight geometric transformation (GT)–augmented support vector machine (SVM) framework for early diagnosis of skin cancer. It effectively addresses the class imbalance issues present in the datasets and improves the detection of positive cases. The novel pipeline integrates preprocessing, morphology preserving GT, rotation-invariant texture feature extraction, feature validation, and feature scaling for performing binary classification using an optimized SVM framework. This framework provides balanced and accurate lesion classification even if image samples are insufficient. Experimental results have successfully achieved a true positive rate (TPR) of 93% on PH2 and 81.1% on International Skin Imaging Collaboration 2016 (ISIC-2016) dataset, which is better than conventional ML models. These findings prove that proposed GT-SVM framework is a lightweight, interpretable, and computationally efficient approach for early skin cancer diagnosis. Future developments shall explore multi-class lesion classification, validation across diverse clinical datasets, and hybrid feature fusion.
Copyright (c) 2026 Shikha Malik, Vaibhav V. Dixit http://creativecommons.org/licenses/by-sa/4.0
Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market
The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.
Copyright (c) 2026 Yosef Budiman, Gilang Adi Prasetyo, Asma’ Khoirunnisa’, Hanifah Mar’atush Shalihah, Muhamad Riyan Maulana, Yanuar Agung Fadlullah, Sugiri Sugiri http://creativecommons.org/licenses/by-sa/4.0
Real-time object detection for autonomous driving: a comparative study of YOLO and Faster R-CNN
Over the past few years, object detection has experienced remarkable progress and development, primarily driven by the development of one-stage and two-stage detection algorithms. Among these, Faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO) have achieved notable success due to their strong performance and computational efficiency. Object detection plays a crucial role in various applications, particularly in autonomous driving systems, where accurate detection of pedestrians, vehicles, and road signs is essential for ensuring safety and reliability. This paper conducts a comparative evaluation of YOLO and Faster R-CNN to analyze their performance in autonomous driving environments. The experiments were conducted using the KITTI open-source dataset, which is widely used for benchmarking object detection models. All experiments were performed on an NVIDIA RTX A5000 GPU to ensure efficient computation, with implementations developed using Python version 3.9.13. The experimental findings indicate that YOLO surpasses Faster R-CNN in performance, attaining an accuracy rate of 90%. These findings highlight the effectiveness of YOLO for real-time object detection tasks, making it a suitable and preferred choice for time-sensitive applications such as autonomous driving systems.
Copyright (c) 2026 Madhura M. Bhosale, Yogesh S. Angal http://creativecommons.org/licenses/by-sa/4.0
YOLO-based deep learning for tooth detection, segmentation, and numbering in panoramic radiographs
Panoramic dental radiographs are essential for diagnosis and treatment planning, but are difficult to interpret due to overlapping anatomical structures and limited image quality. Automating tooth detection, segmentation, and numbering can enhance clinical efficiency and reduce observer variability. This study developed a you only look once version 8 (YOLOv8)-based deep learning model for automatic detection and segmentation of teeth in panoramic radiographs. A dataset of 302 images containing 9,009 annotated teeth was divided into 70% training, 20% validation, and 10% testing sets. Teeth were labeled using the Federation Dentaire Internationale (FDI) two-digit numbering system with bounding boxes and segmentation masks. Model performance was assessed using precision, recall, F1-score, and mean average precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5–0.95. The model achieved bounding box precision, recall, and F1-score of 0.9075, 0.9352, and 0.9212, respectively, and segmentation scores of 0.9078, 0.9344, and 0.9209. Bounding box mAP@0.5 reached 0.9510 and mask mAP@0.5 was 0.9504, while stricter thresholds lowered performance to 0.7763 and 0.6896. Class-wise analysis showed high accuracy overall but reduced performance on posterior teeth due to anatomical overlap and peripheral image quality. YOLOv8 enables fast, accurate, and robust tooth detection and segmentation, supporting real-time computer-aided diagnosis in orthodontics, prosthodontics, and forensic dentistry.
Copyright (c) 2026 Sri Oktamuliani, Luqyana Mahdiyah, Haritsul Haq, Nesa Perdana Putri, Wulandani Liza Putri http://creativecommons.org/licenses/by-sa/4.0
Efficient deep learning for automated corneal ulcer severity classification from fluorescein images
Corneal ulcers can cause permanent vision loss if not diagnosed and managed promptly, particularly in settings with limited access to ophthalmology services. This study aims to develop an automated deep learning approach for classifying corneal ulcer severity from fluorescein slit-lamp images. An EfficientNetV2-S–based model is employed, incorporating corneal area masking to suppress non-relevant regions and class distribution–based augmentation to address data imbalance. To improve evaluation reliability, a leakage-aware data splitting strategy is applied before and after augmentation. Experimental results show that the proposed approach achieves a maximum validation accuracy of 95.93% under non-leakage conditions for the category classification scenario, while maintaining high training efficiency. These results demonstrate that the proposed method provides a robust and efficient solution for automated corneal ulcer severity assessment and has the potential to support clinical decision-making in ophthalmic practice.
Copyright (c) 2026 Rodiah, Indah Sinthya Permata Sari, Matrissya Hermita, Sarifuddin Madenda, Diana Tri Susetianingtias http://creativecommons.org/licenses/by-sa/4.0
Smart parking management system: a seamless parking solution using YOLO and QR code payment technology
The inefficiencies of traditional parking systems, including manual entry and reliance on sensors, as well as slow payment lines, contribute to congestion and a lower level of user satisfaction. This paper suggests a smart parking management system (SPMS) based on the license plate recognition (LPR) technology using you only look once version 5 (YOLOv5)modelincombination with a QR code based payment system, to automate the parking system. It’s able to detect vehicle license plates in real-time, which means that data doesn’t have to be entered manually, and it also avoids the need for physical sensors, which cuts down on infrastructure costs. With a centralized database, the efficient tracking of vehicles is achieved, and QR-based payment will facilitate contactless payments at exit. Experimental evaluation shows that the proposed system out performs than the other systems, with an accuracy of 98.09% and recall of 98.25% in case of LPR. This system drastically decreases processing time, congestion and improves overall user experience. SPMS is more efficient, scalable, and reliable than traditional and existing smart parking systems. The results show that the proposed method is a cost-effective and practical solution for solving the modern parking management problem.
Copyright (c) 2026 Sumit Kumar, Ruchi Rani, Sanjeev Kumar Pippal http://creativecommons.org/licenses/by-sa/4.0
Symmetry index-based gait improvement prediction using a CNN-LSTM-attention framework
The shortcomings of existing clinical assessments are addressed by using a deep learning (DL) framework. This study will assess the gait improvement of lower limb fractured patients using the publicly available GaitRec dataset. The dataset contains the vertical ground reaction force (GRF) data used to compute biomechanical kinetic features. This framework considers hip, knee, ankle, and calcaneus fractures as lower limb fracture class, and the symmetry indices computation is done between affected and unaffected limbs, and their temporal changes were examined to analyze and track the rehabilitation progress of individual subject. The framework successfully identifies the reduction in the asymmetry between left and right leg features. The overall gait improvement in patients is computed using initial and final session composite asymmetry index (ASI) values and if it is at least 30% then overall gait is improved, demonstrating its robustness and clinical relevance. The suggested convolutional neural network (CNN)-long short-term memory (LSTM)-attention hybrid model outperformed with regression metrics of root mean square error (RMSE) of 1.09, mean absolute error (MAE) by 0.64, and R2 score of 0.98 and classification metrics of accuracy 97.1%, precision of 97.6%, recall of 96%, and F1-score of 96.8% by capturing both the local patterns and temporal dynamics of the gait data.
Copyright (c) 2026 Pushpalatha Obanna, Premkumar Ramesh http://creativecommons.org/licenses/by-sa/4.0
Context-aware AgriBot using dual intent and entity transformer and hybrid deep learning model
The agriculture sector has gone through vast technological improvement, leading to increased productivity and sustainability. This research introduces AgriBot, a context-aware virtual assistant (VA) that helps farmers in rice cultivation and identifies diseases while providing instant suggestions for subsequent task. Using the RASA framework, AgriBot has been designed to understand the farmer's queries. Dual intent and entity transformer (DIET) classifier has been used for entity classification, achieving training and testing accuracy of up to 98% and 97%, respectively. Additionally, the system incorporates machine learning (ML) models for rice disease detection, utilizing a dataset of 4,624 images covering three major rice diseases: bacterial blight, brown spot, and blast. Among the tested models—neural network (NN), random forest (RF), support vector machine (SVM) and naive Bayes (NB) achieved an accuracy of up to 99.9%, demonstrating excellent classification performance. Using text-based query handling with image-based disease identification and instant suggestion makes it a more robust support system for the farmers.
Copyright (c) 2026 Binod Deka, Ridip Dev Choudhury, Utpal Barman http://creativecommons.org/licenses/by-sa/4.0
Comparative evaluation of transfer learning models and Grad-CAM interpretability for brain tumor detection from MRI
Brain tumor classification plays an important role in early diagnosis and treatment planning. The current study aims to evaluate and compare the performance of five pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, MobileNet, Xception, and InceptionV3 using magnetic resonance imaging (MRI) images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. To enhance model performance and address class imbalance, transfer learning and data augmentation techniques were employed. To boost model interpretability, heatmaps of important areas in tumor classification were produced through gradient-weighted class activation mapping (Grad-CAM). MobileNet was the most accurate with 97% and was more precise and more sensitive. The Grad-CAM visualizations showed the models were attending to clinically relevant features, which increased the interpretability. This comparative study demonstrates the effectiveness of the integration of explainable artificial intelligence (XAI) in deep learning pipelines for reliable brain tumor diagnosis.
Copyright (c) 2026 Md. Firoz Hasan, Md. Awal Hadi, Sumaiya Nasrin, Md. Raisul Islam, Md. Atik Shahriar, Md. Hasan Moon, Tanvir Ahmed Momin, Dewan Mamun Raza http://creativecommons.org/licenses/by-sa/4.0
Multi-stage hybrid YOLO-driven and MobileNetV2-CNN variants for robust fish freshness classification
This study presents a multi-stage hybrid representation learning framework for robust fish freshness classification to address the critical challenge of reliable quality assessment in unpreserved food supply chains. The proposed pipeline operates in two stages: stage-1 employs you only look once (YOLO)v8n as feature-gated detector to validate inputs and eliminate non-fish images, while stage-2 leverages transfer-learned MobileNetV2 variants enhanced with convolutional neural network (CNN) layers, fine-tuning, adaptive learning rate schedulers, and expanded fully connected layers for hierarchical classification into three freshness classes i.e., highly fresh, fresh, and not fresh. The framework has been trained and evaluated on two curated datasets comprising 4,500 fish and non-fish images and 11,111 freshness-labeled including augmented dataset to increase diversity. The experimental results showed significant improvements. The best performance model transfer learning (TL)-MobileNetV2 + CNN + fine-tuning achieved 98.07% training accuracy and 67.72% validation accuracy on 80:20 split, and training accuracy of 97.57%, validation accuracy of 97.21% through 10-fold cross-validation. The comparative benchmarking confirmed that dual-stage design outperformed baseline MobileNetV2 and YOLOv5s models across precision, recall, and F1-score. The findings highlighted significant value of integrating detection-driven validation with transfer-learning classification, and propose new benchmark for intelligent freshness monitoring. For future work, this study aims to explore attention-based models, data integration, and species diversity.
Copyright (c) 2026 Raseeda Hamzah, Rosniza Roslan, Amni Munira Khidir, Lala Septem Riza http://creativecommons.org/licenses/by-sa/4.0
Indian sign language video generation using attention-enhanced generative adversarial network
Sign language (SL) is the primary mode of communication for the Deaf signers. Despite advancements in deep learning, SL recognition, translation, and video generation face challenges like blurriness and inconsistencies. This research proposes a novel sign language translation (SLT) approach for Indian sign language (ISL) using an attention-driven generative adversarial network (GAN). The preprocessing pipeline includes video frame extraction, skeletal joint coordinate detection via OpenPose, and dynamic time warping (DTW) for pose data refinement. The squeeze and excitation (SE) attention mechanism enhances 2D convolutional layers, allowing the generator to focus on relevant skeletal pose sequences. A motion discriminator refines motion authenticity. Performance evaluation using two SL datasets demonstrates significant improvements in structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and temporal consistency metric (TCM) scores, achieving 99.60 (%) as SSIM, 31.10 dB as PSNR, and 0.9111 as TCM. The proposed model outperforms standard GAN and dynamic GAN in SL video generation.
Copyright (c) 2026 Prachi Pramod Waghmare, Ashwini Mangesh Deshpande http://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning model for the task scheduling in cloud computing
Task scheduling plays a crucial role in optimizing performance, reducing costs, and enhancing system reliability by efficiently allocating resources to workloads. Traditional task scheduling methods lack the ability to efficiently manage workloads and resource distribution, leading to potential inefficiencies in performance and energy consumption. To address these limitations, advanced techniques leveraging deep learning and reinforcement learning are explored. This study proposes a deep learning-based model for task scheduling in cloud computing. The model employs a convolutional neural network (CNN) to predict the optimal machines for task allocation. Additionally, Q-learning is integrated with CNN to facilitate load shifting between machines, ensuring efficient utilization of resources. The dataset used in this work consists of task attributes, such as execution time, resource requirements, which were loaded from a CSV file. Comparative analysis with existing models shows that the proposed approach achieves approximately 94% accuracy and consumes less energy than other models, demonstrating its effectiveness in cloud task scheduling.
Copyright (c) 2026 Kavita Rani, Om Prakash Sangwan, Ritu Garg http://creativecommons.org/licenses/by-sa/4.0
Text emotion recognition based on deep learning and attention with whale optimization algorithm
Text emotion recognition (TER) represents one of the crucial tasks in natural language processing (NLP), and it is highly important for many applications. To improve the performance of TER, advanced deep learning (DL) model GRU-BiGRU-CNN-Att is presented in this research. It is a combination of gated recurrent units (GRU), bidirectional gated recurrent units (BiGRU) with a convolutional neural network (CNN), and an attention mechanism. Using a GRU, BiGRU, and CNN as part of a deep feature extraction model, combined with an attention mechanism that gives important information different weights, the method that is proposed in the present work enhances the quality of the word vectors and leads to increasing sentiment analysis judgment accuracy. The whale optimization algorithm (WOA), which selects the most informative features for further enhancing the model, is used lastly for the optimization of the feature selection process. After such optimization, those chosen features are trained on a multi-layer perceptron (MLP), successfully combining machine learning (ML) and DL approaches for improving TER. A varied corpus of tweets, sentences, and dialogues had been used in the present work for the assessment of the performance of the suggested model. The proposed method achieves 83.76% accuracy in emotion recognition.
Copyright (c) 2026 Tabarek Alwan Tuib, Mohammad-Reza Feizi-Derakhshi, Yaqdhan Mahmood Hussein, Fahad Taha Al-Dhief http://creativecommons.org/licenses/by-sa/4.0
Deep learning intrusion detection for software-defined networking using synthetic minority oversampling
This article proposes an advanced method for network intrusion detection using a combination of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) models, enhanced by synthetic minority oversampling technique (SMOTE) to address class imbalance in datasets like network security laboratory–knowledge discovery in databases (NSL-KDD). The method aims to accurately classify network traffic by learning temporal patterns of both normal and malicious activities. SMOTE is employed to balance the dataset, ensuring that underrepresented attack types receive adequate model attention, thereby improving model robustness. The proposed models (LSTM, GRU, and BiLSTM) are trained and evaluated on the NSL-KDD dataset, with hyperparameter tuning performed through RandomizedSearchCV for optimal performance. The results show a significant improvement in accuracy, precision, recall, and F1-score, with BiLSTM demonstrating the highest performance, achieving near-perfect classification results (99.5% accuracy). This method not only mitigates the issue of class imbalance but also leverages the power of RNNs for sequence modeling, offering a promising solution for effective intrusion detection in modern networks.
Copyright (c) 2026 Prajwalasimha Sindugatta Nagaraja, Navya Rajashekara, Pushpa Bangalore Ramesh, Druva Kumar Siddaraju, Santhosh Kumar Ramachandragowda, Trupti Shripad Tagare http://creativecommons.org/licenses/by-sa/4.0
Feature-guided transformer approach for detecting distributed denial of service attacks
Distributed denial of service (DDoS) attacks continues to pose serious risks to modern networks, with their growing intensity making early detection both critical and challenging. Conventional machine learning (ML) models often struggle with the nonlinear and highly dynamic nature of attack traffic, which motivates the use of advanced architectures. In this study investigate a transformer-based classifier for DDoS detection on the CIC-DDoS2019 dataset. The workflow included preprocessing, feature scaling, and domain-guided feature selection. Logistic regression (LR) was employed as a baseline, achieving 92.1% accuracy and F1-score of 0.90, thereby revealing the limitations of linear models. The transformer, after hyperparameter tuning and 5-fold cross-validation, reached an average accuracy of 99.95% with precision, recall, and F1-scores all above 99.9%. The model demonstrated stable convergence and generalization across folds. These results highlight the strength of attention mechanisms in capturing feature dependencies, while also pointing to future directions such as real-time deployment, explainability, and resilience to zero-day attacks.
Copyright (c) 2026 Lokeshwaran Kanagaraj, Raguraman Purushothaman, Sathya Subramanian, Durga Devi Saravanan, Komal Kumar Napa, Cornelius Karunakaran, Billa Manindhar http://creativecommons.org/licenses/by-sa/4.0
An optimized deep learning framework for brain tumor classification using magnetic resonance imaging
Accurate and interpretable classification of brain tumors in magnetic resonance imaging (MRI) scans plays a crucial role in early diagnosis and effective treatment planning. This study introduces a deep learning (DL) framework based on a customized YOLOv5m architecture integrated with a bidirectional feature pyramid network (BiFPN) for multi-class brain tumor classification. The integration of BiFPN enhances multi-scale feature fusion, improving detection across varied tumor types, while YOLOv5m ensures real-time inference capabilities. To mitigate class imbalance, a class-weighted cross-entropy loss is adopted. The model is evaluated on multiple performance metrics, achieving a test accuracy of 88.86%, precision of 88.70%, recall of 88.20%, and F1-score of 88.25%. It also reports a mean average precision (mAP@0.5) of 94.36%, with high class-wise average precision (AP) for glioma, meningioma, pituitary, and no-tumor categories. Computational time for training (12484.81 seconds) and testing (146.82 seconds) confirms the model’s feasibility for real-time clinical deployment. To support interpretability, gradient-weighted class activation mapping (Grad-CAM) is integrated for visualizing class-discriminative regions, helping clinicians understand the model’s predictions. A gradio-based user interface is also developed, enabling intuitive interaction with the system.
Copyright (c) 2026 Komal Kumar Napa, Rajkumar Govindarajan, Senthil Murugan Janakiraman, Jayanthi Arumugam http://creativecommons.org/licenses/by-sa/4.0
Enhancing early detection of autism spectrum disorder through ensemble-based machine learning classifiers
Autism spectrum disorder (ASD) is a developmental disability characterized by significant social, communication, and behavioral challenges. Machine learning is a practical approach for autism detection. The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms. This ensemble approach is designed to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process. This methodology addresses the urgent need for early and accurate ASD identification, enabling timely interventions. Leveraging complex data analysis, it offers deeper diagnostic insights, facilitating informed clinical decisions and advancing ASD research. The methodology's accessibility across healthcare settings marks a significant step forward in making early ASD detection more universally available, showcasing the transformative potential of machine learning in healthcare. In deploying the “ensemble-based machine learning classifier” for ASD diagnosis, this study utilizes an extensive dataset comprising behavioral and medical profiles from diverse demographics, including toddlers, children, adolescents, and adults with ASD. Upon the preliminary analysis, the dataset enables the methodology to learn from a wide array of ASD manifestations, ensuring its robustness and applicability across different age groups and severity levels.
Copyright (c) 2026 Shabeena Lylath, Laxmi B. Rananavare http://creativecommons.org/licenses/by-sa/4.0
Adaptive binary capsule network for complex image recognition
Glaucoma and cataracts are leading causes of blindness worldwide, emphasizing the need for early detection. Convolutional neural networks (CNNs) have shown promise in detecting ocular diseases, but require extensive training datasets. However, medical datasets are scarce, limited, and imbalanced, prompting the use of time-consuming data augmentation techniques. To address these limitations, a computationally efficient and robust capsule network (CapsNet) model was proposed. The model features a novel adaptive contrast spatial filtering (ACSF) algorithm and incorporates a local binary pattern (LBP) algorithm to enhance robustness. This study achieved good recognition accuracy, with scores of 96.90% on the combined dataset, 96.45% on the cataract-only dataset, and 96.78% on the glaucoma-only dataset. The model's performance is comparable to state-of-the-art models, demonstrating its potential to support ophthalmologists in diagnosing cataracts and glaucoma-related eye issues.
Copyright (c) 2026 Mavis Serwaa Yeboah, Patrick Kwabena Mensah, Adebayo Felix Adekoya, Mighty Abra Ayidzoe http://creativecommons.org/licenses/by-sa/4.0
Deep learning for categorizing microsatellite stability in colorectal cancer
Cancer remains a significant global health challenge, with its incidence rising steadily in recent decades. In colorectal cancer (CRC), microsatellite instability (MSI), and microsatellite stability (MSS) are important biomarkers that influence treatment decisions and patient outcomes. Accurate MSI classification is critical but traditional methods can be costly and time-consuming. This study explores the potential of deep learning to classify MSI and MSS in CRC. A large dataset of CRC patients with confirmed MSI and MSS status was utilized, obtained through standard testing images. Deep learning models were applied to histopathological images, analyzing tissue features from digital slides. Convolutional neural network (CNN) and residual network (ResNet)-18 models demonstrated high accuracy in distinguishing between MSI and MSS CRCs. The best-performing model, which integrated genomic and histopathological data, achieved an area under the curve (AUC) receiver operating characteristic (ROC) of 0.85, indicating strong discrimination capability. The findings suggest that deep learning could be a valuable tool for clinical decision-making and personalized medicine in CRC.
Copyright (c) 2026 Sofyan El Idrissi, Yassine Drider, Ikram Ben Abdel Ouahab, Mohammed Bouhorma, Fatiha El Ouaai http://creativecommons.org/licenses/by-sa/4.0
EEB7-UNet: a deep learning framework for automated segmentation of fractured C-spine vertebrae
Accurate identification of vertebral fracture (VF) regions in computed tomography (CT) images is crucial for surgeons prior to treatment planning, but remains challenging due to irregular vertebral boundaries, low contrast, noise, and image unevenness. Recent advancements in deep learning have shown promising results compared to conventional manual diagnosis methods in detecting anomalies and segmenting regions of interest in medical imaging. In this study, a deep learning model, enhanced EfficientNetB7 U-Net (EEB7-UNet), is proposed to segment the fractured cervical vertebrae. It includes custom data augmentation to increase the data size and a hybrid learning rate scheduler strategy technique for faster convergence, which increases the generalizability and robustness of the model. The proposed model achieved an improved dice score index of 95.53% and Jaccard coefficient index of 93.85% on the test dataset. Furthermore, the EEB7-UNet has emerged as a moderate size with 98.6 MB. The approach yields superior performance in terms of dice score index and Jaccard coefficient index compared to the other state of the art convolutional neural network (CNN) used as an encoder in the U-Net. This research also compared the performance of the proposed model with three other studies in similar contexts, reported in the literature.
Copyright (c) 2026 Abhishek Kumar Pandey, Pateel G. P., Kedarnath Senapati http://creativecommons.org/licenses/by-sa/4.0
Performance of majority voting transfer learning deep learning monkeypox disease detection
Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
Copyright (c) 2026 Nur Nafiiyah, Muhammad Nurul Huda http://creativecommons.org/licenses/by-sa/4.0
Deep learning-based prognostic modeling of brain tumors
Headline accuracy is not enough to move a deep-learning brain tumor magnetic resonance imaging (MRI) classifier into the clinic. A model also needs repeated-split validation, trustworthy probabilities, a statistical sanity check, and a small enough footprint for hospital hardware. This study cover all four in one framework. Five convolutional neural network (CNN) backbones (visual geometry group (VGG)-16, residual network (ResNet)-50V2, MobileNetV2, EfficientNetB0, and dense convolutional network (DenseNet)-121) are trained on 4,600 public brain MRI scans (2,513 tumor, 2,087 healthy) under a shared two-phase transfer-learning recipe. The usual single split is replaced by stratified 5-fold cross-validation (CV), with paired McNemar and DeLong tests and 1,000 bootstrap resamples. Calibration is judged by expected calibration error (ECE), Brier, reliability diagrams, and temperature scaling. VGG16 wins mean accuracy (98.72 ± 0.42%); ResNet50V2 has the tightest area under the curve (AUC) (0.9986 ± 0.0006); the two are statistically tied. MobileNetV2 is the best-calibrated model out of the box (ECE = 0.0021) with only 2.59 M parameters, making it the most deployable. A four-level confidence-based risk score turns calibrated probabilities into triage tags. This study also flag a measured 3.23% format-duplicate leakage in the public dataset and presents the framework as a radiologist co-pilot, not a prognostic model.
Copyright (c) 2026 Laiali Almazaydeh, Arar Al Tawil http://creativecommons.org/licenses/by-sa/4.0
Unsupervised voice activity detection based on the envelope's fractal dimension
Currently, voice activity detection (VAD) is utilized in many fields, including forensics, healthcare, and medicine, to detect vocal anomalies, as well as in telecommunications and mobile telephony. Due to its importance and the difficulty of distinguishing between speech and nonspeech segments, especially in noisy environments (low signal-to-noise ratio (SNR)), this area remains under continuous development. Most existing VAD algorithms require predefined thresholds or training data, which reduces their compatibility. This study proposes an unsupervised VAD system that utilizes the Katz algorithm to calculate the fractal dimension of envelopes obtained through a single frequency filtering (SFF) approach. This method allows for high temporal and frequency resolution. The proposed VAD algorithm does not require any training data and is suitable for various types of noise and SNRs. To evaluate the effectiveness of the proposed method, two different databases are used: the Texas Instruments Massachusetts Institute of Technology (TIMIT) database and the King Saud University (KSU) Arabic speech database. The experimental results reveal an average detection accuracy of 96.86%, demonstrating its considerable value in various applications.
Copyright (c) 2026 Nesrine Abajaddi, Youssef Elfahm, Laila Elmazouzi, Ilham Mounir, Badia Mounir, Abdelmajid Farchi http://creativecommons.org/licenses/by-sa/4.0
Genetic algorithm-optimized BERTopic with SHAP explainability for institutional research trend analysis
Institutional research grant titles constitute short-text grey literature characterized by heterogeneous semantic structures, making topic identification and research trend analysis challenging. This study proposes an integrated bidirectional encoder representation from transformers-based topic modeling (BERTopic) framework combining genetic algorithm (GA)-based hyperparameter optimization and Shapley additive explanations (SHAP)-based interpretability to improve semantic topic quality and model transparency. GA was applied to optimize dimensionality-reduction and density-based clustering parameters, while SHAP was used to estimate the contribution of bigram features to the surrogate classifier’s predictions of BERTopic-generated topic labels. Experimental results demonstrated that the proposed framework improved topic coherence from 0.367 to 0.543 while reducing the outlier ratio from 21.12% to 13.55%. In addition, the number of topics decreased from 46 to 10, resulting in a more compact and less fragmented topic structure. The resulting topic structure revealed dominant themes related to higher education, religious moderation, Islamic counseling, halal tourism, and sharia banking. Overall, the proposed framework contributes to the development of more coherent, interpretable, and semantically robust topic modeling for institutional short-text grey literature analysis.
Copyright (c) 2026 Muhammad Dedi Irawan, Yustria Handika Siregar, Hewa Majeed Zangana, Ali Ikhwan http://creativecommons.org/licenses/by-sa/4.0
Analysis of normalization technique on multi objective preference analysis method
Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Copyright (c) 2026 Fristi Riandari, Gabriel Ardi Hutagalung, Ferry Fachrizal http://creativecommons.org/licenses/by-sa/4.0
Experimental validation of vision-based hybrid navigation for differential-drive robots
Reliable mobile-robot deployment requires mapping, localization, global planning, and real-time obstacle avoidance to operate consistently under sensor noise and physical constraints. This study presents a vision-based hybrid navigation framework for a differential-drive mobile robot in a warehouse-like environment. The main contribution lies in the system-level integration and experimental validation of established techniques rather than the development of a new standalone navigation algorithm. A ceiling-mounted camera converts top-view images into an occupancy-grid map and world coordinates, while a convolutional neural network (CNN) recognizes goal markers. Odometry–augmented reality University of Cordoba (ArUco) fusion is used to correct accumulated localization drift. Particle swarm optimization (PSO) generates smooth global paths offline, whereas the dynamic window approach (DWA) performs real-time local motion control. Experiments in a 3.3 m × 2.4 m workspace achieved average obstacle-localization errors of 0.959 cm and 0.696 cm along the x- and y-axes, respectively, and a goal-recognition accuracy of 99%. The DWA controller required 12.61±2.42 ms per cycle, while rapidly-exploring random tree (RRT) and PSO required 4–7 s and 477–692 s, respectively, for global path generation. Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.
Copyright (c) 2026 Thanh Quang Duc Le, Trong Hieu Luu http://creativecommons.org/licenses/by-sa/4.0
The role of big data in precision medicine and healthcare monitoring using the MapReduce framework
Data analytics has become a cornerstone of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. Most healthcare data are high-dimensional data that not only require longer computational time but also affect the accuracy of analysis. In order to overcome these issues, the MapReduce based big data healthcare monitoring framework is proposed. The proposed work comprises preprocessing, the MapReduce framework, and data classification. The preprocessing can be done using improved min-max normalization, and the big data can be handled using the improved support vector machine (SVM)-recursive feature elimination (RFE) method. Finally, the classification can be done using a deep Q-network (DQN). The performance of the proposed method is analyzed in terms of accuracy, precision, F-measure, and Matthew's correlation coefficient (MCC).
Copyright (c) 2026 Meenakshi Sankarasubramanian, Meena Chavan, Govindan Manoharan Karthik, Jhansi Pandiri, Arumalla Nagaraju, Idimadakala Madhavilatha http://creativecommons.org/licenses/by-sa/4.0
Prioritizing ransomware indicators of compromise using algorithmic scoring for enhanced threat detection
Ransomware is a very serious cybersecurity threat. Its attack methods are continually evolving, which means that it is not very easy to detect it quickly. A big challenge faced in the ransomware defense is how to prioritize indicators of compromise (IoC) efficiently. This paper introduces a hybrid IoC ranking scheme based on static and dynamic analysis of the behavior of commonly circulating ransomware variants. Each IoC is given a weighted score according to its significance for investigations based on actual patterns of occurrence and contextual behavior. Experimental results clearly separate high-confidence indicators from low-confidence ones. Deleting shadow copy and connecting to Tor2web receive the highest rank scores of about 99.99, while older behaviors like locking screen show lower relevance, around 73.11. The proposed algorithm has a linear time complexity of O(n·m) for score calculation and a bounded space complexity of O(n·m), allowing it to scale for large IoC sets. The findings show that this ranked IoC framework enhances early ransomware detection and helps prioritize responses based on evidence in current security systems.
Copyright (c) 2026 Krishna Prasad D. Subramanya, Prasanna Kumar H. Ramakrishna http://creativecommons.org/licenses/by-sa/4.0
Metaheuristic optimization for atrial fibrillation detection: feature extraction, selection, and hyperparameter tuning
Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and intervention. This study presents a multi-objective optimization approach for AF detection, focusing on feature extraction, selection, and neural network hyperparameter tuning. The methodology uses cross-validation during the training of the two concatenated ECG dataset features and simultaneously minimizes the error rate on the separate validation folds of each dataset and reduces the number of selected features, enhancing model generalization and efficiency. Particle swarm optimization (PSO), grey wolf optimization (GWO), and differential evolution (DE) algorithms were implemented to navigate this multi-objective space. While all three algorithms were explored, the final solution, demonstrating a superior trade-off between accuracy and feature reduction, was obtained using DE. This approach effectively identifies optimal feature subsets and neural network configurations, yielding a robust and compact AF detection model. The proposed approach has shown promising results, with the model achieving accuracies of 96.38% and 90.69%, and corresponding area under the curve (AUC) values of 0.99 and 0.96, for the first and second datasets, respectively, using 10 optimally selected features.
Copyright (c) 2026 Zaid Nouna, Hamid Bouyghf, Mohammed Nahid, Issa Sabiri http://creativecommons.org/licenses/by-sa/4.0
Predictive model based on machine learning to identify sleep-related health problems
Sleep quality has become a growing public health issue worldwide, mainly due to a lack of awareness about its long-term consequences. Despite existing strategies to address this problem, there remains a need for more effective approaches. In this study, an early detection model for sleep disorders was implemented using the extreme gradient boosting (XGBoost) algorithm, following the knowledge discovery in databases (KDD) methodology, which includes the phases of selection, preprocessing, transformation, data mining, and interpretation. A dataset extracted from the Kaggle platform in CSV format was used, consisting of 374 records. With an overall accuracy of 91.5%, a recall of 100% for the insomnia class, and a precision of 100% for sleep apnea, the proposed model demonstrated exceptional performance. It also received an area under the curve (AUC) of 0.909 and an average F1-score of 0.913. With a mean accuracy of 91%, a 95% confidence interval (0.89–0.94), and a p-value of 0.0012, cross-validation confirmed its robustness and showed a statistically significant change from the baseline model. The error rates remained within clinically acceptable ranges, confirming its applicability as a diagnostic support tool. Overall, the results demonstrate the effectiveness of the model in identifying patterns related to sleep disorders.
Copyright (c) 2026 Laberiano Andrade-Arenas, Inoc Rubio Paucar, Margarita Giraldo Retuerto, Cesar Yactayo-Arias http://creativecommons.org/licenses/by-sa/4.0
A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network
This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.
Copyright (c) 2026 Ahmad Zarkasi, Hadipurnawan Satria, Anggina Primanita, Deris Stiawan http://creativecommons.org/licenses/by-sa/4.0
Experimental evaluation of an artificial intelligence system for automated mango classification
This study proposes the design and implementation of an intelligent mango sorting system capable of classifying mangoes into three categories: unripe, ripe, and rotten. The system integrates a programmable logic controller (PLC) to operate a conveyor mechanism, with user interaction facilitated through a human-machine interface (HMI) touchscreen panel. Image acquisition is performed using a universal serial bus (USB) webcam, while image processing and classification are handled by the CiRA CORE software utilizing artificial intelligence (AI) techniques. The classification results are transmitted to an Arduino microcontroller, which controls pneumatic actuators responsible for the physical sorting process. Experimental results demonstrate that the system can accurately classify mangoes with an overall success rate of 90%, indicating its potential for practical application in automated agricultural product sorting.
Copyright (c) 2026 Surasit Phokha, Wiriya Dangton, Viroch Sukontanakarn, Phisit Srinoi http://creativecommons.org/licenses/by-sa/4.0
Emotion recognition of electroencephalogram using hybrid convolutional neural networks and vision transformer
Electroencephalogram (EEG) signal-based emotion classification faces challenges in spatio-temporal complexity and inter-channel redundancy. This paper proposes a 2D convolutional neural network (CNN) and vision transformer (ViT) approach. Relevant emotions require signal extraction in the 4-40 Hz frequency band using the discrete wavelet transform (DWT). This study uses the SEED dataset from 15 subjects, with 62 channels reduced to 12. Every 5-second segment is decomposed using DWT into four frequency bands, which are then mapped to a 2D spatial representation for CNNs and ViTs. Experiment results show that the DWT-2D CNN-ViT achieves the best accuracy of 87% with a final loss of 0.087. In the meantime, the CNN-only model (85%), the ViT-only model (77%), and the DWT-only model (47%). Testing with several optimizers also shows that AdamW provides the highest performance with the fastest training time of 12.34 minutes. These results demonstrate that this integration can produce more efficient and stable learning. These findings indicate that the combination of DWT and the CNN-ViT hybrid architecture is effective for accurate, stable EEG-based emotion recognition and is potentially applicable to real-time emotion-monitoring systems.
Copyright (c) 2026 Esmeralda Contessa Djamal, Revan Vio Endriansyah, Daswara Djajasasmita http://creativecommons.org/licenses/by-sa/4.0
Intravenous immunoglobulin resistance prediction in Kawasaki disease using oversampled transformer embeddings
Kawasaki disease (KD) is a leading cause of acquired heart disease in children under five. Although intravenous immunoglobulin (IVIG) treatment is usually effective, 10–20% of cases are resistant and at higher risk for coronary complications. Early prediction of IVIG resistance is critical but difficult due to the rarity of KD and imbalanced clinical data. To address this, we propose a novel technique called sentence transformer embeddings with synthetic minority over-sampling technique (SMOTE) oversampling (STESO), which leverages the complementary strengths of transformer-based representation learning and synthetic oversampling. Pretrained models such as paraphrase-MiniLM-L3-v2 are used to convert tabular clinical data into dense text-based embeddings, capturing deeper semantic relationships across features. By coupling these rich embeddings with SMOTE, we balance class distributions directly in the semantic space, enabling traditional machine learning (ML) models to more effectively detect minority (resistant) cases. This synergy yielded substantial improvements in sensitivity and F1-score, with random forest (RF) combined with STESO (RF-STESO) achieving the highest overall performance. Among the models evaluated, our proposed model attained best result as accuracy of 0.85, a sensitivity of 0.81, a specificity of 0.89, and an F1-score of 0.85. Our results underscore that the joint use of transformer embeddings and oversampling is more effective than either approach in isolation, offering a promising pathway for rare disease prediction tasks such as IVIG resistance prediction in KD.
Copyright (c) 2026 Namitha Thattarassery Nanappan, Raghavendra Srinivasaiah, Vinith Rejathalal http://creativecommons.org/licenses/by-sa/4.0
Indonesia Islamic students' belief towards the performance of artificial intelligence: perceptions, impacts, and anticipations
This research aims to i) determine the belief level of Islamic university students towards AI for learning and ii) what factors related to student confidence in using AI for learning. This study employed a mixed-methods sequential explanatory design. The research subjects were students of the strata 1, Universitas Islam Negeri Syarif Hidayatullah, and students of Universitas Islam Negeri Sunan Kalijaga, totaling 411 students. The research sample was obtained using random techniques. The quantitative data collection method is questionnaire-based, and data in-depth analysis is carried out qualitatively using the focus group discussion (FGD) technique. The quantitative data analysis method was carried out using descriptive statistics, and the qualitative study was carried out thematically. This research found that the majority of students have a high level of confidence in the performance of artificial intelligence (AI) as a tool for them. This level of belief is significantly and positively related to understanding of the AI platform used, lecturer support and guidance, parental support and guidance, and support from fellow students. However, belief in AI cannot be absolute because it is feared that it will result in poor use. AI platform users, especially students, lecturers, researchers, and anyone who uses it, need to recheck and deepen its accuracy and correctness.
Copyright (c) 2026 Yuni Sugiarti, Ence Oos Mukhamad Anwas, Fitroh, Janu Arlinwibowo, Ihsan Maulana Anwas http://creativecommons.org/licenses/by-sa/4.0
A review of brain signal analysis in food perception studies
Food perception is more than just taste. It is a multisensory experience shaped by smell, texture, and even how the food looks. The human brain works to make sense of all these signals, influencing everything from cravings to food choices. This review looks at more than 40 studies that used electroencephalography (EEG) and other brainwave analysis tools to understand how to process food related stimuli. In order to identify patterns associated with attention, mood, and appetite, these studies looked at brain responses using techniques including event related potentials (ERPs) and spectral analysis. Current research shows that human desire for food, level of focus on it, and the feelings it evokes are all correlated with certain brain signals. However, majority of the studies investigated individual senses in isolation and focused on basic taste attributes such as sweet, salty, or bitter. The complexity of eating in real life, where several senses interact, is missed by this. Furthermore, a lot of research is lab based, short term, and does not represent real world situations. Future research must adopt practical, multimodal methods in order to fully understand how humans perceive food. Such studies could help improve food design, predict consumer behavior, and even support interventions for eating disorders.
Copyright (c) 2026 Harish S. Sinai Velingkar, Roopa R. Kulkarni, Prashant Patavardhan, Abdul Haq Nalband http://creativecommons.org/licenses/by-sa/4.0