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
Exploring artificial intelligence in vocational learning: teachers’ perspective from Indonesia
The rapid development of artificial intelligence (AI) in education has expanded its potential applications. However, empirical evidence from vocational education in developing countries remains limited, particularly regarding differences between certified and uncertified vocational high school (VHS) teachers’ perspectives. This study investigates VHS teachers’ perceptions of AI use in the learning process by explicitly comparing certified and uncertified teachers in Indonesia. Using a quantitative approach combined with data-mining techniques applied to open-ended survey responses, data were collected from 65 VHS teachers in the Special Region of Yogyakarta (DIY). Group differences were examined using a non parametric Mann-Whitney U test. The findings indicate that both certified and uncertified teachers demonstrate consistently positive perceptions of AI in instructional planning, implementation, and assessment, with no statistically significant differences between the two groups. Importantly, this result suggests that openness toward AI integration is not determined by certification status but reflects broader pedagogical orientations among vocational teachers. Teachers perceive AI primarily as a pedagogical partner rather than a substitute for professional educators. The study underscores the need of structured AI-focused professional development and policy support through adequate infrastructure and targeted training to enhance the effectiveness of AI adoption in improving the quality of vocational education in Indonesia.
Copyright (c) 2026 Yuliansah, Mar’atus Sholikah, Sutirman http://creativecommons.org/licenses/by-sa/4.0
Applications of machine intelligence and decision analytics in hospitality: a systematic review
This systematic review synthesizes how machine intelligence (MI) and decision analytics (DA) are deployed across hospitality functions (customer relationship management (CRM)/personalization, revenue management (RM) and pricing, and operations) and clarifies barriers and research frontiers. Following a PRISMA-oriented protocol for the 2008–2025 period, the study searched Scopus, Web of Science, IEEE/Elsevier, and SpringerLink using combined terms for MI/artificial intelligence (AI)/machine learning (ML) and hospitality/tourism with CRM, revenue/pricing, forecasting, and operations. The inclusion criteria focused on peer-reviewed English studies reporting concrete models/applications or empirical evidence. Studies were thematically coded into four streams: CRM and personalization; RM and dynamic pricing; operations and scheduling; and governance (privacy/ethics/skills). The findings consistently show that MI/DA improves customer segmentation, demand forecasting, dynamic pricing (revenue per available room (RevPAR)), and workforce/maintenance efficiency. Yet, adoption is slowed by data governance (privacy/general data protection regulation (GDPR)) and siloed systems. Unlike prior broad IT/e Tourism reviews, this study integrates recent MI advances (spatiotemporal deep learning, real-time decisioning, and human–AI teaming) into a hospitality-specific decision pipeline and outlines a testable research agenda around scalability/drift, privacy-by-design, and controlled field experiments.
Copyright (c) 2026 Omar Jawabreh, Ehab Abdul Raheem Alshatnawi, Rami Mahmoud http://creativecommons.org/licenses/by-sa/4.0
Pneumothorax detection using a learning focal point architecture
Automatic image segmentation and feature analysis play a crucial role in improving the accuracy and efficiency of disease diagnosis and treatment within modern medical practice. This study propose the use of the learning focal point (LFP) architecture, which is based on the LFP algorithm, to perform effective segmentation of medical images by dividing each image in the dataset into multiple meaningful zones. This zonal segmentation strategy enables the precise extraction of critical regions of interest that are most relevant for pathological analysis. The proposed approach is specifically applied to the detection of common pneumothorax in lung imaging, a condition that requires timely and accurate diagnosis. By concentrating on essential lung zones, the LFP architecture enhances the reliability and robustness of pneumothorax identification. The results demonstrate that this method has the potential to significantly assist clinicians by providing more accurate diagnostic support and facilitating earlier medical intervention, ultimately improving patient outcomes.
Copyright (c) 2026 Salah-Eddine Mansour, Bouabid Qabliyane, Abdelhak Sakhi, Zakaria Khoudi, Mohamed Baslam http://creativecommons.org/licenses/by-sa/4.0
Mapping global ethical AI principles into Indonesian higher education: a framework for responsible institutional implementation
Artificial intelligence (AI) is an essential component of higher education's digital revolution. But its application unlocks a chain of ethics issues that must be resolved in an organized manner. This study uses mapping between United Nations Educational, Scientific, and Cultural Organization (UNESCO) and Organisation for Economic Co-operation and Development (OECD) guidelines and illustrates whether both guidelines can actually be implemented by Indonesian universities. In this study, a literature review and analysis of the content of the AI policy framework at the international level were conducted which were then applied to understand the operating environment in higher education. The findings in this study emphasize eight contextually meaningful ethical norms such as fairness, transparency, accountability, data protection, sustainability, inclusion, AI literacy, and ethical governance. Each of these values is combined with real-world practices such as algorithmic audits, multidisciplinary coordination, regulations for data encryption, and the formation of an AI ethics committee. In addition, this study produces a strategic narrative that can serve as a guide for universities in Indonesia when developing AI systems. The contribution of this study is the creation of a framework that can be applied to provide information to stakeholders on how to align AI-based applications with international standards while remaining oriented towards local values and laws in Indonesia.
Copyright (c) 2026 Zaqqi Yamani, Sarifah Putri Raflesia, Dinda Lestarini, Ghita Athalina, Purwita Sari http://creativecommons.org/licenses/by-sa/4.0
Performance-optimized boosted hybrid ensemble model for diabetes risk prediction
The proposed boosted hybrid ensemble (BHE) machine learning (ML) model utilizes the classification power which reduces the overfitting by bagging and generates better results using random forest (RF) and extreme gradient boosting (XGBoost). The paper presents the importance and impact of secondary features in type 2 diabetes prediction utilizing real-time self reported and hospital data. The research study shows that age, gender, body mass index (BMI), and glucose are the key prime factors and are also influence by the other factors like demographic conditions, eating, and activity styles to some extents. The paper presents transfer learning (TL) on the basis on standard Pima Indians diabetes dataset (PIMA) to apply hybrid 2-layer BHE model to predict and classify the records into diabetic and non diabetic class providing explanations to factors contributing to it. The result section shows the highest 98% accuracy for BHE with optimized model presenting recommendations as per careful considerations of World Health Organization (WHO) and American Diabetes Association (ADA) standards. The paper throws light on the need of life-style factors considerations and correction to establish causation and refine preventive strategies in avoiding or postponing type-2 occurrences in youth people. This paper present perfect integration of multifactorial data with high reliability of artificial intelligence (AI)-driven healthcare explainable models to generate recommendations utilizing TLs.
Copyright (c) 2026 Prajakta Bhosale-Dhamdhere, Ganesh Pathak http://creativecommons.org/licenses/by-sa/4.0
Advanced inferential statistics and data mining for chlorophyll distribution clustering
This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.
Copyright (c) 2026 Felix Reba, Toha Saifudin, Rimuljo Hendradi http://creativecommons.org/licenses/by-sa/4.0
AI tools in teaching: EFL teachers’ perceptions of usefulness, challenges, and the role of designation and AI-training
This study aims to investigate the integration of artificial intelligence (AI) into the teaching of English as a foreign language (EFL) at Bangladeshi Universities, focusing on teachers' perceptions of its benefits and challenges. A mixed-methods research design was employed, with a 19-item survey administered to 123 EFL teachers across 20 universities. Qualitative data were collected through semi-structured interviews with 5 teachers. Reliability and validity were assessed through pre-testing and Cronbach's alpha for internal consistency. Member checking and triangulation also contributed to the trustworthiness of qualitative data collected. Quantitative analysis used mean, standard deviation (SD), factor analysis, and chi-square tests, complemented by qualitative thematic analysis. Chi-square test results indicate no significant difference in teachers' designations regarding challenges (p >0.05). However, teachers with AI training do significantly better than those without in recognizing AI-generated EFL content (p =0.03). The qualitative findings show that teachers perceive AI as a tool that enhances teaching strategies, offers better time management, and facilitates innovative instructional practices. However, concerns about maintaining academic integrity prevail. Despite these challenges, the study emphasizes an optimistic outlook towards the role of AI in teaching English. It suggests integrating AI tools into EFL classrooms and proposes structured training programs to fully leverage their potential to enhance teaching effectiveness and improve learning outcomes.
Copyright (c) 2026 Md. Mahadhi Hasan, Kaniz Fatema, Rashed Mahmud http://creativecommons.org/licenses/by-sa/4.0
Detection of autism spectrum disorder using multilayer perceptron classifier
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by distinctive challenges in both verbal and nonverbal communication, social interaction, and repetitive behaviors. However, the diagnosis of ASD usually occurs within a clinical setting, conducted by licensed professionals, and often involves lengthy and costly procedures. On the other hand, machine learning holds significant promise for improving diagnostic and intervention research within the behavioral sciences, particularly in research concerning ASD disease. Hence, a deep investigation of a machine learning algorithm for ASD detection is crucial. Therefore, this paper presented a new system for differentiating the ASD samples from non-ASD (i.e., healthy) samples. The samples of ASD have been compiled from toddlers. The multilayer perceptron (MLP) algorithm is used to classify ASD samples from non-ASD samples. The proposed MLP classifier is implemented based on different numbers of neurons (i.e., nodes). In other words, the proposed MLP classifier started with 10 neurons and finished with 50 neurons with an increment step of 5 neurons. The outcomes demonstrate that the MLP classifier acquired different results concerning the number of neurons. The MLP obtained the best performance, reaching an accuracy rate of 100% in identifying ASD cases.
Copyright (c) 2026 Ahmed Q. Hadi, Saif H. Alrubaee, Fahad Taha Al-Dhief, Ammar AbdRaba Sakran http://creativecommons.org/licenses/by-sa/4.0
A scalable low-cost internet of things-based electronic nose for identifying chemical ripening in fruits
Indiscriminate use of chemical agents like calcium carbide and ethephon for the ripening of fruits poses grave health hazards, emitting carcinogenic and neurotoxic compounds. Here we present a new, scalable, inexpensive, internet of things (IoT)-enabled electronic nose (e-nose) AI-Bot system for the detection of chemically ripened fruits. This would involve the development of a system that uses an MQ-3 gas sensor to quantify the ethanol content, as well as an MQ-135 gas sensor with an ESP32 microcontroller to quantify even further the amount of volatile organic compounds (VOCs) suggestive of artificial ripening. Flutter-based mobile application allows real-time monitoring, ripening classification using machine learning (ML) algorithms, and logging the historical data. A small sample was taken for inter-document feature literature mining, modelling sensor behaviors according to voltage dividers and gas concentration resistance laws for robust calibration and classification performance. Validation studies were performed on mango, banana, and papaya fruit in the laboratory environment. Total 75 samples (25 each of banana, mango, papaya across 3 trials) of fruit were tested. The implemented system achieved 95% for banana, 92% for mango, and 90% classification accuracy for papaya when cross validated.
Copyright (c) 2026 Amit R. Welekar, Gaurav Vishnu Londhe, Amit Pimpalkar, Abhrendu Bhattacharya, Nilesh Shelke, R. Jeberson Retna Raj http://creativecommons.org/licenses/by-sa/4.0
Architectural design of an internet of things-based framework for road bike speed optimization
This research aims to develop an internet of things (IoT) system framework to predict cyclists’ optimal speed in road cycling using multisensor data and machine learning. The primary issue raised is the lack of an intelligent system capable of integrating physiological, performance, and environmental data in real-time speeds for cyclists. The designed framework consists of four functional layers: data acquisition layer; data processing and feature layer; predictive modeling layer; and recommendations and output layer. Modeling is carried out using gradient boosting regression (GBR), performed end-to-end with validation on real cyclist activity data. The test results demonstrate that the system can provide precise optimal speed estimates and offer pacing zone recommendations that positively impact athlete performance strategies. This research contributes novelty in the form of an adaptive multivariate prediction approach and a modular IoT architecture design that can be implemented on cloud and edge platforms.
Copyright (c) 2026 Tigor Hamonangan Nasution, Opim Salim Sitompul, Fahmi Fahmi, Muhammad Anggia Muchtar http://creativecommons.org/licenses/by-sa/4.0
Navigating the new frontier: large language models and their implications for education
This survey characterizes the contributions of large language models (LLMs) to technology enhanced learning by relating their capabilities to actual educational functions, making comparisons with traditional models of language. The contributions for this study are: i) introduce an education centered taxonomy that classifies LLM use by four key functions personalization and adaptivity, assessment and evaluation, profiling and prediction, and intelligent tutoring with illustrations from deployed systems and tools; ii) give a domain-based comparison of where LLMs outperform traditional models (sentiment analysis with sarcasm, context-aware question answering, and abstractive summarization) and why those advantages will mean something to e-learning practice; iii) synthesize six cross-cutting risks, including computational cost/carbon, privacy, bias and hallucination, labor displacement, interpretability, and the limits of human-like judgment, and provide practical design/research implications; and iv) report on a transparent review protocol that got the initial corpus down to 50 key articles, allowing for modifications and future updates from other interested researchers. In sum, the discussion about LLMs in education has been pushed past the broad strokes to a situation where there is a comprehensive vocabulary for what LLMs can do, and how they may or may not responsibly improve learning experiences, educator workflows, and systems/learning design in e-learning.
Copyright (c) 2026 Laila Boullous, Mustapha Hain, Adil Chergui, Brahim Elbhiri http://creativecommons.org/licenses/by-sa/4.0
Modified gorilla troops optimization for the quadratic assignment problem
Balancing exploration and exploitation remain a fundamental challenge in artificial intelligence-based optimization, particularly when addressing discrete combinatorial problems such as the quadratic assignment problem (QAP). The gorilla troops optimizer (GTO), inspired by the collective social behavior of gorillas, has shown promising results in continuous domains but faces limitations when directly applied to discrete optimization. To address this, the present study introduces a modified gorilla troops optimizer (MGTO), a novel discrete adaptation designed specifically for the QAP. The proposed MGTO strategically integrates a swapping-based diversification mechanism to enhance exploration within discrete solution spaces, while a modified uniform crossover operator promotes effective exploitation of high-quality solutions. Extensive experiments on benchmark instances from the quadratic assignment problem library (QAPLIB) show that MGTO achieves superior convergence behavior and solution quality compared with several state-of-the-art algorithms. These results demonstrate MGTO’s capacity to maintain a balanced equilibrium between exploration and exploitation, effectively navigating complex discrete landscapes to yield high-quality solutions with strong computational efficiency.
Copyright (c) 2026 Hussein Fouad Almazini, Salah Mortada, Hassan Al-Mazini http://creativecommons.org/licenses/by-sa/4.0
From audio to image: gunshot classification using Mel spectrogram convolutional neural networks
Accurate identification of firearm types from acoustic signals is essential for modern public safety and forensic applications. Traditional gunshot analysis methods often rely on physical evidence or handcrafted audio features, which can be unreliable under noisy and reverberant conditions. This study presents a systematic investigation of gunshot sound classification using Mel spectrogram representations and convolutional neural networks (CNNs). Raw audio signals are transformed into Mel spectrogram images, enabling firearm classification to be formulated as an image recognition problem. Thirteen CNN architectures, ranging from lightweight to deep models, are evaluated under a unified experimental protocol to analyze both classification performance and computational efficiency. Experiments are conducted on a publicly available multi-firearm dataset recorded in semi-controlled real-world environments. The results demonstrate that Mel spectrogram–based CNN models achieve classification accuracy exceeding 94%, while moderate-complexity architectures provide a favorable balance between accuracy and efficiency. The findings highlight the importance of representation–architecture alignment and offer practical design guidelines for selecting deployable CNN models in real-time gunshot detection systems.
Copyright (c) 2026 Peerapol Khunarsa, Pafan Doungpaisan http://creativecommons.org/licenses/by-sa/4.0
Hybrid automated road crack segmentation using morphological operations and boundary tracing
Cracks in the road surface are one of the early indicators of structural damage that has an impact on safety and infrastructure maintenance costs. Accurate early detection is a challenge in complex visual conditions such as uneven lighting and varied asphalt textures. This study proposes an efficient and fully automated hybrid segmentation method to detect cracks in road surface imagery. This method consists of several main stages: image enhancement using contrast limited adaptive histogram equalization (CLAHE), initial segmentation through a combination of Otsu's thresholding, adaptive Gaussian thresholding, and Canny edge detection, followed by mask enhancement with morphological operations (closing, opening, and erosion). The DeepCrack dataset is used as a source of test data. The evaluation results showed high performance with detection accuracy reaching 95.82%. These findings show that the proposed method is not only precise and sensitive, but also adaptive to visual variation without the need for manual training or parameters. A major novelty lies in the integration of three classic segmentation methods in one morphology pipeline that is computationally lightweight yet competitive, making it potential for real-world applications of automated inspection systems.
Copyright (c) 2026 Ida Ayu Ari Angreni, Diyanti1, Vega Valentine http://creativecommons.org/licenses/by-sa/4.0
A hybrid deep learning approach for BoT-IoT intrusion detection
Internet of things (IoT) devices enhance quality of life and industrial operations but pose significant security risks, necessitating intelligent intrusion detection systems (IDS) to combat evolving cyber threats. This paper proposes a novel IDS framework integrating bio-inspired heuristic feature selection, a generative adversarial network (GAN)-based data augmentation, and an ensemble classifier combining ResNet, AlexNet, and MobileNet. The methodology, tested on the botnet (BoT)-IoT dataset, follows four stages: preprocessing, feature augmentation, feature selection, and ensemble classification. Evaluated on benchmarks including CIC-IDS-2018, NSL-KDD, and UNSW-NB15, the model achieved accuracies of 98.2%, 99.1%, 97.6%, and 98.4%, respectively, with consistently high precision, recall, and F1-scores, demonstrating robust detection of diverse cyberattacks. Beyond accuracy, the framework optimizes processing time for large-scale IoT data, addressing scalability challenges in real-time threat mitigation. By synergizing feature optimization, synthetic data generation, and deep learning architectures, the solution enhances detection rates while minimizing computational overhead. Comparative analysis highlights its superior performance over existing methods, positioning it as a vital tool for securing IoT ecosystems against unauthorized access and malicious activities. The results underscore its potential to fortify IoT network security, balancing efficiency, adaptability, and computational feasibility for practical deployment in resource-constrained environments.
Copyright (c) 2026 Khalid Altarawneh, Ghayth AlMahadin, Ibrahim Altarawni http://creativecommons.org/licenses/by-sa/4.0
Multi-agent autonomous GeoAI framework for scalable and self-improving geospatial intelligence
Large language models (LLMs) have recently expanded the scope of automation across many application domains. In geographic information systems (GIS), however, many tasks still require specialized expertise and remain difficult for non-expert users. Recent studies have explored LLM-based geospatial analysis under a single-agent paradigm, but these early systems remain limited by weak coordination, limited error recovery, and dependence on proprietary artifacts. This study proposes multi-agent autonomous geospatial artificial intelligence (MA-GeoAI), a multi-agent architecture in which the planner, coder, validator, debugger, and knowledge agents collaborate through the LangGraph framework. The framework was evaluated on three case studies: population exposure assessment, mobility pattern analysis, and county-level mortality modeling. Unlike general-purpose multi-agent LLM frameworks, MA-GeoAI embeds spatial semantics, coordinate reference system (CRS) consistency checks, geometry validation, and operation-aware coordination directly into the control loop. Across repeated runs, all evaluated systems completed the controlled artifact contract; therefore, the analysis focuses on auditability, runtime, fallback behavior, and reproducibility rather than binary task-completion superiority.
Copyright (c) 2026 Kim-Son Nguyen, The-Vinh Nguyen, Van-Viet Nguyen, Thi-Minh-Hue Luong, Huu-Khanh Nguyen, Duc-Binh Nguyen http://creativecommons.org/licenses/by-sa/4.0
Personalized learning with learning style using fuzzy for university students performance
The main challenges of traditional learning systems are time-space constraints and teacher-centeredness. The emergence of information technology has given rise to e-learning systems characterized by teacher centred strategy components and one-size-fits-all strategies. Furthermore, the concept of personalization is presented through learning technology that provides educational content to the students learning style. This research develops a personalized system that aligns teaching strategies with students' learning styles using the Myers-Briggs Type Indicator (MBTI). The emphasis is on adaptive and revising teaching strategies to improve student learning performance. The system is developed to create student profiles to determine their learning styles based on the MBTI and fuzzy. The system was tested with undergraduate students at the information systems department in University of Bengkulu. Research shows that students in the experimental group have higher post-test scores, greater learning achievement and performance than the control group. Fuzzy clustering based personalized e-learning could improve university student performance. The use of personalized online learning significantly affects learning management system (LMS) integration, lecturers, and curriculum development.
Copyright (c) 2026 Endina Putri Purwandari, Endang Widi Winarni, Siti Soraya Abdul Rahman, Jafar Nashrudin Al Azam http://creativecommons.org/licenses/by-sa/4.0
Adaptive proportional integral control using neural networks for secondary frequency regulation in microgrids
Microgrids with high renewable energy integration face a challenge in maintaining frequency stability due to the reduced inertia of inverter-based generation and the intermittent nature of these sources. Although primary frequency regulation using virtual synchronous generator (VSG) strategies can provide fast support, it cannot fully bring the system frequency back to its nominal value. This limitation highlights the importance of secondary frequency regulation, which is implemented using proportional integral (PI) controllers. However, fixed parameter PI regulators often fail to adapt effectively to varying loads and fluctuating renewable generation. This paper proposes an adaptive secondary control strategy for microgrids that combines offline optimization with real time learning. Grey wolf optimization (GWO) is first applied offline to determine the optimal PI gains for multiple disturbance scenarios. These datasets are then used to train an artificial neural network (ANN), which updates the PI parameters in real time to achieve adaptive performance. The proposed control is implemented in a hybrid microgrid with a diesel generator, a permanent magnet synchronous generator (PMSG) wind turbine for primary support and a fuel cell for secondary regulation. Simulation results show that the adaptive PI controller improves frequency recovery and reduces steady-state error compared to conventional fixed gain PI.
Copyright (c) 2026 Belkasem Imodane, Mohamed Benydir, Sana Mouslim, Abdellah El Idrissi, Mohamed Ajaamoum, Brahim Bouachrine http://creativecommons.org/licenses/by-sa/4.0
Fine-tuning convolutional neural network for artificial intelligence generated image detection enhancement
The relationship between art and technology has changed how people engage with creativity, leading to the industrialization of the field. Various digital media have been utilized in the endeavour of art creation, such as artificial intelligence (AI) generation for images. The utilization of AI-generated art has yielded negative reactions due to its exploitative nature on pre-existing artworks without the creator’s consent, which raises plagiarism concerns. This research utilized convolutional neural network (CNN) to help detect such images to reduce public concerns on the abuse of AI images. The algorithm is proposed to detect such images as it involves spatial convolution within two-dimensional spaces, matching the nature of images. The model was developed from pre-existing architectures, namely EfficientNetB1 and Xception, which was pre-trained on ImageNet classification task with the modification of inclusion or exclusion of dropout in the top layer. After assessing the models, removing top layer dropout from EfficientNetB1 model improved it to reach the F1-score of 97.66% compared to 97.44% in the base model and Xception with a dropout layer yields lower F1-score of 95.56% compared to 97.07% in the base model.
Copyright (c) 2026 Steven Vincent Hendrawan, Moeljono Widjaja, Alethea Suryadibrata http://creativecommons.org/licenses/by-sa/4.0
Stacking ensemble techniques for automated peripheral blood cell classification using Inception v3 features
Robust distinction of blood cells is crucial in clinical evaluation. Manual examination is slow and exposed to errors. This work investigates using machine learning (ML) techniques for automated classification of eight categories of peripheral blood cell types from multi-color images. The Inception v3 network was used to extract features, a split of 66%/34% were used to evaluate the model along with 20-fold cross-validation. To reduce computational complexity, principal component analysis (PCA) was used to reduce the 2048-dimensional feature vectors to 100 components. Among all classifiers used, the highest performance without using PCA was achieved using the support vector machine (SVM) with an accuracy equal to 93.4% and an area under the curve (AUC) of 0.996. Using PCA, affected monocytes and immature granulocytes most due to the slight reduction in the accuracy and AUC which became 90.1% and to 0.991 respectively. Results were further enhanced when a stacked ensemble of neural network (NN), logistic regression (LR), and SVM were used, achieving an accuracy of 95.2% and an AUC of 0.998. The obtained findings confirmed the effectiveness of using stacked ensembles in providing a robust, high accuracy framework for automated blood cell classification, while PCA efficiently reduced dimensions with minimal performance loss.
Copyright (c) 2026 Marwa Mawfaq Mohamedsheet Al-Hatab, Maysaloon Abed Qasim, Nawar A. Sultan http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence-based risk assessment in agro-industry using supervised neural networks
The coffee supply chain involves high production volumes, complex multi actor interactions, and increasing sustainability requirements, yet remains highly vulnerable to risks dimension. This study aims to develop and evaluate a decision-support framework that improves the accuracy and consistency of sustainability risk classification in the coffee supply chain. The proposed framework integrates failure mode and effect analysis (FMEA) with a supervised artificial neural network (ANN) using backpropagation (BP) to enable data-driven and adaptive risk assessment. Empirical data was collected from 55 respondents, resulting in the identification of 35 supply chain risk factors. These data were used to train and validate an ANN-based classification model implemented in a Python environment, with standard preprocessing and stratified data partitioning to ensure robustness. The ANN classified risks into five categories using supervised learning. The results demonstrate strong predictive performance, achieving overall accuracy of 98.97%, with precision, recall, and F1-scores exceeding 96.8% across all risk classes. Confusion matrix analysis confirms reliable generalization and minimal misclassification. The findings indicate that integrating FMEA with ANN-BP significantly enhances risk classification compared to conventional qualitative approaches. The proposed framework provides a scalable and reliable decision-support tool for dynamic risk scoring, supporting enhancement of sustainable practices in agro-industrial coffee supply chains.
Copyright (c) 2026 Imam Santoso, Izzum Wafi’uddin, Naila Maulidina Lu’ayya, Annisa’u Choirun, Siti Asmaul Mustaniroh, Dodyk Pranowo, Ainur Rofiq http://creativecommons.org/licenses/by-sa/4.0
Cutting-edge algorithms for reliable failure prediction in metro train systems
This study investigated various machine learning algorithms on dataset for failure prediction within metro train systems. The data indicated strong linear relationships within the dataset, making linear models such as support vector machines (SVMs) viable, as well as logistic regression analysis. For example, the least absolute shrinkage and selection operator (LASSO) regularization method used in feature selection had profound implications, leading to enhanced performance through the identification of pertinent attributes. Some advanced models like gradient boosting machines (GBMs), convolutional neural networks (CNNs), and kernel SVMs were found to outperform the conventional methods because they are capable of recognizing any complicated trends or non-linear relationships present in data sets. Combining strong learners can produce an ensemble model that improves forecast performance, while top-performing models are used in the ensemble method to enhance prediction accuracy. These findings would help professionals in the metro train industry choose appropriate machine learning methods to support preventive maintenance strategies, minimizing costs while enhancing operational effectiveness and safety.
Copyright (c) 2026 Sana Chakri, Naoual Mouhni, Faouzia Ennaama http://creativecommons.org/licenses/by-sa/4.0
Improved boosting-based machine learning algorithms for network intrusion detection in wireless sensor network
Intrusion detection is essential for protecting wireless sensor networks (WSNs) from evolving cyberattacks. This paper proposes an enhanced boosting-based framework that integrates generative adversarial networks (GANs) to address data imbalance, and Harris hawk optimization (HHO) for efficient feature selection. Six boosting algorithms, including adaptive boosting (AdaBoost), gradient boosting (GB), extreme gradient boosting (XGBoost), light gradient‑boosting machine (LightGBM), categorical boosting (CatBoost), and histogram-based GB, were evaluated to determine the most effective configuration. The proposed system achieves an accuracy of 99.18% with a detection time of 12.7 ms on a dataset for intrusion detection systems in WSN (WSN-DS dataset), significantly outperforming the existing boosting-based intrusion detection models. By combining data balancing and feature optimization, the framework enhances both accuracy and resource efficiency, providing a scalable and robust approach for real time threat detection in resource-constrained environments. The results confirm the potential of hybrid boosting methods coupled with advanced data generation and optimization strategies to strengthen the resilience of modern WSNs against emerging network attacks.
Copyright (c) 2026 Said Ouhmi, Housni Khalid, Mbarek Marwan, Hassan Silkhi, Abdelkarim Ait Temghart http://creativecommons.org/licenses/by-sa/4.0
Combination of binary particle swarm optimization and random forest for stroke disease prediction
Stroke is a leading cause of death and disability worldwide, making early risk prediction critical for prevention. Machine learning methods such as random forest (RF) have shown strong predictive performance, but accuracy can be further improved through effective feature selection. This research proposes an integrated model that combines binary particle swarm optimization (BPSO) for feature selection with RF for stroke risk classification. Experiments were conducted on two public datasets: the stroke prediction dataset (SPD) and the brain stroke dataset (BSD). Data preprocessing included handling missing values, normalization, and the synthetic minority oversampling technique (SMOTE) to mitigate the minority and majority classes. BPSO was employed to select the most informative features, followed by RF for classification. The BPSO-RF model delivered superior accuracies of 96.13% on the SPD and 96.07% on the BSD, outperforming competing classifiers and feature selection techniques. Important features such as gender, age, work type, residence type, average glucose level, body mass index (BMI), and smoking status were consistently identified as key predictors. These results indicate that integrating swarm intelligence with ensemble learning can effectively improve stroke risk prediction and support clinical decision-making.
Copyright (c) 2026 Sutikno, Rismiyati, Khadijah, Abdul Karim http://creativecommons.org/licenses/by-sa/4.0
Comparing global and variety-specific ensemble models for avocado maturity prediction with near-infrared
Ensuring accurate, non-destructive maturity classification of avocados is critical to supply chain optimization in agro-industrial systems. This study presents a predictive framework that integrates near-infrared (NIR) spectroscopy with ensemble stacking machine learning (ML) to enhance the precision of avocado ripeness assessment. The proposed methodology compares global versus variety-specific models for 'Hass' and 'Fuerte' avocado types, leveraging spectral data (900–1,700 nm) and multiple base classifiers, including random forest (RF), gradient boosting (GB), support vector machines (SVMs), decision trees (DT), k-nearest neighbors (KNN), and categorical boosting (CatBoost), combined via linear regression as a meta-learner. Experimental results revealed that the stacking models outperformed individual learners, with variety-specific GB model achieving the highest performance (Matthews correlation coefficient (MCC) =0.679, area under the curve (AUC) =0.931). These findings highlight the critical importance of varietal specificity in model calibration and demonstrate how ensemble strategies can improve robustness, scalability, and interpretability in intelligent agricultural systems. The proposed model provides a computationally efficient solution for real-time quality control and supports the deployment of AI-powered systems within agricultural supply chains in developing regions.
Copyright (c) 2026 Christian Ovalle, Jhon Garcia Jimenez, Jose Briones Zuñiga http://creativecommons.org/licenses/by-sa/4.0
A hybrid machine learning model for optimized mixed-crop recommendation
Farmers today encounter more challenges when selecting appropriate variety of crops depending on their farm soil nutrients and climate. This research will assist farmers in choosing suitable mixed-crops depending on the individual farms soil and climate conditions in Andhra Pradesh, India. Using the dataset sourced from Indian Institute of Soil Science (IISS), Bhopal with 2,552 entries. Previous studies focused on only single-crop recommendations. This work proposes a novel hybrid mixed-crop recommendation system (CRS) that incorporates several machine learning (ML) techniques comprise of random forest-ExtraTrees (RF-ExtraTrees), decision tree-C4.5 (DT-C4.5), extreme gradient boosting-gradient boosting (XGBoost-GBoost), quadratic discriminant analysis-linear discriminant analysis (QDA-LDA), and support vector machine-stochastic gradient descent (SVM-SGD) were utilized to recommend mixed-crops. To enhance the reliability of the training process, 20% of the dataset was held in reserve for validation to analyze model performance. The result of the proposed work shows that all the hybrid ML models applied were viable, and RF ExtraTrees has achieved 95.91% best accuracy, 95.08% precision, and 95.91% recall, when contrasted to the other ML models.
Copyright (c) 2026 Ahmed Mohammed Gimba, Pradeep Kumar Mishra http://creativecommons.org/licenses/by-sa/4.0
Yarn inspection and sorting system using robotic vision and machine learning
The increasing demand for automation in the textile industry, particularly in quality inspection processes, underscores the need for intelligent and cost effective solutions. Conventional methods of yarn classification and sorting remain labor-intensive, time-consuming, and susceptible to human error, resulting in inconsistent quality control. This study introduces an automated system for yarn inspection and sorting that integrates robotic vision, machine learning, and position-based visual servoing (PBVS) for real-time motion control. The proposed system combines Raspberry Pi-based machine learning with computer vision utilizing a 4-degree-of-freedom (4-DOF) robotic manipulator and a webcam, enabling precise pick-and-place operations based on yarn classification into four categories: good, striped, moldy, and dirty. Experimental results validate the system’s effectiveness, achieving an average deviation of 0.375 mm along the x-axis, 0.69 mm along the y-axis, and 0.675 mm along the z-axis, resulting in an overall position error of 0.58 mm. These results demonstrate the system’s robustness and reliability in dynamic industrial environments. The novelty of this research lies in leveraging a low-cost embedded architecture with advanced visual servoing for textile automation, reducing operational errors, improving efficiency, and supporting industry 4.0 adoption.
Copyright (c) 2026 Emmanuel Agung Nugroho, Joga Dharma Setiawan, Deni Kurnia, Nanang Roni Wibowo http://creativecommons.org/licenses/by-sa/4.0
Brain tumor detection using VGG-16 model
Research in medical image analysis, specifically through deep convolutional networks, addresses the challenges of manually analyzing large magnetic resonance imaging (MRI) image volumes for brain tumor detection. The manual analysis is time-consuming, tedious, and prone to inaccuracies due to subtle visual similarities between normal tissue and tumor cells. This research aims to automate tumor detection, increasing accuracy and efficiency in medical treatments. This study aimed to develop a model capable of classifying brain tumors 2D MRI images, and the convolutional neural network (CNN)-based model successfully achieved an accuracy of 99.21% but suffered from noticeable Overfitting. Implementing the independent tests set and early stopping mitigated this issue, making the model more reliable for production deployment and demonstrating its potential in supporting physicians in detecting brain tumors, thereby enhancing treatment efficiency. The use of Python, TensorFlow, and Keras facilitated the development of the proposed solution, focusing on a diverse set of MRI images with varying tumor sizes, locations, shapes, and intensities.
Copyright (c) 2026 Aicha Oussous, Abderrahmane Ez-Zahout, Soumia Ziti http://creativecommons.org/licenses/by-sa/4.0
Intestinal disorders categorization in endoscopic images using deep learning architectures
Gastroenterology is revolutionized by advancements in artificial intelligence (AI). As the gastrointestinal (GI) tract is consulted, globally 40% of the world's and 18% of the Indian population are affected. AI is a reliable sword for diagnosing issues related to the GI tract. The learning capabilities of deep learning (DL) techniques make it widely helpful in medical investigations. The variety of data available in the medical sector generates the need for an appropriate model for every problem domain. The purpose of this research is to explore the significance of medical image pre-processing and the implementation of pre-trained DL models on endoscopic images for the diagnosis of disease. Convolutional neural network (CNN)-based architectures have robust diagnostic potential for medical images. It can assist physicians as a tool for disease analysis, screening and help in investigating further needs. The paper also provides a comparative performance framework showing CNN architectures and preprocessing techniques for endoscopic images to highlight the key points important for investigating GI tract related diseases. The endoscopic images were trained over VGG-16, ResNet-50 and DenseNet-121, DL models. The result suggests that VGG-16 and ResNet-50 gave promising results with an accuracy maximum of 87.50%.
Copyright (c) 2026 Esha Saxena, Suraiya Parveen, Mohd. Abdul Ahad, Meenakshi Yadav, Mohammad Anas http://creativecommons.org/licenses/by-sa/4.0
Accurate detection of Alzheimer’s disease using machine learning model on magnetic resonance imaging data
The rapid identification and diagnosis of Alzheimer's disease (AD) is essential for initiating early intervention and effective treatment planning. Magnetic resonance imaging (MRI) provides valuable structural insights into the pathological alterations in the brain associated with AD. Early and accurate detection of AD is critical for initiating timely interventions. This study presents a classical machine learning (ML) approach for detecting AD using structured features extracted from MRI metadata, such as mini-mental state examination (MMSE) scores, brain volume metrics, and cognitive attributes. Unlike deep learning models that rely on raw imaging data, the interpretable framework offers reduced computational complexity and better alignment with real-world clinical constraints. Models such as random forest (RF) and extreme gradient boosting (XGBoost) achieved up to 85% accuracy, showing strong potential for deployment in resource-limited environments. The results demonstrate the potential of classical ML in supporting early AD diagnosis, particularly in low-resource clinical settings. Moreover, the proposed approach offers a computationally efficient and interpretable alternative to deep learning models, facilitating adoption in real-world healthcare environments.
Copyright (c) 2026 Rahman Md Mojnur, Md. Tanjil Sarker, Hezerul Abdul Karim, Fahmid Al Farid, Aziah Ali, Wan-Noorshahida Mohd-Isa http://creativecommons.org/licenses/by-sa/4.0
Machine learning-enabled joint antenna selection and precoding
Joint antenna selection (AS) and precoding design is essential for improving spectral efficiency and energy efficiency in multi-antenna wireless communication systems. However, conventional optimization-based solutions rely on exhaustive search and iterative processing, leading to high computational complexity that limits real-time applicability. This work proposes a machine learning-enabled framework that shifts the computational burden from online operation to offline training. Optimal AS and precoding decisions are first generated offline using model-based optimization under diverse channel conditions. A supervised machine learning model is then trained to learn the relationship between channel state information (CSI) and optimal transmission configurations. During online operation, the trained model enables fast and efficient AS with significantly reduced processing time. Numerical results demonstrate that the proposed approach achieves near-optimal system performance while substantially lowering computational complexity, making it well suited for real-time and next-generation wireless communication systems.
Copyright (c) 2026 Monica Nilesh Kalbande, Kanala Sai Madhuri, M. Venkateswara Rao, Saradha Rani Sabbavarapu, Rajyalakshmi Uppada, Lakshmi Durga Rajamahendravarapu http://creativecommons.org/licenses/by-sa/4.0
LogRegression: human action pattern recognition for yoga and kavayat for children
Poses of human pattern recognition through machine learning is an essential facet of several applications, including health, surveillance, and sports analysis. For children yoga and kavayat (mock drill) increases the physical as well as mental health. Through the analysis of motion data this work discriminates between varied actions with high precision, contribution valued insights for monitoring and analysis in real-time. Here, this system that influences technique called as machine learning specifically logistic regression, to precisely discriminate and classify physical action patterns (child pose estimation) of children. Analogous to the propagation of false information, identifying physical actions is essential for upholding integrity and efficiency across various domains. The novel system demonstrates a towering accuracy of 98.00%, highlighting its effectiveness in recognizing and classifying physical action patterns.
Copyright (c) 2026 Vanita Babanne, Pankaj M. Agarkar http://creativecommons.org/licenses/by-sa/4.0
Enhancing fake news detection: a hybrid BERT-XGBoost model for improved performance and interpretability
The widespread spread of fake news poses a serious threat to the integrity of information. The dominant approach to detection involves end-to-end fine-tuning of large transformer models like bidirectional encoder representations from transformers (BERT), which, despite achieving high accuracy, often function as opaque “black boxes” with limited interpretability. This paper proposes and validates a hybrid, decoupled architecture that proves to be a more practical and powerful alternative. We first fine-tune a DistilBERT model on the full WELFake dataset of 71,537 articles after cleaning to create domain-specific embeddings. These high-dimensional vectors are then used as input features to train a robust extreme gradient boosting (XGBoost) classifier. The results demonstrate that the hybrid model achieves a state-of-the-art accuracy of 99.76%, slightly surpassing the already high performance of a standard end-to-end fine-tuned model. Crucially, this approach provides this top-tier performance while offering significant advantages in model interpretability through feature importance analysis. This work establishes that a decoupled architecture is not just a viable alternative but a superior practical strategy for combating misinformation, successfully balancing state-of-the-art accuracy with essential model transparency.
Copyright (c) 2026 Nishant Vasantkumar Hegde, Suneesh Bare, Namruth Reddy, Rajat Gondkar Aravinda, Minal Moharir, Aamir Ibrahim http://creativecommons.org/licenses/by-sa/4.0
Adaptive synthetic-based arrhythmia classification using machine learning techniques
Cardiac arrhythmias, characterized by irregular heart rhythms, pose a significant challenge for timely and accurate diagnosis. This paper presents an advanced framework for arrhythmia classification that addresses the class imbalance issue using Adaptive Synthetic (ADASYN) sampling combined with a comprehensive set of machine learning techniques. We implemented various classifiers, including Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron, Gradient Boosting, AdaBoost, Light Gradient Boosting, CatBoost, and Extreme Gradient Boosting. Our experimental results demonstrate that Random Forest, Gradient Boosting, Light Gradient Boosting, and XGBoost achieved a remarkable accuracy of 97%. Other models, such as Decision Tree and Logistic Regression, also performed well, achieving 95% and 94% accuracy, respectively. KNN and Naive Bayes yielded 93% and 81% accuracy, respectively, while AdaBoost underperformed with an accuracy of 24%. Precision scores across the models remained high, except for Naive Bayes and AdaBoost. All models, except AdaBoost, demonstrated excellent recall. Our proposed methodology outperforms previous works, setting a new benchmark for arrhythmia classification. These findings emphasize the effectiveness of integrating ADASYN with machine learning techniques to enhance arrhythmia detection, with significant potential for improving clinical diagnostic processes and patient outcomes.
Copyright (c) 2026 Md. Rabiul Islam, Tapan Kumar Godder, Ahsan-Ul-Ambia, Ferdib-Al-Islam, Bulbul Ahamed, Anindya Nag, Ariful Islam http://creativecommons.org/licenses/by-sa/4.0
Ensuring effective cervical cancer diseases diagnosis system using ensemble machine learning model
Worldwide, cervical cancer is a major public health concern, and early detection is essential for successful management and treatment. Ensemble machine learning (ML) has emerged as a promising approach for improving the accuracy and reliability of cervical cancer diagnosis systems. This study evaluated the performance of various ensemble ML models for cervical cancer diagnosis using a large dataset of cervical cell images. The performance of different ensemble models was compared, including random forest (RF), gradient boosting (GB), and stacking, with conventional ML models, such as logistic regression and support vector machine (SVM). The bagging ensemble model developed reported training of 0.991667, 0.827586, 1.0, and 0.90566 for accuracy, precision, recall, and F-measure (F1-score), respectively. Furthermore, the interpretability of ensemble models was investigated using feature importance and partial dependence plots. The interpretability analysis revealed that ensemble models can provide valuable insights into the key features and factors that contribute to cervical cancer diagnosis. In conclusion, the findings suggest that ensemble ML is a promising approach for developing accurate and reliable cervical cancer diagnosis systems and improving the interpretability of these models.
Copyright (c) 2026 Oluwatobi Akinlade, Jafar Abdollahi, Misan Paul Etchie, Sunday Adeola Ajagbe, Oluwaseyi Omotayo Alabi, Bambo Ayo Adeyanju http://creativecommons.org/licenses/by-sa/4.0
Spark-powered bioactivity prediction: a comparison of machine learning approaches
The arduous and expensive nature of drug discovery has long been a bottleneck in scientific progress. However, recent breakthroughs in computational power, notably machine learning (ML) and artificial intelligence (AI), are profoundly transforming the field. Automated machine learning (AutoML) presents itself as a significant advancement, streamlining model selection, and hyperparameter tuning. This study delves into the potential of AutoML to accelerate drug discovery by comparing it to classical ML techniques. The focus lies on predicting the bioactivity of epidermal growth factor receptor (EGFR), a critical protein implicated in many cancers. By utilizing the scalability of Apache Spark, vast and diverse datasets encompassing biological, chemical, and genomic data tied to EGFR are processed. This comparative analysis aims to evaluate the comparative performance of both approaches, thereby contributing actionable insights to drug discovery research.
Copyright (c) 2026 Nazif Tchagafo, Abderrahmane Ez-Zahout, Belaid Ahiod http://creativecommons.org/licenses/by-sa/4.0
Enhancing diabetes prediction: integrating machine learning with explainable artificial intelligence
Early detection of diabetes is critical in preventing disease progression and improving patient outcomes. This work combines Explainable Artificial Intelligence (XAI) and machine learning to enhance understanding and prediction of diabetes using the Pima Indian Diabetes Dataset. The machine learning models used in this study are Random Forest, Logistic regression and Gradient Boosting, which resulted in the best accuracy of 93.2%. Some of the pre-processing steps taken were handling of missing data, normalization, feature scaling and Synthetic Minority Over-sampling Technique (SMOTE) for handling class imbalance. Use of SHAP and LIME XAI methods has proven that glucose, BMI and insulin are the most crucial features when it comes to prediction. These techniques further enhance the trust of the clinicians and stakeholders by improving the understanding of how the features contribute to individual predictions, which enhances the model prediction as a whole. The findings prove that there is indeed a marked improvement in the understanding of the machine learning models and their predictions with no compromise on performance. This study highlights the benefits of using XAI in machine learning so that there is accuracy and ease of interpretation with immense power within the developed models.
Copyright (c) 2026 Shaik Abdul Jaffar, Shadab Siddiqui http://creativecommons.org/licenses/by-sa/4.0
Dynamic portfolio optimization using differential evolution: a Markowitz modern portfolio theory approach
An optimal investment portfolio is one of the main focuses in the financial world to minimize risk while maximizing returns. However, the challenge that arises is how to choose the right asset allocation amidst dynamic market uncertainty. This study aims to optimize portfolios based on Markowitz modern portfolio theory (MPT) by using the differential evolution (DE) algorithm as an optimization technique. The data used includes stocks, bonds, and other financial instruments taken from trusted data sources, such as Bloomberg and Yahoo finance, with an observation period of the last five years. The results show that this approach succeeds in finding optimal portfolios with the right asset weights, higher expected returns, and minimized risks compared to conventional approaches. The implication of this research is that the DE algorithm can be effectively used to address portfolio optimization problems in complex and volatile market environments, offering a more adaptive solution for investors to maximize their returns.
Copyright (c) 2026 Hengki Tamando Sihotang, Jonson Manurung, Bambang Saras Yulistiawan, Galih Prakoso Rizky A. http://creativecommons.org/licenses/by-sa/4.0
Optimized classification of student performance outcomes using LEE feature selection in the context of educational data mining
Student speculative victory is a vital area that needs to be predicted to improve the quality of education and aid the institutional decision making. This research work has to planned to use learning based enhanced evaluation (LEE) feature selection method with real world educational datasets for optimized data mining approach to predict student performance. High dimensionality and irrelevant features are common problems with enhanced models, affecting classification accuracy and efficiency. LEE feature algorithm is used to extract important features, that enhance the performance of the model, reduce the calculation quantity of the model. The methodology consists of pre-processing of the dataset, feature selection using LEE algorithm, and testing four classifiers namely support vector machine (SVM), k-nearest neighbor (KNN), adaptive learning, and naïve Bayes. The incorporation of LEE improves the model’s ability by reducing noise and highlighting the influential features. Experimental results show that optimized techniques are better in terms of accuracy and robustness than others. The models are evaluated based on important performance metrics such as accuracy, precision, recall, F1-score, and training time. The enhanced approach will help to add to the literature of the field of educational data mining (EDM), providing a practical and effective way of predicting student performance in real academic settings.
Copyright (c) 2026 Kishore Kumar Kamarajugadda, Movva Pavani, Rani Vanathi Gurusamy, Nagarajan Karthikeyan, Pavan Kumar Nidumolu, Desidi Narsimha Reddy, Muniappan Ramaraj, Rajasekaran Nithya http://creativecommons.org/licenses/by-sa/4.0
Computed tomography imaging radiomics: a novel approach to early-stage non-small cell lung cancer prediction
Radiomics shows promise as non-invasive method for enhancing clinical staging of non-small cell lung cancer (NSCLC) by using quantitative information from computed tomography (CT) scans. This study presents radiomics-based machine learning (ML) approach for staging NSCLC patients into clinical stages I, II, and III based on shape, intensity, and texture features. CT images of 369 NSCLC patients are collected from the cancer imaging archive (TCIA), and extracted 107 radiomic features following image biomarker standardization initiative (IBSI) protocol. The analysis of the sources of variability due to different imaging protocols, using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), showed that these effects were resolved through ComBat harmonization. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) are used for feature selection. Five ML algorithms: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) were used, with an 80:20 train-test split and 10-fold cross-validation. The classifier is assessed using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic (AUROC) curve. The RFE and RF classifier combination performed the best with AUROC of 0.9307 and accuracy of 0.8114. This study illustrates the use of radiomics models in non-invasive classification of NSCLC stages and it is role in clinical decision making.
Copyright (c) 2026 Raviteja Balekai, Mallikarjun S. Holi http://creativecommons.org/licenses/by-sa/4.0
Prediction of new student admissions to higher education using support vector machines
Higher education institutions across various regions operate using systems that generate large amounts of data. This data is stored and utilized for strategic decision-making, providing significant business value to these institutions. Support vector machine (SVM) has become popular due to its strong generalization capability, high prediction accuracy, and faster training speed. SVM employs kernels as tuning parameters. This study aims to enhance the accuracy of student admissions prediction in higher education institutions using the SVM classification model. The SVM model was applied to a dataset comprising 5,936 records with four attributes and was evaluated using the use training set, 10-fold cross-validation, and percentage splits of 70%–30% and 80%–20%. Initially, the SVM-kernel model achieved high accuracy but failed to identify any true positive instances, indicating its inability to detect the minority “not accepted” class due to severe class imbalance. After applying class balancing techniques, the model’s performance improved significantly in terms of area under the curve (AUC), F-measure, and Matthews correlation coefficient (MCC), reflecting a more balanced classification between majority and minority classes. The SVM with Pearson VII function-based universal kernel (PUK) and classifier version 4.5 (C4.5) models achieved the best performance, indicating that class balancing effectively enhances both sensitivity and fairness in predictive classification.
Copyright (c) 2026 Neni Purwati, Windya Harieska Pramujati, A. Aviv Mahmudi, Mira Febriana Sesunan, Yahya http://creativecommons.org/licenses/by-sa/4.0
Multilingual signs recognition using recurrent neural network
Recognition of sign language is a crucial step towards providing individuals with hearing and speech impairment meaningful communication, but the fact that there are a number of distinct sign languages and gestures remain complex makes it a challenge to the current automated systems. The present paper describes a real-time multilingual sign language recognition system that is based on a recurrent neural network with long short-term memory (RNN-LSTM) with hand landmark MediaPipe-based hand landmark detection to successfully receive spatial and temporal gesture features. The proposed system was trained and tested over a self-collected set of alphabet gestures of the Chinese, American, and Indian sign language, including one hand and two-hand gestures, and was run with Keras with extensive performance evaluation metrics. The strength and generalization abilities of the suggested approach as part of different gesture patterns and variations in users are confirmed by experimental outcomes that indicate high recognition rates of 99.58%, 99.62%, and 99.63% of the Chinese, American, and Indian sign languages, respectively. These results demonstrate the promise of the given framework as a dedicated assistive system of communication and give it a solid base to continue its development to the point of the system of the continuous sign language recognition (CSLR) and multimodal translators.
Copyright (c) 2026 Thouseef Ulla Khan, Dileep Marichi Ramachandra http://creativecommons.org/licenses/by-sa/4.0
Handwriting-based personality classification on Indian samples using long-short term memory
Traditional handwriting analysis methods have historically faced criticism for their lack of scientific basis, but more contemporary models based on layered artificial neural network (ANN) architecture have evidently been more successful. In the proposed model, a deep neural network (DNN) layered, long-short term memory (LSTM) model with contextual analysis has been proposed for handwriting-based personality classification. The model has been trained over a manually curated verbose dataset of ~6,000 Indian handwriting sample images, varying across genders, age groups, and regions. The classification is based on the five major personality traits. The proposed framework achieved an accuracy of 97.75%, which is over 10% better than the next best performing model on a comparably numerically bigger dataset; demonstrating the enhanced potential of context based neural networks on handwriting-based personality prediction when coupled with an appropriately varied and unbiased dataset.
Copyright (c) 2026 Pradeep Kumar Mishra, Gouri Shankar Mishra, Ali Imam Abidi, Tarun Maini, Amit Kumar http://creativecommons.org/licenses/by-sa/4.0
Tomato leaf disease classification using DenseNet-121 with data augmentation and fine-tuning
In recent years, accurate classification of agricultural images has become increasingly important to support precision farming and crop disease monitoring. However, achieving reliable performance remains challenging due to visual similarity between disease classes and dataset variability. This study presents an applied evaluation of DenseNet-121 combined with data augmentation and fine-tuning for multi-class tomato leaf disease classification. Experiments were conducted using a publicly available tomato leaf image dataset consisting of 5,000 images across 10 classes. All images were resized to 64×64 pixels and split into 80% training and 20% testing sets using a stratified strategy. Data augmentation was applied exclusively to the training data to improve generalization. The experimental results show a progressive performance improvement across training stages, achieving a final classification accuracy of 98.44% with a loss of 4.72% after fine-tuning. Per-class evaluation indicates strong performance across most disease categories, with minor confusion observed among visually similar classes. While the results demonstrate the effectiveness of the proposed training strategy under controlled experimental conditions, further validation using real-field images is required. Overall, this study shows the potential of DenseNet-121 with transfer learning to support tomato leaf disease classification in precision agriculture applications.
Copyright (c) 2026 Sufajar Butsianto, Anggi Muhammad Rifa’i http://creativecommons.org/licenses/by-sa/4.0
Predictive modeling for crop suitability and productivity using machine learning techniques
With the increasing global population and rising food demand, improving agricultural productivity through data-driven decision support systems has become essential. This study proposes a cross-validated meta-stacking ensemble framework for multi-class crop suitability prediction using soil nutrient and environmental parameters. The dataset consists of 2,200 samples covering 22 crop types and seven predictor variables, including nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall. Six machine learning (ML) models—random forest (RF), decision tree (DT), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), support vector classifier (SVC), and k-nearest neighbors (KNN)—were trained and optimized using RandomizedSearchCV with k-fold cross-validation. A stacked ensemble model was then developed to combine heterogeneous learners and improve predictive robustness. Experimental results demonstrate that the RF model achieved an accuracy of 99.36%, while the proposed cross-validated meta-stacking ensemble achieved comparable performance with improved generalization stability. Precision, recall, and F1-score values of 0.99 indicate consistent classification across all crop classes. Feature importance analysis revealed N, K, and rainfall as the most influential predictors. Model robustness was evaluated using cross-validation and an independent test split to minimize overfitting risk. The findings highlight the effectiveness of ensemble learning for sustainable recommendation systems.
Copyright (c) 2026 Gulaganjihalli Ningegowda Shwetha, Bhat Geetalaxmi Jairam http://creativecommons.org/licenses/by-sa/4.0
Machine learning approaches for anomaly detection of Jakarta air quality index
Anomalies in time series data are observations that deviate markedly from surrounding values or overall patterns. Air quality index (AQI) data, which vary over time, provide a suitable context for anomaly detection. Time series anomaly detection can be done with machine learning approaches like long short-term memory (LSTM) and extreme gradient boosting (XGBoost). These methods have advantages over conventional methods in handling nonlinearity and large data dimensions. This study compares LSTM and XGBoost methods for detecting anomalies in Jakarta's hourly AQI data. The dataset was obtained from the AirNow website and covers the period from January 1, 2018, to December 31, 2023. Anomalies in the observed data were labeled using moving range (MR) (2) and (3) approaches with three and four-sigma thresholds, and feature engineering (FE) was applied to improve model performance. The results indicate that LSTM is more suitable than XGBoost for forecasting and classification tasks in AQI data. LSTM achieved an average mean absolute percentage error (MAPE) of 10.3840%, a root mean square error (RMSE) of 10.5913, and a balanced accuracy (BACC) of 0.9424 under MR (2) labeling with the four-sigma rule. The anomalies detected mostly occurred between 21:00 and 09:00 and during the rainy season.
Copyright (c) 2026 Muhammad Rizky Nurhambali, Yenni Angraini, Anwar Fitrianto http://creativecommons.org/licenses/by-sa/4.0
Image enhancement combined with EfficientNet-B7 for grading classification of diabetic retinopathy
Diabetic retinopathy (DR) is a complication caused by poorly managed diabetes that affects the eyes. According to the World Health Organization (WHO), 422 million people worldwide have suffered from DR in the past ten years. Manual detection using retinal fundus images is time-consuming and requires experienced ophthalmologists. This study proposes a deep learning method using the pre-trained model EfficientNet-B7 to identify this disease automatically. Five levels of DR will be classified: no-DR, mild-DR, moderate-DR, severe-DR, and proliferative-DR. The model was trained using "APTOS 2019 blindness detection" dataset, and image augmentation was performed. Image segmentation techniques such as contrast limited adaptive histogram equalization (CLAHE) and real enhanced super resolution generative adversarial network (Real-ESRGAN) were applied during preprocessing to improve the model's accuracy significantly. The implementation of CLAHE resulted in the validation accuracy improvement from 76.6% to 83.4% compared to no segmentation, while the combination of Real-ESRGAN and CLAHE increased the accuracy to 93.7%. Future research can explore the combination of CLAHE with other image processing techniques apart from the Real-ESRGAN model.
Copyright (c) 2026 Nina Sevani, Edy Kristianto, Albert Salomo http://creativecommons.org/licenses/by-sa/4.0
Diagnosing tuberculosis from X-ray imaging based on contrast limited adaptive histogram equalization
Tuberculosis is a serious threat, and one of the effective data types for diagnosing tuberculosis is chest X-ray data. In this paper, we hypothesize the effect of image enhancement on the effectiveness of deep learning models in the problem of diagnosing pulmonary tuberculosis from chest X-ray images. To clarify the hypothesis, we have designed a data processing process with an image enhancement step using the contrast limited adaptive histogram equalization (CLAHE) technique to enhance the quality of input chest X-ray data, and the experiments were conducted with a standard dataset that was published on the Kaggle system. The evaluation is performed comprehensively with popular convolutional neural network architectures, including DenseNet201, DenseNet121, EfficientNetB0, and MobileNetV2, compared in two scenarios with and without the image enhancement step. Experiments have shown that the image enhancement step effectively improves the classification performance of all models, clearly through important scores such as area under curve (AUC), accuracy, F1-score, precision, and recall. The best result tested is the EfficientNetB0 model with 0.925926 accuracy score, 0.970732 AUC score, 0.904762 precision score, 0.95 recall score, and 0.926829 F1-score. In addition, qualitative analysis using gradient-weighted class activation mapping (Grad-CAM) shows that the resulting models have shown a focus on the lung region, reflecting the interpretability and suitability for radiologist expertise.
Copyright (c) 2026 Nguyen Trong Vinh, Lam Thanh Hien, Ha Manh Toan, Do Nang Toan http://creativecommons.org/licenses/by-sa/4.0
Hyperparameter optimization of deep residual recurrent fusion models for facial emotion recognition
Deep learning facial emotion recognition (FER) is widely applied in healthcare, education, and human–computer interaction. However, many deep learning models suffer from suboptimal hyperparameter configurations that reduce accuracy and stability. This study proposes three deep residual recurrent fusion models that integrates residual blocks with recurrent neural networks (bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and gated recurrent unit (GRU)) to capture both spatial and temporal features. A systematic hyperparameter optimization strategy was applied, tuning kernel size, filter size, recurrent units, batch size, learning rate, dropout, and weight decay to balance generalization and computational efficiency. The models were evaluated on four benchmark datasets: FER2013, FERPlus, RAF-DB, and CK+. The results show that optimized configurations achieved outstanding accuracy, reaching 99.85% on FER2013, 99.99% on FERPlus, and 100% on RAF-DB and CK+. These findings demonstrate that careful hyperparameter tuning significantly enhances feature extraction, mitigates vanishing gradient and overfitting issues, and improves generalization across diverse datasets. The proposed framework highlights the importance of optimization in advancing robust FER systems for real-world applications.
Copyright (c) 2026 Muhammad Munsarif, Ku Ruhana Ku-Mahamud http://creativecommons.org/licenses/by-sa/4.0
Utilization of depth-wise and spatially separable convolutional network fusion for classification of white blood cells
White blood cells (WBCs) are an essential part of the human immune system, playing a significant role in fighting diseases and infections. Their detection and classification from microscopic blood images is a crucial step in diagnosing various diseases. Looking at cells by hand is still key, but it takes a lot of work and mistakes can happen. So, this study tries to improve how to find and sort WBCs using some cool computer tricks. The study tackling issues like cells being on top of each other, looking different, and not having a ton of data. To achieve this, image enhancement techniques were applied using contrast enhancement algorithm, contrast-limited adaptive histogram equalization (CLAHE), and image segmentation techniques using color isolation are employed, which contributes to more accurate separation of overlapping cells, and enables faster and more efficient diagnosis. To efficiently complete the classification process after the segmentation process, a neural network structure consisting of combining three types of convolutional layers (depthwise, spatially, and convolution) was used. To evaluate the proposed technique, experiments were conducted using an open-source blood cell count and detection (BCCD) dataset from the Kaggle platform, and resulted in achieving a classification accuracy of 99.06% and an F1-score of 99.05%. This highlight of the model’s ability to efficiently deal with the challenges associated with WBC classification.
Copyright (c) 2026 Firas Muneam Bachay, Ali Abbas Alzaheiree, Hasanain Ali Hussein, Ahmed Nooruldeen Alsafi, Mohammed Hasan Abdulameer http://creativecommons.org/licenses/by-sa/4.0
Performance analysis of intelligent controllers for permanent magnet synchronous motor drive systems
Permanent magnet synchronous motors (PMSMs) are utilized in robotics and automation applications due to their exceptional performance, compact dimensions, and minimal maintenance needs. To ensure dynamic operation and achieve outstanding performance, accurate rotor position detection and real-time control methods are required, independent of the motor's mathematical model. Several intelligent controllers based on soft-computing tools were designed and tested to evaluate the performance of the PMSM drive system. The performance of these intelligent controllers was compared to that of a conventional proportional-integral-derivative (PID) controller, which adjusts its parameters according to the mathematical model of the PMSM. The results indicate that the proposed intelligent controllers outperform the conventional PID controller in controlling the speed of the PMSM. The deep learning-based controller achieved the best results among all evaluated controllers, demonstrating rapid response, minimal overshoot (less than 0.35%), and improved capabilities for handling disturbances or changes in motor parameters.
Copyright (c) 2026 Kasim Mousa Al-Aubidy, Izziyyah M. Alsudi, Abdullah F. Al-Saoudi http://creativecommons.org/licenses/by-sa/4.0
DriveShield: attention-based hybrid neural network for intrusion detection in automotive controller area networks
Vehicle network security is important as increasing amounts of connected technology are being added to vehicles nowadays, putting them at risk of cyberattacks. This paper presents DriveShield, a novel real-time intrusion detection system (IDS) that is the first to combine gated recurrent units (GRU), convolutional neural networks (CNN), and long short-term memory (LSTM) with an attention mechanism. The systematic pre-processing pipeline, which includes feature engineering, the synthetic minority oversampling technique (SMOTE) for class balancing, and normalization. The model was validated on the open training intrusion detection system (OTIDS) dataset and the Hacking and Countermeasure Research Lab (HCRL) car hacking dataset. In the HCRL dataset, the model had an accuracy of 96.30% with F1-scores as high as 96% for all kinds of attacks. On the OTIDS dataset, it performed very well in terms of generalization, with a highest accuracy of 99.78% and a weighted F1-score of 99.78%. The addition of an attention mechanism enabled the model to concentrate on the most significant features, providing better adaptability to changing threats. These findings demonstrate the efficacy, scalability, and reliability of the system for in-vehicle network security. The future research will focus on performance on lower-frequency attacks through the study of unsupervised learning methods and real-world deployment trials.
Copyright (c) 2026 Vismaya Kootayi Kunnacheri, Arul Leena Rose Peter Joseph http://creativecommons.org/licenses/by-sa/4.0
Forecasting world sugar contract futures using long short-term memory technique with multi-step ahead forecasting strategy
Time series analysis using stochastic and dynamic models for data forecasting is a key in assisting planning and decision-making processes in various sectors. Long short-term memory (LSTM), with its advantage in understanding patterns and non-linearity in sequential data, is applied in a multi-step ahead forecasting strategy on world sugar futures prices. Fluctuations in sugar prices have a significant impact on the agriculture, trade, and food industry sectors. Forecasting sugar prices becomes a crucial tool for industries, investors, and traders to anticipate changes and make informed decisions. The objectives of this study are to identify the best strategy for forecasting the world sugar contract price and to perform forecasting using the best model. The research results indicate that hyperparameter tuning in LSTM models produces varied combinations and effects. Furthermore, the recursive strategy is suitable for long-term forecasting, while the direct strategy is appropriate for short-term forecasting. Forecasting values for long-term periods remains challenging in achieving high accuracy.
Copyright (c) 2026 Khairil Anwar Notodiputro, Kayla Fakhriyya Jasmine, Indahwati, Wandee Wanishsakpong http://creativecommons.org/licenses/by-sa/4.0
ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection
Valvular heart disease (VHD) is a significant global health issue, contributing to increased morbidity and mortality rates, particularly in aging populations. Current diagnostic methods, such as echocardiography and manual auscultation, face limitations in accessibility and accuracy, particularly in resource-constrained environments. This study introduces ValveHealthNet, a lightweight deep learning model designed to classify various VHDs using heart sound recordings. Leveraging a dataset of over 10,000 heart sounds, minimal preprocessing was applied by converting the audio signals into power spectra before feeding them into a convolutional neural network (CNN) combined with a bidirectional long short-term memory (BiLSTM) network. This model achieved impressive results, with an accuracy of 98% in training and testing and 98.4% through 10-fold cross-validation. This highly efficient model can be used in embedded systems, providing a cost-effective, AI-driven solution for early detection of VHD in settings where advanced diagnostic tools may be unavailable.
Copyright (c) 2026 Ausilah Alfraihat, Wafaa Al-Sharu, Ali Mohammad Alqudah http://creativecommons.org/licenses/by-sa/4.0
Vision transformer and hybrid models for Malayalam handwritten word recognition
Transformer-based architectures and attention mechanisms have revolutionized the field of image recognition. This study focuses on offline handwritten Malayalam word recognition, addressing the lack of publicly available datasets for this low-resource language. A new Malayalam word dataset (MWD) comprising 20,850 samples across 139 classes was developed to support research in this domain. The vision transformer (ViT) was employed for advanced feature extraction, and multiple recognition models—feed-forward neural network (FFNN), global average pooling (GAP), bidirectional long short-term memory (BiLSTM), and attention based feed-forward neural network (AFFNN)—were evaluated. Among these, AFFNN achieved the highest accuracy of 98.56%, establishing the proposed vision transformer-based attention handwritten word recognition (ViTA-HWR) model as a robust framework for handwritten Malayalam word recognition and valuable contribution to regional language processing.
Copyright (c) 2026 Anju Arangil Thazhath, Binu Poothakuzhiyil Chacko, Mohamed Basheer Kizhakke Parambath http://creativecommons.org/licenses/by-sa/4.0
Optimizing papaya yield: the evaluation of deep learning models for automated disease detection
The current research will create a robust and successful deep learning (DL) system to recognize and classify papaya leaf diseases. The traditional disease detection techniques are both time-consuming and unreliable, and extensively rely on expert knowledge, therefore limiting them in terms of scalability in agricultural practice. To tackle this issue, the convolutional neural network (CNN)-based method is suggested and tested on the BDPapayaLeaf that includes 2,159 images of papaya leaf with four disease categories and healthy papaya leaves, i.e., anthracnose, bacterial spot, leaf curl (reversal), and ring spot. The data was split into training 80%, validation 10%, and testing 10% data. Pictures were downscaled to 224×224 and normalized before training. Six trained CNN structures VGG16, VGG19, InceptionV3, DenseNet121, MobileNetV2, and ResNet50 were examined. The top model in terms of classification accuracy, according to them, was InceptionV3 with 89% in terms of classification accuracy, showing a high level of performance on true positive and false negative. The findings indicate that DL is an effective and precise method of automated detection of papaya leaf disease and is useful in improving precision and reliability in agricultural diagnostics.
Copyright (c) 2026 Tejas Rana, Chintan Thacker http://creativecommons.org/licenses/by-sa/4.0
Explainable deep learning framework for advanced deepfake video manipulation detection
The growing sophistication of deepfake technologies has emerged as a critical threat to the credibility of digital media by generating highly realistic yet fabricated visual content. This erodes public trust, elevates security vulnerabilities, and challenges information integrity across online platforms. Despite notable advancements, existing research still suffers from limited data diversity, insufficient model explainability, and inadequate model evaluation. To overcome this limitation, a framework for detecting deepfake video manipulation by using a transfer learning approach was introduced. Each extracted frame was processed by a convolutional neural network (CNN)-based model to obtain frame-level predictions, which were subsequently aggregated to produce the final video-level prediction using a predefined threshold. The publicly available, widely adopted FaceForensics++ dataset was used, which contains high-quality videos generated using advanced manipulation techniques. Various CNN architectures, including Xception, Densenet121, InceptionResNetV2, ResNet50, and EfficientNetB3, were explored along with rigorous hyperparameter tuning. Among these, the Xception architecture outperformed others by achieving a test accuracy of 94.5%. Gradient-weighted class activation mapping (Grad-CAM), generalized gradient-based visual explanations (Grad-CAM++), and Shapley additive explanations (SHAP) were employed to enhance model explainability by visualizing the key regions that influence deepfake detection. The research offers an effective approach to address deepfake threats and safeguard information integrity in contemporary industry 4.0.
Copyright (c) 2026 Shahrin Islam, Bibhas Roy Chowdhury Piyas, Fatama Jannat Tisha, Abu Saleh Musa Miah, Sadia Rahman, Shazzad Hossen, Md Abdus Samad Kamal http://creativecommons.org/licenses/by-sa/4.0
A novel deep learning-based pollinator species classification using the multimodal species-image regularization framework
Pollinators, such as bees, butterflies, and other insects, are essential to maintaining biodiversity and ensuring agricultural productivity, with over 80% of flowering plants and 75% of global food crops relying on them for successful reproduction. However, pollinator populations are facing significant declines due to environmental changes, habitat destruction, and climate change, posing substantial risks to ecosystems and global food security. This paper introduces the multimodal species-image regularization (MSIR) framework for automating the classification of pollinator species using both binary classification (pollinator vs. non-pollinator) and multiclass classification (bee genera). The framework integrates multimodal data, including visual images of pollinators and species details such as genus, family, and environmental factors, to improve accuracy and scalability. The system leverages the multimodal contrastive learning framework (MCLF) to align both image and species-detail features into a unified embedding space, enabling mor effective classification. Additionally, the framework applies image-species prototype regularization (ISPR) and species-detail prototype regularization (SDPR) to further enhance the classification accuracy by regularizing the tunable weights based on prototype alignment. The proposed deep learning (DL) model is evaluated against traditional machine learning (ML) methods, such as random forest (RF), and demonstrates superior performance on key metrics, including accuracy, precision, recall, and F1-score.
Copyright (c) 2026 Pooja Hadimane, Ashoka Kukkuvada http://creativecommons.org/licenses/by-sa/4.0
Parkinsonian gait classification in older adults using time–frequency spectrograms and 2D convolutional neural network
Gait disorders in adults aged 50 years and above are a common concern and are often linked to reduced mobility, a higher risk of falls, and a lower quality of life. This study presents a deep learning-based approach to detect gait disorders using vertical ground reaction force (vGRF) signals. The data were collected from older adults, including individuals with Parkinson’s disease (PD) and healthy controls, using force-sensitive resistor sensors. The raw signals were first processed using band-pass filtering and wavelet denoising to remove noise and unwanted variations. After that, the signals were converted into time–frequency representations using the continuous wavelet transform (CWT). These representations were then used as input to a convolutional neural network (CNN) for classification. The model achieved a validation accuracy of 93.48%, with precision, recall, and F1-score all above 92% for both groups. The results show that combining CWT with CNN provides a reliable and efficient way to detect gait disorders. This approach can support clinical evaluation by offering a practical and scalable method for analyzing gait patterns in older adults.
Copyright (c) 2026 Kazi Ashikur Rahman, Ezreen Farina Shair, Nur Zawani Saharuddin, Muhammad Hazwan Adlin Jumaris http://creativecommons.org/licenses/by-sa/4.0
VisionEyeNet: a customized deep learning framework for early diagnosis of keratitis and uveitis
Keratitis and uveitis are increasingly prevalent ocular disorders, often linked to delayed detection and limited specialist access, particularly in rural healthcare settings. These diseases can lead to severe visual impairment or irreversible blindness if not identified at an early stage. Traditional diagnostic approaches are manual, time-consuming, and prone to human error, making them challenging for large-scale screening. To address these limitations, this study presents VisionEyeNet, a framework for automatic classification of keratitis and uveitis. VisionEyeNet integrates MobileNetV2 and DenseNet121 within a fusion architecture, along with image enhancement methods such as adaptive gamma correction and specular reflection suppression. The model was trained and evaluated on a curated dataset of 1,860 slit-lamp images (960 uveitis and 900 keratitis) using a patient-wise split (71.5% training, 8.4% validation, and 20% testing). On the independent test set, it achieved 98.0% accuracy (95% CI: 97.1–98.8%) with balanced performance across classes. Inference analysis showed an average processing time of 51±2 ms per image, supporting real-time use. These results indicate that VisionEyeNet has strong potential as a clinically useful decision-support tool, particularly in resource-limited settings.
Copyright (c) 2026 Somashekhar Bannur Mayigowda, Raghavendra Kodandarama, Sudhamani Mallaiah, Manjunath Naganna, Jamuna, Kiran Kumar B. S. http://creativecommons.org/licenses/by-sa/4.0
Makespan and energy-aware workflow scheduler for heterogeneous cloud computing platform
Scientific workflows, typically modelled as complex directed acyclic graphs (DAGs), are increasingly executed on heterogeneous cloud platforms to achieve high performance and scalability. However, as workflow sizes grow, energy consumption, and operational cost have become critical concerns, especially under global carbon-emission constraints. Although dynamic voltage and frequency scaling (DVFS) offers significant potential for energy savings, existing workflow scheduling methods fail to fully exploit heterogeneous processors that contain both high-performance and energy efficient cores, resulting in suboptimal makespan and energy utilization. To address this gap, the makespan and energy-aware workflow scheduler (MEAWS) is proposed as a multi-core DVFS-enabled scheduling framework designed to optimize both execution time and energy consumption in heterogeneous cloud environments. Extensive simulations using scientific workflows demonstrate that MEAWS reduces makespan by up to 88.75% and 70.4%, and lowers energy usage by 41.59% and 47.15% when compared with reliable and efficient workflow scheduling (REWS) and multi-objective workflow scheduling (MOWS). These improvements highlight the effectiveness of MEAWS in enhancing the sustainability and efficiency of scientific workflow execution.
Copyright (c) 2026 Rashmi Kambalapalli Anjaneya Reddy, Vikas Reddy Shivaram Reddy http://creativecommons.org/licenses/by-sa/4.0
Hybrid deep learning for sentiment analysis of online student experiences
The COVID-19 pandemic disrupted millions of lives worldwide, and social media platforms became a significant outlet for people to share their emotions and experiences, providing valuable insights into the challenges and opportunities of remote education. This paper analyzes student sentiments about online learning during the pandemic using Twitter data. An experimental approach is developed to analyze public comments, focusing on the sentiment expressed in tweets related to online education. A hybrid deep learning model, based on the logistic regression (LR) sentiment model, is used to predict sentiment from a large dataset of online learning-related tweets. After performing n-gram analysis to extract key topics, tweets are classified into sentiment classes. The proposed convolutional long short term memory (Conv-LSTM) and convolutional bidirectional long short-term memory (Conv-BiLSTM) models are trained on tweets annotated with granular sentiment classifications, achieving validation accuracies of 93% and 95%, respectively. This work provides meaningful insights into the emotional effects of online learning during the pandemic, contributing to the understanding of students' experiences and challenges in remote education.
Copyright (c) 2026 Raja Ouadad, Hicham Mouncif http://creativecommons.org/licenses/by-sa/4.0
A transfer hybrid deep learning approach for advanced intrusion detection in IoT-based smart home security
As smart home environments increasingly rely on interconnected internet of things (IoT) devices, they face growing cyber threats originating both externally from malicious actors and internally from compromised or malfunctioning IoT devices. These threats, including unauthorized access, distributed denial of service (DDoS) attacks, and data exfiltration, pose significant risks to the security and privacy of smart home inhabitants. This paper introduces an advanced intrusion detection system (IDS) specifically designed to enhance the security of IoT-based smart home networks. Leveraging a hybrid deep learning approach combining convolutional neural networks (CNN) and long short-term memory (LSTM) models, complemented by transfer learning (TL) and hyper-parameter optimization techniques, our proposed IDS efficiently identifies both external and intra-network threats. Using the IoTID20 dataset, which simulates realistic attack scenarios, the IDS was trained and evaluated to detect abnormal behavior effectively within smart home networks. CNN layers extract spatial features from network traffic, while LSTM layers capture temporal dependencies, enabling robust detection against a range of cyber-threats. Evaluation results demonstrate the IDS’s high detection accuracy and exceptional F1-scores, validating its effectiveness in safeguarding IoT-based smart homes from evolving threats.
Copyright (c) 2026 Mouad Choukhairi, Ouail Choukhairi, Youssef Fakhri, Ali Choukri http://creativecommons.org/licenses/by-sa/4.0
Effects of sparse datasets on time interval-aware self-attention sequential recommendation models
Recommendation models serve as crucial filters in managing information, yet they face a few crucial challenges, such as capturing user-item interaction behaviors in sparse datasets. Data sparsity refers to an issue where there is a lack of interactions or missing values in the recommendation dataset. A sparse dataset with a massive number of missing values and interactions leads to more dynamic user behaviors, which suffers a poor recommendation quality. The self-attention mechanism from Transformer can alleviate the effects of data sparsity in datasets by assigning weights to items of interaction behaviors. This allows the model to capture the user dependencies in complex user behavior, which is beneficial for sparse datasets with patterns that are not immediately apparent. This approach has shown its capability to handle large and sparse datasets, as seen in time interval-aware self-attention sequential recommendation model (TiSASRec). It utilized the self-attention mechanism, considering the timestamp and absolute positions of items to estimate the higher attention weights to show the importance of recent items. Thus, this study aims to investigate the effects of sparse datasets by comparing the performance of TiSASRec model with self-attention based sequential recommendation model (SASRec), which excludes time interval-awareness.
Copyright (c) 2026 Weishan Ooi, Lee Yeng Ong, Meng-Chew Leow http://creativecommons.org/licenses/by-sa/4.0
RGB-D salient object detection with local feature and semantic segmentation
Red, green, blue–depth (RGB-D) salient object detection (SOD) focuses on identifying visually prominent objects by simulating human visual perception. While existing RGB-D SOD methods have demonstrated results, there remain challenges in effectively leveraging extrinsic cues and enhancing feature representation. To address these limitations, novel RGB-D SOD model with local feature extraction and semantic segmentation (LFSS) is introduced, which is built on an encoder-decoder architecture. The encoder preprocesses the input images by merging RGB and depth data through a channel and spatial attention (CSA) module. A local feature extraction module further refines this fusion. The decoder consists of three key modules: i) the multi-feature extraction (MFE) module enhances base features through diverse convolutional operations; ii) the semantic segmentation enhancement (SSE) module optimizes features via spatial pyramid pooling and atrous convolution; and iii) the local/global agreement and edge detection (LGE) module that enables multi-level feature interaction and edge detection. These modules work sequentially to enhance and extract salient objects. LFSS is evaluated on six standard RGB-D SOD datasets (NJU2K, NLPR, STERE, LFSD, SSD, SIP) by four metrics, outperforming the comparison models with up to 1.2% F-measure improvement. LFSS is found to be a versatile model, offering valuable applications in engineering.
Copyright (c) 2026 Zhang Wang, Kim On Chin, Rayner Alfred, Junyi Chai, Rundong Zhang, Soo See Chai http://creativecommons.org/licenses/by-sa/4.0
K-fold ensemble 3D convolutional neural network for predicting MGMT promoter methylation in glioblastoma
Medical intervention is necessary for brain tumors, which represent a critical health threat. Chemotherapy response and patient survival outcomes depend on the methylation status of the O-methylguanine-DNA methyltransferase (MGMT) promoter. Biopsy and laboratory testing currently provide the only method to obtain this specific information. This study investigates a non invasive technique for measuring MGMT promoter methylation through magnetic resonance imaging (MRI) scanning. The BraTS 2021 dataset provided fluid attenuated inversion recovery (FLAIR) and contrast-enhanced T1 (T1ce) MRI data to develop a 3D convolutional neural network (CNN) system. The model used five-fold stratified cross-validation for training and testing to create a reliable assessment method. Prediction accuracy improved through the use of an ensemble that combined the best models from each cross-validation fold. The model achieved an average accuracy of 0.718 and an area under the curve (AUC) of 0.727 on the validation data. The results demonstrate that MRI features can provide essential molecular details despite using restricted imaging techniques. The proposed framework shows that deep learning enables early non-invasive detection of MGMT promoter methylation status in glioblastoma (GBM). The methods help doctors with treatment planning while also identifying patients who will benefit from temozolomide-based therapies.
Copyright (c) 2026 Manzoor Mohammad, Burra Vijaya Babu http://creativecommons.org/licenses/by-sa/4.0
Deep hybrid models for bitcoin forecasting: EMD, CEEMDAN,and LSTM in comparison
In this study, an artificial neural network (ANN) was developed to forecast Bitcoin prices using one of the most successful deep learning architectures for time series analysis: long short-term memory (LSTM) networks. This model was enhanced with a signal processing layer that reduces the impact of the instrument’s high volatility on prediction accuracy by applying two signal decomposition techniques: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). This study is motivated by the major fluctuations in Bitcoin prices, which make precise forecasting difficult but crucial for experts and investors. This findings demonstrate that forecasting performance improves when decomposition techniques are used. In particular, compared to the conventional LSTM and EMD-LSTM models, the CEEMDAN-LSTM model achieved the highest accuracy, with a mean absolute error (MAE) of 167.837 and a root mean square error (RMSE) of 255.673, outperforming both EMD-LSTM (MAE =168.785, RMSE =256.042) and the standard LSTM (MAE =169.516, RMSE=256.225). The combination of CEEMDAN and LSTM results in a more reliable model that can accurately capture short-term fluctuations in Bitcoin prices.
Copyright (c) 2026 Ayoub Aarabi, Maryem Ait Moulay, Issam Bouganssa, Abdelali Lasfar http://creativecommons.org/licenses/by-sa/4.0
Performance comparison of deep learning models for concrete crack detection on mobile devices
Concrete crack detection is essential for structural maintenance, yet traditional manual inspection methods are time-consuming and require specialized expertise. While deep learning offers promising solutions, existing models often demand high computational resources unsuitable for mobile deployment. This research evaluates three convolutional neural network (CNN) architectures, namely mobile network (MobileNet), visual geometry group-16 (VGG-16), and residual network-50 (ResNet-50), to identify an optimal model for practical mobile-based crack detection. A dataset of 1,634 images was collected from online databases and field documentation, categorized into 10 classes across three severity levels: i) severe cracks requiring urgent repair (30%); ii) cracks requiring monitoring (40%); and iii) minor cracks (30%). The models were trained using standardized parameters with 224×224-pixel RGB input, rectified linear unit (ReLU) activation, and softmax classification. Systematic parameter optimization was conducted across epochs, learning rate, dropout rate, and optimizer selection, with stochastic gradient descent (SGD) identified as the optimal optimizer. Experimental results demonstrate that MobileNet achieves the best performance with 80% accuracy and a compact model size of 13.1 megabytes. This study concludes that MobileNet provides an optimal balance between detection accuracy and computational efficiency, enabling practical field deployment for automated concrete crack detection, with expert verification recommended for critical structural assessments.
Copyright (c) 2026 Sarapee Chunkaew, Somporn Ruang-On, Prawit Nuengmatcha, Kritaphat Songsri-in http://creativecommons.org/licenses/by-sa/4.0
Dynamic optimization using long short-term memory and genetic algorithms for predicting marine data
This study aims to develop an accurate and efficient ocean data prediction model to tackle the challenges posed by climate change and complex oceanographic dynamics. The main goal is to use long short-term memory (LSTM) networks along with genetic algorithms (GA) to predict four key ocean factors at once: sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a (Chl-a). An experimental quantitative approach is employed, utilizing satellite data from the Banda Sea region. This approach involves time series modeling using LSTM, which is optimized by GA for hyperparameters such as the number of neurons and batch size. The results show that the combined LSTM-GA model greatly improves prediction accuracy and successfully identifies seasonal trends and irregular changes in all variables, even when there is a lot of noise. Tests reveal that the optimal configuration varies for each variable, and the GA optimization process can expedite model convergence by as little as 10 epochs. These findings underscore the effectiveness of integrating evolutionary techniques in training deep learning (DL) models for ocean data. The implications of this research include potential applications in adaptive ocean monitoring systems, early warning initiatives, and data-driven planning in marine resource management.
Copyright (c) 2026 Mukhlis, Indra Jaya, Sri Nurdiati, Karlisa Priandana, Irman Hermadi http://creativecommons.org/licenses/by-sa/4.0
Identification of areas of influence using a thematic modeling approach and belief function theory
Social networks have become essential platforms for the dissemination of information and the exchange of ideas. They are transforming the way influencers interact and exert influence over their peers. On platforms such as Twitter, Facebook and Instagram, influence is expressed through social interactions such as retweets, likes, mentions, comments and shares. These activities play a key role in amplifying messages and shaping opinions. Studying the practices of influencers allows for a more precise identification of the domains in which their impact is particularly significant. However, this task is complex. It requires rigorous methods capable of integrating various forms of social engagement while managing the uncertainties associated with heterogeneous data. In this context, we propose a method to identify the domain of influence of a social media influencer. This approach combines thematic modeling Latent Dirichlet Allocation (LDA) with Belief Function Theory (BFT) to analyze social interactions and dominant topics of interest. By incorporating indicators such as retweets, likes and mentions, the method provides a robust framework for evaluating the influencer's impact across different domains. It thus offers precise tools for researchers, practitioners and decision-makers aiming to better understand these complex dynamics.
Copyright (c) 2026 Fatima-Zahrae Sifi, Wafae Sabbar, Amal El Mzabi http://creativecommons.org/licenses/by-sa/4.0
Hybrid convolutional neural network-bidirectional long short-term memory model for Arabic sentence readability assessment
In the current educational landscape, a large number of educators prefer using generative artificial intelligence techniques to produce textual content to be presented for learning. However, these generated texts may not meet the specific needs of learners or align with their abilities. Many traditional methods and techniques can be employed to assess the complexity of a text, such as traditional readability formulas, but these techniques are time consuming and labor-intensive. In this paper, we introduce a deep learning approach for automatically evaluating the readability of Arabic texts by analyzing and classifying sentences into different difficulty levels within educational content. The initial stage of the proposed approach is preprocessing textual content and leveraging natural language processing (NLP) methodologies for feature extraction, such as Word2Vec. The approach then concentrates on refining and evaluating a deep learning model to classify text into different readability levels. This paper introduces a hybrid classification model that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) layers, attaining an accuracy of 96.68%. This model demonstrates the significance of applying hybrid deep learning models in analyzing educational materials and establishes a foundation for subsequent progress in the field of automated Arabic readability assessment.
Copyright (c) 2026 Mohamed Amine Ouassil, Mohammed Jebbari, Rabia Rachidi, Mouaad Errami, Soufiane Hamida, Bouchaib Cherradi, Abdelhadi Raihani http://creativecommons.org/licenses/by-sa/4.0
Review of advancements in AI-assisted lung sound analysis for respiratory illness diagnosis in noisy environments
For several centuries, research has been carried out to address respiratory ailments, which are among the most detrimental to human health. The advent of the stethoscope in the 19th century has facilitated the identification of respiratory sounds. This innovation represents a significant advancement in the identification and diagnosis of numerous respiratory ailments. In Malaysia, public hospitals have traditionally employed stethoscopes in their emergency departments. However, the precision of readings obtained through this method is susceptible to interference from ambient noise, uneven terrain, and suboptimal acoustic performance, particularly during medical transportation. Consequently, this can result in erroneous diagnoses and inappropriate treatment. Potential remedies for addressing the challenges associated with assessing respiratory sounds during medical transportation include advancements in stethoscope technology, novel auditory techniques, and reduced levels of background noise within the transportation environment. The present investigation concerns the effects of developing a new machine learning (ML) algorithm for the assessment of lung sound in conditions of high ambient noise. The objective is to devise a ML algorithm that can categorize acute respiratory illnesses based on their level of urgency in the presence of ambient noise.
Copyright (c) 2026 Reshma Sreejith, R. Kanesaraj Ramasamy, Wan-Noorshahida Mohd-Isa, Junaidi Abdullah http://creativecommons.org/licenses/by-sa/4.0
Double direction optimization: a new metaheuristic that performs exploitation and exploration simultaneously
This research constructs a novel method called double direction optimization (DDO). DDO is constructed based on swarm intelligence (SI) approach and it does not use any metaphor. As its name suggests, it employs a novel algorithm by performing exploitation and exploration simultaneously which is transformed into two sequential searches. In the 1st search, the motion toward the highest quality agent is combined with the motion toward a randomly taken higher quality agent. In the 2nd search, the motion toward the finest entity is combined with the motion relative to a randomly taken agent. In this work, the efficacy of the DDO is assessed using three use cases: 23 functions, four engineering problems, and an economic emission dispatch (EED) problem. In this assessment, there are five metaheuristics that become the benchmark: crayfish optimization algorithm (COA), hiking optimization (HO), osprey optimization algorithm (OOA), carpet weaver optimization (CWO), and dollmaker optimization algorithm (DOA). The result indicates the supremacy of DDO in high dimension functions and competitiveness of DDO in fixed dimension multimodal functions, four engineering problems, and the EED problem.
Copyright (c) 2026 Purba Daru Kusuma, Helmy Widyantara http://creativecommons.org/licenses/by-sa/4.0
Pre-driving fatigue screening from short-term heart rate variability with subject-independent validation
This study evaluates fatigue screening from 30-second electrocardiogram (ECG) recordings using short-term heart rate variability (HRV) features in a pre-driving context. The dataset comprises 99 participants (one session each) with fatigue labels derived from the Karolinska sleepiness scale (KSS), where the primary label (K1) defines non-fit as KSS ≥ 7. A subject-independent logistic-regression model was trained under a leave-one-subject-out (LOSO) scheme. Probabilities were calibrated using Platt scaling and evaluated through threshold-free metrics (receiver operating characteristic (ROC)-area under the curve (AUC), precision-recall (PR)-AUC) as well as calibration performance using the Brier score. The model achieved ROC-AUC =0.687 (95% confidence interval: 0.591–0.776), PR-AUC =0.621, and a Brier score of 0.200. At the operating threshold t = 0.255, the model achieved sensitivity of 1.000 with no false negatives, while specificity remained 0.091 (95% confidence interval: 0.030–0.140). Reliability analysis indicated reasonable calibration in the operational probability range. These findings support short-term HRV derived from ECG as a screening tool that prioritizes avoiding missed non-fit cases, paired with a triage scheme (fit/review/non-fit) to manage uncertainty near the decision threshold. Future work should incorporate ECG morphology and signal quality cues and aim to improve specificity without sacrificing sensitivity.
Copyright (c) 2026 Tia Haryanti, Eri Prasetyo Wibowo, Wahyu Kusuma Raharja, Rossi Septy Wahyuni, Ilmiyati Sari http://creativecommons.org/licenses/by-sa/4.0
High-gain antenna arrays for millimetre-wave energy harvesting: architectures, challenges, and future directions
The rapid expansion of fifth-generation (5G)/sixth-generation (6G) networks and internet of things (IoT) ecosystems has intensified the need for self sustaining power solutions to support billions of wireless devices. Millimetre-wave (mmWave) energy harvesting (EH) emerges as a viable alternative to traditional battery-powered systems, leveraging ambient radio frequency (RF) signals to provide continuous energy for IoT, smart sensor networks, and next-generation wireless applications. However, several challenges hinder its widespread adoption, including high path loss, low RF to-direct current (DC) conversion efficiency, and the trade-off between high gain and wide bandwidth. This paper presents a comprehensive review of high-gain mmWave antenna arrays, exploring state-of-the-art advancements in beamforming techniques, phased arrays, metasurface-enhanced rectennas, and multi-band EH architectures. We analyse existing methodologies, identifying key research gaps such as scalability constraints, material limitations, and real-world deployment challenges. Additionally, we highlight emerging trends, including artificial intelligence (AI)-driven adaptive beamforming, intelligent metasurfaces, and cost-effective fabrication techniques, which can significantly improve mmWave RF EH efficiency. By addressing these gaps, this study provides insights into future research directions for developing high-performance, scalable, and commercially viable mmWave EH solutions. The findings pave the way for the practical deployment of battery-free IoT devices, smart city infrastructures, and energy-autonomous wireless communication networks in the 6G era.
Copyright (c) 2026 Shalini Mirle Gajendra, Naveen Kalenahalli Bhoganna http://creativecommons.org/licenses/by-sa/4.0
Improving the quality of images using Wasserstein generative adversarial networks for image restoration
In the present digital age, it is crucial to preserve personal memories and historical photographs in their original form, and this is made possible through image restoration. This paper presents a dynamic multi-scale Wasserstein generative adversarial network with gradient penalty (WGAN GP) framework that combines colorization and image denoising, addressing the limitations of distinct restoration models that denoise and colorize images in parallel. The proposed system adapts to hierarchical image features, stabilizes training, and enhances fine-grained texture reconstruction. The model is trained on CelebA, Places365, and ImageNet datasets. The need for repeated retraining is required, and there are still no guarantees of robustness under various degradations such as fading, saturation loss, and sensor noise. The results show peak signal-to-noise ratio (PSNR) of 24.5 dB and structural similarity index measure (SSIM) of 0.74, outperforming Pix2Pix, CycleGAN, denoising generative adversarial network (D‑GAN), and enhanced super‑resolution generative adversarial network (ESRGAN) in efficiency and robustness. In contrast to previous GAN-based restoration methods that treat denoising and colorization as separate problems, the presented multi-scale WGAN-GP applied a generator-discriminator model, resulting in less training redundancy and similar SSIM results while using ~55-65% less number of training epochs than ESRGAN and DeblurGAN. In the future, the model will integrate attention and transformer-based refinement to enhance detail recovery and perceptual realism further.
Copyright (c) 2026 Aruna Pavate, Surekha Janrao, Rohini Patil, Maganti Venkatesh, Shudhodhan Bokefode, Yunfei Li, Ubaldo Comite http://creativecommons.org/licenses/by-sa/4.0
Class imbalance resolution in IoT networks using advanced elk herd optimization with SMOTE and iteratively fine-tuned deep BiLSTM
The "internet of things (IoT)" mentions to a system in which multiple network protocols are used to link together disparate devices that are always sharing information with one another. The hope is that this study will add to the existing body of knowledge by suggesting ways to make intrusion detection systems (IDS) more effective. Training datasets for the proposed model were collected from telemetry of network (ToN)-IoT network traffic. After cleaning and normalizing the datasets, the synthetic minority over sampling technique (SMOTE) is used to balance the datasets that are imbalanced. Optimal sampling rates are critical for resolving class imbalance, as SMOTE's efficiency is dependent on it for instances involving minority classes. Improving classification accuracy through finding appropriate sample rates for input datasets is the goal of this paper's introduction of advanced elk herd optimization (AEHO) with SMOTE. Finally, a deep bidirectional long short-term memory (deep BiLSTM) model based on deep learning is used to classify attacks. The fine-tuning technique is used during testing to update the high limits and when combined with the data balancing mechanism and AEHO-based-SMOTE, the results greatly enhance the classification techniques. Deep BiLSTM performs better than 90% in every category: classification, recall, precision, and F1-score.
Copyright (c) 2026 Srikanth Mudiyanur Sriramappa, Ananda Babu Jayachandra, Vasantha Kumara Mahadevachar, Ashwini Kailas, T. G. Keerthan Kumar http://creativecommons.org/licenses/by-sa/4.0
Enhancing phishing website detection: a comparative study of SMOTETomek-XGB and SMOTEENN-XGB
In the evolving landscape of cybersecurity, phishing websites continue to be a persistent threat, challenging detection methods due to the significant class imbalance between phishing and legitimate websites. This study evaluates the effectiveness of two advanced hybrid-resampling techniques SMOTETomek and SMOTEENN integrated with the extreme gradient boosting (XGBoost) classifier to enhance phishing website detection. SMOTETomek combines the synthetic minority over-sampling technique (SMOTE) with Tomek links, creating synthetic examples and eliminating overlapping instances to address dataset imbalance. SMOTEENN, on the other hand, merges SMOTE with edited nearest neighbors (ENN) to improve class balance through synthetic sample generation and noise reduction. The comparative analysis reveals that both methods significantly enhance classification performance, SMOTETomek-XGB consistently outperforms SMOTEENN-XGB across key evaluation metrics, including accuracy, F1 score, recall, and receiver operating characteristic - area under the curve (ROC-AUC), underscoring its superior effectiveness in distinguishing phishing sites from legitimate ones. This study offers practical insights into the application of advanced resampling methods for improving machine learning model performance in cybersecurity.
Copyright (c) 2026 Kamal Omari, Ayoub Oukhatar http://creativecommons.org/licenses/by-sa/4.0
Smart system to control lighting in a smart library
Smart light controllers revolutionize energy efficiency and convenience by controlling lights with precision. The main problem with such control structures is that the existing lighting systems are outdated and inefficient in places such as libraries, leading to unnecessary energy consumption. In this study, we propose the implementation of an intelligent light-emitting diode (LED) lighting control system that optimizes energy in libraries. The system utilizes passive infrared (PIR) sensors and a counter mechanism. When an aisle is occupied, the LEDs operate in highlight mode; however, they switch to medium-light mode when no presence is detected for a predetermined period. For this process, we propose a new control system with the random forest prediction algorithm for displacement patterns. We also designed a mathematical algorithm with three matrices derived from previous work on lighting. The algorithmic process is applied to ensure that the proposed system has enhanced maintainability, the simulation methodology generated scenarios based on occupancy data collected from real library environments, MATLAB was used to create dynamic simulations that reflected these user behaviors, with the power consumption of each LED calculated over an eight-hour operating period, the proposed system achieves potential savings between 23% to 39%.
Copyright (c) 2026 Ouissale Elansari, Hicham Jamouli http://creativecommons.org/licenses/by-sa/4.0
Adaptive multi-scale convolutional network for plant leaf disease detection and classification
Plant disease detection is a critical task in modern agriculture, directly impacting crop yield, food security, and sustainable farming practices. Traditional methods rely on expert visual inspection, which is time consuming, inconsistent, and inaccessible in remote areas. This study introduces advanced deep learning (DL) framework, the adaptive multi-scale convolutional network (AMS-ConvNet) optimized for accurate and efficient plant disease identification. Hierarchical feature extraction network (HFEN) integrates the multi-domain attention framework (MDAF) and adaptive scale fusion module (ASFM) to enhance feature extraction and address challenges such as complex natural backgrounds, non-uniform leaf structures, and varying environmental conditions. The proposed framework employs pre trained knowledge adaptation (PTKA) techniques to improve generalization and overcome data scarcity. Comprehensive evaluations on multiple datasets demonstrate the model's better performance, achieving state-of-the-art metrics in precision, recall, F1-score, and accuracy. Furthermore, this approach ensures scalability and adaptability, making it suitable for real field conditions. The study emphasizes the importance of robust, automated solutions in minimizing crop losses, reducing labor costs, and enhancing agricultural sustainability through precision disease management.
Copyright (c) 2026 Tejashwini C. Gadag, D. R. Kumar Raja http://creativecommons.org/licenses/by-sa/4.0
Deep learning-based integrated XAI for photovoltaic power forecasting considering actual power production period
This paper proposes a deep learning (DL)-based integrated explainable artificial intelligence (XAI) framework for photovoltaic (PV) power forecasting, explicitly considering the actual power production period to improve operational reliability. The framework uses solar irradiance, ambient temperature, and relative humidity as input features and evaluates nine DL architectures, including artificial neural networks (ANN), recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), CNN-LSTM, CNN-BiLSTM, RNN-LSTM, and RNN-BiLSTM. Model performance is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results show that the residual-based RNN-LSTM model provides highest forecasting accuracy, achieving MAE of 1.21 kW, MAPE of 5.12%, and RMSE of 2.24 kW. In comparison, the LSTM and BiLSTM models exhibit substantially higher prediction errors, with MAPEs exceeding 21%, while hybrid convolutional models show moderate improvements but remain inferior. To enhance model transparency, XAI techniques are integrated to interpret feature contributions. The analysis confirms that solar irradiance is the dominant influencing factor, while temperature and humidity introduce secondary nonlinear effects captured effectively by recurrent architectures. The proposed framework provides a high-accuracy and interpretable solution for PV power forecasting, supporting reliable energy management and smart grid applications.
Copyright (c) 2026 Promphak Boonraksa, Warunee Srisongkram, Kedsara Palachai, Boonruang Marungsri, Terapong Boonraksa http://creativecommons.org/licenses/by-sa/4.0