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 3.9 (Q2 CiteScore Best Quartile), with an SNIP of 0.800 and an SJR of 0.365. 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
Review of artificial intelligence in smart wearable devices under internet of things communication
This paper aims to provide a review about the role of artificial intelligence (AI) in wearable devices, specifically smartwatches, fitness trackers, smart clothes, and smart eyewear. Machine learning (ML) and deep learning (DL) play essential roles in the development of these devices, thanks to their advanced algorithms with the support of the internet of things (IoT) framework. AI functionalities and metrology are detailed in these wearables, highlighting the use of convolutional neural networks (CNN) and recurrent neural networks (RNN) for applications such as activity recognition, health monitoring, and personalized recommendations. The paper demonstrates the AI implementation in smart devices, including stress detection by heart rate variability (HRV), personalizing fitness recommendations, muscle activity monitoring, and real-time image recognition. Challenges and potential solutions are discussed for a deep comprehension of the AI development in wearable devices.
Copyright (c) 2026 Minh Long Hoang, Guido Matrella, Paolo Ciampolini http://creativecommons.org/licenses/by-sa/4.0
Review of ChatGPT tools in education systems based on literature
Artificial intelligence (AI) has rapidly reshaped modern education, with ChatGPT emerging as one of the most influential generative AI tools supporting teaching, learning, and academic administration. This review synthesizes evidence from 65 peer-reviewed studies published since 2022 to evaluate ChatGPT’s educational applications, benefits, constraints, and ethical implications. Findings indicate that ChatGPT enhances personalized learning, academic writing, digital literacy, and instructional efficiency, while offering scalable support for large classrooms. Comparative analyses reveal that ChatGPT demonstrates superior linguistic coherence and reasoning compared to Gemini, Bing Chat/Copilot, and Claude. However, concerns persist regarding hallucinations, academic dishonesty, data privacy, infrastructural disparities, and faculty readiness. The review highlights the need for responsible governance frameworks, AI literacy programs, and equitable institutional policies. Future directions include longitudinal research on learning outcomes, inclusive AI design, cross-cultural adoption patterns, and evolving teacher–student dynamics in AI-augmented environments.
Copyright (c) 2026 Siva Prasad Reddy K. V., Parvathi Malepati, Khadarbadar Pullamma, Gangapuram Mallikarjunachari, Sareddy Venkata Rami Reddy, Anil Kumar Vadaga, Sai Shashank Gudla http://creativecommons.org/licenses/by-sa/4.0
Evolutionary trends in automatic speech recognition with artificial intelligence: a systematic literature review
Human beings depend greatly on communication and continually seek ways to overcome language barriers. Automatic speech recognition (ASR) has emerged as a vital tool for enhancing human interaction. Early ASR research relied on probabilistic models, particularly the hidden Markov model (HMM) and Gaussian mixture model (GMM), with mel-frequency cepstral coefficients (MFCCs) for feature extraction, leading to the creation of Audrey at Bell Laboratories. Subsequently, artificial intelligence (AI) approaches, especially deep learning, have transformed ASR and produced systems such as Jasper, Whisper, Google Assistant, Microsoft Cortana, Apple Siri, and Amazon Alexa. This paper presents a systematic literature review that examines ASR’s evolution, the AI architectures employed, their features, strengths and weaknesses, and the performance gains achieved since AI was integrated into probabilistic modelling. A snowballing approach was used to identify relevant studies from Google Scholar and Scopus to address five research questions, iterating through backward and forward searches until no new information was found. Findings reveal that ASR dates back to the 1920s with the Radio Rex toy and has since advanced through architectures including deep learning, recurrent neural networks (RNN), support vector machines (SVM), and transformers, all contributing to improved performance measured by reduced word error rates (WER).
Copyright (c) 2026 Gabriel Oluwatobi Sobola, Emmanuel Adetiba, Olabode Idowu-Bismark, Abdultaofeek Abayomi Raymond Jules Kala, Surendra Colin Thakur, Sibusiso Moyo http://creativecommons.org/licenses/by-sa/4.0
Securing cloud data with machine learning: trends, gaps, and performance metrics
The increasing reliance on cloud computing has raised significant concerns about the security of data access control, as traditional models are insufficient in managing the dynamic and large-scale nature of cloud environments. This review evaluates machine learning (ML)-based approaches to improve cloud data security, with a particular focus on advancements in anomaly detection and insider threat prevention. Deep learning (DL) models emerge as the most dominant, utilized by 47% of the studies due to their superior ability to process large datasets and adapt to real-time environments. Random forest models are also prominent, being adopted in 20% of the studies for their strong performance in anomaly detection and categorization. TensorFlow stands out as the most widely used tool, featuring in nearly 37% of the reviewed works, while datasets like Amazon Access and computer emergency response team (CERT) are employed in 20% and 13% of the research, respectively. Anomaly detection and prevention are critical priorities, accounting for 41.2% of the research objectives. However, gaps remain, with 21.7% of the studies noting adversarial vulnerabilities and 13% identifying limitations in dataset diversity. The review recommends further development of ML models to address these challenges, expanding dataset diversity, and improving real-time monitoring techniques to enhance cloud data security.
Copyright (c) 2026 Blessing Ifeoluwa Omogbehin, Tshiamo Sigwele, Thabo Semong, Aone Maenge, Zhivko Nedev, Hlomani Hlomani http://creativecommons.org/licenses/by-sa/4.0
Exploring artificial intelligence adoption challenges: bridging the technology gap for marketing advancements
Artificial intelligence (AI) holds immense potential to enhance marketing strategies for micro, small, and medium enterprises (MSMEs). However, significant barriers, including financial constraints, limited digital literacy, and inadequate government support, hinder its widespread adoption. This study compares AI adoption challenges across two Indonesian MSME ecosystems; tourism-oriented Bali and agrarian Garut using the diffusion of innovation (DOI) lens and complementary model⎯technology organization environment (TOE), technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT). The findings reveal that MSMEs in Bali exhibit characteristics of "early adopters" and "early majority," driven by the demands of the tourism sector. In contrast, MSMEs in Garut align more with "late majority" and "laggard" profiles, constrained by infrastructural and resource limitations. By mapping these regional disparities onto the DOI curve, this study provides actionable insights for policymakers. It advocates for the development of ecosystem-aware, tailored strategies to bridge the digital divide and foster inclusive digital transformation, enabling MSMEs across diverse regions to leverage AI for a sustainable competitive advantage.
Copyright (c) 2026 Susi Susanti Tindaon, Jonas Meylan Freddy Banurea http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence in writing: unveiling a research landscape
This study examines the expanding research landscape of artificial intelligence (AI) in writing, a field that continues to reshape the way ideas are produced, refined, and communicated. While AI has been widely examined in education and technology, limited research has mapped its thematic evolution and ethical dimensions in writing. To address this gap, 1,596 publications indexed in Scopus between 2021 and 2024 were analyzed using bibliometric mapping tools such as Scopus analyzer and VOSviewer. The analysis covers publication patterns, collaboration networks, and keyword relationships to trace the intellectual structure of the field. The results indicate a sharp increase in scholarly output over the past four years, supported by contributions from multiple disciplines, including computer science, social sciences, and education. Several thematic clusters were identified, centering on AI-assisted creative writing, authorship ethics, educational use, and cross-sector innovation. Despite these advances, ethical frameworks and responsible AI applications in writing remain underexplored. This paper offers a comprehensive overview of current trends and presents a foundation for future research on how AI can be integrated into writing practices responsibly and in ways that uphold human creativity and academic integrity.
Copyright (c) 2026 Wan Rusydiah Salehudin, Zilal Saari, Hafiza Abas http://creativecommons.org/licenses/by-sa/4.0
A survey on leveraging artificial intelligence tools for enhancing advanced mathematical education and problem-solving
Artificial intelligence (AI) has increasingly shaped education, with ChatGPT developed by OpenAI, emerging as a prominent tool due to its ability to generate contextually relevant language and support learning. This survey investigates the integration of ChatGPT into mathematics education, focusing on three dimensions. First, it explores innovative strategies for creating interactive and personalized learning environments that adapt to individual student needs. Second, it evaluates ChatGPT’s specific advantages in mathematics instruction, including providing tailored feedback, assisting with problem-solving, and deepening conceptual understanding. Third, it addresses the challenges of adopting ChatGPT in advanced mathematics education, such as risks of over-reliance, the necessity of balancing AI with traditional pedagogy, and the importance of ongoing professional development for educators. Recent studies highlight ChatGPT’s potential to solve complex mathematical problems, such as those in linear algebra and word problems, while also noting limitations related to accuracy and the preservation of critical thinking skills. The findings demonstrate that ChatGPT can significantly enhance mathematics education by supporting personalized learning and complex problem-solving. Therefore, this study will contribute to the discourse on AI in education by identifying opportunities, challenges, and implications for equity, pedagogy, and the responsible integration of ChatGPT in future classrooms.
Copyright (c) 2026 Hadeel N. Abosaooda, Syaiba Balqish Ariffin, Osamah Mohammed Alyasiri http://creativecommons.org/licenses/by-sa/4.0
A comprehensive survey of cyberbullying on social media: challenges, detection, and AI-based prevention
Cyberbullying is a pervasive issue in the digital landscape, particularly on social media platforms, where individuals engage in online harassment, intimidation, and abuse. Unlike traditional bullying, cyberbullying has a broader reach, anonymity, and persistence, making it a growing concern for mental health, social well-being, and online safety. This paper provides a comprehensive survey of cyberbullying trends, its psychological and social impacts, and the role of social media in amplifying the problem. It explores existing detection and prevention strategies, including artificial intelligence (AI)-driven approaches, policy frameworks, and platform-based moderation techniques. Furthermore, it discusses challenges in enforcement, the limitations of automated detection systems, and the need for improved legal measures. This paper uniquely contributes an integrated perspective on cyberbullying detection and prevention by synthesizing current research across psychological, sociocultural, and technical dimensions. It emphasizes underexplored gaps such as multilingual detection, real-time moderation, and cross-platform enforcement, and proposes a layered framework to guide future research and policy.
Copyright (c) 2026 Ammar Odeh, Osama Alhaj Hassan, Anas Abu Taleb, Abobakr Aboshgifa, Nabil Belhaj http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence in orthodontics: modeling decision support systems for treatment planning
Orthodontic treatment planning involves complex clinical decision-making that can benefit from artificial intelligence (AI). This study evaluates machine learning and deep learning models—including random forest, AdaBoost, gradient boosting, and artificial neural networks (ANNs)—for predicting orthodontic treatment strategies using a dataset of 612 anonymized patient records with 66 clinically validated features across four categories (extraction, non-extraction, functional appliance, and orthopedic case). Preprocessing included imputation, normalization, and the synthetic minority oversampling technique (SMOTE) for class imbalance, while performance was assessed via 10-fold cross-validation. Results showed that ANNs achieved the highest balanced accuracy (0.83), F1-score (0.84), and receiver operating characteristic area under the curve (ROC-AUC) (0.90), outperforming ensemble and baseline models. Shapley additive explanations (SHAP) analysis confirmed clinically meaningful predictors such as vertical face proportions and mandibular plane angle, enhancing interpretability. Although promising, the study is limited by its single-institution dataset and lack of external validation. Future research should incorporate multicenter, multimodal datasets and interpretable-by-design frameworks to enable clinically trusted AI decision-support systems in orthodontics.
Copyright (c) 2026 Sowmya Lakshmi Belur Subramanya, Advaith Vijaya Mohan, Achala Varsha Vishlavath Premalatha, Manchikanti Varunsai http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence framework for multi-stage lung disease detection with audio signals
Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
Copyright (c) 2026 Bandreddi Venkata Seshukumari, Jyothirmayi Tayi, Rajeshkhanna Bhuthkuri, Bhavani Madireddy, Jhansi Yellapu, Bodapati Venkata Rajanna, Nitalaksheswara Rao Kolukula, Siva Sairam Prasad Kodali, Jayasree Pinajala, James Stephen Meka, Chilakala Rami Reddy http://creativecommons.org/licenses/by-sa/4.0
Real-time object detection to classify export quality of mangosteen using variants of you only look once version 8
Mangosteen is one of the leading export commodities from Indonesia. Despite its great economic potential, only about 25% of Indonesian mangosteens meet export standards, mainly due to visual defects such as yellow sap and spots on the skin of the fruit. The process of sorting export worthy mangosteens has been done manually, which tends to be time consuming and inconsistent. Therefore, this study aims to utilize artificial intelligence technology in building a real-time image recognition model to improve the efficiency and accuracy of the export-quality mangosteen sorting process. This study uses you only look once version 8 (YOLOv8) as an image recognition model with YOLOv8 variants, including nano, small, medium, large, and extra large variants. The results of the study using 4,014 primary and 255 secondary data of mangosteen, the highest performance is reached by YOLOv8 medium 82% of accuracy, 0.856 of mean average precision (mAP)50, and 0.616 of mAP50-95. This result is obtained from 70% training, 20% validation, and 10% testing data with epoch stop 85. These results indicate that the model can provide good performance in mangosteen export quality classification. This research contributes to the fields of agricultural technology and artificial intelligence by offering an innovative solution to a practical problem, enhancing efficiency, accuracy, and scalability in export-quality mangosteen sorting.
Copyright (c) 2026 Dian Sa’adillah Maylawati, Mi’raj Fuadi, Kurniawan Yuniarto, Yuhendra AP, Rizky Rahmat Nugraha, Akbar Hidayatullah Harahap, Agung Wahana http://creativecommons.org/licenses/by-sa/4.0
Automated data exploration with mutual information in natural language to visualization
Transcribing natural language to visualization (NL2VIS) has been investigated for years but still suffer from several fundamental limitations (e.g., feature selection). Although large language models (LLMs) are good candidates but they incur computation cost and hard to trace their made decisions. To alleviate this problem, we introduced an alternative information-theoretic framework that utilized mutual information (MI) to quantify the statistical relationship between utterances and database features. In our approach, kernel density estimation (KDE) and neural estimation techniques were utilized to estimate MI, and to optimize a diversity-promoting objective balancing feature relevance and redundancy. We also introduced the information coverage ratio (ICR) to quantify the amount of information content preserved in feature selection decisions. In our experiments, we found that the proposed approach improved information-theoretic metrics, with F1-score of 0.863 and an ICR of 0.891. We observed that these improvements did not come at the cost of traditional benchmarks: validity reached 88.9%, legality 85.2%, and chart-type accuracy 87.6%. Moreover, significance tests (p < 0.001) and large effect sizes (Cohen’s d > 0.8) further supported that these improvements were meaningful for feature selection. Thus, this study provides a mathematical framework for applications requiring analytical validity that extends beyond NL2VIS to other machine learning contexts.
Copyright (c) 2026 HueLuong-Thi-Minh, Vinh-The Nguyen, Van-Viet Nguyen, Kim-Son Nguyen, Huu-Khanh Nguyen http://creativecommons.org/licenses/by-sa/4.0
Malware detection using convolutional neural network-di strategy polar fox optimization algorithm
Malware attacks have escalated significantly with an increase of internet users and connected devices. With the rise of various types of malwares released by the hackers, constructing new competitive methods are necessary to identify the advanced malware. However, conventional malware detection struggles to identify new and evolving malware variants accurately because of its dependence on handcrafted features and static-signature based methods. To address this problem, this research proposes convolutional neural network (CNN) based di strategy polar fox optimization algorithm (DSPFOA) for malware detection to fine-tune the CNN parameters effectively which later assists to overcome the limitations of CNN. The model integrates the sine chaotic mapping and Cauchy operator mutation as DSPFOA prevents the model from local optima issue, and extends search space solution, also enhance convergence. This ensures that the CNN learns highly discriminative features which makes the system more accurate and robust in detecting both known and evolving malware variants. The CNN DSPFOA achieves a high accuracy of 99.65 and 99.76% by utilizing BIG2015 and Malimg dataset respectively compared to existing methods like masked self-supervised model with swin transformer (MalSort).
Copyright (c) 2026 Parvathi Sathenahalli Jayaprakash, Yogeesh Ambalagere Chandrashekaraiah http://creativecommons.org/licenses/by-sa/4.0
An AI-driven framework for efficient and accurate calibration of electricity meters using extreme gradient boosting
Calibration testing plays a vital role in electricity meter manufacturing to guarantee measurement accuracy and compliance with industry standards. In practice, however, conventional calibration methods are often hindered by lengthy test cycles and the high cost of expanding test bench capacity. This study proposes a data-driven approach to address these limitations by applying machine learning techniques to optimize calibration testing. An extreme gradient boosting (XGBoost) regression model, enhanced through systematic hyperparameter tuning and feature engineering, was developed to predict calibration outcomes using data obtained from existing production test benches. When evaluated under real manufacturing line conditions, the proposed method shortened calibration runtime by about 55% compared with manual procedures relying on power supply units (PSU) and standard meter calculations, while maintaining reliable measurement accuracy. The framework also achieved lower root mean square error (RMSE), demonstrating improved predictive performance. In addition to reporting these results, the study describes the preprocessing pipeline, model selection process, and optimization strategy, providing a practical and replicable framework for integrating artificial intelligence (AI) into industrial calibration processes.
Copyright (c) 2026 Rosalina, Nunik Afriliana http://creativecommons.org/licenses/by-sa/4.0
Interpretable artificial intelligence system for personalized cognitive stimulation
The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.
Copyright (c) 2026 Rubén Baena-Navarro, Yulieth Carriazo-Regino, Mario Macea-Anaya http://creativecommons.org/licenses/by-sa/4.0
Serious game intelligent transportation system based on internet of things
This research examines the implementation of the preference ranking organization method for enrichment evaluation (PROMETHEE) approach for multi-criteria decision-making in a character recommendation system for serious games. The method calculates character skill values across multiple criteria and generates rankings of the best characters according to game environment conditions derived from closed-circuit television (CCTV) based traffic detection. Image processing algorithms were applied to classify congestion levels into quiet, moderate, and busy categories, which directly influence gameplay modes. Experimental results show that PROMETHEE rankings vary across maps (e.g., A6 ranked highest in quiet mode, while B2 dominated in busy mode), demonstrating the system’s contextual adaptability. Usability testing with 50 participants yielded an average system usability scale (SUS) score of 78.9, while expert evaluation using game design factor questionnaire (GDFQ) produced a mean of 4.19/5, both indicating high acceptance and positive user experience. These findings confirm that PROMETHEE is effective in generating context-aware recommendations, providing both strategic depth and engagement. The study concludes that integrating traffic data into serious game design can enrich intelligent transportation systems (ITS) education and awareness, with future improvements possible through real-time player feedback adaptation and machine learning–based traffic prediction.
Copyright (c) 2026 Fresy Nugroho, I Gusti Putu Asto Buditjahjanto, Dwi Pebrianti, Jehad A. H. Hammad, Moch Fachri, Tri Mukti Lestari, Dian Maharani, Alfina Nurrahma’N http://creativecommons.org/licenses/by-sa/4.0
Explainable hybrid models for cardiovascular disease detection and mortality prediction
The impact of cardiovascular diseases (CVDs) is devastating, with 20.5 million deaths annually. Early detection and prediction tools exist, but current approaches struggle to balance predictive performance with clinical interpretability. In this work, a two-stage machine learning (ML) framework is proposed for heart disease detection and mortality prediction in heart failure patients. Logistic regression (LR), random forest (RF), and gradient boosting (GB) models were trained using the publicly available heart failure datasets, and their performance was compared, then a stacked ensemble approach was employed to enhance prediction accuracy. Model interpretability was achieved through Shapley additive explanations (SHAP), which provide global feature rankings and specific patient attributes, supporting explainable artificial intelligence (XAI) in clinical practice. The GB model achieved the highest performance in the first stage with a receiver operating characteristic area under the curve (ROC AUC) of 96% and an accuracy of 89% on internal testing, while external validation confirmed strong generalization (ROC AUC of 94%). In the second stage, stacked ensemble model was employed and achieved marginal improvements. Two interactive web applications were developed to enable real-time predictions with SHAP visualizations. The results demonstrate that combining high-performance ML models with interpretable outputs can significantly improve trust in real-world healthcare environments.
Copyright (c) 2026 Ali Al-Ataby, Hussain Attia http://creativecommons.org/licenses/by-sa/4.0
Comparison of image enhancement methods for pratima theft detection using artificial intelligence
The theft of pratima in Balinese temples threatens the spiritual and cultural balance of the community. These sacred objects, regarded as manifestations of God in Hinduism, hold profound religious significance, and their loss represents both material and spiritual desecration. To address this issue, this study investigates a security system that leverages image enhancement for low-light detection. Four techniques—contrast limited adaptive histogram equalization (CLAHE), adaptive histogram equalization (AHE), histogram equalization (HE), and gamma correction—were evaluated to improve image quality. CLAHE yielded the lowest mean squared error (MSE) of 21.16 and the highest peak signal-to-noise ratio (PSNR) of 38.13 dB. For object detection, VGG-19 and AlexNet were assessed. The best configuration, VGG-19 with HE, reached 83.33% accuracy and 93.75% recall, and achieved a receiver operating characteristic area under the curve (ROC AUC) of 0.90±0.02 across five runs. Thresholds derived from the ROC analysis were selected using the Youden J statistic to balance sensitivity and specificity. The approach outperformed lightweight and classical baselines in AUC, indicating superior discrimination under low illumination. These findings show that superior image quality does not always align with higher detection accuracy, and they highlight the importance of pairing effective enhancement with robust detectors for temple security. The study contributes practical insights for preserving Balinese cultural and spiritual heritage by strengthening efforts to protect pratima against theft.
Copyright (c) 2026 Made Sudarma, Ni Wayan Sri Ariyani, I Putu Agus Eka Darma Udayana, Ida Bagus Gde Pranatayana, Lie Jasa http://creativecommons.org/licenses/by-sa/4.0
Efficient data streaming in dynamic vehicular networks: a hybrid controller for seamless connectivity
The demand for highly efficient data transmission is being increasingly demanded for dynamic vehicular networks, especially in the case of internet-of-vehicle (IoV). The current data transmission methods are known to encounter inefficiencies in terms of unreliable routing and restricted scalability. Evolving studies have found artificial intelligence (AI) based schemes more suitable to address these issues; however, there are no significant innovations towards developing a potential framework that can not only increase data transmission performance but also minimize the analytical overheads of AI. Hence, this paper presents a novel baseline framework by introducing an optimized controller structure at anchor points with the inclusion of novel ideologies of orientation degree and selection of mediating node. The proposed model witnesses 32.3 dB of signal quality, 857 kbps throughput, 81 ms delay, and 171 ms of response time, exhibiting much better performance in contrast to the frequently used data transmission method. The proposed model contributes to a solid foundation for any futuristic AI model for efficient and reliable data transmission in IoV.
Copyright (c) 2026 Prathibha Thimmappa, Mayuri Kundu http://creativecommons.org/licenses/by-sa/4.0
Real-time intelligent virtual assistant based on retrieval augmented generation
Improving user experience in accessing information on organizational websites remains a challenge. Users often face complex navigation and multi step searches that slow information retrieval. This study introduces the real time intelligent virtual assistant (RIVA), which integrates large language models (LLMs) with the retrieval-augmented generation (RAG) framework to support real-time interaction with website content. The system was implemented on the Universitas Pendidikan Ganesha (Undiksha) website using a WordPress content management system (CMS) and developed following the design science research (DSR) approach, which includes six stages: problem identification, solution objectives, design and development, demonstration, evaluation, and communication. The retrieval-augmented generation assessment (RAGAS) evaluation indicated that the combined model of text-embedding-ada-002 and semantic chunking yielded the best results, with context precision=0.83, context recall=0.90, response relevancy=0.91, faithfulness=0.83, and answer correctness=0.85. User experience questionnaire (UEQ) testing performed well, particularly in the novelty and stimulation dimensions. These results demonstrate that RIVA can provide users with access to relevant and engaging information. As a result, future research will focus on improving retrieval and developing adaptive semantic chunking for structured and complex data.
Copyright (c) 2026 I Ketut Resika Arthana, Ni Putu Novita Puspa Dewi, Gede Arna Jude Saskara, I Made Ardwi Pradnyana, Luh Indrayani http://creativecommons.org/licenses/by-sa/4.0
Enhancing vehicular ad hoc network security through a trust based vehicular model for attack mitigation
In vehicular ad-hoc networks (VANETs), ensuring secure and reliable communication is essential due to the growing threat of cyber-attacks. As attacks can disrupt data transmission and compromise user privacy and network integrity, it is vital to develop robust security solutions. Hence, this work introduces a trust-based vehicular security (TVS) model, which leverages trust metrics to enhance VANET security. The main objective was to establish secure connections between vehicles and infrastructure nodes, effectively mitigating attacks while maintaining higher throughput. The methodology integrated a dynamic trust evaluation model to prevent malicious activities and ensure secure data transmission. The TVS model’s performance was compared to an existing VANET model, showing improved results in terms of detection rate, misclassification rate, and throughput. The findings demonstrate an average misclassification rate of 22.75%, a detection rate of 14.77%, and a throughput of 11.45%, highlighting the superior effectiveness of the TVS model in attack-prone environments when compared with existing VANET models. The TVS model provides a promising security solution for VANETs, offering enhanced protection against denial-of-service (DoS) attacks and spoofing (cyber-attacks) with better accuracy and network performance. The novelty lies in the dynamic, multi-trust-based approach for secure communication in vehicular networks.
Copyright (c) 2026 Shilpa, Thiruvenkadam Prasanth http://creativecommons.org/licenses/by-sa/4.0
Benchmarking machine learning models for natural disaster prediction with synthetic IoT data
Natural disasters pose severe threats to human life and infrastructure, demanding robust early warning systems (EWS) supported by machine learning (ML) and internet of things (IoT)-based sensing. This study benchmarks ML models for predicting floods and earthquakes using synthetic IoT sensor data. A dataset comprising nine environmental and seismic parameters was generated and labeled into three classes: no disaster, flood, and earthquake, where the feature preprocessing was applied during model training. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models were trained and evaluated using accuracy, precision, recall, and F1-score. Experimental results on the World-A test set show that ensemble models consistently outperform LR, with XGBoost and RF achieving F1-scores of up to 97%and99%,respectively, compared to79%forLR.Anindependenttestonthe separately generated World-B dataset revealed that ensemble models maintained higher generalization capability with F1-scores of 80% for XGBoost and 78% for RF. In contrast, LR showed substantial degradation with an F1-score of 54%. Stress testing on the World-B dataset under simulated situations, such as sensor failures, noise injection, and extreme weather events, confirms the resilience performance of ensemble models in comparison to LR. These results demonstrate the usefulness of ensemble learning in handling unpredictable IoT data for disaster prediction and highlight their potential integration into intelligent EWS. Future work will focus on expanding the framework to include cross-time prediction, incorporating additional environmental features, and deploying the models in real-time IoT systems for field validation.
Copyright (c) 2026 Moath Alsafasfeh, Abdullah Alhasanat, Atheer Bassel, Mohanad Alhasanat http://creativecommons.org/licenses/by-sa/4.0
Design of Antasena: an AI-powered maritime surveillance and anomaly detection system for security decision support
Indonesia’s vast maritime territory faces serious challenges from illegal fishing, smuggling, and habitat destruction. To address these, the Indonesian Navy (TNI-AL) developed Antasena, an artificial intelligence (AI)-powered smart dashboard integrating automatic identification system (AIS) data, satellite imagery, and conservation metrics. Antasena leverages advanced anomaly detection algorithms, achieving 95.3% accuracy, 94.7% precision, 94.2% recall, and a 96.8% receiver operating characteristic-area under the curve (ROC-AUC) score in identifying vessel anomalies, including unauthorized fishing and smuggling activities. Using the analyze, design, develop, implement, and evaluate (ADDIE) framework, the system supports real-time maritime surveillance and biodiversity monitoring in conservation zones. The main contributions of this study include the development of a user-centric AI-based dashboard for maritime anomaly detection, the integration of multi-source data with machine learning models, and validation through operational field tests with maritime authorities. Antasena offers a scalable and effective solution to strengthen maritime security and protect Indonesia’s marine resources.
Copyright (c) 2026 Arif Badrudin, Siswo Hadi Sumantri, Rudy Agus Gemilang Gultom, I Nengah Putra Apriyanto, Umi Laili Yuhana, Fitria Dwi Ratnasari http://creativecommons.org/licenses/by-sa/4.0
Enhancing medical language models with big data technologies
In this study, we present an end-to-end, big-data–driven framework for continuously enriching and fine-tuning large language models (LLMs) with the latest professional and scientific medical knowledge. Streaming updates from premier sources such as The New England Journal of Medicine (NEJM) are ingested via an Apache Kafka cluster for low-latency delivery and durably archived in a three-node Apache Hadoop (Hadoop distributed file system (HDFS)) system. Each new article is preprocessed into high dimensional embeddings and indexed in a Milvus vector database to enable sub-second semantic retrieval over millions of records. At query or batch time, our retrieval-augmented generation (RAG) module retrieves the top-k relevant embeddings from Milvus and injects them into prompts for DeepSeek-R1, GPT-4o-mini, and Llama 3, models which are hosted, fine tuned, and served via Ollama on an NVIDIA GeForce RTX 3050 Ti GPU for efficient inference and continual learning. The enriched outputs are seamlessly delivered to end users through a Telegram bot programmed in Python using the Telebot library, linking the RAG-enhanced LLMs to an intuitive chat interface. Our Kafka, HDFS, Milvus, RAG, LLM, or Telegram bot pipeline demonstrably improves factual accuracy and topical currency of AI-generated medical insights across clinical decision support, patient engagement and education, drug discovery and development, virtual health assistants, and mental health support, laying the groundwork for truly intelligent, responsive, and data-driven healthcare solutions.
Copyright (c) 2026 Ayoub Allali, Ibtihal Abouchabaka, Najat Rafalia http://creativecommons.org/licenses/by-sa/4.0
Portable system for real-time traffic volume and speed estimation using YOLOv10
Accurate traffic data is essential for effective transportation planning and policymaking. However, in many regions, especially those lacking intelligent infrastructure, data collection remains dependent on manual methods that are labor-intensive, time-consuming, and susceptible to human error. While advanced systems such as closed-circuit television (CCTV) and area traffic control systems (ATCS) offer automation, their high cost and infrastructure requirements limit widespread adoption. This study proposes a portable, low-cost, and real-time traffic monitoring system based on the YOLOv10 object detection algorithm. The system operates using only a smartphone-grade camera (1080 p, 60 fps) and a standard laptop, eliminating the need for expensive installations. It detects, classifies, and counts vehicles as they pass through a predefined region of interest (ROI), and also estimates their speed based on time–distance measurements. Field evaluations using five one-hour urban traffic videos showed excellent agreement with manual counts, achieving a mean absolute percentage error (MAPE) of just 0.30%. Speed estimation trials conducted on sample clips also demonstrated consistent and plausible results. These findings highlight the system’s potential as a scalable and accurate alternative for traffic monitoring in infrastructure-limited environments.
Copyright (c) 2026 Ida Bagus Sradha Nanda, Masrono Yugihartiman, Eko Primadi Hendri, I Made Suartika http://creativecommons.org/licenses/by-sa/4.0
Secure and interoperable electronic health record exchange using blockchain and ECDHE-based access control
Electronic health records (EHRs) act as comprehensive records of health related transactions and essential resources of data in the healthcare sector. However, the integrity and security problems of EHR continue to be inflexible. The architecture of blockchain-enabled EHR addresses the issues of integrity efficiently. This paper developed the decentralized patient centric healthcare data management (PCHDM) system using blockchain enabled EHR architecture for addressing problems with access control, record privacy and data confidentiality. This framework places the patient at the control center, ensuring secure storage of EHR records and obtaining efficient data management through the integration of blockchain and interplanetary file system (IPFS). To prevent access by unauthorized users, the proposed elliptic curve Diffie-Hellman ephemeral (ECDHE) mechanism incorporates smart contract-enabled access control for managing EHR transactions and enforcing access strategies. This architecture incorporates hyperledger fabric endorsement policies (HFRP) to address scalability problems while preserving patient privacy and securing medical data. The developed method secures the EHR data and facilitates the data exchange across heterogeneous healthcare platforms, ensuring standard communication among different EHR systems. The architecture is assessed with parameters of time for block creation, the computational overhead of transaction with encryption key size and EHR upload and download time.
Copyright (c) 2026 Krishna Prasad Narasimha Rao, Selvan Chinnaiyan http://creativecommons.org/licenses/by-sa/4.0
Single hidden layer feedforward neural networks for indoor air quality prediction
Indoor air quality (IAQ) has become a problem because it affects human health, comfort, and productivity. Predicting air quality is a complex task due to the dynamic nature of IAQ variable values simultaneously. In this study, the single hidden layer feedforward neural networks model is used, namely radial basis function (RBF), self-organizing maps (SOM)-RBF, and extreme learning machine (ELM) to classify IAQ. This study also observed the effect of the number of neurons in the hidden layer on the model accuracy and overfitting of each network. The experimental results show that the number of neurons in the hidden layer can affect the accuracy of the RBF and SOM-RBF models. Among the three models used, RBF produces very good training data accuracy but also the most significant overfitting value. The largest overall accuracy was obtained using SOM-RBF, with a value of 86.37%.
Copyright (c) 2026 Dwi Marisa Midyanti, Syamsul Bahri, Ilhamsyah, Zalikhah Khairunnisa, Hafizhah Insani Midyanti http://creativecommons.org/licenses/by-sa/4.0
Change detection and classification of satellite images using convolutional neural network
Satellite and airborne imagery, collectively known as earth observation imagery, are images of the earth collected from spaceborne or airborne platforms such as satellites and aircraft. Over the last 100 years, with the fast development of aviation, space exploration, and imaging technologies, the coming together of these technologies has been inevitable. Earth observation imagery has many applications in regional planning, geology, reconnaissance, fishing, meteorology, oceanography, agriculture, biodiversity conservation, forestry, landscape, intelligence, cartography, education, and warfare. With the rise in the number of these airborne and spaceborne imaging platforms being deployed by government and private entities alike, the capability to sift through and analyze vast amounts of data generated by these platforms is the need of the hour. With the exponential improvement in the computational capabilities of computers over the last half a century, analysts are exceedingly moving towards the practice of artificial intelligence, machine learning (ML), and computer vision solutions to automate a large part of the processes employed in analyzing earth observation imagery. This work recommends a workflow to perceive and classify changes in earth observation imagery of a given area by utilizing the vast flexibility that convolutional neural networks (CNN) provide.
Copyright (c) 2026 Raghavendra Srinivasaiah, Santosh Kumar Jankatti, Manjunath Ramanna Lamani, Niranjana Shravanabelagola Jinachandra http://creativecommons.org/licenses/by-sa/4.0
Deep learning-based cervical cancer detection via colposcopy images integrated into an Android mobile application
Cervical cancer is a form of cancer that develops in the cells of the cervix, the lower part of the uterus that connects the uterus to the vagina. Early detection is essential for improving the chances of recovery from cervical cancer. One method for early detection is colposcopy image analysis, a medical procedure that examines the cervix and captures images for evaluation. These images were analyzed to observe color changes after the visual inspection with acetic acid (VIA) process. However, this analysis requires experienced and specially trained medical personnel. To address this challenge, a system that can automatically classify cervical cancer images is needed. Therefore, researchers proposed designing and developing an Android mobile application to enable early detection of cervical cancer using the convolutional neural network (CNN) algorithm. The CNN model was tested using test data to evaluate its performance. The optimized CNN model utilizing the ResNet50 architecture achieved 86% test accuracy, 85% precision, and 87% recall. The test results indicate that the model's accuracy is consistent before and after its implementation on the mobile application, confirming the effectiveness of both the model and its implementation as diagnostic tools.
Copyright (c) 2026 Retno Supriyanti, Arsil Kultura Anzil, Yogi Ramadhani, Suroso, Wahyu Widanarto, Muhammad Alqaaf, Kartika Dwi Hapsari, Futiat Diana Kartika http://creativecommons.org/licenses/by-sa/4.0
Human activity recognition using selective kernel network-2D convolutional neural network with ArcFace loss
Human activity recognition (HAR) is a widely adopted technique in applications requiring accurate identification of human actions. However, HAR approaches often face challenges in generalizing across complex datasets with multi-view variations, resulting in reduced classification accuracy. Existing classifiers face shortcomings in predicting human activities due to the presence of irrelevant video frames, leading to frequent misclassifications. This research proposes a selective kernel network-2D convolutional neural network with additive angular margin loss for deep face recognition (SKN-2D-CNN with ArcFace loss) to recognize human activity effectively. SKN dynamically adapts the receptive field for learning multi scale spatial features, enhancing the recognition of intricate human activities with varying motion scales. In the embedding space, ArcFace loss introduces an angular margin penalty that improves inter-class separability and intra class compactness for recognition. Together, the proposed method minimizes misclassification in human activity by improving the robustness of feature representation. Feature extraction using visual geometry group 19 (VGG19) captures spatial features like edges, textures and shapes from video frames, enhancing the model’s ability to differentiate between complex human activities. The proposed method achieves high accuracy of 99.16 and 98.75% on the UCF101 and HMDB-51 datasets, respectively, when compared with existing methods such as CNN and bidirectional gated recurrent unit (BiGRU).
Copyright (c) 2026 Banushri Srinivasaiah, Jagadeesha Ramegowda http://creativecommons.org/licenses/by-sa/4.0
Multi-scale features assisted knowledge distillation vision transformer for land cover segmentation and classification
The most significant problem in remote sensing interpretation is semantic segmentation, which attempts to give each pixel in the image a particular class. This research work follows the various steps, such as pre-processing, segmentation, and classification. Initially, high spatial resolution remote sensing images (RSI) are collected from the open-source dataset. In the pre processing stage, an improved guided filter (Imp-GF) is used to remove various noises from images. Next, the segmentation is done by using a knowledge distillation-based vision transformer approach integrated with an atrous spatial multi-scale pyramidal module (KD-MuViTPy). Based on the segmented image, land cover classes such as vegetation, urban areas, forest, water bodies, and roads are classified. The proposed method outperformed the Bhuvan satellite dataset, achieving better accuracy, precision, recall, F1 score, Dice score, intersection over union (IoU), and Kappa score at values of 98.01%, 98.99%, 97.49%, 98.23%, 98.23%, 96.55%, and 95.91%, respectively.
Copyright (c) 2026 Sujata Arjun Gaikwad, Vijaya Musande http://creativecommons.org/licenses/by-sa/4.0
TAHRF: enhancing personalized tourism recommendations with dynamic adaptation
The rapid growth of online tourism data intensifies information overload, while conventional recommender systems struggle with sparsity, cold-start issues, and single-criteria ratings. This paper presents the trust-aware hybrid recommendation framework (TAHRF), which integrates user-item trust propagation, multi-criteria ratings, and dynamic preference adaptation. TAHRF employs Euclidean-Jaccard trust metrics, item connectivity, and rating consistency, combined with a feedback-driven weighting mechanism. Experiments on TripAdvisor datasets show superior performance: mean absolute error (MAE) reduced to 0.98 (restaurants) and 0.71 (hotels), outperforming multi-criteria tensor-based collaborative filtering (MC-TeCF) baselines. TAHRF also achieves higher precision@5, with coverage maintained under extreme sparsity. Ablation studies confirm the critical role of trust propagation, multi-criteria analysis, and adaptive weighting. TAHRF advances personalized, transparent, and adaptive tourism recommendations.
Copyright (c) 2026 Mohamed Badouch, Mehdi Boutaounte http://creativecommons.org/licenses/by-sa/4.0
Centrality-optimized coalition formation: a genetic algorithm approach with leadership attributes
In graph theory, centrality is often assessed using traditional methods such as closeness centrality, which measures the average shortest path length between nodes in a network. In this study, we primarily focus on developing the proposed approach and demonstrating its effectiveness through initial experimental results. A novel genetic algorithm (GA)–based method named centrality–optimized leadership coalition formation (COLCF) has been designed. It emphasizes actual agent distances according to closeness centrality and leadership attributes in group formation. We detail the COLCF algorithm, present empirical case studies, and provide efficiency comparisons. In accordance with our simulation results, the proposed algorithm is capable of capitalizing on the ideal coalition structure for achieving high closeness centrality when incorporated with leadership attributes. The experimental results demonstrate the algorithm’s robustness and effectiveness in addressing complex coalition formation challenges.
Copyright (c) 2026 Anon Sukstrienwong, Sorapak Pukdesree http://creativecommons.org/licenses/by-sa/4.0
Optimization of maximum power point tracking in wind energy systems: a comparative study of ant colony and genetic algorithms
This research focuses on optimizing maximum power point tracking (MPPT) in wind energy conversion systems (WECS) using ant colony optimization (ACO) and genetic algorithm (GA). The study evaluates these two metaheuristic techniques to optimize the parameters of a proportional integral-derivative (PID) controller in order to maximize power output in a permanent magnet synchronous generator (PMSG)-based system. Simulations conducted in MATLAB/Simulink show that both ACO and GA effectively enhance MPPT performance by improving power output, DC bus voltage regulation, and torque stability. The results demonstrate the potential of metaheuristic algorithms to optimize wind energy conversion efficiency and support sustainable energy development.
Copyright (c) 2026 Najoua Mrabet, Chirine Benzazah, Mohssine Chakib, Adil Ziraoui, Ahmed El Akkary, Najma Laaroussi http://creativecommons.org/licenses/by-sa/4.0
Robust UAV localization of ground sensors in urban environments via path loss refinement and geometric selection
Localizing ground sensors with unmanned aerial vehicles (UAVs) in dense urban environments is challenging because multipath and non-line-of-sight (NLoS) propagation distorts path loss (PL) measurements. This paper proposes a two-stage UAV localization framework that refines PL data and selects geometrically stable waypoint subsets before position estimation. In stage 1, PL samples are spatially smoothed by averaging measurements at neighboring UAV waypoints to reduce localized fluctuations. In stage 2, waypoint subsets are filtered using non-collinearity and non-adjacency constraints, and sensor positions are estimated using weighted least squares (WLS) and particle swarm optimization (PSO), with final estimates averaged across valid subsets. Wireless InSite ray-tracing simulations show that the framework reduces mean absolute error (MAE) from over 150 m to approximately 8.5 m. The proposed approach improves the practicality of UAV-assisted localization for urban internet of things (IoT) sensor deployments.
Copyright (c) 2026 Ahmed M. A. A. Elngar, Heng Siong Lim, Yee Kit Chan, Yaser Awadh Bakhuraisa, Ida Wahidah http://creativecommons.org/licenses/by-sa/4.0
Brain tumor segmentation and classification using artificial hummingbird optimization algorithm
The time and medical personnel experience are the only factors that determine whether brain tumors can be manually identified from numerous magnetic resonance imaging (MRI) pictures in medical practice. Many frameworks based on brain tumors are diagnosed using both deep learning and machine learning. This study proposes a Wasserstein deep convolutional generative adversarial network (WDCGAN) optimized using the artificial hummingbird optimization algorithm (AHBOA) for brain tumor segmentation and classification (SCBT). First, the BraTS dataset is used to gather the input data. Then it is pre-processed consuming adaptive self guided filtering (ASGF) and the result is segmented using fuzzy possibilistic C-ordered mean clustering (FPCOMC). After that, features are extracted using the dual tree complex discrete wavelet transform (DT-CDWT). The characteristics of feature extracted are fed to WDCGAN for effectively categorize the various parameters. Then the proposed MATLAB is used to implement the technique, and the performance measurements like F1-score, accuracy, error rate, precision, sensitivity, mean square error, receiver operating characteristic (ROC), and computational time are analyzed. The WDCGAN-AHBOA-SCBT method significantly improves precision in SCBT by integrating adaptive optimization strategies, resulting in 32.18, 32.75, and 32.90% higher precision in contrast to current techniques. This demonstrates that the approach is more accurate and effective, making it a reliable tool for medical diagnosis.
Copyright (c) 2026 Radhakrishnan Karthikeyan, Arappaleeswaran Muruganandham http://creativecommons.org/licenses/by-sa/4.0
Optimizing Javanese script recognition using fine-tuned ResNet-18 and transfer learning
Javanese script, known as Aksara Jawa, is an ancient script used in historical and cultural texts. However, its complex character structure poses challenges for accurate recognition in modern digital applications. This study proposes an optimized classification approach for Aksara Jawa using a fine-tuned ResNet-18 model combined with the Adam optimization algorithm and transfer learning on the Hanacaraka image dataset. By leveraging the residual learning framework of ResNet-18, the model effectively captures deep spatial features of the script while reducing vanishing gradient issues. Fine-tuning is applied to enhance model adaptability, ensuring robust feature extraction specific to Javanese characters. Experimental results demonstrate that the fine-tuned ResNet-18 outperforms conventional deep learning architectures in recognizing Aksara Jawa characters, achieving 93% precision, 91% recall, 91% F1-score, and 91% accuracy. The study further explores the impact of hyperparameter tuning, data augmentation, and dropout regularization on model performance. The findings highlight the effectiveness of transfer learning in resource-limited scenarios, making it a feasible solution for optical character recognition (OCR) applications in Javanese script digitization. This research contributes to the preservation of cultural heritage through advancements in deep learning-based script recognition.
Copyright (c) 2026 Nur Fateah, Subhan, Yahya Nur Ifriza http://creativecommons.org/licenses/by-sa/4.0
Development of generalized principal component analysis using multiple imputation genetic algorithm
In this study, we propose an innovative method called the integrated GPCA MIGA, which integrates the multiple imputation genetic algorithm (MIGA) and generalized principal component analysis (GPCA) to perform missing value imputation and data dimensionality reduction simultaneously. The approximated original data produced by GPCA serves as the basis for MIGA to update missing values in the next iteration. At the same time, GPCA refines the low-dimensional representation using the latest imputation results from MIGA, thereby balancing the accuracy of missing value imputation and the stability of dimensionality reduction. The objective of this study is to evaluate the performance of the integrated GPCA-MIGA and analyze trends in human development at the district/city level in Indonesia. The findings of this study show that the integrated GPCA-MIGA effectively reduces the dimensionality of data containing missing values compared to other methods. The integrated GPCA-MIGA method was applied to human development data. The results were then visualized using a biplot, which revealed that human development trends in Jayawijaya from 2019 to 2022 indicate progress in school enrollment rates for ages 16–18 years.
Copyright (c) 2026 Fahrezal Zubedi, I Made Sumertajaya, Khairil Anwar Notodiputro, Utami Dyah Syafitri http://creativecommons.org/licenses/by-sa/4.0
Classifying mental workload of esports players using machine learning
Electrodermal activity (EDA) peak counts, derived from both tonic and phasic components, are widely used as physiological proxies for mental workload in cognitively demanding tasks, such as esports. However, their specificity remains uncertain, particularly given potential confounding effect of time-on-task. This study analyzes 92 competitive gameplay sessions from a multimodal esports dataset using three decomposition techniques: convex decomposition (cvxEDA), sparse deconvolution (sparseEDA), and time varying sympathetic activity (TVSymp). From each method, phasic, and tonic peak counts (TPC), as well as their normalized rates, were extracted. We examined their relationship with self-reported workload through correlation analyses, partial correlations controlling for session duration, and linear mixed-effects models (LMMs). While both peak types exhibited strong positive correlations with gameplay duration (r=0.915 for phasic and r=0.856 for tonic), their association with perceived workload vanished once time was accounted for. Across methods, TVSymp yielded the highest discriminative validity with an area under curve (AUC) of 0.880 in classifying high versus low workload. Machine learning (ML) classifiers trained solely on EDA-based features under a leave-one-subject-out (LOSO) scheme outperformed multimodal models that incorporated heart rate variability (HRV). These results underscore need to disentangle temporal structure from cognitive signals when interpreting EDA and call into question the assumption that EDA peak counts alone reliably encode mental workload across individuals.
Copyright (c) 2026 Aisy Al Fawwaz, Osmalina Nur Rahma, Sayyidul Istighfar Ittaqillah, Angeline Shane Kurniawan, Revita Novianti Putri, Richa Varyan, Aura Adinda, Khusnul Ain, Rifai Chai http://creativecommons.org/licenses/by-sa/4.0
Anefficient ensemble tree-based framework for intrusion detection in industrial internet of things networks
The increasing complexity of cyber threats in industrial internet of things (IIoT) environments necessitates robust, scalable, and efficient intrusion detection systems (IDS). This study presents a novel ensemble tree-based framework that integrates gradient boosting-based machine learning models, including XGBoost, LightGBM, AdaBoost, and CatBoost, with mutual information (MI) feature selection and synthetic minority over-sampling technique (SMOTE) to enhance multiclass intrusion detection performance. The framework is designed to handle large-scale, imbalanced datasets efficiently while maintaining high classification accuracy. Performance evaluation using the telemetry of network (ToN)-IoT benchmark dataset demonstrates that the proposed models achieve a high accuracy of 99.43%, with a strong precision-recall balance and an F1-score, ensuring minimal false positive rates of 0.08%. By leveraging MI for optimal feature selection and SMOTE for data balancing, this approach effectively enhances detection capabilities in highly dynamic network environments. The lightweight architecture and reduced execution time make the framework well-suited for deployment in edge or fog nodes within smart industrial environments. The proposed solution provides a scalable and adaptable methodology for securing IIoT networks, making it applicable for real-time intrusion monitoring and further cybersecurity advancements in industrial systems.
Copyright (c) 2026 Mouad Choukhairi, Oumaima Chentoufi, Ouail Choukhairi, Youssef Fakhri http://creativecommons.org/licenses/by-sa/4.0
Predicting trapped victims in debris using signal analysis ensemble classification
One major difficulty in pervasive computing is trapped human detection in search and rescue (SAR) scenarios. Accurately identifying trapped individuals is challenging due to noisy data and the curse of dimensionality. When non-line-of-sight (NLOS) conditions are present during catastrophic occurrences, the curse of dimensionality can result in blind spots in detections because of noise and uncorrelated data. Because machine learning algorithms are incredibly accurate, this work focuses on using ultra wideband (UWB) radar waves to detect individuals in NLOS scenarios and leveraging wireless communication to harmonize information. The paper uses ensemble methods to extract features using independent component analysis (ICA) and evaluate classification performance on both static and dynamic datasets. The testing results confirm the effectiveness of the proposed strategy, with classification accuracies of 87.20% for dynamic data and 88.00% for static data. Lastly, during SAR operations, our approach can assist engineers and scientists in making quick decisions.
Copyright (c) 2026 Enoch Adama Jiya, Ilesanmi B. Oluwafemi, Olayinka O. Ogundile, Oluwaseyi P. Babalola http://creativecommons.org/licenses/by-sa/4.0
Automated ergonomic sitting postures detection for office workstation using XGBoost method
Sedentary office work increases musculoskeletal risk, underscoring the need for non-intrusive, real-time posture monitoring. This study presents a computer vision approach that classifies ergonomic versus non-ergonomic sitting postures using upper body key points extracted by MoveNet thunder. Images from 30 participants were captured from frontal and side views, and labeled according to SNI 9011:2021 criteria. Seventeen key points were detected, with head-to-hip landmarks retained, then normalized and centered. Three classifiers—adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and a multi-layer perceptron (MLP)—were trained and evaluated with 10-fold stratified cross-validation. XGBoost achieved the best performance, with accuracy 93.0%±1.9%, precision 94.6%, recall 91.4%, F1-score 92.9%, and area under the receiver operating characteristic curve (ROC-AUC) 0.974±0.010, outperforming MLP and AdaBoost. The method supports privacy-preserving, on-device inference and is suitable for integration into smart office systems to reduce exposure to high-risk postures. Limitations include controlled capture conditions and an upper body focus; future work will expand posture taxonomy and real-world deployment.
Copyright (c) 2026 Theresia Amelia Pawitra, Farida Djumiati Sitania, Anindita Septiarini, Hamdani http://creativecommons.org/licenses/by-sa/4.0
Air quality prediction using boosting-based machine learning models for sustainable environment
High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.
Copyright (c) 2026 Ahmad Fauzi, Maharina, Jamaludin Indra, Ayu Ratna Juwita, Agustia Hananto, Euis Nurlaelasari http://creativecommons.org/licenses/by-sa/4.0
Feature selection in supervised machine learning: a case study of poverty dataset in West Java, Indonesia
West Java, one of the largest provinces in Indonesia with a population exceeding 50 million, reported a poverty rate of 7.62% in 2023. Data from the national socio-economic survey or survei sosial ekonomi nasional (SUSENAS) show that poverty is multidimensional, encompassing aspects of employment, education, sanitation, housing, food security, technology, and government assistance. Addressing this complexity requires identifying the most influential factors that determine household welfare. This study applies and compares three feature selection approaches—filter, wrapper, and embedded—to the SUSENAS dataset to evaluate their effectiveness in identifying key poverty determinants. By prioritizing variables with the strongest predictive power, the study provides an evidence-based framework for more efficient and targeted poverty alleviation strategies. Results indicate that the information filter method combined with random forest (RF) and the least absolute shrinkage and selection operator (LASSO) embedded method combined with logistic regression (LR) deliver the best performance, improving model accuracy while reducing more than 65% of irrelevant features. The selected indicators highlight critical sectors such as food security, housing, and access to technology, which can serve as short-term policy priorities. In the long term, broader interventions in education, employment, sanitation, and government support are recommended. These findings demonstrate how data-driven feature selection can guide effective policy design for reducing poverty in West Java.
Copyright (c) 2026 Sean Marshelle, Septian Rahardiantoro, Anang Kurnia http://creativecommons.org/licenses/by-sa/4.0
AI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks
Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.
Copyright (c) 2026 Kassem Danach, Hassan Rkein, Alaaeddine Ramadan, Hassan Harb, Bassam Hamdar http://creativecommons.org/licenses/by-sa/4.0
An efficient method to improve machine learning decoders using automorphisms group
The decoding of error-correcting codes (ECCs) is a critical aspect of communication systems, yet traditional decoding techniques can often be computationally demanding or ineffective for certain codes, necessitating innovative approaches. In this study, we introduce a hybrid approach that combines machine learning and automorphism techniques to optimize the decoding process. Specifically, we train multilayer perceptron (MLP) models to learn the mapping between error syndromes and their corresponding errors. While these models exhibit robust learning capabilities, their performance sometimes does not reach 100%. To mitigate this limitation, we exploit the automorphism group of the code—a set of structure-preserving transformations—to convert the errors that the MLP struggles to decode into ones it can process more effectively. We use a minimum number of p permutations, pre-calculating and storing all possible automorphisms to ensure computational efficiency. Our experimental results reveal that this hybrid approach substantially enhances the decoding performance of the MLP model, presenting a promising avenue for decoding ECCs. Importantly, this approach is not limited to MLP models and can be applied to any machine learning model with a learning score less than 100%, broadening its applicability and impact. By integrating machine learning with traditional algebraic coding theory, we propose a new paradigm that holds the potential to revolutionize the design of decoding systems, making them more efficient and effective.
Copyright (c) 2026 Imrane Chemseddine Idrissi, Said Nouh, El Mehdi Bellfkih, Mohammed El Assad, Abdelaziz Marzak http://creativecommons.org/licenses/by-sa/4.0
Evaluation of artificial intelligence algorithms to estimate water quality parameters using satellite images
The Ciénaga de la Virgen (Virgen Swamp) is a coastal lagoon in Cartagena de Indias that provides multiple ecosystem services in northern Bolívar. This ecosystem has faced anthropogenic pressure from city growth and improper water resource management, including wastewater and agrochemical discharges. Consequently, environmental authorities must monitor certain sites within the water body and extrapolate the data across its entire expanse. In this study, predictive tools are applied to determine water quality parameters such as chlorophyll-a (CL-a), dissolved oxygen (DO), total suspended solids (TSS), and salinity. This is achieved by correlating traditionally obtained data with the spectral response of medium-resolution satellite images, adjusted using artificial intelligence (AI) algorithms. Support vector machine (SVM) algorithms were used for regression, random forests (RF), and artificial neural networks (ANN), achieving an accuracy of 79% for CL-a, 95% for DO, 89% for TSS, and 96% for salinity. Validation was performed using mean absolute percentage error (MAPE) statistical metrics and root mean square error (RMSE).
Copyright (c) 2026 Julio Cesar Anaya-Valenzuela, Gloria Yaneth Florez-Yepes, Yeison Alberto Garcés-Gómez http://creativecommons.org/licenses/by-sa/4.0
An artificial intelligence technology for promoting hom-thong banana agriculture system
The hom-thong banana, being a high-value Thai export variety, is facing significant risk from disease outbreaks affecting crop yield and quality. Traditional visual inspection methods in detection of diseases are labor consuming, error-prone. This research addresses these limitations by developing a new artificial intelligence (AI)-based automatic disease detection system for the hom-thong banana industry on top of cutting-edge computer vision technology. The study employed deep learning object detection models, contrasting Roboflow, you only look once (YOLO)v11, and YOLOv12 architectures, which were trained on a large dataset of 2,576 images of Thai banana plantations. With systematic data augmentation techniques, the dataset was augmented to 6,184 images of seven types of disease under varied environmental conditions. The method entailed extensive preprocessing and evaluation of performance through precision, recall, and mean average precision (mAP) metrics. Outcomes indicated that YOLOv12 outperformed with 93.3% accuracy, 83.3% sensitivity, and 86.3% mAP@50 compared to standard inspection schemes. This research is applicable to Thailand's smart agriculture initiative by providing farmers with low-cost, accurate, and effective disease monitoring equipment. The application of this AI system has the ability to enhance the yield of crops, reduce losses, and enhance the competitiveness of Thai banana exports in the global market, in support of sustainable agricultural development.
Copyright (c) 2026 Ratsames Tanveenukool, Suwit Somsuphaprungyos, Boonyarit Nokkurth, Likit Chamuthai, Patumwadee Bonguleaum, Parinya Natho http://creativecommons.org/licenses/by-sa/4.0
Dynamic attack pattern-aware intelligent cyber-physical intrusion detection system for internet of things-edge networks
The proliferation of Internet of Things (IoT) technologies, coupled with the convergence of edge computing infrastructures, has revolutionized modern cyber-physical systems (CPS). However, the inherently distributed architecture of these systems increases their vulnerability to advanced network-level cyber threats, posing significant challenges to data integrity and system reliability. Traditional machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS) often fall short in identifying evolving attack vectors due to their limited adaptability. To address these limitations, this paper introduces a novel Dynamic Attack Pattern-Aware Improvised Weighted Gradient Boosting (DAPA-IWGB) model designed to enhance real-time threat detection and adaptive response within IoT-edge-enabled CPS environments. The DAPA-IWGB framework synergizes gradient tree boosting with an enhanced loss function handling covariate shift, while incorporating statistical monitoring mechanisms for dynamic covariate shift recognition and continuous learning. Comprehensive experimental validation using two prominent benchmark datasets ToN-IoT and UNSW-NB15 demonstrates the proposed model’s robustness and superior performance, achieving detection accuracies of 99.921% and 99.93%, respectively. Comparative evaluations highlight substantial improvements in detection accuracy, adaptability, and reliability over existing IDS solutions. The results affirm the effectiveness of the DAPA-IWGB model in fortifying the security posture of distributed IoT-based CPS against sophisticated and evolving cyber threats.
Copyright (c) 2026 Vishala Ibasapura Lakshminarayanappa, Kempahanumaiah M. Ravikumar http://creativecommons.org/licenses/by-sa/4.0
Pneumonia classification from chest X-rays using significant feature selection and machine learning
The chest X-ray images of normal lungs differ only subtly from those of lungs with pneumonia, making image-based diagnosis highly challenging. To address this issue, we developed a machine learning (ML)-based, lightweight, end-to-end Python package that processes chest X-ray images, implements robust feature selection methods, and classifies the images using various algorithms. While many studies have focused on improving classification accuracy using newer methods, few have addressed the interpretability of the extracted features or the growing computational demands of complex models. We used four publicly available datasets and extracted first-order, textural, and transform-based radiomic features to test our package. Features were selected using the Shapley additive explanations (SHAP) combined with recursive feature elimination (RFE) and stability selection algorithms. Our final solution contains a method that extracts a finite set of features identified by stability selection and feeds them as inputs into classical ML algorithms. Our model achieved 98% accuracy on the primary dataset, and 97%±1, 96%±2, and 94%±2% accuracy on the other three datasets. Our approach is fast, self-contained, and requires only an ideal set of features, making it suitable for resource-constrained clinical environments.
Copyright (c) 2026 Yugandhar Chodagam, Manjunatha Hiremath http://creativecommons.org/licenses/by-sa/4.0
Web-based geothermal drilling stuck pipe prediction using decision tree algorithm
In geothermal drilling operations, data from rig-mounted sensors play a crucial role in maintaining operational efficiency and preventing drilling failures. However, sensor uncertainties and complex subsurface conditions can lead to stuck pipe incidents, causing significant non-productive time and financial losses. This study proposes web-based drilling monitoring system integrated with machine learning (ML) to predict stuck pipe occurrences in geothermal drilling. Several ML algorithms—decision tree (DT), random forest (RF), naïve Bayes (NB), multilayer perceptron (MLP), and support vector machine (SVM)—were evaluated using geothermal drilling data from an Indonesian geothermal project conducted in 2023. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied to the training dataset. Feature selection was performed using the correlation coefficient method, and predictions were generated using a 5 minute sliding window. Among the evaluated models, the DT consistently demonstrated superior performance across multiple prediction horizons (PH), achieving an accuracy of 97.4%, precision of 98.6%, recall of 72.9%, and a ROC-AUC of 0.729 using the top five selected features. The trained model was integrated into web-based monitoring platform that provides visualization and predictive alerts. This system enables early detection and better decision-making, helping improve drilling efficiency, reduce stuck pipe risks, and enhance operational safety.
Copyright (c) 2026 Rosyihan Muhtadlor, Nur Rohman Rosyid, Anni Karimatul Fauziyyah, Lalu Hendra Permana Setiawan, Irfan Saputra, Pavel Stasa, Filip Benes, Muhammad Syafrudin, Ganjar Alfian http://creativecommons.org/licenses/by-sa/4.0
Explainable rice yield from Sentinel-1 and Sentinel-2 satellite data for food security
Reliable, explainable crop-yield estimates are essential for food-security planning in data-sparse regions. We present a transparent pipeline for district-level (regency) rice yield prediction in Indonesia that fuses Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 normalized difference vegetation index (NDVI), and weather/reanalysis features. The system standardizes inputs per province, fixes a 16-day temporal key, and uses a small, auditable ensemble of tree models (gradient boosting+light gradient-boosting machine (LightGBM)). Trained on ≤2023 data and evaluated on a 2024 temporal hold-out, a joint West Java ∪ South Sulawesi model achieves root mean square error (RMSE)≈0.80 t/ha, mean absolute error (MAE)≈0.48 t/ha, and R-squared (R²)≈0.33 at regency scale. Feature importances and Shapley additive explanations (SHAP) confirm that phenology (NDVI peak, integral, green-up/senescence), SAR backscatter (vertical transmit-vertical receive/vertical transmit-horizontal receive (VV/VH)), and wind/pressure are consistent drivers under monsoon conditions. The workflow supports routine, one-click provincial updates and produces parity maps and error bars for actionable diagnostics. These results demonstrate that combining Sentinel-1, Sentinel-2, and basic meteorology delivers accurate, interpretable, and operational yield signals suited to Indonesia’s food security needs, while providing a clear recipe for scaling to additional provinces.
Copyright (c) 2026 Dhimas Tribuana, Usman Sattar, Baharuddin Mide, Dayanti Dayanti http://creativecommons.org/licenses/by-sa/4.0
Predicting university student dropouts in Latin America using machine learning
In the university context, student dropout has become one of the most recurring problems, both in the short and long term. The objective of this research was to develop a predictive model using the random forest (RF) algorithm to identify patterns associated with university dropout. To achieve this, the knowledge discovery in databases (KDD) methodology was applied, which encompasses the stages of selection, preprocessing, transformation, data mining, and interpretation of results. The RF model demonstrated superior performance compared to other evaluated models, achieving an accuracy of 87%, a precision of 86%, a recall of 85%, an F1-score of 85%, and an receiver operating characteristic (ROC) area under the curve (AUC) of 0.91, highlighting its high predictive capability compared to other techniques analyzed. Therefore, the application of the proposed model is recommended in various university institutions in order to identify potential dropout cases at an early stage.
Copyright (c) 2026 Laberiano Andrade-Arenas, Inoc Rubio Paucar, Margarita Giraldo Retuerto, Cesar Yactayo-Arias http://creativecommons.org/licenses/by-sa/4.0
Deformable spatial pyramid pooling-enhanced EfficientNet with weighted feature fusion for pomegranate fruit disease diagnosis
Pomegranate is a fruit of high nutritional and economic importance. Still, it is highly susceptible to different diseases during its growing stages, leading to significant yield losses and financial setbacks for farmers. This article proposes a novel disease detection model that integrates handcrafted features with deep features extracted using a developed deformable spatial pyramid pooling (DSPP)-EfficientNet architecture. Handcrafted features such as color (RGB and HSV histograms), texture features from gray level co occurrence matrix (GLCM), and shape attributes extracted from contour descriptors and Hu moments are captured and fused with deep features by weighted fusion strategy, resulted in the most discriminative information. The fused features are categorized using a support vector machine (SVM) in a classification phase, which effectively classifies different classes of pomegranate fruit diseases. The combined deep and handcrafted features obtained 96.66% accuracy, 96.26% precision, 96.50% recall, 96.37% F1 score, and 95.64% specificity on the pomegranate fruit disease dataset which compared to existing techniques.
Copyright (c) 2026 Harish Bommenahalli Mallikarjunaiah, Balaji Prabhu Baluvaneralu Veeranna http://creativecommons.org/licenses/by-sa/4.0
Stroke prediction using data balancing method and extreme gradient boosting
Stroke is one of the leading causes of death worldwide, creating an urgent need for effective early detection systems, particularly because conventional methods often struggle with class imbalance and produce biased evaluations. Previous studies have primarily focused on accuracy while overlooking model consistency, data pre-processing quality, and probability-based evaluation. This study evaluates model performance under three conditions: original data using extreme gradient boosting (XGBoost) with scale_pos_weight, original data using the easy ensemble classifier, and class-balanced data generated using random oversampling (ROS), adaptive synthetic sampling (ADASYN), and synthetic minority over-sampling technique (SMOTE). Each model underwent missing value handling, normalization, feature preparation, and hyperparameter optimization using grid search. Performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), confidence intervals, calibration curves, Shapley additive explanations (SHAP), decision curve analysis (DCA), and external validation. The results demonstrate that data resampling significantly improves performance, with the XGBoost-SMOTE combination achieving the best results, including an accuracy of 0.99, AUROC of 0.998, and AUPRC of 0.986, outperforming the other approaches. This method provides more consistent and balanced predictions, supporting the application of artificial intelligence for early stroke risk identification.
Copyright (c) 2026 Abd Mizwar A. Rahim, Anna Baita, Firman Asharudin, Wahid Miftahul Ashari, Walidy Rahman Hakim, Andriyan Dwi Putra, Supriatin, Eko Pramono http://creativecommons.org/licenses/by-sa/4.0
Detection and forecasting of mental health disorders using machine learning models on social media data
The detection and classification of depression and other mental disorders have become crucial in the modern era, particularly with the growing reliance on social media for self-expression. Existing systems often face challenges like limited prediction accuracy, difficulty forecasting future mental illnesses, and handling both clinical and non-clinical data. This study proposes a novel analytical model that not only screens individuals' current mental health status from social media content but also predicts the likelihood of future mental health issues. The proposed methodology integrates classical machine learning (ML) models, ensemble learning approaches, and pretrained models for enhanced detection and forecasting accuracy. The outcome shows that pre-trained language models accomplished maximized F1-score and overall performance significantly better than conventional ML and ensemble models. The system outperforms existing methods with a significant accuracy improvement, achieving 90.9% overall accuracy, a 7.2% improvement over traditional ML classifiers, 5.8% over ensemble models, and 11.3% over language models.
Copyright (c) 2026 Chaithra Indavara Venkateshagowda, Roopashree Hejjajji Ranganathasharma, Yogeesh Ambalagere Chandrashekaraiah, Narve Lakshminarayan Taranath http://creativecommons.org/licenses/by-sa/4.0
Comparison between ensemble and linear methods for website phishing detection
In the current digitalized world, the notion of cybersecurity has become crucial in everyday life, and the issue of privacy takes the leading role in the technological agenda of the global community. One such social engineering attack that is currently prevalent is phishing, which is a common technique used by cybercriminals to intercept sensitive data. Despite the presence of certain limitations which can restrict its usefulness, machine learning (ML) has evolved into an interesting approach to identify phishing attacks. Cloud ML is an effective solution that uses cloud computing solutions to create, train, and deploy models that provide a faster and more accurate result as well as support large datasets. This paper compares the ensemble method of Amazon SageMaker’s AutoML tool, AutoGluon, with the linear method of SageMaker’s linear learner algorithm for website phishing detection. Key factors examined include training techniques, training time, batch transform time, endpoint prediction time, and model accuracy. The results demonstrate that while AutoGluon outperforms linear learner in terms of accuracy and prediction speed, linear learner is faster in training and batch transform processes.
Copyright (c) 2026 Saba Hussein Rashid, Saba Alaa Abdulwahhab, Farah Amer Abdulaziz http://creativecommons.org/licenses/by-sa/4.0
Deep feature-based multi-class Alzheimer’s disease classification with statistical performance evaluation
This study evaluated the performance of multiple machine learning classifiers for the classification of Alzheimer’s disease (AD) stages using deep features extracted from a pre-trained SqueezeNet model. Magnetic resonance imaging (MRI) scans were processed through SqueezeNet to generate high-dimensional feature vectors, which were then used as achieved an accuracy of 94.78% input to six classifiers: k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), neural network (NN), naive Bayes (NB), and logistic regression (LR). Models were assessed using a 70/30% training-testing split and 5-, 10-, and 20-fold stratified cross validation. Principal component analysis (PCA) was applied to retain 99% of variance. On the original dataset consisting of 6,400 images, KNN has achieved 97.48% accuracy and 0.998 area under the curve (AUC), and when a larger dataset of 44,000 images was used it achieved an accuracy and of 94.78% and an AUC of 0.987, demonstrating the system’s robustness across scales. Statistical tests, including paired t-tests and Wilcoxon signed-rank tests, confirmed that KNN has significantly leveraged from PCA. These outcomes demonstrate that combining deep feature extraction with PCA improved the reliability and efficiency of the classifier for AD stage prediction.
Copyright (c) 2026 Maysaloon Abed Qasim, Marwa Mawfaq Mohamedsheet Al-Hatab, Lubab H. Albak http://creativecommons.org/licenses/by-sa/4.0
Autoencoder and GAN-aided plant disease detection in rice and cotton via hybrid feature extraction and decision tree classification
In agriculture, crop diseases caused by pathogens, including bacteria, viruses, and fungi, pose a significant threat to the effectiveness of agricultural productivity. Some major crops in India such as rice and cotton are adversely impacted, leading to economic loss and loss of production. Timely intervention and sustainable agriculture depend on proper and early identification of diseases. In this paper, we propose a novel plant disease detection framework that integrates generative adversarial network (GAN) based image denoising with feature extraction and decision tree (DT) classification. The GAN module effectively removes noise from agricultural images, enhancing quality and stability under challenging imaging conditions. Following denoising, a combination of color, texture, and gradient features is extracted to obtain rich and discriminative patterns, which are then used to train a DT classifier for disease identification. Experiments are conducted on benchmark datasets comprising rice and cotton leaf images. The proposed system achieves superior performance, with 98.70% accuracy, 98.20% precision, 97.22% recall, and 98.50% F1 score, outperforming existing methods. These results demonstrate that the GAN-based denoising approach, combined with traditional feature-based classification, offers a robust, efficient, and practical solution for modern agricultural disease monitoring systems.
Copyright (c) 2026 Anandraddi Naduvinamani, Jayshri Rudagi, Mallikarjun Anandhalli http://creativecommons.org/licenses/by-sa/4.0
Explainable deep learning for scalable record linkage: a TabNet-based framework for structured data integration
Record linkage is considered a fundamental process for ensuring data quality and reliability, with critical applications in domains such as healthcare, finance, and commerce. A machine learning-based approach for optimizing record linkage in structured datasets is presented in this paper. By integrating hybrid blocking methods (combining standard blocking and sorted neighborhood approaches) with advanced similarity measures, computational overhead is significantly reduced while high accuracy is maintained. The performance of TabNet, a deep learning model designed for tabular data, is compared with traditional deep neural networks (DNNs) in the classification phase. Experimental results on a synthetic dataset of 5,000 records demonstrate that comparable precision and recall are achieved by TabNet to DNNs while execution time is reduced by over 79%. The scalability and efficiency of the proposed method are highlighted by these findings, making it well-suited for large-scale data management tasks. Practical and computationally efficient solutions for record linkage in the era of big data are contributed to by this work.
Copyright (c) 2026 Fatima Zahrae Saber, Ali Choukri, Mohamed Amnai, Abderrahim Waga http://creativecommons.org/licenses/by-sa/4.0
YOLOv5: an improved algorithm for real-time detection of industrial defective pieces
The rapid advancement of communication technologies and the growing demand for artificial intelligence are transforming traditional manufacturing into smart industries. Robotic arms and smart vision cameras are widely adopted to support industrial internet of things (IIoT) applications. Beyond enhancing production efficiency and quality, these technologies play a crucial role in cost reduction, energy savings, and improving operator safety. In this article, we propose an intelligent industrial system using an improved version of the you only look once (YOLO) algorithm for defect detection on production lines. The system integrates robots and cameras to automate defect inspection and classification of manufactured pieces. An updated YOLOv5 model is designed as an end-to-end solution for detecting surface defects in three specific regions. We trained and evaluated the model using custom data tailored to the inspected pieces. The system achieved a 99% mean average precision (mAP) and an 80% recall rate. Additionally, it delivers a 99% detection rate at high speed, enabling real-time surface defect detection. This method not only accurately predicts defective locations but also provides size information, which is critical for assessing the quality of newly produced pieces.
Copyright (c) 2026 Abdelaziz Elbaghdadi, Yassine Yazid1, Ahmed El Oualkadi, Antonio Guerrero-González, Soufiane Mezroui http://creativecommons.org/licenses/by-sa/4.0
Intelligent plant disease detection using twin attention optimal convolutional neural network
Farming is one of the most important ways for people in India to make a living. Rice is a staple food, and when farmers successfully harvest rice crops, pests often attack them, which costs agriculture a lot of money. There are now a lot of new AI-based ways to help with this problem in rice plants. But those ways don’t work very well because they take a long time and make mistakes when sorting things. This article talks about a new hybrid deep learning (DL) method for finding leaf diseases in rice plants. This process has four main steps: pre-processing, segmentation, feature extraction, and classification. A hybrid DL-based twin attention convolutional neural network (CNN) model classifies segmented images into healthy and unhealthy leaves. But this method has the problem of overfitting. An optimization method based on chaotic slime mould (CSM) solves this problem. The proposed method is compared with bidirectional long short-term memory (Bi-LSTM), recurrent neural network (RNN), deep neural network (DNN), and deep belief network (DBN). The suggested method has an overall accuracy of 99.56%, an F-measure of 99.21%, a sensitivity of 99.16%, a specificity of 98.56%, a precision of 99.26%, a mean absolute error (MAE) of 0.004, a mean squared error (MSE) of 0.004, and a root mean square error (RMSE) of 0.06.
Copyright (c) 2026 Prameetha Pai, Namitha Sunkadakatte Jagannatha, Sowmya Thimmegowda, Amutha Somasundram, Nisarga Gondi http://creativecommons.org/licenses/by-sa/4.0
Improving efficiency of autism detection based on facial image landmarks
Autism is a serious mental health problem with long-term effects on life. Therefore, early diagnosis is a topical issue for effective treatment. This study proposes a novel facial landmark transformation-based data augmentation method that allows for the generation of geometric transformations related to facial geometry. This method increases the generalizability and provides a perspective on the role of facial regions in autism detection. The proposed augmentation method ensures the generation of variants that are consistent with the facial image structure and the nature of the facial image. Next, conduct a comprehensive and comparative study with EfficientNet-B0, EfficientNet-B4, ResNet-18, ResNet-50, ResNet-101, MobileNet-V2, DenseNet-121 and DenseNet-201. Also analyze the model's attention over the main regions of the face that are related to facial landmarks. The results clearly show that the models trained with the proposed method outperform the default augmentation method. Specifically, when averaging the measures across the tested models, the results are 0.905417 for accuracy, 0.962133 for area under the curve (AUC), 0.9198 for precision, 0.888333 for recall, and 0.903678 for F1-score. Furthermore, when analyzing the gradient-weighted class activation mapping (Grad CAM) heatmaps, the high-value regions are clearly concentrated on the main areas of the face. Source code is published on GitLab platform.
Copyright (c) 2026 Nguyen Trong Tung, Ngo Duc Vinh, Ha Manh Toan, Do Nang Toan http://creativecommons.org/licenses/by-sa/4.0
Hybrid convolutional networks, hidden Markov models, and autoencoders for enhanced recognition
Recognition problems, including object detection, scene understanding, and fine-grained categorisation, are popular subjects in computer vision. However, it is challenging to model spatial coherence and contextual dependencies in response to changes in configurations. Human Vs computers' ability in perception-although convolutional neural networks (CNNs) do well in the extraction of features, they have high dependence on local receptive fields and are not able to capture long-range spatial relationships and high-order interactions. To alleviate the shortcomings of the current approaches, we present an enhanced hybrid CNNs two dimensional hidden Markov model (2D-HMM) framework that combines 2D-HMM, Markov random fields (MRF) and variational autoencoders (VAEs) into a single model. The model employs 2D-HMMs for pairwise spatial modelling, MRFs for higher order context, and VAEs for stable latent representation learning. Tested on the MNIST and CIFAR-10 benchmark datasets, our approach consistently outperforms the state-of-the-art performance by 98.2% and 89.5%, respectively, with high robustness to noise and occlusion. Results from ablation studies further show that MRFs improve recall by 1.6% and VAEs improve precision by 1.3%, suggesting that they complement each other sufficiently with respect to overall testing performance. This work unifies deep learning and probabilistic graphical models, leading to more interpretable, scalable, and accurate recognition systems.
Copyright (c) 2026 Driss Naji, Kamal Elhattab, Abdelali Joumad, Abdelouahed Ait Ider, Abdelkbir Ouisaadane, Azzeddine Idhmad http://creativecommons.org/licenses/by-sa/4.0
A novel BERT-long short-term memory hybrid model for effective credit card fraud detection
In the rapidly evolving landscape of financial transactions, the detection of fraudulent activities remains a critical challenge for financial institutions worldwide. This study introduces a novel bidirectional encoder representation from transformers (BERT)–long short-term memory (LSTM) hybrid model that integrates both textual and numerical data to enhance credit card fraud detection. Leveraging BERT for deep contextual embeddings and LSTM for sequence analysis, the model provides a comprehensive approach that surpasses traditional fraud detection systems primarily based on numerical analysis. On the validation set, the model achieved a recall of 100% and an accuracy of 99.11%, highlighting strong effectiveness in identifying fraudulent transactions under class imbalance. Through rigorous evaluation, the model demonstrated exceptional accuracy and reliability, promising improvements in fraud detection and mitigation. This paper details the development and validation of the hybrid model, emphasizing its use of mixed data types to capture complex patterns in transaction data. The results indicate a new frontier in fraud detection by combining natural language processing (NLP) and sequential data analysis to create a robust solution for real-world applications, supporting the security and integrity of financial systems globally.
Copyright (c) 2026 Oussama Ndama, Safae Ndama, Ismail Bensassi, El Mokhtar En-Naimi http://creativecommons.org/licenses/by-sa/4.0
Classification of Cihateup duck egg fertility using convolutional neural network EfficientNet-B3
Accurate detection of egg fertility is crucial to improve hatching success in duck farming. Conventional candling methods rely heavily on human expertise, making them subjective and error-prone. This study proposes an automated classification system for Cihateup duck egg fertility using candling images and a convolutional neural network (CNN) based on the EfficientNet-B3 architecture. Image enhancement techniques, including contrast limited adaptive histogram equalization (CLAHE), unsharp masking, and adaptive thresholding, were applied to improve image quality and feature visibility. The dataset consisted of fertile and infertile egg images captured at two incubation stages: the first 24 hours and the 8th–15th days. Data were split into training, validation, and testing sets with a ratio of 70:15:15. Experimental results show that image enhancement significantly improves classification performance. Without enhancement, the model achieved an accuracy of 49% with an area under curve (AUC) of 0.4226, indicating poor discrimination capability. With image enhancement, the proposed method achieved accuracies of 77% for the first 24 hours dataset and 80% for the 8th–15th days dataset, with AUC values of 0.9962 and 0.9317, respectively. These results demonstrate that EfficientNet-B3 combined with image enhancement provides an effective and computationally efficient solution for automated fertility detection of Cihateup duck eggs.
Copyright (c) 2026 Evi Dewi Sri Mulyani, Teuku Mufizar, Dani Rohpandi, Ayu Djuliani, Egi Rahmatulloh, Rinaldi Satia Aulia Rahmat http://creativecommons.org/licenses/by-sa/4.0
Neuro-DANet: dual attention deep neural network long short term memory for autism spectrum disorder detection
Autism spectrum disorder (ASD) is neurological illness affects ability of individuals to communicate and interact socially, and it is diagnosed in any time. Early detection of ASD is especially significant due to its subtle characteristics and high costs associated with the detection process. Traditional deep learning (DL) models struggle to capture intricate spatiotemporal dependencies in functional magnetic resonance imaging (fMRI) data, resulting in minimized detection performance and poor generalization. To address these drawbacks, the proposed Neuro-DANet combines a dual-attention deep neural network (DA-DNN) with long short term memory (LSTM) to efficiently learn spatial and temporal features from fMRI scans. The continuous wavelet transform (CWT) is used to extract multi-scale features and the principal component analysis (PCA) is utilized to dimensionality reduction, which enhances robustness and efficacy. The dual self-attention mechanism improves the interpretability of the model by focusing on critical brain regions and time steps that are most relevant to ASD severity. The developed Neuro-DANet obtains the highest accuracy of 98.51% on autism brain imaging data exchange (ABIDE)-I and 98.81% on ABIDE-II datasets when compared with traditional algorithms.
Copyright (c) 2026 Sujatha Hanumantharayappa, Manjula Rudragouda Bharamagoudra http://creativecommons.org/licenses/by-sa/4.0
Adaptive transformer architecture for scalable earth observation via hyperspectral imaging
Hyperspectral Image (HSI) classification is one of the critical processes involved in remote sensing application that plays a crucial role towards earth observation. Owing to complex spatial-spectral relationship and high dimensionality, it is quite a challenging task to subject HSI content to conventional data analytics or existing methods. Hence, the proposed study introduces a novel computational model known as Adaptive Spectra-Spatial Transformer (ASST) to address these ongoing challenges and shortcoming of existing Artificial Intelligence (AI) based modelling. The proposed model contributes towards a novel transformer-based architecture where a distinct spectral-spatial attention method has been used with transformer encoder. This novel combination facilitates highly adaptive and contextually enriched feature extraction. Tested on universally standard HSI dataset of Pavia University, the proposed ASST model has been benchmarked with notice 97.26% of overall accuracy and faster processing duration computed via training and response time in contrast to frequently adopted ML and DL models. The accomplished study outcomes truly exhibited highly improved feature representation as well as robust performance against class imbalance problems towards scalable data analysis of HSI contents for earth observation.
Copyright (c) 2026 Devendra Kumar Saragoor Madanayaka, Devanathan Muthukrishnan http://creativecommons.org/licenses/by-sa/4.0
Adaptive deformable feature augmentation and refinement network for scene text detection and recognition
Scene text recognition (STR) is the task of detecting and identifying text within images captured from natural scenes, a challenging process due to variations in text appearance, orientation, and background complexity. The proposed methodology, adaptive deformable feature augmentation and refinement network (ADFARN), is designed to address these challenges by combining deformable convolutional networks for robust enhanced feature extraction with a novel deep feature refinement (FRE) that leverages refinement for precise text localization. This approach enhances the differentiation between text and background, significantly improving recognition accuracy. The ADFARN methodology includes a comprehensive process of feature extraction, deep feature augmentation module (DFAM), and the generation of score and threshold maps through differentiable binarization. The adaptive nature of the model allows it to handle low resolution and partially occluded text effectively, further increasing its robustness. Additionally, the proposed method aligns visual and textual features seamlessly. Extensive performance evaluation on the common objects in context (COCO)-Text dataset demonstrates that ADFARN outperforms existing state-of-the-art methods in terms of precision, recall, and F1-scores, establishing it as a highly effective solution for STR in real world applications.
Copyright (c) 2026 Ratnamala S. Patil, Geeta Hanji, Rakesh Hudud http://creativecommons.org/licenses/by-sa/4.0
Adaptive feature fusion network for fetal head segmentation in ultrasound images
The measurement of fetal biometrics from ultrasound images plays a vital role in assessing potential development during pregnancy. However, existing fetal segmentation methods failed to accurately segment and asses the head circumference that gives inaccurate segmentation results. To overcome this limitation, a feature feedback and global feature with adaptive feature fusion network (FGA–Net) model is proposed to enhance fetal head segmentation (FHS). It involves four key components for feature extraction, fusion, and correction, respectively. The adaptive feature fusion module (AFFM) and correction map integrate the local features and global features and refine the features to enhance accurate FHS from the ultrasound images efficiently. Initially, ultrasound images are obtained from the two publicly available datasets and preprocessed using normalization and data augmentation techniques. Finally, preprocessed images are fed to FHS by proposed FGA Net utilizing EfficientNet-B0 as the backbone network for efficient feature extraction. Experimental results of proposed FGA-Net are evaluated using the dice coefficient (DC) of 95.78% and 98.95% for FH-PS-AoP and HC-18 datasets, which shows better results than the existing segmentation approaches like inverted bottleneck patch expanding (IBPE) method.
Copyright (c) 2026 Vimala Nagabotu, Pavan Kumar Reddy Sana, B. Lakshmi Bhavani, Donapati Srikanth Reddy3 http://creativecommons.org/licenses/by-sa/4.0
Efficient YOLO-based models for real-time ceramic crack detection
The following research work systematically compares four variants of you only look once (YOLO), namely, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 proposed recently, considering the key properties required to perform ceramic surface crack detection tasks with high computational efficiency, real-time inference speed, and low memory usage. A total of 300 images of ceramic surfaces were collected with manually labeled cracks and divided into training, validation, and testing sets in portions of 263, 22, and 15 images, respectively. Each of the four YOLO variants was trained for 50 and 100 epochs, and each was evaluated regarding mean average precision (mAP), inference time, model size, and computational complexity in giga floating point operations per second (GFLOPs). YOLOv9 produced the highest accuracy with mAP values as high as 0.752-0.79 but the highest cost in terms of increased computational complexity. However, among these methods, YOLOv8 can produce the fastest inference (~2-2.3 ms) with a small memory footprint (~6 MB) with an acceptable accuracy of ~0.65-0.67. The results showed that YOLOv8 is the most feasible to deploy in resource constrained industrial automation environments. By offering this comparative study, the research attempts to provide hints for the selection of appropriate YOLO-based models by practitioners in quality control applications related to ceramic manufacturing.
Copyright (c) 2026 Benchalak Maungmeesri, Sasithorn Khonthon, Dechrit Maneetham, Padma Nyoman Crisnapati http://creativecommons.org/licenses/by-sa/4.0
Hybrid texture-deep feature fusion for mammogram classification: a patient-level, calibrated evaluation
We propose a lightweight computer-aided diagnosis (CAD) framework that fuses four sub-band discrete wavelet transform gray-level co-occurrence matrix (DWT–GLCM) texture features with fine-tuned ResNet-50 embeddings under a strict, patient-level, leak-free evaluation protocol. Experiments were conducted on two public datasets: mammographic image analysis society (MIAS) (normal vs. abnormal) and curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) (benign vs. malignant). Five-fold cross-validation (CV) was confined to the training portion, operating thresholds were fixed on the validation split to target high recall, and the held-out test set was evaluated once. Performance was assessed using accuracy, F1-score, receiver operating characteristic (ROC)-area under the curve (AUC) with bootstrap 95% confidence intervals (CI), precision-recall (PR)-AUC, and calibration metrics (Brier score, expected calibration error). The proposed fusion model achieved ROC-AUC on MIAS (0.992) and strong performance on CBIS-DDSM (0.896), with consistent PR characteristics. Calibration analysis indicated reliable probability estimates and clinically interpretable decisions at a 95% sensitivity operating point. Ablation experiments revealed substantial gains over texture-only baselines and parity with convolutional neural network (CNN)-only models, highlighting fusion as a simple yet well-calibrated alternative for screening-oriented workflows. This study underscores the necessity of patient-level evaluation, explicit operating-point selection, and calibration reporting to ensure clinically meaningful CAD performance in mammography.
Copyright (c) 2026 Muhammad Subali, Lulu Mawaddah Wisudawati, Teresa http://creativecommons.org/licenses/by-sa/4.0
Gradient descent optimization based weighted federated learning for privacy-preserving framework
Federated learning (FL) is a disseminated machine learning (ML) paradigm that gained significant consideration in modern days, particularly in a domain of the internet of things (IoT). FL saves communication bandwidth when compared to centralized ML processes by eliminating the need to transmit raw client data to a central server, thereby enhancing data privacy. Nevertheless, participant privacy is still compromised through inference attacks and similar threats. Additionally, a data excellence provided through clients can differs significantly, and excessive inclusion of low-quality data during training may degrade the overall performance of the global model. Hence, this research introduces a gradient descent optimization assisted weighted federated learning (GDO-WFL) method for privacy preservation. The proposed GDO-WFL approach is significantly efficient as it strengthens privacy preservation through reducing exposure to inference attacks and optimises gradient updates for secure learning. Through weighting client contributions based on data quality, an undesirable effect of low-quality data can be minimised, helping to maintain a strength as well as accuracy of the global model. The experimental results illustrate a proposed GDO-WFL approach maintains an overall accuracy of 99.3 and 91.5% on MNIST and CIFAR-10 datasets as compared to the existing method of FedlabX method.
Copyright (c) 2026 Gururaj Prakash Murthy, Chandrashekhar Pomu Chavan http://creativecommons.org/licenses/by-sa/4.0
Bridging hybrid deep learning detection and lightweight handcrafted features for robust single sample face recognition
Single sample face recognition (SSFR) remains a challenging task due to the limitation of having only one reference image per identity, which reduces embedding diversity and decreases robustness under variations of pose, expression, and illumination. This study proposed a hybrid framework that integrates deep learning-based detection through anchor box optimization and non-maximum suppression (NMS) with lightweight handcrafted feature extraction using local binary pattern (LBP). The detection stage leverages deep learning to ensure robust face localisation, while LBP maintains computational efficiency under limited-sample conditions. The training process showed accuracy improvement from 47.5% at the initial epoch to 98.0% at epoch 72, while testing accuracy stabilized at 85-88% with the best value of 87.9%. Evaluation on 48 new facial images achieved 89.6% accuracy, 95.3% precision, 91.1% recall, 93.1% F1-score, and 0.94 area under the receiver operating characteristic curve (AUC ROC). Real-world implementation on Android and iOS-based attendance applications further validated the model, reaching 88.46% accuracy across 52 tests under 50-400 lux illumination. The findings proved that the proposed hybrid design provides improved accuracy and stability compared with previous approaches.
Copyright (c) 2026 Faulinda Ely Nastiti, Sopingi, Dedy Hariyadi, Sri Sumarlinda http://creativecommons.org/licenses/by-sa/4.0
Deep intelligence for sustainable farming: a swarm-empowered data analytics architecture
The inclusion of complex patterns of data in precision agriculture (PA) induces a greater degree of challenges from the perspective of carrying out conventional analytical operations. Although proliferated use of artificial intelligence (AI) has been noticed to yield some promising results to address such issues, yet they too have many shortcomings. Hence, the current manuscript introduces an innovative hybrid AI scheme towards enhancing the analytical operations necessary for decision-making in smart farming. The proposed scheme hybridizes a deep neural network (DNN) with a novel swarm intelligence (SI) model for optimizing the performance of its adopted deep learning (DL) model. Tested on a standard dataset of agriculture, the proposed model exhibited a 10% increase in accuracy and 40% faster response time when compared with conventional machine learning (ML) models, DL models, and SI models. The study contributes to a novel benchmark towards time-efficient, scalable, and intelligent analytics on PA.
Copyright (c) 2026 Kiran Muniswamy Panduranga, Roopashree Hejjaji Ranganathasharma http://creativecommons.org/licenses/by-sa/4.0
Deep learning-based spam detection for WhatsApp chatbot fallback reduction
Chatbots on WhatsApp are widely used for customer service, but their effectiveness is often undermined by fallback responses when user input cannot be understood. A major cause of these fallbacks is unsolicited spam, which disrupts interactions and reduces service quality. This study develops and evaluates a spam detection system aimed at reducing fallback rates and enhancing user experience. A comparative analysis was conducted between traditional machine learning models (support vector machine (SVM) and decision tree (DT)) and advanced deep learning architectures, including long short-term memory (LSTM) variants (vanilla, bidirectional, stacked, convolutional neural network (CNN)-LSTM, and encoder-decoder) and transformer-based models (bidirectional encoder representations from transformers (BERT)-base, DistilBERT, and cross-lingual language model robustly optimized BERT pretraining approach (XLM-ROBERTa)). Using 170,000 messages sampled from 18 million interactions collected between July 2022 and December 2023, the models were assessed with standard evaluation metrics. Results show that CNN-LSTM and DistilBERT achieved the most robust performance. CNN-LSTM attained a precision of 0.92, recall of 0.91, F1-score of 0.91, and accuracy of 0.94, while DistilBERT achieved precision of 0.92, recall of 0.89, F1-score of 0.90, and accuracy of 0.93. These findings highlight their superior ability to capture contextual patterns in spam messages. Implementing such models is expected to significantly lower fallback rates, thereby improving chatbot reliability and user satisfaction.
Copyright (c) 2026 Satrio Sadewo, Amalia Zahra http://creativecommons.org/licenses/by-sa/4.0
Evaluating the detected communities using traditional algorithms on keyword co-occurrence networks
Community detection is one of the most significant research areas in network analysis, which helps to understand the internal structure of large networks. This work utilizes the traditional community detection methods on a keyword co-occurrence graph derived from the Scopus bibliographic database. This research article primarily focused on the index keywords of deep learning driven publications obtained from three major network Scopus bibliometric datasets (SBD), namely SBD_1 as 2006-2013, SBD_2 as 2014-2016, and SBD_3 as 2017. For this proposed model framework, the existing traditional algorithms, including Louvain, greedy modularity optimization (GMO), Leiden, Infomap, speaker-listener label propagation algorithm (SLPA), Walktrap, SpinGlass, K-Clique, and Clauset, Newman and Moore (CNM) methods are applied to detect communities from the network and carried out through Python. Comparisons among these algorithms, Leiden, SpinGlass, and Louvain are considered as better algorithms for our work based on the detected communities, modularity score and other metrics to evaluate the performance of detected communities from the network. This research proposes an ideology for the selection process of algorithms that depends on different factors like network characteristics, network structure, dataset size, and computational efficiency. This analysis suggests a unique perspective on the effectiveness of each method in the Scopus bibliometric network and its potential to enhance research topic exploration.
Copyright (c) 2026 Kiruthika R., Krishnaveni Sakarapani http://creativecommons.org/licenses/by-sa/4.0
Text summarization: BART, RF, and hybrid BART-RF algorithm comparison
Data and information accumulate quantitatively and qualitatively. Abundant text data are posted on the internet. The number correlates to the complexity of the summarization. Automatic text summarization (ATS) is one of the most challenging tasks in natural language processing (NLP). ATS approached in three ways: extractive, abstractive, and hybrid. Hybrid approach combines both extractive and abstractive. This research tests and compares performance of bidirectional auto-regressive transformer (BART) and random forest (RF) individually and the performance combination of hybrid BART and RF in ATS. The research shows that individually, BART and RF recall-oriented understudy for gisting evaluation (ROUGE) scores are having quite differences. Consecutively, ROUGE RF scores in R1, R2, and RL are 51.45, 45.52, and 54.58 respectively. Meanwhile, ROUGE BART scores are 32.78, 16.17, and 32.19. Consecutively, average ROUGE RF, BART, and RF×BART F-measure are 45.73, 21.38, and 31.31. RF has the highest average score. ATS hybrid RF×BART is shown to be performed better than the default BART. The average ROUGE F-measures for RF×BART obtain moderate score at 31.31. This score is better than the default BART’s ROUGE score. RF×BART can be an alternative to the effective hybrid approach.
Copyright (c) 2026 Muhammad Adib Zamzam, Agus Buono, Toto Haryanto http://creativecommons.org/licenses/by-sa/4.0
Adversarial attacks have a great popularity in the artificial intelligence (AI) domain. In the natural language processing (NLP) field, various techniques have been used to evaluate the vulnerability of deep learning (DL) models. It is observed that while most studies focused on generating adversarial examples in English language, Arabic adversarial attacks have received little attention. This paper presents a two-step method to create adversarial examples in Arabic language: first, the most important words are identified. Then, the proposed transformation algorithm is applied. Only small and imperceptible manipulations based on common mistakes in Arabic writing mislead the popular pre-trained language model (PLM) bidirectional encoder representations from transformers (BERT) retrained on the book reviews in Arabic dataset (BRAD) on the sentiment analysis (SA) task and decrease its performance: the classification accuracy was reduced by an average of 3.44%. This drop in accuracy shows that the model was successfully attacked.
Copyright (c) 2026 Safae Laatyaoui, Mohammed Saber http://creativecommons.org/licenses/by-sa/4.0
Scalable resume screening using large language model Meta AI version 3
This research paper explores the use of large language model Meta AI 3 (LLaMA 3) for automating the resume screening process. Traditional resume screening methods that rely on keyword searching and human review can be inefficient, biased, and fail to identify qualified candidates. LLaMA 3, trained on large-scale text datasets, has the potential to accurately analyze resumes by understanding context and semantic details beyond simple keyword matching.The study presents a system that converts resume PDFs to text, inputs the text along with the job description into the LLaMA 3 model, and generates a ranked list of candidates with reasoning for their job fit. This discusses the data preparation, model setup, and performance evaluation of this system. Results show LLaMA 3 can rapidly process batches of resumes while reducing human bias in the screening process. The system aims to streamline hiring by automating the initial resume screening stage to surface top candidates for further in-depth evaluation. Key benefits include improved accuracy in identifying relevant skills, reduced bias compared to human screeners, and significant time savings for recruiters. The paper also examines ethical considerations around using AI for hiring decisions. Overall, this work demonstrates the promising application of large language models (LLMs) like LLaMA 3 to transform and enhance resume screening practices.
Copyright (c) 2026 Asmita Deshmukh, Anjali Raut, Vedant Deshmukh http://creativecommons.org/licenses/by-sa/4.0
Enhanced framework for detecting Vietnamese hate and offensive spans
The rise of hate and offensive content on social media platforms, such as Facebook and Twitter, has emerged as an escalating concern, especially in Vietnam. Consequently, detecting hate and offensive spans in Vietnamese text is an essential area of research. This study introduces ViHateOff, an advanced framework that combines a hated speech dictionary (HSD) automatically constructed from the Vietnamese hate and offensive spans (ViHOS) dataset with the pre-trained language models for Vietnamese (PhoBERT)-large language model to enhance the detection of offensive expressions. The framework functions through two primary modules. First, it constructs an HSD from the ViHOS dataset, which serves as a reference for identifying hate and offensive language in Vietnamese text. Second, the framework integrates the PhoBERT-large language model with HSD, enhancing the detection of harmful words in the input text. Experimental results demonstrate that the proposed framework significantly outperforms existing state-of-the-art (SOTA), achieving an F1-score of 0.8693 on the all spans subset and 0.8709 on the multiple-spans subset representing relative improvements of over 10% compared to the strongest baseline.
Copyright (c) 2026 Dinh-Hong Vu, Tuong Le http://creativecommons.org/licenses/by-sa/4.0
Unmanned aerial vehicle systems: key management and intrusion response techniques
Protecting information is crucial for unmanned aerial vehicle (UAV) network communications, particularly during delicate tasks such as surveillance and reconnaissance. While encryption safeguards data privacy, managing and distributing keys in UAV settings poses significant challenges due to the vehicles' mobility and limited processing power. This research proposes an efficient key management scheme utilizing elliptic curve cryptography (ECC) and bilinear pairings, complemented by a lightweight intrusion detection system (IDS). The method employs behavior-based anomaly detection, using cluster-based watchdogs and trust assessment to identify and isolate harmful nodes. Additionally, applying compression techniques before encryption helps to reduce transmission load. Simulation in NS2 demonstrates performance improvements of 6-10% in throughput, 4-6% in packet delivery ratio (PDR), and a 13-17% reduction in delay compared to elliptic curve cryptography Diffie–Hellman (ECCDH) and pairwise encryption methods.
Copyright (c) 2026 Reshma C. Sonawane, A. Muthukrishnan http://creativecommons.org/licenses/by-sa/4.0
Life balloon: a paradigm shift in earthquake safety-intelligent IoT detection and protection system for optimal resilience
Internet of things (IoT) applications for environmental monitoring have greatly improved due to advances in hardware and software technologies. Given the significant economic and societal impacts of earthquakes, there is an increasing need to develop effective earthquake early warning systems (EEWS). However, designing such intelligent systems remains challenging because of inefficient classification methods and limitations in high-fidelity sensing capabilities. To reduce the devastating effects of earthquakes, this paper proposes an earthquake detection and protection system. The system’s primary function is to detect seismic signals and activate a specially designed airbag (life balloon) unit that protects occupants in apartment buildings. In addition, the unit helps maintain necessary oxygen levels, thereby improving occupant safety during seismic events. The proposed system also includes a communication method that transmits critical information about the affected area to relevant parties. Early data transmission enables rapid response and guides the efficient deployment of required resources, making aftershock management more effective. By combining advanced sensor technologies with efficient communication methods, the proposed system aims to enhance safety and emergency management while providing comprehensive protection and support during seismic events. Experimental results show that the proposed method achieves approximately 95% sensitivity and 94.2% accuracy.
Copyright (c) 2026 Tawfiq Alrawashdeh, Sumaya Abusaleh, Malek Z. Alksasbeh, Khalid Ali Alemerien http://creativecommons.org/licenses/by-sa/4.0
A review of modern techniques for plant disease identification and weed detection in precision agriculture
Plant disease identification and weed detection are critical components of precision agriculture, aimed at ensuring high crop yields and sustainable farming practices. These processes involve the use of advanced machine learning and deep learning techniques to automatically identify and classify plant diseases and distinguish between crops and weeds in agricultural fields. Traditional methods for managing these challenges are often labor intensive, prone to errors, and environmentally unsustainable, necessitating the development of automated, accurate, and scalable solutions. This survey provides a comprehensive review of the state-of-the-art approaches, including pixel-based, region-based, and spectral-based methods, and evaluates their effectiveness in various agricultural contexts. Additionally, it identifies significant challenges such as data scarcity, model generalization, and computational constraints, while proposing potential research directions to address these gaps. The findings aim to guide future research in developing more robust and interpretable models that can be deployed in real-world agricultural environments, ultimately contributing to more efficient, precise, and sustainable farming practices.
Copyright (c) 2026 Mohammad Naseera, Arpita Gupta http://creativecommons.org/licenses/by-sa/4.0
Instagram influencer classification using fine-tuned BERT model
Influencer marketing has emerged as a powerful strategy in today’s digital world, where social media stars can influence how people think about products. However, the rapid growth of influencers and social media users presents novel challenges for brands in identifying suitable influencers for their marketing goals. Traditional approaches that rely on popularity and follower count are no longer the primary metrics for determining an influencer’s ability to affect consumer behavior. To address this gap, this study proposed an influencer classification to enhance audience targeting and marketing effectiveness. By utilizing deep learning, specifically fine tuned bidirectional encoder representations from transformers (BERT), influencer classification was carried out for Instagram users in Indonesia based on their post captions. The multilingual BERT model is optimized through hyperparameter tuning, including learning rate, batch size, and stop word removal variation. With an outstanding 80% accuracy, the model performs best in situations where stop words are not removed. This study on influencer classification using a fine-tuned BERT model has demonstrated the effectiveness of BERT in enhancing influencer selection. It contributes to the digital marketing domain by showcasing the potential of deep learning for social media analysis and content classification, paving the way for future data-driven marketing strategies.
Copyright (c) 2026 Ni Putu Sutramiani, Ni Made Dita Dwikasari, I Nyoman Prayana Trisna, I Wayan Agus Surya Darma http://creativecommons.org/licenses/by-sa/4.0
Exploring cutting-edge research, applications, and future directions in artificial intelligence across diverse domains
This issue highlights the most recent advances in artificial intelligence (AI) research, which cover a wide range of applications, methodologies, and emerging technologies. The gathered works highlight AI's transformative potential in a variety of fields, including healthcare, environmental monitoring, energy systems, intelligent transportation, cybersecurity, smart agriculture, and human-computer interactions. The featured studies demonstrate novel applications of deep learning, convolutional neural networks, vision transformers, reinforcement learning, ensemble methods, and explainable AI techniques, with a focus on both performance optimisation and interpretability. The issue also delves into AI integration with IoT, blockchain, big data, and mobile platforms, showcasing scalable and real-time solutions for dynamic, data-intensive settings. Aside from technical accomplishments, the contributions address practical issues such as model generalisation, feature selection, data quality, privacy, and ethical concerns. These works show how AI is improving decision-making, predictive capabilities, and operational efficiency while addressing complex societal and industrial issues. Looking ahead, this issue encourages reflection on AI development trajectories, emphasising the importance of robust, explainable, and adaptive systems that balance computational power and interpretability. This issue aims to inform, inspire, and guide practitioners and researchers in shaping the next generation of intelligent technologies by providing a comprehensive overview of cutting-edge AI research and real-world applications.
Copyright (c) 2026 Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0