Journal of Data Science and Intelligent Systems
The Journal of Data Science and Intelligent Systems (JDSIS) is an international, peer-reviewed, interdisciplinary journal that provides in-depth coverage of the latest advances in the closely related fields of data science and intelligent systems. JDSIS considers researches that focus on data integration, data information and knowledge extraction, and data application in a wide range of fields, including health, education, agriculture, biology, medicine, finance, environment, engineering, commerce, and industry. By integrating of data with computer science, artificial intelligence, and other appropriate methods, the scope of JDSIS covers the entire process of areas of Data Science and Intelligent Systems. The journal is a Gold Open Access journal, online readers don't have to pay any fee.
Spatiotemporal Analysis of Climate–Forest Fire Correlation in Peninsular Malaysia Using SARIMAX and Ensemble Models
Forest fires in Peninsular Malaysia continue to threaten biodiversity, public health, and economic activity, with risks most evident in peat swamp forests where prolonged dry periods increase fuel flammability. This study analyzes 22 years of fire occurrence (2001–2023) across peat swamp forest regions in Pahang, Selangor, Johor, and Terengganu to examine long-term climate–fire relationships and to develop predictive tools for fire-risk assessment. The modeling framework was organized into three sequential stages. Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) was applied for temporal modeling, while an ensemble framework combining Generalized Linear Models and Maximum Entropy estimated fire occurrence probability. A hybrid system subsequently linked SARIMAX-derived climatic projections with ensemble classifiers to generate forward-looking estimates. The results reveal a recurring seasonal pattern, characterized by a clear March peak and noticeably higher fire activity during drought years, including 2005 and 2014. SARIMAX produced the strongest temporal accuracy (average Mean Absolute Scaled Error = 0.1902), demonstrating substantially lower forecast error (around 81%) than a simple seasonal repetition benchmark. Spatial probability modeling showed strong climatic separability at the district–monthly scale (area under the curve = 1; Continuous Boyce Index = 0.67). Projection outputs suggest a gradual redistribution of hotspot intensity from Sabak Bernam toward inland districts such as Pekan, although these estimates remain conditional on projected climatic patterns. Overall, the integrated framework offers a climate-informed foundation for operational monitoring and longer-term management of peat swamp forest fires in Peninsular Malaysia. Received: 28 December 2025 | Revised: 9 June 2026 | Accepted: 17 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Zenodo at https://zenodo.org/records/11542164. Author Contribution Statement Grace Kian Hwai Ling: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Weng Howe Chan: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
An Integrated UTAUT, Machine Learning, and Association Rule Mining Analysis of Password Security Behavior Among Educational Personnel
This study examined password security behavior among educational personnel at Krabi Technical College using an integrated Unified Theory of Acceptanceand Use of Technology (UTAUT), machine learning, and association rule mining framework. Based on 210 survey responses and 50 variables, behavioral intention (BI) and use behavior (UB) were operationalized as composite scores derived from UTAUT-aligned Likert-scale items. In contrast, password behaviors captured practical routines such as password reuse, recovery capability, composition practices, and perceived attackability. Explanatory results showed that BI was mainly associated with hedonic motivation (β = 0.414), performance expectancy (β = 0.285), and facilitating conditions (β = 0.162), while UB was associated with BI (β = 0.444), habit (β = 0.206), and social influence (β = 0.142). Although the structural equation model indicated an excellent global fit, severe multicollinearity among constructs necessitated interpreting path coefficients as conditional associations rather than independent causal effects. Predictive analysis using five-fold cross-validation showed that construct-only ridge regression achieved the best performance for BI (RMSE = 0.491, R 2 = 0.650) and UB (RMSE = 0.553, R 2 = 0.640), with no statistically significant improvement over standard linear regression. Association rule mining revealed co-occurrence patterns centered on facilitating conditions, recovery capability, habit, and social influence. The findings support a parsimonious, interpretable approach to cybersecurity behavior analysis and provide actionable guidance for institutional password training, recovery support, and policy design. Received: 25 April 2026 | Revised: 16 June 2026 | Accepted: 8 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/pjarupunphol/Datasets/blob/main/Krabi_Technical.zip. Author Contribution Statement Pita Jarupunphol: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Sujira Jeennoo: Conceptualization, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Project administration.
A Hybrid Approach for Prominent Node Detection in Social Media Networks Using Modified Cluster Walktrap with Analytical Hierarchy Process
The rapid growth of social media platforms has significantly increased the complexity of online interaction networks, creating new challenges in suspicious community detection and influential node identification. Conventional community detection approaches often face limitations in handling heterogeneous social network structures and identifying structurally significant nodes using multiple decision parameters simultaneously. To address these limitations, this study proposes a hybrid framework integrating modified cluster Walktrap (MCW) with the Analytical Hierarchy Process (AHP) for suspicious community detection and prominent node analysis in social media networks. In the first phase, the proposed MCW approach utilizes probability-driven random-walk analysis and modularity optimization to identify structurally similar communities in bipartite graph structures. In the second phase, AHP is applied to evaluate influential nodes using multiple graph-theoretic parameters, including mean distance, degree centrality, and betweenness centrality. The proposed framework was experimentally evaluated using the publicly available KONECT Facebook social network dataset. Experimental results demonstrate that the proposed MCW + AHP framework achieved improved clustering effectiveness, reduced overlapping community formation, and more structurally consistent community detection compared with conventional Cluster Edge Betweenness and Walktrap approaches. The framework also enabled balanced multi-criteria influential node evaluation, where Node 101 was identified as the most structurally significant node within the analyzed network. Overall, the proposed framework provides a computationally feasible and effective approach for structural community analysis and influential node identification in complex social media environments. Received: 12 April 2026 | Revised: 23 June 2026 | Accepted: 3 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in KONECT at https://networkrepository.com/socfb.php. Author Contribution Statement Faz Mohammad: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Shweta Vikram: Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Artificial Intelligence, Financial Access, and the Path to Sustainable Development: Insights from G-7 Countries
Achieving sustainable development in advanced economies requires reconciling rapid technological progress with growing environmental constraints. This study examines whether artificial intelligence (AI) innovation can enhance environmental sustainability alongside economic growth in the G-7 countries over the period 1990–2019. Using the load capacity curve (LCC) framework and the load capacity factor (LCF) as a measure of ecological sustainability, the analysis integrates AI innovation, financial accessibility, globalization, urbanization, and income dynamics. This study contributes to the literature by providing the first evidence on the role of AI innovation in shaping environmental carrying capacity within the LCC framework for advanced economies. Panel autoregressive distributed lag estimates reveal strong support for the LCC hypothesis, indicating a nonlinear income–environment relationship. AI innovation and financial accessibility significantly improve the LCF, suggesting that technological progress and inclusive finance can promote sustainability through efficiency gains and cleaner production. In contrast, globalization and urbanization reduce environmental carrying capacity, particularly at higher levels of ecological pressure. These findings highlight the importance of AI-driven innovation and green finance in sustainability strategies, while underscoring the need for stronger environmental governance to manage globalization and urban expansion in advanced economies. Received: 15 January 2026 | Revised: 10 March 2026 | Accepted: 26 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are publicly available from the following sources: 1) Load Capacity Factor (LCF): Global Footprint Network – https://data.footprintnetwork.org 2) Economic Growth (GDP) and Urbanization: World Bank, World Development Indicators (WDI) – https://databank.worldbank.org/source/world-development-indicators 3) Artificial Intelligence (AI) Patent Data: Our World in Data – https://ourworldindata.org 4) Financial Accessibility: Global Financial Inclusion (Global Findex) Database, World Bank – https://globalfindex.worldbank.org 5) Globalization Index: KOF Swiss Economic Institute – https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html All datasets are openly accessible and do not require special permission for academic use. Author Contribution Statement Mohammad Ridwan: Conceptualization, Methodology, Software, Investigation, Writing – review & editing. Kabaly P Subramanian: Conceptualization, Software, Validation, Resources, Writing – original draft, Writing – review & editing. Jaheer Mukthar K.P.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Binata Rani Sen: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Minghui Jiang: Conceptualization, Formal analysis, Writing – review & editing, Visualization. Mujeeb Saif Mohsen Al Absy: Investigation, Resources, Data curation, Writing – review & editing.
A Deep Reinforcement Learning–Based Adaptive Framework for Early DDoS Attack Detection and Prevention in Heterogeneous Networks
Distributed Denial-of-Service (DDoS) attacks are constantly growing in scale, intensity, and attack plan strategies, which are causing significant threats to the heterogeneous network environments, including Internet of Things, edge, cloud, Software-Defined Networks, and 5G networks. This paper presents an adaptive framework of deep reinforcement learning (DRL) in early-stage detection and proactive mitigation of DDoS attacks. The proposed DRL agent operates in active network environments to develop policies through online learning. This helps it address new and unexpected attack patterns. Traditional machine learning and deep learning models depend on unchanging training methods, which run their training process from a fixed point in time. The framework combines the flow-level feature analysis and the packet-level feature analysis with the Deep Q-Network–based learning mechanism to facilitate real-time decision-making. Numerous experiments were carried out on benchmark datasets and heterogeneous traffic simulations. This indicates that the suggested methodology can be considered stable and offers an approximation of 98% accuracy, 96% precision, 96% recall, and 96% macro F1-score on the datasets with up to 300,000 network flows. Further flow analysis shows that flow duration, packet-level variability, and distribution of destination ports are critical in differentiating between benign and malicious traffic. The findings prove that the developed framework can offer detection and mitigation of DDoS threats, which are scalable, robust, and adaptive, to facilitate the creation of intelligent and self-educating cybersecurity systems in complex network settings. Received: 6 March 2026 | Revised: 3 June 2026 | Accepted: 23 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The datasets used in this study, including CICDDoS2019, NSL-KDD, and UNSW-NB15, are publicly available through their respective repositories. The implementation details, preprocessing procedures, model configurations, and experimental settings are described within the manuscript to facilitate reproducibility. The source code and trained model configurations are available from the corresponding author upon reasonable request and will be considered for public repository release in future work to further enhance research transparency and reproducibility or can be accessed at https://www.kaggle.com/datasets/dhoogla/cicddos2019, https://www.kaggle.com/datasets/hassan06/nslkdd, and https://www.kaggle.com/datasets/mrwellsdavid/unswnb15. Author Contribution Statement Sujit Sutradhar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Joy Lal Sarkar: Writing – review & editing, Visualization, Supervision, Project administration. Abhijit Biswas: Writing – review & editing, Supervision, Project administration.
A Narrative Review of GAN-Based Synthetic Data Generation in Disease Prediction
Synthetic data generation has emerged as an important approach in the healthcare field to address data scarcity, disease class imbalance, and privacy restrictions that limit access to patient data. Among generative approaches, generative adversarial networks (GANs) have gained increasing attention, especially because of their ability to generate realistic data across complex data distributions such as medical imaging, electronic health records (EHRs), laboratory data, and phenotype codes. This narrative review focuses on the evolution of major GAN architectures and their application in disease prediction. The original GAN introduced the adversarial paradigm, while Deep Convolutional GAN advanced image generation and became widely used in MRI, CT, and histopathology tasks. Wasserstein GAN variants (WGAN and WGAN-GP) improve training stability and prove to be more suitable for tabular and structured healthcare data such as EHRs. More specialized architectures, including Conditional Tabular GAN and Medical GAN, further extended synthetic data generation to mixed-type datasets and sparse diagnostic records. The review also examines evaluation practices based on data fidelity, downstream utility, and privacy preservation, including differential privacy and resistance to membership inference attacks. Overall, the literature shows that GAN-generated synthetic data can support disease prediction research, but important challenges remain in benchmarking, reproducibility, interpretability, and ethical deployment. Emerging directions include hybrid GAN-diffusion models, federated training strategies, and standardized evaluation frameworks to support clinically reliable and privacy-preserving adoption. Received: 29 November 2025 | Revised: 10 April 2026 | Accepted: 13 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement No new primary datasets were generated or analyzed in this study. This article is a narrative literature review based on previously published studies. The datasets discussed in the manuscript are available from the original publications and associated repositories cited below. The structured EHR datasets reviewed in this article are available at https://doi.org/10.1145/3636424, reference number [6], and https://doi.org/10.1016/j.compbiomed.2023.107188, reference number [7]. The multicenter diabetic EHR dataset is available at https://doi.org/10.1016/j.compbiomed.2023.107188, reference number [7]. Time-series and longitudinal healthcare datasets, including ECG-related datasets, are available at https://doi.org/10.1038/s41746-024-01409-w, reference number [8], and https://doi.org/10.1109/icoa62581.2024.10753742, reference number [9]. Medical imaging datasets used in GAN-based synthetic image generation, augmentation, and reconstruction studies are available at https://doi.org/10.1016/j.compbiomed.2025.109834, reference number [10]; https://doi.org/10.1148/radiol.232471, reference number [11]; https://doi.org/10.1016/j.compbiomed.2022.105382, reference number [20]; https://doi.org/10.1109/tiptekno63488.2024.10755233, reference number [26]; https://doi.org/10.1007/978-981-96-1185-0_38, reference number [29]; and https://doi.org/10.1016/j.compbiomed.2025.110094, reference number [37]. The Breast Cancer Wisconsin (Diagnostic), Lung Cancer Patient, and Fetal Cardiotocography (CTG) datasets are available at https://doi.org/10.1007/s41060-025-00816-w, reference number [14], and https://github.com/Halal-Abdulrahman-Ahmed/MedSynth_GANVariants. The Breast Cancer Wisconsin (Diagnostic) dataset is also available from the UCI Machine Learning Repository at https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic, and the Fetal Cardiotocography dataset is available from the UCI Machine Learning Repository at https://archive.ics.uci.edu/dataset/193/cardiotocography. The cardiovascular dataset was obtained from King Faisal Specialist Hospital and Research Centre and is subject to access restrictions as stated in the original article at https://doi.org/10.3390/s24237673, reference number [33]. The brain MRI dataset is available at https://brain-development.org/ixi-dataset/. Additional synthetic EHR and longitudinal health record datasets discussed in relation to MedGAN and mixed-type EHR generation are available at https://proceedings.mlr.press/v68/choi17a.html and https://doi.org/10.1038/s41746-023-00834-7, reference number [36]. Where datasets are not separately hosted in an open repository, access conditions and availability are governed by the original cited publications. Author Contribution Statement Swathi Ganesan: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Nalinda Somasiri: Visualization, Supervision, Project administration, Funding acquisition.
AI-Assisted Synthetic Dataset Generation and Validation for Demand Forecasting in the Context of Mass Customization
Mass customization emerged as a strategic response to the increased consumer interest in personalized products, combining the efficiency of mass production with the flexibility of individual customization. Yet, this manufacturing paradigm introduced significant complexities in demand forecasting, where traditional methods struggled to account for the high variability in customer preferences. The lack of accessible, high-quality datasets that capture real customization behavior makes this challenge more difficult. To address this gap, this study develops and validates a large-scale synthetic dataset simulating 500,000 laptop sales. Each sale corresponded to a customized product configuration tailored to an individual customer profile, thereby reflecting the dynamics of real-world mass customization contexts. The dataset was generated using a structured rule-based framework, supported by artificial intelligence (AI)-assisted reasoning and grounded in domain knowledge and manufacturing literature. This framework was designed to reproduce realistic customer behavior, configuration patterns, and seasonal demand fluctuations. As a result, the generated data captured heterogeneous customer profiles and varied product configurations. Its realism and coherence were assessed through statistical distribution analysis, logical consistency checks, and seasonal pattern verification. The validation results provide a reliable basis for evaluating and benchmarking demand forecasting models. Overall, this work shows that combining rule-based design with AI-assisted reasoning is effective for the generation of realistic synthetic datasets for demand forecasting in mass customization contexts where real data is unavailable. Received: 16 January 2026 | Revised: 22 April 2026 | Accepted: 20 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://doi.org/10.34740/kaggle/dsv/15258985. Author Contribution Statement Nouhaila El Assad: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Visualization. Kawtar El Haouti: Methodology, Validation. Soumaya Zayrit: Software, Data curation. Salah-eddine Mokhlis: Validation, Formal analysis. Mohamed Rhouzali: Formal analysis, Investigation. Najat Messaoudi: Validation, Writing – review & editing, Supervision, Project administration.
Correction, Reconstruction, and Modeling of Experimental Data Using LI Transforms
This paper examines mathematical methods of using the linear invariant (LI) transforms for the correction, reconstruction, and modeling of experimental data—one-dimensional and multidimensional signals including images distorted during processing by LI systems, which in the 1D case are the linear stationary or time-invariant systems. Methods enable data processing with a minimum of initial information and computational resources; they are simple, require minimal resources for numerical calculations, and can be effectively used to process data of large volumes. LI methods have virtually no restrictions on the class of processing functions, which must be locally integrable in the domain under consideration. LI methods are effective and designed to solve some typical signal processing problems often encountered in practice. This paper provides examples of the practical application of LI methods for signal processing of electronic devices, time-of-flight neutron spectrometers, image processing, etc. Mathematical LI methods can improve the quality of data processing and enhance the effective parameters of processing systems without solving complex scientific, technical, and technological problems or creating expensive equipment. Received: 27 November 2025 | Revised: 16 April 2026 | Accepted: 22 May 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The program codes used in this study are publicly available in the author's domain of the GitHub library at https://github.com/novikov-borodin/lst-data-proc. These codes can be used for free, but references to the author's work and the author's code domain are required. Author Contribution Statement Andrey Novikov-Borodin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
mathematical processing of signals and images
multidimensional convolution-type equation
Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
Toward Fully Automated Prompting: An LLM-Powered Genetic Algorithm for Prompt Generation, Optimization, and Self-Evaluation
Large language models (LLMs) have gained recognition as valuable assets across virtually all industries, yet they rely heavily on manually crafted input prompts. In real-world applications, the dependence on specialized staff, skilled prompt engineering, and domain-specific knowledge often leads to suboptimal performance and increased costs. In this study, we investigate the use of genetic algorithms (GAs) to generate, evolve, and judge LLM prompts in a completely autonomous fashion. The main novelty presented here is the integration of general-purpose LLM-guided genetic operators with LLM-based fitness evaluation, enabling prompt optimization without human intervention. Full prompting automation is possible via the customization of a standard GA implementation to handle textual individuals, which are manipulated by LLM-guided genetic operators that iteratively create and enhance candidate prompts. Additionally, LLMs are employed to assess the correctness of outputs, forming the basis of our GA’s fitness function. Our experimental results indicate that our approach produces solutions that are, on average, 22% more correct than those generated by humans. This conclusion is supported by extensive testing utilizing 10 public datasets and 6 modern LLMs by OpenAI, Meta, and MistralAI. Ablation studies and sensitivity analysis further substantiate our approach’s robustness under probabilistic scenarios. Our findings suggest that this GA-driven prompt engineering approach can produce superior solutions compared with those written by prompt engineers who possess technical skills but lack domain-specific knowledge and a full understanding of vendor-specific prompting idiosyncrasies. Ultimately, this study highlights the viability and potential of fully automated optimization for minimizing human effort in writing performant prompts. Received: 26 November 2025 | Revised: 28 April 2026 | Accepted: 25 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest related to this work. Data Availability Statement All data that support the findings of this study are in the public domain and openly available on GitHub at https://www.github.com/leloss/gallm. Author Contribution Statement Leandro A. Loss: Conceptualization, Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Pratikkumar Dhuvad: Conceptualization, Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Comparative Study of Machine Learning Algorithms for Crystal Structure Predictive Modeling of Li-ion Battery Cathodes
Identification of the crystal structure of the cathodes of Li-ion batteries is important to improve energy storage technologies. This research has three goals: (1) to determine the important parameters to predict the crystal systems (monoclinic, orthorhombic, and triclinic); (2) to evaluate different machine learning algorithms to make the correct prediction; and (3) to give insights on how to optimize materials used in batteries. We have studied a Kaggle dataset comprising a number of physicochemical descriptors such as chemical formula, energy above the convex hull, band gap, number of atomic sites in the crystal, density, and volume. We evaluate and compare systematically the predictive performance of 10 machine learning algorithms, including Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, Decision Tree, Gradient Boosting, XGBoost, Naive Bayes, AdaBoost, and CatBoost. The final model with the best predictive performance resulted from the optimized stacking model that achieved an accuracy of 72.06% and an Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) of 0.8438 after removing leaky features and applying advanced validation methods. These results are consistent with past research that reveals that ensemble learning methods can be used to extract intricate trends in materials information. Nevertheless, this study is based on one dataset with a rather small sample size, which can impact the broader applicability of the findings. Received: 25 November 2025 | Revised: 17 April 2026 | Accepted: 24 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/divyansh22/crystal-system-properties-for-liion-batteries/data. All source code, model implementation, and experimental workfloware available from the GitHub repository at https://github.com/alokchauhan-collab/Crystal-Structure-Predictive-Modeling-of-Li-ion-Battery-Cathodes. Author Contribution Statement Mega Novita: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Project administration. Alok Singh Chauhan: Conceptualization, Methodology, Software, Formal analysis, Writing – review & editing, Visualization, Supervision. Rizky Muliani Dwi Ujianti: Software, Investigation, Data curation, Visualization. Sigit Ristanto: Methodology, Validation, Writing – review & editing, Project administration. Bambang Agus Herlambang: Writing – review & editing. Kazuyoshi Ogasawara: Resources, Writing – review & editing. Chong Geng Ma: Resources. Mikhail Brik: Resources, Writing – review & editing, Supervision. Michal Piasecki: Resources, Writing – review & editing.
Leveraging a Hybrid Fuzzy C-Means-PCA Model for Identifying Speech Therapy Needs in Children
This study proposes a hybrid unsupervised learning framework that integrates principal component analysis (PCA) and Fuzzy C-Means (FCM) to support early identification of speech therapy needs in children. The model utilizes secondary structured datasets containing acoustic–prosodic features such as MFCC, jitter, shimmer, harmonic-to-noise ratio, pitch, duration, fluency, and temporal indicators. PCA was applied to reduce dimensional redundancy and address the high-dimensional, low-sample-size characteristics of pediatric speech data, producing four principal components that retained 83.27% of the total variance. These components were subsequently clustered using FCM to capture partial membership patterns that reflect the continuous nature of children's speech deviations. The proposed PCA-FCM model achieved the best cluster compactness and separation, with a 25.6% improvement in the XieBeni (XB) index compared to the baseline FCM model (XB = 0.421). Three interpretable clusters including Normal, Mild Deviation, and Severe Deviation were identified, each associated with distinct acoustic–prosodic profiles. These findings demonstrate the potential of hybrid unsupervised learning to provide an objective, interpretable, and efficient early-screening mechanism for guiding personalized speech therapy interventions in children. Received: 19 November 2025 | Revised: 16 April 2026 | Accepted: 11 May 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The analytical dataset supporting this study is publicly available in Figshare at https://doi.org/10.6084/M9.FIGSHARE.31410669. Author Contribution Statement Muhammad Rizal Haris: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Muhammad Faisal: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Supervision. Rizki Yusliana Bakti: Methodology, Software, Validation, Data curation, Project administration. Muhyiddin AM Hayat: Validation, Formal analysis, Investigation, Resources, Project administration. Titik Khawa Abd Rahman: Conceptualization, Methodology, Software, Validation, Writing – review & editing, Project administration. Muhammad Syafaat S. Kuba: Validation, Writing – review & editing, Supervision. Titin Wahyuni: Data curation, Software, Project administration.
A Machine Learning Framework for Automated Detection of Perceived Stress in Female Students
Stress is a mental state induced by difficult circumstances. COVID-19 has severely impacted the level of stress and mental health of individuals around the globe. Pharmaceutical treatment (hypnotic or sedative drugs) has shown its ineffectiveness with several unwanted side effects to reduce mental stress. Mindfulness practices have proven their potency to reduce perceived stress (PS) and enhance satisfaction with life (SWL) without any negative consequences. This study aims to detect PS in female students and study the impact of Heartfulness meditation in reducing it. In this work, statistical exploratory data analysis (EDA) is utilized to evaluate the effectiveness of Heartfulness meditation. PS and SWL scores of Week 0 and Week 20 data are recorded from two different age groups of female students and analyzed to detect stressed students using machine learning modules. The highest PS detection accuracy 66.88% and F1-score 65.30% for group-1 are obtained using the support vector machine classifier. EDA indicates that there is a significant decrease in the PS levels and an increase in the SWL after 20 weeks of Heartfulness meditation. The results of this study indicate that the practice of Heartfulness meditation offers significant advantages to female students, regardless of age. Moreover, it was found that the practice effectively reduces stress levels among the participants. Received: 18 November 2025 | Revised: 19 May 2026 | Accepted: 27 May 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Kapil Gupta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization.
Contrastive Pretrain and Supervised Fine-Tune for Land-Cover Classification in Data-Scarce Countries
Deep neural networks are widely used to extract patterns and features from satellite imagery in Earth Observation, but their reliance on large labeled datasets limits deployment in data-scarce regions where ground surveys are expensive or infeasible. This study introduces a novel operational pipeline that couples SimCLR contrastive pretraining on unlabeled Sentinel-2 tiles with lightweight supervised fine-tuning to deliver tile-level predictions that are aggregated into district-level land-cover statistics, explicitly targeting administrative reporting rather than pixel-wise mapping. The key methodological contribution is this tile-to-district design—together with domain-aware, conservative augmentations and RGB-only pretraining—that aims to learn transferable representations under seasonality and atmospheric variability while requiring only a small expert-labeled set. The approach is evaluated in challenging contexts (with a pilot case study in Lesotho)and can generate updated district summaries within hours once trained, supporting rapid deployment where labels and compute are constrained. Benchmarking against ESA WorldCover and Google Dynamic World shows strong coherence of aggregated class distributions using cosine similarity, complemented by standard classification metrics on labeled tiles, indicating that reliable administrative-level indicators can be produced even without per-pixel agreement. These results underscore the practical relevance of the method for national statistical offices, humanitarian programs,and monitoring initiatives that need scalable, affordable land-cover statistics while minimizing dependence on extensive field campaigns. Received: 1 October 2025 | Revised: 3 March 2026 | Accepted: 23 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Gianfausto Bottini: Conceptualization, Methodology, Soft ware, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review &editing, Visualization. Francesco Nicola Tubiello: Resources, Writing – review &editing, Supervision, Project administration. Pengyu Hao: Writing – review &editing.
A Lightweight Human Pose Estimation Algorithm Based on Improved YOLO11-Pose
As computer vision technology is applied to human pose estimation, current human pose estimation models suffer from problems such as large computational load and parameter count and slow inference speed. This paper proposes an improved lightweight human pose estimation algorithm based on YOLO11-Pose. By introducing Dysample dynamic upsampling in the Neck section to replace nearest neighbor interpolation upsampling, the accuracy of human body keypoint recognition is improved; RCSPELAN is proposed, and the C3k2 module in the model is replaced to enhance feature extraction and reduce computation and parameter requirements; meanwhile, a DESD detection head is proposed by employing detail-enhancement convolution, which effectively captures key details of the human body and reduces model parameters and computational complexity. On the MS COCO human keypoint dataset, mAP50 increased by 0.3%. At the same time, the number of model parameters decreased by 29%, and the computational cost dropped by 13.5%, resulting in a lightweight model. Received: 9 November 2025 | Revised: 6 February 2026 | Accepted: 27 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement This paper utilizes the MS COCO human keypoint dataset, which is publicly available at https://cocodataset.org/#download. Author Contribution Statement Yang Gao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Project administration. Guanglei Qiang: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Visualization, Supervision, Project administration. Fujiang Yuan: Methodology, Software, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Supervision.
Reliability Modeling and Predictive Maintenance Integration for an Induced Draft Fan System Using Semi-Markov Process and Machine Learning-Based Predictive Models
The proposed research enhances the dependability and efficiency levels of induced draft fan systems in thermal power plants due to the combination of semi-Markov process reliability modeling and machine learning-based predictive maintenance (PdM)methods. The system in question is a three-fan system that has two working units and one standby unit that runs cold but is not operational to give redundancy.The most important reliability metrics,such as mean time to system failure,system availability,repairman busy period, and predicted downtime,are analyzed analytically with the semi-Markov and regenerative point methods.In order to supplement the analytical reliability model,a PdM framework through machine learning with the help of a Random Forest classifier is created to predict possible failure learning conditions based on historical data on its operation.The model examines the parameters including the failure rate,the repair rate,the downtimes,and the operational capacity to detect the initial signs of system degradation.The proposed structure can greatly ensure the minimization of unplanned breakdowns and maximization of overall system performance due to the possible proactive scheduling of maintenance activities.Hybrid probabilistic reliability analysis and data-driven predictive modeling offer a solution to increasing operational decision-making in thermal power plants’maintenance systems.The suggested methodology shows that there is a prospect of relying on both reliability engineering and machine learning to realize effective real-time monitoring, enhanced availability,and cost-effective maintenance planning of vital industrial infrastructure. Received: 2 November 2025 | Revised: 27 March 2026 | Accepted: 16 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author (Kaushal Kumar) via email upon reasonable request. Author Contribution Statement Prawar Chaudhary: Conceptualization, Methodology, Software, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Kaushal Kumar: Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration.
Plithogenic Fuzzy Soft Expert Set and Plithogenic Fuzzy Rough Set with Upside-Down Logic
In 1965, Zadeh introduced the notion of a fuzzy set, which has supported decades of successful applications across diverse domains involving vagueness and uncertainty. Fuzzy sets have since been extended in many directions using a variety of concepts. Representative examples include fuzzy soft expert sets, fuzzy T-rough sets, and plithogenic sets. Fuzzy soft expert sets associate a fuzzy subset with each parameter–expert–opinion triple, thereby assigning graded membership values to decision alternatives. Fuzzy T-rough sets approximate fuzzy subsets through T-similarity relations, producing lower and upper bounds that formalize uncertainty. Plithogenic sets describe elements via attribute-dependent memberships together with a contradiction mapping between attribute values. Upside-Down Logic models structured context changes under which truth and falsity are interchanged, reversing evaluations while maintaining internal consistency across contexts. To the best of our knowledge, a unified framework that explicitly fuses the plithogenic fuzzy-set perspective with fuzzy soft expert sets and fuzzy T-rough sets has not yet been established. To address this gap, this paper revisits the Plithogenic Fuzzy Soft Expert Set, the Plithogenic Fuzzy Rough Set, and the classical fuzzy set, and examines how Upside-Down Logic can be incorporated into these settings to capture real-world reversal phenomena. Received: 2 November 2025 | Revised: 19 January 2026 | Accepted: 24 February 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Takaaki Fujita: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration.
Underwater Seafood Detection Using Deep Learning and Data Augmentation
Accurate seafood detection underwater is still a real challenge for modern fisheries, especially when it comes to resource monitoring or automated harvesting. In real-world conditions, underwater images are often blurry, discolored, or cluttered with background noise, all of which make feature extraction harder. On top of that,many marine species have irregular, elongated body shapes, which adds another layer of difficulty for reliable detection. To tackle these problems, we developed an underwater seafood detection model called Seafood Detection Deep Learning (SDDL). It builds on the YOLOv8 framework but integrates dynamic snake convolution to better handle elongated features like sea urchin spines or sea cucumber tentacles. This change lets the network deal more effectively with anisotropic shapes.Before training, we apply Contrast Limited Adaptive Histogram Equalization to boost image contrast and bring out finer details. We also use Focal Loss during training to reduce the impact of class imbalance. The dataset we used contains 5543 underwater images, which provides a realistic and fairly challenging testbed. SDDL achieves 84.22% mAP50 and 48.74% mAP50–95 on this dataset. When we compared it against several representative methods, SDDL gave more consistent results under difficult imaging conditions. Overall,these findings suggest the model holds good promise for deployment in underwater robotic systems, helping enable accurate seafood detection and supporting intelligent, automated harvesting in fisheries. Received: 28 September 2025 | Revised: 27 March 2026 | Accepted: 24 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The URPC dataset that support the findings of this study is available in Github at https://github.com/mousecpn/DG-YOLO. Author Contribution Statement Hanyu Jiang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration, Funding acquisition. Zhichao Zheng: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review &editing, Supervision.
Time Series Prediction Network for Modeling Channel Correlation Based on Wavelet Transform
With the advancement of technology, multi-channel time series prediction tasks have become a focal point of research. However, due to the complex and intertwined correlations between channels, it is challenging to model their interactions accurately and flexibly. Furthermore, most existing methods typically model data from either the time domain or frequency domain alone, leading to inefficient utilization of information. To overcome these challenges, we introduce MCCWT-Net, a multi-channel time series forecasting framework built upon the wavelet transform. Unlike transformer-based models, MCCWT-Net does not rely on self-attention mechanisms, resulting in a more efficient training process. The wavelet transform, when applied in the frequency domain, can be used to extract and reflect features with predictability (such as periodicity) and act as a filter to reduce noise, instead of relying solely on raw historical data. Our model incorporates a specific number of channels and, while modeling long-term features, effectively captures inter-channel correlation information in the frequency domain through wavelet transform, combining it with information from the time domain. Experimental results demonstrate that our model achieves advanced performance across multiple datasets. Received: 27 September 2025 | Revised: 18 March 2026 | Accepted: 27 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The ETT dataset was acquired at https://github.com/zhouhaoyi/ETDataset. The Exchange dataset was acquired at https://github.com/thuml/Autoformer. The Weather dataset was acquired at https://www.bgc-jena.mpg.de/wetter/. The Traffic dataset was acquired at http://pems.dot.ca.gov/. The Electricity dataset was acquired at https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014. Author Contribution Statement Hanyu Jiang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration, Funding acquisition. Zhichao Zheng: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision.
A Fairness-Aware Machine Learning Framework for Sexual and Reproductive Health: Evaluating Algorithmic Bias Across Models
Advances in computational infrastructure and the widespread adoption of electronic health record (EHR) systems have accelerated the integration of artificial intelligence (AI) and machine learning (ML) into sexual and reproductive health (SRH) services. These technologies enhance diagnostic accuracy, support clinical decision-making, and enable predictive analytics using diverse healthcare data. However, biases within training datasets can produce unfair outcomes, particularly for underrepresented groups. This study proposes a fairness-aware ML framework designed to detect and mitigate algorithmic bias in SRH services. The framework is evaluated using two open-source datasets: a large SRH dataset from England (2014–2015) containing 2,126,413 records and the PCOS dataset covering the top 75 countries, enabling assessment of generalizability and intersectional fairness. It integrates pre-processing, in-processing, and post-processing techniques, including model-specific and group-specific thresholding. Results show that on the SRH England dataset, logistic regression (LR) achieved near-optimal parity fairness with minimal performance loss, improving disparate impact from 0.99 to 1.00 while maintaining 0.66 accuracy. Random Forest (RF) and Gradient Boosting (GB) exhibited larger fairness shifts, with disparate impact decreasing from 0.94 to 0.66 (RF) and 0.93 to 0.77 (GB), though accuracy remained stable. On the PCOS dataset, LR reduced bias with only a 1.96% accuracy drop, while GB improved performance but saw fairness decline, with disparate impact falling from 1.08 to 0.57. RF improved fairness but experienced a 28% accuracy reduction. Overall, the findings show that fairness-aware ML can substantially reduce bias, though equity–performance trade-offs vary across models and datasets. Received: 16 September 2025 | Revised: 21 January 2026 | Accepted: 31 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The PCOS dataset that support the findings of this study is openly available on Kaggle at https://www.kaggle.com/datasets/ankushpanday1/pcos-prediction-datasettop-75-countries. The NHS Sexual and Reproductive Health (SRH) Services England 2014–15 dataset that support the findings of this study is openly available through NHS Digital at https://digital.nhs. uk/data-and-information/publications/statistical/sexual-and-reproductive-health-services/sexual-and-reproductive-health-servicesengland-2014-15. Author Contribution Statement Efosa Osagie: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Shemi Ayo-Ogbor: Investigation, Writing – original draft. Rebecca Balasundaram: Investigation, Writing – original draft.
HyperCloudy Number and (m,n)-SuperHyperCloudy Number
Uncertainty modeling is fundamental to decision-making across diverse domains, and numerous frameworks, such as Fuzzy Sets, Rough Sets, Hesitant Fuzzy Sets, and Plithogenic Sets, have been developed to capture different facets of imprecision. Among these, HyperFuzzy Sets and their recursive generalization, SuperHyperFuzzy Sets, assign set-valued membership degrees at multiple hierarchical levels to represent uncertainty more richly. A Cloudy Number assigns to each real value a closed interval within [0,1], modeling uncertainty via lower and upper membership levels. Cloudy Numbers have deep connections with Fuzzy Numbers and real numbers, and substantial related research has been conducted. However, the counterparts of HyperFuzzy Numbers and SuperHyperFuzzy Numbers in the setting of Cloudy Numbers remain unexplored. In this paper, we introduce two new constructs: the HyperCloudy Number and the (m,n)-SuperHyperCloudy Number. Inspired by the HyperFuzzy Number and the (m,n)-SuperHyperFuzzy Number, these concepts hierarchically generalize the existing notion of a Cloudy Number. We establish their fundamental properties and demonstrate their applicability using concrete examples. Received: 12 September 2025 | Revised: 23 October 2025 | Accepted: 29 October 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. This research is purely theoretical, involving no data collection or analysis. We encourage future researchers to pursue empirical investigations to further develop and validate the concepts introduced here. Author Contribution Statement Takaaki Fujita: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration.
Three-Mode Upside-Down Logic Within Plithogenic Neutrosophic Sets: Handling Contradiction and Truth Inversion for Applications in Decision Science
Reversal effects are common in real-world reasoning: a claim that is initially regarded as false may later be treated as true. Upside-Down Logic offers a formal way to model such reversals via context-dependent transformations that exchange truth and falsity, thereby capturing ambiguity and shifts in judgment over time. A (finite) Plithogenic Set describes elements through membership degrees conditioned on attribute values and is equipped with contradiction functions defined on those values, so it both subsumes and generalizes the (finite) classical fuzzy, intuitionistic, and neutrosophic frameworks. Established notions such as fuzzy sets and neutrosophic sets also admit plithogenic counterparts, including (finite) Plithogenic Fuzzy Sets and (finite) Plithogenic Neutrosophic Sets. De-Plithogenication denotes a systematic procedure for neutralizing contradictions in a plithogenic structure—for example, by resetting or transforming conflicting attribute relations into consistent, contradiction-free configurations. A Plithogenic Neutrosophic Set additionally assigns degrees of truth, indeterminacy, and falsity in the presence of such contradictions, thereby enriching neutrosophic semantics with explicit context sensitivity. We develop Three-Mode Upside-Down Logic, an enriched Upside-Down framework, alongside De-Plithogenication, and we investigate their interplay within Plithogenic Neutrosophic Sets. Because the present work is mainly theoretical, we expect future research to assess the practical effectiveness of these notions through computational experiments and related quantitative investigations. Received: 12 September 2025 | Revised: 23 October 2025 | Accepted: 20 January 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Takaaki Fujita: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Database Optimization for Low-Latency Analytics with Adaptive Indexing
Data are rapidly produced in various fields such as finance, e-commerce, healthcare, and the Internet of Things (IoT), resulting in a constantly growing need for real-time analytics that can scale up. Traditional relational database management systems (RDBMSs) are often unable to keep up with high-speed processing requirements due to the limitations of ACID properties. In this report, auto optimized stream is introduced as a self-adaptive optimization framework to enhance real-time analytical pipelines powered by PostgreSQL, Kafka, and Flink in terms of both scalability and performance. The major difference between the proposed framework and the static optimization methods, which are used by Oracle, is that it is a continuous process where live query workloads and data ingestion are monitored regularly, thus allowing for the optimization of indexing strategies, partitioning schemes, and stream processing configurations. The framework employs rule-based decision-making to improve query execution, ingestion throughput, and resource utilization across the board. The adaptive method, when compared to static optimization methods, allows lower query latency, better stream stability, and higher CPU efficiency while causing minimal overhead and keeping full tuning action traceability. These findings not only prove the practical viability of adaptive database systems for real-time analytics but also lay down a steppingstone for future systems. Received: 8 September 2025 | Revised: 24 November 2025 | Accepted: 12 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in "vega-datasets" at https://github.com/vega/vega/blob/main/docs/data/seattle-weather.csv. Author Contribution Statement Lakmali Karunarathne: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration. Swathi Ganesan: Investigation. Kavindu Karunarathne: Methodology, Software, Formal analysis, Resources, Writing — original draft, Writing — review & editing, Visualization. Nalinda Somasiri: Investigation.
Modeling Markers for Detection of Psychiatric Disorders Using EEG Signals
The diagnosis of mental (psychiatric) disorders is challenging, and there is a lack of consensus on objective diagnostic criteria that are based on definitive signs that accompany the disorder. There is a need, therefore, to develop objective tools for the examination of these disorders. We present here a novel machine learning (ML) approach that accurately identifies disorders. The approach uses electroencephalography (EEG) signals for diagnosis, which are processed to extract novel region based markers that are found to contain key information about the types of disorders. Subsequently, a support vector machine (SVM) classifier is modeled, integrated with sequential feature (marker) selection (SFS), which identifies optimal and compact marker subsets for disorder detection. The proposed system has been validated using a publicly available dataset. The developed model was benchmarked against existing models and was shown to perform superior to the models it was extensively compared with; it demonstrated a 98.33% accuracy in detecting obsessive-compulsive disorder (OCD). Our findings indicate that an accurate psychiatric diagnosis system can be achieved using EEG signals with significantly fewer, and more interpretable markers. This simpler and transparent approach improves the practicality and trustworthiness of AI/ML-driven diagnostic tools, making them more suitable for real-world clinical integration and understanding by medical professionals. Received: 5 September 2025 | Revised: 17 November 2025 | Accepted: 15 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in OSF repository at https://osf.io/8bsvr/. Author Contribution Statement Steven Chris: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Debasish Mishra: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision. Lakshman Varanasi: Methodology, Resources, Writing – review & editing, Supervision, Funding acquisition. Varun Viswanathan: Methodology, Writing – review & editing, Supervision.
Correlation-Based Feature Selection for Efficient Intrusion Detection: Comparative Evaluation of Machine Learning Models on CICIDS-2017
The increased growth of network traffic has enabled digital communication, global connectivity, driving advances in e-commerce, and cloud computing. However, this growth also increases the risk of cyberattacks, making effective and efficient intrusion detection systems (IDS) essential. Although many studies have applied machine learning (ML) and deep learning (DL) to improve detection accuracy, the majority pay less attention to computational efficiency, which is critical for real-time deployment. The proposed study evaluates three widely used ML models, namely, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), on the CICIDS-2017 dataset, with and without correlation-based feature selection (CFS). Results show that RF and XGBoost achieved the highest accuracy (0.998) with very low false positive rates, while MLP lagged behind in both detection and runtime performance. Notably, applying CFS reduced RF's training time by 35% without sacrificing accuracy. The proposed study's findings confirm that ensemble models, particularly RF and XGBoost, provide a strong balance between accuracy and efficiency. Moreover, feature selection emerges as a simple yet effective strategy to lower computational cost, making IDS more practical for large-scale, real-time network environments. Received: 1 September 2025 | Revised: 22 December 2025 | Accepted: 20 January 2026 Conflicts of Interest Xuyang Shi is a specialist for Journal of Data Science and Intelligent Systems and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB), Canada, at http://cicresearch.ca/CICDataset/CIC-IDS-2017/. Author Contribution Statement Bilal Rafique: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Sania Kanwal: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Xuyang Shi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Rapid Identification of Pathogenic Bacteria from Raman Spectra with a CNN–Transformer Hybrid Architecture
Bacterial identification from Raman spectra offers a promising label-free and nondestructive approach, providing molecular fingerprints at the single-cell level. However, practical implementation is constrained by low signal-to-noise ratios arising from short acquisition times, severe class imbalance across bacterial species, and high inter- and intra-species spectral variability. This study presents a two-stage convolutional neural network (CNN)–Transformer pipeline evaluated on the Bacteria-ID dataset, covering 30 bacterial species across approximately 63,000 spectra. Preprocessing combined baseline subtraction, fast Fourier transform, and wavelet decomposition to improve signal quality prior to training. Class imbalance was addressed through synthetic minority oversampling technique and class-weighted loss, while mixed precision computation reduced GPU overhead. Hyperparameters were optimized via Bayesian search using Optuna. The CNN stem extracts local Raman peak features, while the Transformer encoder captures long-range spectral dependencies that convolutional layers alone cannot model efficiently. On the independent test set, the model achieved approximately 85% accuracy and weighted F1, surpassing ResNet (82.2%) and RamanNet (84.7%) evaluated under identical conditions. The lowest-performing species improved from 31% F1 in the unoptimized baseline to approximately 70% in the final configuration. External validation on spectra from alternative instruments or clinical settings has not yet been conducted and represents the most important direction for future work. Extensions toward MRSA/MSSA classification and antibiotic response prediction are planned. Received: 31 August 2025 | Revised: 13 November 2025 | Accepted: 30 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement This study uses the publicly available Bacteria-ID dataset (Ho et al., 2019). No new data were generated. The specific train/validation/test splits, trained model weights, and figure-generation outputs can be obtained from the corresponding author upon reasonable request for academic use. Source code is not publicly shared currently Author Contribution Statement Apoorv Patel: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Hongying Meng: Resources, Writing – review & editing, Supervision, Project administration.
An Asynchronous Parallel Data Loading Optimization Algorithm for Deep Learning Applications
This study aims to address the concern of inefficient data loading, which often leads to computationally inefficient deep learning workflows and becomes a bottleneck for scalability, especially under resource constraints. An asynchronous parallel data loading optimization algorithm is proposed that will revolutionize the data-training pipeline by enabling multi-threaded concurrent data loading and model training on multiple devices. The two-dimensional array structure and special hash table used ensure the invariance of the data distribution and concurrency safety, which is independent of loading and training processes and supported by rigorous mathematical proof. Experimental results from the CIFAR-10 dataset vividly demonstrate that this method represents a significant improvement over state-of-the-art baselines, achieving a throughput of approximately 3,250 samples/second, 87% GPU utilization, and a 40% reduction in training time, while maintaining the statistical integrity of the original dataset. This paper proposes a solution that does not bind users to a specific framework and increases efficiency without requiring the purchase of expensive hardware. This resolution makes deep learning achievable and reproducible, while reducing the time cost of research and student training exercises. Received: 31 August 2025 | Revised: 10 February 2026 | Accepted: 30 March 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://www.cs.toronto.edu/~kriz/cifar.html. Author Contribution Statement Xingjun Lin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
High-Resolution Evaluation of WRF Cumulus Parameterization Schemes for Rainfall Prediction in Thailand
This study evaluates the performance of cumulus parameterization schemes under a default SBU-YLin within the weather research and forecasting (WRF) model to enhance rainfall forecasting in Thailand's tropical climate. Using a high-resolution nested domain setup (8.1 × 8.1 km, 2.7 × 2.7 km, and 0.9 × 0.9 km), simulations are validated against 128 Thai Meteorological Department stations from September 21 to 23, 2024. The New Tiedtke (RAIN-16) scheme achieved the most balanced performance, with underestimation and overestimation rates of approximately 50% and mean underestimation and overestimation of −12.75 mm and 13.30 mm, respectively. However, the SBU-YLin scheme reduced biases, root mean square error (RMSE), and mean absolute error (MAE) but could not resolve heavy rainfall events, especially in Domains 2 and 3, which are considered high-resolution domains. In Domain 1, very heavy rainfall was overestimated with an RMSE of 98.50 mm; Domain 2 showed an overestimation of rainfall, and Domain 3 had a substantial overestimation of rainfall associated with localized convective activity. However, the SBU-YLin scheme reduced the biases, RMSE, and MAE, but at higher computational demands. Results showed that Domains 2 and 3 increased accuracy with finer spatial resolution, but it was challenging to simulate heavy rainfall events. The selected 3 days are an intense rainfall event that is characteristic of monsoonal convection in Thailand. This brief time span enables WRF schemes to be examined at a resolution in extreme conditions. The temporal coverage of future research is going to be extended to a seasonal-level assessment. Received: 14 August 2025 | Revised: 3 February 2026 | Accepted: 23 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data used in this study were obtained from TMD with permission and are not publicly available. Author Contribution Statement Usa Wannasingha Humphries: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Muhammad Waqas: Validation, Investigation, Data curation, Writing – original draft, Visualization. Boonlert Archevarahuprok: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation. Porntip Dechpichai: Validation, Visualization. Angkool Wangwongchai: Conceptualization, Methodology, Software, Formal analysis, Writing – review & editing, Supervision, Project administration. Boobphachard Chansawang: Writing – review & editing, Visualization.
Predicting and Reducing Wireless Customer Churn Using the AI2-Sriya Expert Index (SXI)
The telecommunication sector has experienced swift technological development, with wireless services being the forces of change. One of the key issues in this sector is customer churn that affects profitability and market competitiveness. As customer expectations increase and competition becomes fiercer, telecom providers should be concerned with retaining current customers through better services. This study examines churn prediction in the wireless telecommunication sector via machine learning (ML) methods, focusing on the performance of the AI2 -Sriya Expert Index (SXI) model. AI2 (AI Square), launched by Sriya.AI, improves standard AI with a second layer of AI to facilitate adaptive learning. Fundamentally, SXI is a super feature that aggregates outputs from 5–10 ML models into one dynamic score that accurately determines the probability of customer churn. This method condenses complex, multi-dimensional data into two-dimensional data for enhanced decision-making and predictive analysis. In the comparison, conventional models such as XGBoost had 67% accuracy and 0.68 AUC, other research employing LightGBM and CatBoost had AUC values around 0.9, and internal benchmarking showed TabNet and FT-Transformer achieving AUCs of 0.65 and 0.66, respectively. CatBoost with hyperparameter optimization obtained an F1-score of 93% and AUC of 0.91. Nevertheless, the SXI model outperformed all of them with 98% accuracy and a 0.98 AUC value. Aside from predictive accuracy, SXI provides actionable business insights through iterative optimization with deep learning. This study shows that SXI has the capability to decrease customer churn from 49.5% to 39.65% (first 20% improvement). Through optimization, churn can be further reduced to 24.78% (50% reduction) and then eventually to 6.44% (87% reduction), providing long-term strategic advantages. These findings validate that the SXI framework offers a robust, precise, and interpretable solution for customer churn in telecom that is substantially better than that of conventional ML models. Received: 28 July 2025 | Revised: 6 September 2025 | Accepted: 26 September 2025 Conflicts of Interest The authors are employees of Sriya, where the AI2 SXI model was developed using the company's resources. The authors declare that this affiliation did not influence the study's results or conclusions. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/abhinav89/telecom-customer. Author Contribution Statement Nikhil Kakkattu Murugan: Software, Investigation, Data curation, Visualization. Reeshabh Kumar: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Prashant Yadav: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing. Mahesh Banavar: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration. Srinivas Kilambi: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Efficiency Analysis and Ranking of the Decision-Making Units Using Bipolar Fuzzy Data Envelopment Analysis
This manuscript introduces a novel extension of Fuzzy Data Envelopment Analysis (FDEA) by incorporating Triangular Bipolar Fuzzy Numbers (BTFNs), thereby proposing the Bipolar Fuzzy DEA (BFDEA) framework to better model environments with uncertainty and conflicting information. The authors define a magnitude function to transform the proposed BFDEA model into a crisp linear programming model, enabling the computation of efficiency scores for Decision-Making Units (DMUs). To address the challenge of ranking fully efficient units, a bipolar super-efficiency model is developed, complemented by a benchmarking methodology that identifies peer DMUs to guide performance improvement of each inefficient DMU. The approach is clearly illustrated through a numerical example involving ten DMUs, with detailed evaluation, ranking, and benchmarking results and compared with the existing fuzzy model. The paper contributes significantly to the literature by enhancing DEA's ability to handle both positive and negative aspects of fuzziness, thus offering a more expressive and realistic efficiency analysis framework in complex decision-making environments. Received: 10 July 2025 | Revised: 22 September 2025 | Accepted: 9 October 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Kshitish Kumar Mohanta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
Generative AI in Healthcare: Applications and Challenges
This scoping review synthesizes recent literature on the applications and challenges of generative artificial intelligence (AI) in healthcare, with the aim of providing a structured overview that is useful to clinicians, data scientists, and health-system decision-makers. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-guided search across PubMed, IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and Google Scholar, we identified and thematically analyzed 86 studies. The review organizes applications into seven themes—medical image processing, medical imaging and radiology interpretation, clinical documentation, drug design, clinical decision and patient–provider support, synthetic data generation, and medical education and training—and maps the model families most commonly used in each theme. On the basis of this synthesis, generative AI demonstrably accelerates diagnostic workflows, augments scarce clinical datasets, personalizes communication, and supports discovery pipelines. However, the review also identifies four interlocking challenge domains that constrain clinical translation: data and security (privacy, bias, contextualization), technology and infrastructure (scalability, interpretability, vendor dependence), ethics and governance (regulation, trust, certification),and human resources (workforce skills, change management). The paper concludes with a translational path and research priorities aimed at closing these gaps. The review is intended to serve as a reference for researchers, practitioners, and policymakers working on the responsible deployment of generative AI in clinical settings. Received: 16 September 2024 | Revised: 9 May 2026 | Accepted: 1 June 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study Author Contribution Statement Wael Rahhal: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.