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.
Explaining and Predicting Behavioral Intention Toward the Halal Phuket AI Tourism Platform: A UTAUT–Machine Learning Hybrid Study
This study examined behavioral intention toward Halal Phuket, an AI-enabled tourism platform supporting Muslim travelers in Phuket, Thailand. Using 450 questionnaire responses from Muslim tourists who interacted with the platform or evaluated its core functions in a realistic usage scenario, the study integrated Unified Theory of Acceptance and Use of Technology (UTAUT)-based explanation with machine learning prediction. The analysis tested performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, and behavioral intention through measurement validation, structural equation modeling (SEM), regression, predictive benchmarking, ablation analysis, and explainability. Measurement results supported construct reliability, convergent validity, discriminant validity, and common-method bias diagnostics. However, the cross-sectional and self-reported nature of the data required cautious interpretation of the SEM paths. All six UTAUT constructs positively predicted behavioral intention, with habit, social influence, and hedonic motivation showing the strongest effects. Among all predictive configurations, the construct-only linear regression model achieved the best performance (root mean squared error (RMSE) = 0.251, R 2 = 0.861), outperforming item-level, hybrid, demographic, and nonlinear alternatives. Ablation findings indicated that removing demographic or expanded feature groups had a limited effect, whereas removing full UTAUT construct families substantially weakened performance, especially when habit and social influence were excluded. Feature-importance and SHapley Additive exPlanations (SHAP) analyses showed broad alignment with regression coefficients, supporting the value of theoretically grounded, parsimonious feature construction. The findings suggest that halal tourism platform developers should prioritize repeated-use support, social trust signals, enjoyable service experience, and practical usability rather than relying only on technical sophistication. Received: 21 April 2026 | Revised: 12 June 2026 | Accepted: 14 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/Halal.zip. Author Contribution Statement Nasith Laosen: Conceptualization, Methodology, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition. Tanagrit Chansaeng: Methodology, Software, Data curation, Writing – review & editing, Visualization. Wipawan Buathong: Investigation, Resources, Data curation, Writing – review & editing. Atipan Saimmai: Validation, Investigation, Resources, Writing – review & editing. Pita Jarupunphol: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Agentic Data Engineering Framework for Autonomous, Real-Time, and Trustworthy AI Systems
The fast expansion of real-time data has revealed the very important weaknesses of traditional data engineering pipelines, especially in the aspects of flexibility, scalability, and resilience. This paper is a proposal for an Agentic Data Engineering Framework that is meant to facilitate autonomous, real-time, and trust-conscious data processing. This framework integrates a multi-agent design that is composed of decision, processing, and trust components and allows pipelines to be dynamically reconfigured based on the condition of systems and characteristics of data. The methodology builds data engineering as a multi-objective optimization and involves latency, accuracy, computational cost, and trust score as a combination of learning. A novel algorithm, Agentic Pipeline Optimization with Trust Feedback, is developed to guide adaptive decision-making, based on reinforcement learning and trust-sensitive rewards modeling. Heterogeneous datasets under controlled conditions are used to perform experimental evaluation. The proposed framework has an accuracy of 94.6 %, latency of 165 ms, throughput of 1980 records/s, and a trust score of 0.91, which is much better than the baseline models. These improvements correspond to approximately 5–10% higher accuracy, 20–30% lower latency, and 20% increased throughput compared to existing approaches. The findings indicate that the framework works well in dynamic data settings as the performance is stable. The results suggest that agentic intelligence is most likely to combine with trust-sensitive optimization to offer a viable route to next-generation data engineering systems, specifically when it comes to Internet of Things (IoT), cloud, and intelligent enterprise applications. Received: 15 April 2026 | Revised: 17 June 2026 | Accepted: 29 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 on the UCI Machine Learning Repository at https://archive.ics.uci.edu/dataset/352/online+retail and on the LogHub Repository (HDFS Log Dataset) at https://github.com/logpai/loghub/tree/master/HDFS. Processed datasets, experimental configurations, and supplementary implementation details are available from the corresponding author upon reasonable request. Author Contribution Statement Parikshit Premchand Sahagal: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration. Swetha Talluri: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization. Pradeep Kumar Sambamurthy: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Hastimal Jangid: Conceptualization, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Abrar Ahmed Syed: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, 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.
Combining Artificial Intelligence with Ayurveda and Siddha Medicine: An Examination of Pulse-Based Disease Diagnosis Systems
Advancements in the fields of Ayurveda and Siddha worldwide have led to an increasing need for objective, noninvasive, technology-based diagnostic methods. Therefore, traditional pulse diagnosis has become a pivotal field in computational research to enhance diagnostic consistency. With the emergence of new biomedical sensing techniques and computational intelligence, automatic pulse diagnosis systems have been developed for pulse signal acquisition and analysis. This review paper introduces the technologies of pulse acquisition, signal preprocessing, feature extraction, machine learning, and deep learning-based methods to diagnose the disease by pulse in an automated system and also analyzed paradigms using sensors such as strain-gauge sensors, piezoelectric sensors, microelectromechanical systems, multichannel pulse sensors, photoplethysmography, piezoelectric wafer active sensors, Internet of Things-enabled devices, and artificial intelligence (AI)-assisted platforms. The preprocessing techniques, such as Butterworth, Chebyshev, Bessel, and Savitsky–Golay filters for enhancement of pulse waveform quality and feature reliability and assessment of the pulse parameters related to arterial stiffness and vascular function, along with K-Nearest Neighbors, support vector machines, Decision Trees, Random Forests, and artificial neural networks, were utilized for dosha classification and disease prediction. The review results show remarkable advances in sensing technology, signal analysis, and intelligent diagnostics algorithms. But there are hurdles, such as nonuniform datasets, explainability gaps, and clinical validation. For future studies, explainable AI, integration with wearables, standardized datasets, and widespread clinical testing are areas of interest to improve health assessment and early disease screening. Received: 30 November 2025 | Revised: 23 June 2026 | Accepted: 30 July 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 Priyadharshini K: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Saravanan K: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.
Using ChatGPT with Prompt Engineering for Personalized Travel Destination Recommendations
Artificial intelligence (AI) has become widely accessible and can support a wide range of use cases such as personalized recommendations. Our study investigates the capabilities of a pre-trained large language model for generating personalized travel destination recommendations. Based on a design science research approach, we evaluate the usability and effectiveness of suggestions. In particular, we explore how additional task-specific or user-specific input can enhance the response effectiveness. User preferences gathered from a survey are used for computational experiments. The results are presented in a second survey, and the participants’ feedback is collected to assess the perceived quality of personalized recommendations. This feedback demonstrates that the model recommendations are positively evaluated with regard to user satisfaction, preference consideration, and timesaving. Yet, the participants also see potential for more detailed and specific recommendations, along with enhanced granularity for the budget breakdown and better transparency through shared sources. We also identify limitations, such as the availability and quality of gathered data, leading to potentially inadequate recommendations, as well as subjective evaluation measures requiring larger and more diverse sample data to confirm the generalizability of the approach. The findings indicate the potential benefits of personalized AI recommendations in the travel industry and suggest areas for improvement. Received: 27 November 2025 | Revised: 5 June 2026 | Accepted: 30 June 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 upon reasonable request. Author Contribution Statement Sarah Castratori: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Project administration. Domenico Dongiovanni: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Project administration. David Grossenbacher: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Project administration. Thomas Hanne: Conceptualization, 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.
Ensuring Turmeric Authenticity: A Web-Based AI Approach for Detecting Rice Flour Adulteration
Turmeric powder is widely used for its culinary and medicinal properties. However, its adulteration with substances similar to the properties of pure turmeric powder poses significant concerns regarding quality, authenticity, and consumer health. This study aimed to develop an artificial intelligence (AI)-based tool for detecting rice flour adulteration in turmeric powder using color values and to further validate the findings through chemical, sensory, and microscopic analyses. Pure turmeric powder was mixed with rice flour (0.1%, 0.5%, 1%, 2.5%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% (w/w)) to create a series of rice flour adulterated turmeric powder samples. The L* , a* , and b* color values were measured and used for the development of the AI tool. Starch iodine complex formation was employed by the iodine test, while sensory evaluation assessed consumer perception to changes in color due to rice flour adulteration in turmeric powder. Microscopic image analysis was utilized to differentiate rice flour adulterated turmeric powder from pure turmeric powder based on the presence of rice flour granules. The results of the iodine test showed its suitability for detecting rice flour adulteration in turmeric powder on a laboratory scale. The AI-based tool enabled rapid and accessible adulteration detection. The findings of this study contribute to enhancing food quality assurance and promoting consumer health by addressing common adulteration practices in turmeric powder. Received: 30 September 2025 | Revised: 22 January 2026 | Accepted: 25 February 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 upon reasonable request. Author Contribution Statement Manhari Palliyaguruge: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Thilina Abekoon: Formal analysis, Data curation, Writing – review & editing. Hirushan Sajindra: Software, Formal analysis, Data curation, Writing – review & editing. Rasanjali Samarakoon: Conceptualization, Validation, Supervision, Project administration. Namal Rathnayake: Validation, Data curation. Upaka Rathnayake: Conceptualization, Validation, Writing – review & editing, Supervision, 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.
A Comparative Analysis of Two Algorithms for TF-IDF-Based Document Similarity
By using Term Frequency–Inverse Document Frequency, this study set out to analyze how different algorithms carry out the task of identifying research articles that are considered to be of similar subjects. Two algorithms were implemented in our study, one of which was a Python algorithm based on cosine similarity and the other in C# using standard statistical measures. The goal was to examine how the ranking of documents to the most similar target article can be influenced by factors such as preprocessing, similarity techniques, and programming languages. The dataset contains a total of 50 abstracts, with two versions. One with common words removed and one left unchanged. The 50th abstract was selected as the target document, and since it included 29 references, we evaluated the top 29 results produced by each algorithm. The Python implementation relied on cosine similarity only, whereas the C# implementation used mean, standard deviation, Z-score, and weighted similarity. Performance was assessed using Spearman’s rank correlation and hit ratio. The results showed that removing common words generally improved performance. The best result (75.86%) came from the C# algorithm using statistical measures with common words included. Overall, the findings highlight that algorithm choice and preprocessing decisions significantly affect document similarity outcomes. Received: 4 September 2025 | Revised: 8 December 2025 | Accepted: 13 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 figshare at https://figshare.com/articles/dataset/Article_Abstracts/32834597?file=66102518. Author Contribution Statement Glory Tendaishe Muindisi: Methodology, Software, Validation, Formal analysis, Investigation, Writing – review & editing, Visualization. Yusra Nuri: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Edip Senyurek: Conceptualization, Resources, Data curation, Supervision, Project administration.
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.