Journal of Data Science and Intelligent Systems
type: channel
description: 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.
publisher: Bon View Publishing PTE. LTD.
language: en
publication name: Journal of Data Science and Intelligent Systems
issn: 2972-3841
items: Artificial Intelligence, Financial Access, and the Path to Sustainable Development: Insights from G-7 Countries
items: A Narrative Review of GAN-Based Synthetic Data Generation in Disease Prediction
items: Correction, Reconstruction, and Modeling of Experimental Data Using LI Transforms
items: Toward Fully Automated Prompting: An LLM-Powered Genetic Algorithm for Prompt Generation, Optimization, and Self-Evaluation
items: Leveraging a Hybrid Fuzzy C-Means-PCA Model for Identifying Speech Therapy Needs in Children
items: A Machine Learning Framework for Automated Detection of Perceived Stress in Female Students
items: Contrastive Pretrain and Supervised Fine-Tune for Land-Cover Classification in Data-Scarce Countries
items: A Lightweight Human Pose Estimation Algorithm Based on Improved YOLO11-Pose
items: Reliability Modeling and Predictive Maintenance Integration for an Induced Draft Fan System Using Semi-Markov Process and Machine Learning-Based Predictive Models
items: Plithogenic Fuzzy Soft Expert Set and Plithogenic Fuzzy Rough Set with Upside-Down Logic
items: Underwater Seafood Detection Using Deep Learning and Data Augmentation
items: A Fairness-Aware Machine Learning Framework for Sexual and Reproductive Health: Evaluating Algorithmic Bias Across Models
items: HyperCloudy Number and (m,n)-SuperHyperCloudy Number
items: Three-Mode Upside-Down Logic Within Plithogenic Neutrosophic Sets: Handling Contradiction and Truth Inversion for Applications in Decision Science
items: Database Optimization for Low-Latency Analytics with Adaptive Indexing
items: Modeling Markers for Detection of Psychiatric Disorders Using EEG Signals
items: Correlation-Based Feature Selection for Efficient Intrusion Detection: Comparative Evaluation of Machine Learning Models on CICIDS-2017
items: Rapid Identification of Pathogenic Bacteria from Raman Spectra with a CNN–Transformer Hybrid Architecture
items: An Asynchronous Parallel Data Loading Optimization Algorithm for Deep Learning Applications
items: High-Resolution Evaluation of WRF Cumulus Parameterization Schemes for Rainfall Prediction in Thailand
items: Predicting and Reducing Wireless Customer Churn Using the AI2-Sriya Expert Index (SXI)
items: Generative AI in Healthcare: Applications and Challenges
items: AI-Powered Soil Temperature Modeling for Sustainable Agriculture in Arid Regions: A Case Study of Bustan, Uzbekistan
items: Robust Histogram Signature-Driven Template Matching for Vehicle Tracking in Traffic Videos
items: Predicting Mental Health Outcomes for Native and Non-native Language Students Through ICT-Based Learning Analytics
items: AI-Driven Climate Analysis in a Semi-arid Region: Uncovering Warming Trends and Meteorological Shifts in Southeastern Morocco
items: Handling Uncertain Information with Fuzzy Logic in Locating New Infrastructure
items: NLP Framework to Safeguard Youngsters Online Using Advanced Transformer-Based Models
items: Exploring the Uses of Artificial Intelligence and ChatGPT in Therapeutic Diet and Nutrition
items: A Systematic Review of Techniques for Early-Stage Alzheimer's Disease Diagnosis Using Machine Learning and Deep Learning

Artificial Intelligence, Financial Access, and the Path to Sustainable Development: Insights from G-7 Countries
type: item
description: 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://data…
subject: Research Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: sustainable development
  topic: keywords
subject: load capacity factor
  topic: keywords
subject: financial accessibility
  topic: keywords
subject: G-7 economies
  topic: keywords
creator: Mohammad Ridwan
creator: Kabaly P. Subramanian
creator: Jaheer Mukthar K.P.
creator: Binata Rani Sen
creator: Minghui Jiang
creator: Mujeeb Saif Mohsen Al Absy
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-12
publication date: 2026-06-12
doi: 10.47852/bonviewJDSIS62029112

A Narrative Review of GAN-Based Synthetic Data Generation in Disease Prediction
type: item
description: 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 i…
subject: Review
  topic: section
subject: generative adversarial networks (GANs)
  topic: keywords
subject: synthetic data generation
  topic: keywords
subject: electronic health records (EHR)
  topic: keywords
subject: AI in healthcare
  topic: keywords
subject: disease prediction
  topic: keywords
creator: Swathi Ganesan
creator: Nalinda Somasiri
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-11
publication date: 2026-06-11
doi: 10.47852/bonviewJDSIS62028542

Correction, Reconstruction, and Modeling of Experimental Data Using LI Transforms
type: item
description: 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.
subject: Research Articles
  topic: section
subject: mathematical processing of signals and images
  topic: keywords
subject: linear stationary and invariant systems
  topic: keywords
subject: multidimensional convolution-type equation
  topic: keywords
creator: Andrey Novikov-Borodin
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-06-25
publication date: 2026-06-25
doi: 10.47852/bonviewJDSIS62028448

Toward Fully Automated Prompting: An LLM-Powered Genetic Algorithm for Prompt Generation, Optimization, and Self-Evaluation
type: item
description: 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 conflict…
subject: Research Articles
  topic: section
subject: genetic algorithms
  topic: keywords
subject: LLM prompt optimization
  topic: keywords
subject: automated prompt engineering
  topic: keywords
subject: LLM-guided genetic operators
  topic: keywords
creator: Leandro A. Loss
creator: Pratikkumar Dhuvad
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-25
publication date: 2026-06-25
doi: 10.47852/bonviewJDSIS62028440

Leveraging a Hybrid Fuzzy C-Means-PCA Model for Identifying Speech Therapy Needs in Children
type: item
description: 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…
subject: Research Articles
  topic: section
subject: Fuzzy C-Means
  topic: keywords
subject: principal component analysis
  topic: keywords
subject: acoustic–prosodic feature
  topic: keywords
subject: speech therapy
  topic: keywords
subject: developmental language disorder
  topic: keywords
creator: Muhammad Rizal Haris
creator: Muhammad Faisal
creator: Rizki Yusliana Bakti
creator: Muhyiddin A. M Hayat
creator: Titik Khawa Abd Rahman
creator: Muhammad Syafaat S. Kuba
creator: Titin Wahyuni
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-05
publication date: 2026-06-05
doi: 10.47852/bonviewJDSIS62028311

A Machine Learning Framework for Automated Detection of Perceived Stress in Female Students
type: item
description: 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.
subject: Research Articles
  topic: section
subject: machine learning
  topic: keywords
subject: data analysis
  topic: keywords
subject: Heartfulness meditation
  topic: keywords
subject: stress
  topic: keywords
subject: satisfaction with life
  topic: keywords
creator: Kapil Gupta
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-07-17
publication date: 2026-07-17
doi: 10.47852/bonviewJDSIS62028284

Contrastive Pretrain and Supervised Fine-Tune for Land-Cover Classification in Data-Scarce Countries
type: item
description: 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 Contributi…
subject: Research Articles
  topic: section
subject: contrastive learning
  topic: keywords
subject: cost-efficient pipeline
  topic: keywords
subject: Sentinel-2
  topic: keywords
subject: low-income food-deficit countries
  topic: keywords
subject: zonal statistics
  topic: keywords
creator: Gianfausto Bottini
creator: Francesco Nicola Tubiello
creator: Pengyu Hao
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-04-30
publication date: 2026-04-30
doi: 10.47852/bonviewJDSIS62027813

A Lightweight Human Pose Estimation Algorithm Based on Improved YOLO11-Pose
type: item
description: 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.
subject: Research Articles
  topic: section
subject: human pose estimation
  topic: keywords
subject: Dysample
  topic: keywords
subject: RCSPELAN
  topic: keywords
subject: DESD
  topic: keywords
subject: lightweight networks
  topic: keywords
creator: Yang Gao
creator: Guanglei Qiang
creator: Fujiang Yuan
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-02
publication date: 2026-06-02
doi: 10.47852/bonviewJDSIS62028142

Reliability Modeling and Predictive Maintenance Integration for an Induced Draft Fan System Using Semi-Markov Process and Machine Learning-Based Predictive Models
type: item
description: 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 Sta…
subject: Research Articles
  topic: section
subject: reliability engineering
  topic: keywords
subject: quality control
  topic: keywords
subject: predictive maintenance
  topic: keywords
subject: machine learning
  topic: keywords
subject: semi-Markov process
  topic: keywords
creator: Prawar Chaudhary
creator: Kaushal Kumar
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-19
publication date: 2026-05-19
doi: 10.47852/bonviewJDSIS62028066

Plithogenic Fuzzy Soft Expert Set and Plithogenic Fuzzy Rough Set with Upside-Down Logic
type: item
description: 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.
subject: Research Articles
  topic: section
subject: Plithogenic Fuzzy Soft Expert Set
  topic: keywords
subject: Plithogenic Fuzzy Rough Set
  topic: keywords
subject: fuzzy set
  topic: keywords
subject: plithogenic set
  topic: keywords
subject: Upside-Down Logic
  topic: keywords
creator: Takaaki Fujita
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-06-24
publication date: 2026-06-24
doi: 10.47852/bonviewJDSIS62028065

Underwater Seafood Detection Using Deep Learning and Data Augmentation
type: item
description: 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, D…
subject: Research Articles
  topic: section
subject: underwater image
  topic: keywords
subject: image enhancement
  topic: keywords
subject: dynamic snake convolution
  topic: keywords
subject: CLAHE
  topic: keywords
subject: YOLOv8
  topic: keywords
creator: Hanyu Jiang
creator: Zhichao Zheng
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-21
publication date: 2026-05-21
doi: 10.47852/bonviewJDSIS62027779

A Fairness-Aware Machine Learning Framework for Sexual and Reproductive Health: Evaluating Algorithmic Bias Across Models
type: item
description: 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 Ava…
subject: Research Articles
  topic: section
subject: sexual and reproductive health
  topic: keywords
subject: algorithmic bias
  topic: keywords
subject: bias mitigation
  topic: keywords
subject: health equity
  topic: keywords
subject: fairness-aware framework
  topic: keywords
creator: Efosa Osagie
creator: Shemi Ayo-Ogbor
creator: Rebecca Balasundaram
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-08
publication date: 2026-05-08
doi: 10.47852/bonviewJDSIS62027678

HyperCloudy Number and (m,n)-SuperHyperCloudy Number
type: item
description: 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.
subject: Research Articles
  topic: section
subject: Fuzzy Set
  topic: keywords
subject: HyperFuzzy Set
  topic: keywords
subject: SuperHyperFuzzy Set
  topic: keywords
subject: Cloudy Number
  topic: keywords
subject: cloud
  topic: keywords
creator: Takaaki Fujita
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-03-12
publication date: 2026-03-12
doi: 10.47852/bonviewJDSIS62027651

Three-Mode Upside-Down Logic Within Plithogenic Neutrosophic Sets: Handling Contradiction and Truth Inversion for Applications in Decision Science
type: item
description: 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, Meth…
subject: Research Articles
  topic: section
subject: Upside-Down Logic
  topic: keywords
subject: Plithogenic Neutrosophic Set
  topic: keywords
subject: uncertain set
  topic: keywords
subject: De-Plithogenication
  topic: keywords
subject: Three-Mode Upside-Down Logic
  topic: keywords
creator: Takaaki Fujita
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-06-18
publication date: 2026-06-18
doi: 10.47852/bonviewJDSIS62027650

Database Optimization for Low-Latency Analytics with Adaptive Indexing
type: item
description: 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…
subject: Research Articles
  topic: section
subject: database optimization
  topic: keywords
subject: indexing
  topic: keywords
subject: real-time data processing
  topic: keywords
subject: PostgreSQL
  topic: keywords
subject: adaptive optimization
  topic: keywords
creator: Lakmali Karunarathne
creator: Swathi Ganesan
creator: Kavindu Karunarathne
creator: Nalinda Somasiri
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-02-24
publication date: 2026-02-24
doi: 10.47852/bonviewJDSIS62027607

Modeling Markers for Detection of Psychiatric Disorders Using EEG Signals
type: item
description: 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: Concep…
subject: Research Articles
  topic: section
subject: disorder markers
  topic: keywords
subject: medical diagnosis
  topic: keywords
subject: mental health
  topic: keywords
subject: psychiatry
  topic: keywords
subject: well-being
  topic: keywords
creator: Steven Chris
creator: Debasish Mishra
creator: Lakshman Varanasi
creator: Varun Viswanathan
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-03-20
publication date: 2026-03-20
starting page: 237
ending page: 246
doi: 10.47852/bonviewJDSIS62027587

Correlation-Based Feature Selection for Efficient Intrusion Detection: Comparative Evaluation of Machine Learning Models on CICIDS-2017
type: item
description: 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, Softwar…
subject: Research Articles
  topic: section
subject: intrusion detection system
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Random Forest
  topic: keywords
subject: MLP
  topic: keywords
subject: feature selection
  topic: keywords
creator: Bilal Rafique
creator: Sania Kanwal
creator: Xuyang Shi
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-04-22
publication date: 2026-04-22
starting page: 193
ending page: 201
doi: 10.47852/bonviewJDSIS62027552

Rapid Identification of Pathogenic Bacteria from Raman Spectra with a CNN–Transformer Hybrid Architecture
type: item
description: 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 …
subject: Research Articles
  topic: section
subject: Raman spectroscopy
  topic: keywords
subject: bacterial identification
  topic: keywords
subject: convolutional neural networks
  topic: keywords
subject: transformer
  topic: keywords
subject: data augmentation
  topic: keywords
creator: Apoorv Patel
creator: Hongying Meng
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-05-28
publication date: 2026-05-28
doi: 10.47852/bonviewJDSIS62027534

An Asynchronous Parallel Data Loading Optimization Algorithm for Deep Learning Applications
type: item
description: 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.
subject: Research Articles
  topic: section
subject: asynchronous data loading
  topic: keywords
subject: deep learning
  topic: keywords
subject: data distribution invariance
  topic: keywords
subject: optimization algorithm
  topic: keywords
subject: concurrency
  topic: keywords
creator: Xingjun Lin
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-05-29
publication date: 2026-05-29
doi: 10.47852/bonviewJDSIS62027528

High-Resolution Evaluation of WRF Cumulus Parameterization Schemes for Rainfall Prediction in Thailand
type: item
description: 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 Humph…
subject: Research Articles
  topic: section
subject: weather research and forecasting
  topic: keywords
subject: rainfall prediction
  topic: keywords
subject: cumulus parameterization schemes
  topic: keywords
subject: numerical weather prediction
  topic: keywords
creator: Usa Wannasingha Humphries
creator: Muhammad Waqas
creator: Boonlert Archevarahuprok
creator: Porntip Dechpichai
creator: Angkool Wangwongchai
creator: Boobphachard Chansawang
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-06-03
publication date: 2026-06-03
doi: 10.47852/bonviewJDSIS62027208

Predicting and Reducing Wireless Customer Churn Using the AI2-Sriya Expert Index (SXI)
type: item
description: 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 s…
subject: Research Articles
  topic: section
subject: SXI
  topic: keywords
subject: predictive modeling
  topic: keywords
subject: customer churn
  topic: keywords
subject: wireless telecommunication industry
  topic: keywords
subject: churn rate prediction
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Nikhil Kakkattu Murugan
creator: Reeshabh Kumar
creator: Prashant Yadav
creator: Mahesh Banavar
creator: Srinivas Kilambi
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-12-10
publication date: 2025-12-10
starting page: 166
ending page: 172
doi: 10.47852/bonviewJDSIS52026969

Generative AI in Healthcare: Applications and Challenges
type: item
description: 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:…
subject: Review
  topic: section
subject: generative AI
  topic: keywords
subject: applications
  topic: keywords
subject: challenges
  topic: keywords
subject: healthcare
  topic: keywords
subject: survey
  topic: keywords
creator: Wael Rahhal
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
date: 2026-07-16
publication date: 2026-07-16
doi: 10.47852/bonviewJDSIS62024354

AI-Powered Soil Temperature Modeling for Sustainable Agriculture in Arid Regions: A Case Study of Bustan, Uzbekistan
type: item
description: Soil temperature is a key determinant of soil health and agricultural productivity, especially in arid regions vulnerable to climate change. This study investigates the use of advanced machine learning models to predict soil temperature variations in Bustan, Uzbekistan, a region facing significant climatic stress. Using 16 years of meteorological data, including atmospheric temperature, humidity, and wind speed, eight machine learning models were evaluated for their ability to predict surface and subsurface (10 cm depth) soil temperatures. Among the models tested, the bi-directional long short-term memory (Bi-LSTM) algorithm demonstrated superior predictive accuracy with R² values exceeding 0.94 for subsurface temperatures. The two-step modeling approach utilized Bi-LSTM outputs from surface temperature predictions to inform subsurface estimates, reflecting a novel methodology for climate-sensitive agriculture. The results provide a practical framework for improving irrigation planning, crop yield forecasting, and sustainable land management in data-scarce arid environments. By demonstrating high accuracy and real-world applicability, this AI-driven model offers a scalable solution for enhancing agricultural resilience in Uzbekistan and similar contexts. Received: 13 June 2025 | Revised: 27 August 2025 | Accepted: 21 November 2025 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 Lakindu Mampitiya: Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization. Namal Rathnayake: Validation, Investigation, Writing – original draft, Writing – review & editing. Kenjabek Rozumbetov: Resources, Data curation. Valery Erkudov: Resources, Data curation. Mirzohid Koriyev: Resources, Data curation. Komali Kantamaneni: Writing – review & editing. Upaka Rathnayake: Concep…
subject: Research Articles
  topic: section
subject: arid climate modeling
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: machine learning
  topic: keywords
subject: soil temperature prediction
  topic: keywords
subject: sustainable agriculture
  topic: keywords
creator: Lakindu Mampitiya
creator: Namal Rathnayake
creator: Kenjabek Rozumbetov
creator: Valery Erkudov
creator: Mirzohid Koriyev
creator: Komali Kantamaneni
creator: Upaka Rathnayake
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-01-14
publication date: 2026-01-14
starting page: 260
ending page: 268
doi: 10.47852/bonviewJDSIS62026463

Robust Histogram Signature-Driven Template Matching for Vehicle Tracking in Traffic Videos
type: item
description: Correlation-based template matching (CTM) is widely used for object detection because of its simplicity and effectiveness in scenarios where grayscale features are sufficient. However, CTM often underperforms when color is a crucial distinguishing factor. To address this limitation, we propose contextual hierarchical composite template matching (CH-CTM), a color-histogram-enhanced CTM algorithm that integrates color information into a traditional correlation framework. CH-CTM augments the correlation index with red, green, and blue histogram comparisons to improve robustness in color-sensitive contexts. We evaluated CH-CTM using five diverse traffic video datasets that include various lighting conditions, vehicle types, sizes, and colors. Twelve experiments were conducted using standard performance metrics. Results demonstrated notable improvements over baseline CTM: CH-CTM achieved a peak accuracy of 98.30%, an average accuracy of 92.43%, and average precision of 92%. These findings confirm the importance of incorporating color information into template matching, which expands CTM's applicability in complex real-world scenarios, particularly in traffic surveillance and object tracking. Received: 17 March 2025 | Revised: 30 April 2025 | Accepted: 19 May 2025 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 in Kaggle at https://www.kaggle.com/datasets/pkdarabi/vehicle-detection-image-dataset, in Kaggle at https://www.kaggle.com/datasets/amitkumargurjar/car-detection-and-tracking-dataset, in Kaggle at https://www.kaggle.com/datasets/brsdincer/vehicle-detection-image- set, in Kaggle at https://www.kaggle.com/datasets/farzadnekouei/top-view-vehicle-detection-image-dataset, and in Kaggle at https://www.kaggle.com/datasets/dtrnngc/ua-detrac-dataset. Author Contribution Statement Aaron Rasheed Rababaah: Conceptualization, Methodology…
subject: Research Articles
  topic: section
subject: correlation-based template matching
  topic: keywords
subject: color histogram
  topic: keywords
subject: enhanced template matching
  topic: keywords
subject: object detection and tracking
  topic: keywords
subject: vehicle tracking
  topic: keywords
subject: traffic videos
  topic: keywords
subject: computer vision
  topic: keywords
creator: Aaron Rasheed Rababaah
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-06-05
publication date: 2025-06-05
starting page: 211
ending page: 223
doi: 10.47852/bonviewJDSIS52025712

Predicting Mental Health Outcomes for Native and Non-native Language Students Through ICT-Based Learning Analytics
type: item
description: This paper presents the impact and assessment of Information and Communication Technology (ICT)-based learning techniques employed by native and non-native language medium schools and predicts the factors that affect the mental health outcomes of students in these medium schools. The implementation of this study has been executed into five stages: (i) implication of reliable & aligned questionnaire associated with this study, (ii) targeting a group of students for data collection, (ii) performing the data reliability and data analysis tasks, (iv) deriving the key factors associated with the objective of this study, and (v) finally, building the prediction models as prototypes based on the textual analysis of the employed questionnaire for the mental health predictions among students. This study is being carried out using questionnaire-based data collected from 400 students in the Indian education system. Here, the medium of schools is Bengali (native) and English (non-native) languages. After data collection, rigorous statistical and machine learning-based data analysis techniques are employed. In the findings, Statistical tests (e.g., Mann–Whitney U, Kaiser-Meyer-Olkin (KMO), and Bartlett) revealed that English-medium students, particularly girls, reported higher levels of perceived stress compared to their Bengali-medium counterparts. Factor analysis confirmed more complex stress structures in English-medium groups, and predictive models achieved over 81% accuracy in classifying stress profiles. These findings suggest that language background is a significant factor in shaping students' psychological well-being in ICT-based education systems. The outcome of this study will be used to derive the key factors for mental health problems among students and build the prediction model that will be derivable from this study. Received: 7 April 2025 | Revised: 19 August 2025 | Accepted: 13 November 2025 Conflicts of Interest The authors declare that they have no conflicts o…
subject: Research Articles
  topic: section
subject: ICT-based learning
  topic: keywords
subject: mental health prediction
  topic: keywords
subject: native and non-native medium schools
  topic: keywords
subject: data analysis
  topic: keywords
subject: predictive modeling
  topic: keywords
subject: student well-being
  topic: keywords
creator: Kunal Ghosh
creator: Saiyed Umer
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-02-10
publication date: 2026-02-10
doi: 10.47852/bonviewJDSIS62025867

AI-Driven Climate Analysis in a Semi-arid Region: Uncovering Warming Trends and Meteorological Shifts in Southeastern Morocco
type: item
description: Climate change is recognized as a global threat, with arid and semi-arid regions highly vulnerable due to fragile ecosystems and limited water. This study analyzes long-term climate trends in Errachidia, Morocco, using high-resolution hourly meteorological data from 2010 to 2024. By applying both time series decomposition and Long Short-Term Memory (LSTM) neural networks, we did identify clear patterns of warming and climatic variability, providing localized insights that support climate adaptation strategies in line with Sustainable Development Goal 13. The results reveal a mean annual temperature increase of 0.1142°C, amounting to approximately 1.6°C over the 14-year period, while seasonal decomposition highlights particularly intense warming during autumn (+0.1666°C per year), with summer temperatures peaking around 35°C between 2022 and 2024. The study showed a frequency of extreme heat events (defined by the 95th percentile) nearing 500 occurrences annually, while the number of cold days has significantly declined since 2020, decreasing by an average of 9.29 events per year. Results also showed wind direction has shifted notably toward the north (from 25.7% to 30.4% occurrence between 2011 and 2023), although wind speed remained largely stable. In this study, we also used LSTM modeling, enhanced with proper inverse scaling and evaluation, which showed improved predictive performance. The temperature model achieved an RMSE of 0.43°C and an MAE of 0.32°C, confirming observed trends and highlighting challenges in modeling extremes in arid environments. The wind speed model showed lower precision due to inherent volatility, but still captured key directional trends. This work provides data-driven climate insights for an underrepresented North African region. Its findings have implications for agriculture, water management, and public health, offering a reproducible framework for future research. By integrating meteorological analysis with artificial intelligence, i…
subject: Research Articles
  topic: section
subject: climate change
  topic: keywords
subject: time series decomposition
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subject: LSTM neural networks
  topic: keywords
subject: temperature trends
  topic: keywords
subject: extreme weather events
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subject: North Africa climate
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creator: Anas Kabbori
creator: Chahrazad Zargane
creator: Azidine Guezzaz
creator: Said Benkirane
creator: Mourade Azrour
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-02-03
publication date: 2026-02-03
doi: 10.47852/bonviewJDSIS62025842

Handling Uncertain Information with Fuzzy Logic in Locating New Infrastructure
type: item
description: Considerable research has been conducted in location optimization for new infrastructures such as highways and rail lines, as well as fixed facilities such as sports arenas, warehouses, and airports. Uncertainty plays a crucial role in determining the final location of such facilities. For example, there may be uncertainties about land-use and site characteristics as well as about demand for the new facilities. There may be disagreements among various stakeholders that complicate reaching a consensus due to budgetary constraints and differing political views. While some uncertainties can be quantitatively represented, others can only be represented qualitatively (e.g., low, medium, or high). Not all of these uncertainties can be precisely and mathematically modeled. While deep learning and other probabilistic techniques have been developed to deal with uncertainties that can be represented numerically, fuzzy logic has been recognized as a preferred choice for handling qualitative uncertainties. This paper identifies situations with uncertainties in locating new infrastructure and offers solutions for handling the qualitative uncertainties with fuzzy logic. An example is presented to illustrate the approach to handling uncertainties that can only be represented qualitatively. The results are promising for future research dealing with uncertainties represented as linguistic variables, thereby improving the decision-making process. The method can be applied in other domains involving uncertainty. Received: 24 December 2024 | Revised: 16 June 2025 | Accepted: 30 June 2025 Conflicts of Interest Manoj K. Jha is an Editorial Board Member for Journal of Data Science and Intelligent Systems and was not involved in the editorial review or the decision to publish this article. 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. Au…
subject: Research Articles
  topic: section
subject: infrastructure optimization
  topic: keywords
subject: uncertainty theory
  topic: keywords
subject: fuzzy logic
  topic: keywords
subject: linguistic hedges
  topic: keywords
creator: Manoj K. Jha
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-08-28
publication date: 2025-08-28
doi: 10.47852/bonviewJDSIS52025076

NLP Framework to Safeguard Youngsters Online Using Advanced Transformer-Based Models
type: item
description: Ensuring the safety and well-being of youngsters in online environments has become increasingly challenging, particularly with the rise of harmful and inappropriate content on social media platforms. This research study focuses on developing a natural language processing (NLP) framework designed to monitor online interactions and identify online harmful conversations. The framework integrates emotion detection with an advanced age verification model to analyze communication patterns and detect inappropriate behavior. When repeated sexually inappropriate behavior exceeds a predefined threshold, the system responds by blocking accounts and issuing notifications to guardians. The NLP framework was evaluated using two traditional machine learning algorithms alongside two advanced models, namely, BERT and RoBERTa, to assess their effectiveness in detecting harmful interactions. These models were trained and tested on datasets containing emotional patterns and real-world social media conversations. The proposed model’s best accuracy result was 95.15%, which shows great promise in addressing inappropriate behavior during a conversation. However, several challenges were identified, including managing imbalanced data and the substantial computational resources required for model training. Despite these limitations, the framework can be used to enhance online safety for youngsters. Received: 21 March 2025 | Revised: 15 October 2025 | Accepted: 3 December 2025 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/mrmorj/hate-speech-and-offensive-language-dataset, https://www.kaggle.com/datasets/debarshichanda/goemotions, and https://www.kaggle.com/datasets/bhavikjikadara/emotions-dataset. Author Contribution Statement Hisham AbouGrad: Conceptualization, Methodology, Validation, Investigation, Da…
subject: Research Articles
  topic: section
subject: sentiment analysis
  topic: keywords
subject: emotion detection
  topic: keywords
subject: NLP framework
  topic: keywords
subject: BERT model
  topic: keywords
subject: RoBERTa model
  topic: keywords
subject: online safety
  topic: keywords
subject: ethical AI
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creator: Hisham AbouGrad
creator: Sankar Santhosh
creator: Salem Alsaid
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
date: 2026-03-25
publication date: 2026-03-25
doi: 10.47852/bonviewJDSIS62025752

Exploring the Uses of Artificial Intelligence and ChatGPT in Therapeutic Diet and Nutrition
type: item
description: The integration of artificial intelligence (AI) into healthcare has revolutionized the field of therapeutic diet and nutrition by providing innovative tools for assessment, education, and personalized care. Among these advancements, conversational AI models, such as ChatGPT, have emerged as valuable assets in enhancing patient engagement and streamlining clinical workflows. This paper examines the utilization of AI in general, and ChatGPT in particular, in therapeutic diet and nutrition, including dietary assessment and planning, patient education, and real-time monitoring. The discussion highlights the benefits of AI-driven tools in improving adherence to dietary interventions and supporting evidence-based decision-making for clinicians and dietitians. These advancements are tempered, however, by ethical considerations including bias in recommendations, privacy concerns, and equitable access. Although challenges remain in ensuring accuracy and fostering trust among practitioners and patients, this paper identifies opportunities for future research for both researchers and practitioners. Through the integration of technology and human expertise, AI can accelerate advances in therapeutic nutrition. Received: 16 March 2025 | Revised: 10 July 2025 | Accepted: 31 July 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 Joseph C. Kush: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
subject: Review
  topic: section
subject: AI
  topic: keywords
subject: ChatGPT
  topic: keywords
subject: therapeutic diet
  topic: keywords
subject: nutrition
  topic: keywords
subject: healthcare technology
  topic: keywords
subject: patient education
  topic: keywords
creator: Joseph C. Kush
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
date: 2025-09-24
publication date: 2025-09-24
doi: 10.47852/bonviewJDSIS52025709

A Systematic Review of Techniques for Early-Stage Alzheimer's Disease Diagnosis Using Machine Learning and Deep Learning
type: item
description: Alzheimer's disease (AD) is a brain-related disease in which the patient's condition worsens over time. Early diagnosis of AD is uniquely challenging due to its primarily neurological and cognitive anomalies, such as beta-amyloid plaque accumulation, tau tangles, and hippocampal atrophy, which are subtle and slowly progressive. Unlike diseases with clear physical or biochemical markers, AD anomalies often overlap with normal aging and require analyzing complex data such as brain imaging and cognitive tests. This study reviews various machine learning and deep learning techniques for early AD detection. The findings reveal that deep learning techniques, especially convolutional neural networks (CNNs), and hybrid architectures such as VGG16 and CNN–long short-term memory models achieve near-perfect performance (accuracy of 99.95% and 99.92%, respectively), while traditional models such as support vector machines excel in smaller datasets (accuracy between 90% and 96%) due to their simplicity and interpretability. The combination of multimodal data (magnetic resonance imaging, positron emission tomography, and cerebrospinal fluid biomarkers) significantly improved diagnostic performance, indicating the potential of innovative artificial intelligence (AI) methods in early AD identification. However, deep learning faces key limitations, including high computational demands, lack of interpretability, and reliance on large, annotated datasets, which can hinder clinical applicability. By pursuing learning techniques, such as transfer learning, to reduce data needs and explainable AI to achieve interpretability, researchers and developers can overcome the bottlenecks and enhance the accuracy and efficiency of real-world AD diagnosis. Received: 17 December 2024 | Revised: 9 April 2025 | Accepted: 1 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresp…
subject: Review
  topic: section
subject: AD
  topic: keywords
subject: mild cognitive impairment
  topic: keywords
subject: MCI
  topic: keywords
subject: MRI
  topic: keywords
subject: neurodegenerative disorders
  topic: keywords
subject: neuroimaging
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subject: PET
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creator: MD Minul Alam
creator: Shahram Latifi
rights: Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
date: 2025-09-05
publication date: 2025-09-05
doi: 10.47852/bonviewJDSIS52025037