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.
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.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
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.
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.
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.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by/4.0/
Database Optimization for Low-Latency Analytics with Adaptive Indexing
Data are rapidly produced in various fields such as finance, e-commerce, healthcare, and the Internet of Things (IoT), resulting in a constantly growing need for real-time analytics that can scale up. Traditional relational database management systems (RDBMSs) are often unable to keep up with high-speed processing requirements due to the limitations of ACID properties. In this report, auto optimized stream is introduced as a self-adaptive optimization framework to enhance real-time analytical pipelines powered by PostgreSQL, Kafka, and Flink in terms of both scalability and performance. The major difference between the proposed framework and the static optimization methods, which are used by Oracle, is that it is a continuous process where live query workloads and data ingestion are monitored regularly, thus allowing for the optimization of indexing strategies, partitioning schemes, and stream processing configurations. The framework employs rule-based decision-making to improve query execution, ingestion throughput, and resource utilization across the board. The adaptive method, when compared to static optimization methods, allows lower query latency, better stream stability, and higher CPU efficiency while causing minimal overhead and keeping full tuning action traceability. These findings not only prove the practical viability of adaptive database systems for real-time analytics but also lay down a steppingstone for future systems. Received: 8 September 2025 | Revised: 24 November 2025 | Accepted: 12 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in "vega-datasets" at https://github.com/vega/vega/blob/main/docs/data/seattle-weather.csv. Author Contribution Statement Lakmali Karunarathne: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration. Swathi Ganesan: Investigation. Kavindu Karunarathne: Methodology, Software, Formal analysis, Resources, Writing — original draft, Writing — review & editing, Visualization. Nalinda Somasiri: Investigation.
Modeling Markers for Detection of Psychiatric Disorders Using EEG Signals
The diagnosis of mental (psychiatric) disorders is challenging, and there is a lack of consensus on objective diagnostic criteria that are based on definitive signs that accompany the disorder. There is a need, therefore, to develop objective tools for the examination of these disorders. We present here a novel machine learning (ML) approach that accurately identifies disorders. The approach uses electroencephalography (EEG) signals for diagnosis, which are processed to extract novel region based markers that are found to contain key information about the types of disorders. Subsequently, a support vector machine (SVM) classifier is modeled, integrated with sequential feature (marker) selection (SFS), which identifies optimal and compact marker subsets for disorder detection. The proposed system has been validated using a publicly available dataset. The developed model was benchmarked against existing models and was shown to perform superior to the models it was extensively compared with; it demonstrated a 98.33% accuracy in detecting obsessive-compulsive disorder (OCD). Our findings indicate that an accurate psychiatric diagnosis system can be achieved using EEG signals with significantly fewer, and more interpretable markers. This simpler and transparent approach improves the practicality and trustworthiness of AI/ML-driven diagnostic tools, making them more suitable for real-world clinical integration and understanding by medical professionals. Received: 5 September 2025 | Revised: 17 November 2025 | Accepted: 15 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in OSF repository at https://osf.io/8bsvr/. Author Contribution Statement Steven Chris: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Debasish Mishra: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision. Lakshman Varanasi: Methodology, Resources, Writing – review & editing, Supervision, Funding acquisition. Varun Viswanathan: Methodology, Writing – review & editing, Supervision.
Correlation-Based Feature Selection for Efficient Intrusion Detection: Comparative Evaluation of Machine Learning Models on CICIDS-2017
The increased growth of network traffic has enabled digital communication, global connectivity, driving advances in e-commerce, and cloud computing. However, this growth also increases the risk of cyberattacks, making effective and efficient intrusion detection systems (IDS) essential. Although many studies have applied machine learning (ML) and deep learning (DL) to improve detection accuracy, the majority pay less attention to computational efficiency, which is critical for real-time deployment. The proposed study evaluates three widely used ML models, namely, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), on the CICIDS-2017 dataset, with and without correlation-based feature selection (CFS). Results show that RF and XGBoost achieved the highest accuracy (0.998) with very low false positive rates, while MLP lagged behind in both detection and runtime performance. Notably, applying CFS reduced RF's training time by 35% without sacrificing accuracy. The proposed study's findings confirm that ensemble models, particularly RF and XGBoost, provide a strong balance between accuracy and efficiency. Moreover, feature selection emerges as a simple yet effective strategy to lower computational cost, making IDS more practical for large-scale, real-time network environments. Received: 1 September 2025 | Revised: 22 December 2025 | Accepted: 20 January 2026 Conflicts of Interest Xuyang Shi is a specialist for Journal of Data Science and Intelligent Systems and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Canadian Institute for Cybersecurity (CIC), University of New Brunswick (UNB), Canada, at http://cicresearch.ca/CICDataset/CIC-IDS-2017/. Author Contribution Statement Bilal Rafique: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Sania Kanwal: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Xuyang Shi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Rapid Identification of Pathogenic Bacteria from Raman Spectra with a CNN–Transformer Hybrid Architecture
Bacterial identification from Raman spectra offers a promising label-free and nondestructive approach, providing molecular fingerprints at the single-cell level. However, practical implementation is constrained by low signal-to-noise ratios arising from short acquisition times, severe class imbalance across bacterial species, and high inter- and intra-species spectral variability. This study presents a two-stage convolutional neural network (CNN)–Transformer pipeline evaluated on the Bacteria-ID dataset, covering 30 bacterial species across approximately 63,000 spectra. Preprocessing combined baseline subtraction, fast Fourier transform, and wavelet decomposition to improve signal quality prior to training. Class imbalance was addressed through synthetic minority oversampling technique and class-weighted loss, while mixed precision computation reduced GPU overhead. Hyperparameters were optimized via Bayesian search using Optuna. The CNN stem extracts local Raman peak features, while the Transformer encoder captures long-range spectral dependencies that convolutional layers alone cannot model efficiently. On the independent test set, the model achieved approximately 85% accuracy and weighted F1, surpassing ResNet (82.2%) and RamanNet (84.7%) evaluated under identical conditions. The lowest-performing species improved from 31% F1 in the unoptimized baseline to approximately 70% in the final configuration. External validation on spectra from alternative instruments or clinical settings has not yet been conducted and represents the most important direction for future work. Extensions toward MRSA/MSSA classification and antibiotic response prediction are planned. Received: 31 August 2025 | Revised: 13 November 2025 | Accepted: 30 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement This study uses the publicly available Bacteria-ID dataset (Ho et al., 2019). No new data were generated. The specific train/validation/test splits, trained model weights, and figure-generation outputs can be obtained from the corresponding author upon reasonable request for academic use. Source code is not publicly shared currently Author Contribution Statement Apoorv Patel: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Hongying Meng: Resources, Writing – review & editing, Supervision, Project administration.
An Asynchronous Parallel Data Loading Optimization Algorithm for Deep Learning Applications
This study aims to address the concern of inefficient data loading, which often leads to computationally inefficient deep learning workflows and becomes a bottleneck for scalability, especially under resource constraints. An asynchronous parallel data loading optimization algorithm is proposed that will revolutionize the data-training pipeline by enabling multi-threaded concurrent data loading and model training on multiple devices. The two-dimensional array structure and special hash table used ensure the invariance of the data distribution and concurrency safety, which is independent of loading and training processes and supported by rigorous mathematical proof. Experimental results from the CIFAR-10 dataset vividly demonstrate that this method represents a significant improvement over state-of-the-art baselines, achieving a throughput of approximately 3,250 samples/second, 87% GPU utilization, and a 40% reduction in training time, while maintaining the statistical integrity of the original dataset. This paper proposes a solution that does not bind users to a specific framework and increases efficiency without requiring the purchase of expensive hardware. This resolution makes deep learning achievable and reproducible, while reducing the time cost of research and student training exercises. Received: 31 August 2025 | Revised: 10 February 2026 | Accepted: 30 March 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://www.cs.toronto.edu/~kriz/cifar.html. Author Contribution Statement Xingjun Lin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
High-Resolution Evaluation of WRF Cumulus Parameterization Schemes for Rainfall Prediction in Thailand
This study evaluates the performance of cumulus parameterization schemes under a default SBU-YLin within the weather research and forecasting (WRF) model to enhance rainfall forecasting in Thailand's tropical climate. Using a high-resolution nested domain setup (8.1 × 8.1 km, 2.7 × 2.7 km, and 0.9 × 0.9 km), simulations are validated against 128 Thai Meteorological Department stations from September 21 to 23, 2024. The New Tiedtke (RAIN-16) scheme achieved the most balanced performance, with underestimation and overestimation rates of approximately 50% and mean underestimation and overestimation of −12.75 mm and 13.30 mm, respectively. However, the SBU-YLin scheme reduced biases, root mean square error (RMSE), and mean absolute error (MAE) but could not resolve heavy rainfall events, especially in Domains 2 and 3, which are considered high-resolution domains. In Domain 1, very heavy rainfall was overestimated with an RMSE of 98.50 mm; Domain 2 showed an overestimation of rainfall, and Domain 3 had a substantial overestimation of rainfall associated with localized convective activity. However, the SBU-YLin scheme reduced the biases, RMSE, and MAE, but at higher computational demands. Results showed that Domains 2 and 3 increased accuracy with finer spatial resolution, but it was challenging to simulate heavy rainfall events. The selected 3 days are an intense rainfall event that is characteristic of monsoonal convection in Thailand. This brief time span enables WRF schemes to be examined at a resolution in extreme conditions. The temporal coverage of future research is going to be extended to a seasonal-level assessment. Received: 14 August 2025 | Revised: 3 February 2026 | Accepted: 23 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data used in this study were obtained from TMD with permission and are not publicly available. Author Contribution Statement Usa Wannasingha Humphries: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Muhammad Waqas: Validation, Investigation, Data curation, Writing – original draft, Visualization. Boonlert Archevarahuprok: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation. Porntip Dechpichai: Validation, Visualization. Angkool Wangwongchai: Conceptualization, Methodology, Software, Formal analysis, Writing – review & editing, Supervision, Project administration. Boobphachard Chansawang: Writing – review & editing, Visualization.
Predicting and Reducing Wireless Customer Churn Using the AI2-Sriya Expert Index (SXI)
The telecommunication sector has experienced swift technological development, with wireless services being the forces of change. One of the key issues in this sector is customer churn that affects profitability and market competitiveness. As customer expectations increase and competition becomes fiercer, telecom providers should be concerned with retaining current customers through better services. This study examines churn prediction in the wireless telecommunication sector via machine learning (ML) methods, focusing on the performance of the AI2 -Sriya Expert Index (SXI) model. AI2 (AI Square), launched by Sriya.AI, improves standard AI with a second layer of AI to facilitate adaptive learning. Fundamentally, SXI is a super feature that aggregates outputs from 5–10 ML models into one dynamic score that accurately determines the probability of customer churn. This method condenses complex, multi-dimensional data into two-dimensional data for enhanced decision-making and predictive analysis. In the comparison, conventional models such as XGBoost had 67% accuracy and 0.68 AUC, other research employing LightGBM and CatBoost had AUC values around 0.9, and internal benchmarking showed TabNet and FT-Transformer achieving AUCs of 0.65 and 0.66, respectively. CatBoost with hyperparameter optimization obtained an F1-score of 93% and AUC of 0.91. Nevertheless, the SXI model outperformed all of them with 98% accuracy and a 0.98 AUC value. Aside from predictive accuracy, SXI provides actionable business insights through iterative optimization with deep learning. This study shows that SXI has the capability to decrease customer churn from 49.5% to 39.65% (first 20% improvement). Through optimization, churn can be further reduced to 24.78% (50% reduction) and then eventually to 6.44% (87% reduction), providing long-term strategic advantages. These findings validate that the SXI framework offers a robust, precise, and interpretable solution for customer churn in telecom that is substantially better than that of conventional ML models. Received: 28 July 2025 | Revised: 6 September 2025 | Accepted: 26 September 2025 Conflicts of Interest The authors are employees of Sriya, where the AI2 SXI model was developed using the company's resources. The authors declare that this affiliation did not influence the study's results or conclusions. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/abhinav89/telecom-customer. Author Contribution Statement Nikhil Kakkattu Murugan: Software, Investigation, Data curation, Visualization. Reeshabh Kumar: Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Prashant Yadav: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing. Mahesh Banavar: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration. Srinivas Kilambi: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Efficiency Analysis and Ranking of the Decision-Making Units Using Bipolar Fuzzy Data Envelopment Analysis
This manuscript introduces a novel extension of Fuzzy Data Envelopment Analysis (FDEA) by incorporating Triangular Bipolar Fuzzy Numbers (BTFNs), thereby proposing the Bipolar Fuzzy DEA (BFDEA) framework to better model environments with uncertainty and conflicting information. The authors define a magnitude function to transform the proposed BFDEA model into a crisp linear programming model, enabling the computation of efficiency scores for Decision-Making Units (DMUs). To address the challenge of ranking fully efficient units, a bipolar super-efficiency model is developed, complemented by a benchmarking methodology that identifies peer DMUs to guide performance improvement of each inefficient DMU. The approach is clearly illustrated through a numerical example involving ten DMUs, with detailed evaluation, ranking, and benchmarking results and compared with the existing fuzzy model. The paper contributes significantly to the literature by enhancing DEA's ability to handle both positive and negative aspects of fuzziness, thus offering a more expressive and realistic efficiency analysis framework in complex decision-making environments. Received: 10 July 2025 | Revised: 22 September 2025 | Accepted: 9 October 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Kshitish Kumar Mohanta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
AI-Powered Soil Temperature Modeling for Sustainable Agriculture in Arid Regions: A Case Study of Bustan, Uzbekistan
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: Conceptualization, Validation, Writing – review & editing, Supervision, Project administration.
Implementation of Smart Contracts in Digital Education Systems
The Blockchain is an emerging technology that is used in various applications for data security and trustworthiness. In the case of a public Blockchain, the data cannot be edited or deleted. In the case of a consortium and private Blockchain, the data can be edited or deleted based on the assigned permission, and the data privacy can be maintained. Blockchain's smart contract provides security to stored data, but it is vulnerable to various security threats. Smart contracts still suffer from different variabilities like distributed denial of service attacks (DDoS), 51% vulnerability attacks, double-spending problems, and mining Pool attacks. The smart contract, run on a Blockchain framework, is the logical contract between two or more anonymous people without involving a third party. Hyperledger and Ethereum are two important frameworks that support the development of smart contracts using Blockchain technology. This paper has tried to analyze the security issues of smart contracts developed on the Ethereum framework. An application of class scheduling management and student attendance management has been designed to validate and generate a smart contract. Received: 31 May 2025 | Revised: 4 August 2025 | Accepted: 29 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data used in this article are virtual data to implement and to establish the algorithm. It is available in GitHub at https://github.com/ashisgitup/e-learning-Blockchain.git. Author Contribution Statement Ashis Kumar Samanta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Project administration.
Robust Histogram Signature-Driven Template Matching for Vehicle Tracking in Traffic Videos
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, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Predicting Mental Health Outcomes for Native and Non-native Language Students Through ICT-Based Learning Analytics
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 of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Kunal Ghosh: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Project administration. Saiyed Umer: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration.
AI-Driven Climate Analysis in a Semi-arid Region: Uncovering Warming Trends and Meteorological Shifts in Southeastern Morocco
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, it highlights the need for targeted climate adaptation in Errachidia and other vulnerable areas across the Global South. Received: 3 April 2025 | Revised: 26 August 2025 | Accepted: 3 December 2025 Conflicts of Interest Azidine Guezzaz 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 authors declare that they have no conflicts of interest to this work. Data Availability Statement The raw meteorological observations used in this study originate from publicly available Aeronautical Meteorological Messages for Errachidia Airport, which are routinely disseminated by official meteorological and aviation weather services. The raw Aeronautical Meteorological Messages data are openly accessible, for example, via the NOAA Aviation Weather Center: https://aviationweather.gov/data/metar/?ids=GMFK. No restrictions apply to the use of the raw data. The processed dataset generated during this study is available from the corresponding author upon request. Author Contribution Statement Anas Kabbori: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization. Chahrazad Zargane: Conceptualization, Validation, Formal analysis, Investigation, Resources, Writing — original draft, Writing — review & editing. Azidine Guezzaz: Methodology, Software, Validation, Resources, Data curation, Writing — original draft, Writing — review & editing, Supervision, Project administration. Said Benkirane: Methodology, Validation, Investigation, Resources, Writing — original draft, Writing — review & editing, Supervision. Mourade Azrour: Software, Validation, Investigation, Resources, Writing — original draft, Writing — review & editing, Visualization, Supervision.
Handling Uncertain Information with Fuzzy Logic in Locating New Infrastructure
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. Author Contribution Statement Manoj K. Jha: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
NLP Framework to Safeguard Youngsters Online Using Advanced Transformer-Based Models
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, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Sankar Santhosh: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Salem Alsaid: Writing – review & editing, Visualization.
Exploring the Uses of Artificial Intelligence and ChatGPT in Therapeutic Diet and Nutrition
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.
A Systematic Review of Techniques for Early-Stage Alzheimer's Disease Diagnosis Using Machine Learning and Deep Learning
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 corresponding author upon reasonable request. Author Contribution Statement Md Minul Alam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review and editing, Visualization. Shahram Latifi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
A Review for Remaining Driving Range Prediction of Electric Vehicles Using Machine Learning Algorithms
With the widespread use of electric vehicles, greenhouse gas emissions can be reduced. Thus, a solution can be proposed to minimize the environmental impact of transportation. However, one of the most important obstacles to the widespread use of electric vehicles is the limited driving range and the accompanying challenges such as range anxiety. Researchers have proposed various methods and algorithms to accurately predict the remaining driving range of electric vehicles to address these challenges. The study is aimed at analyzing and comparing studies that focus on remaining driving range estimation using mathematical-based methods, statistics, and machine learning algorithms. The study includes a comprehensive exploration of the datasets, methods, algorithms, and performance metrics used in the selected studies. It was found that machine learning algorithms, especially Extreme Gradient Boosting and Random Forest, are frequently used for remaining driving range estimation, followed by statistical models such as multiple linear regression. In addition, most of the datasets used are obtained from real-time electric vehicle data. This highlights the importance of real-time data for developing accurate prediction models. In addition, performance metrics such as Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination are widely used metrics to evaluate the performance of the models. The findings obtained within the scope of this study provide valuable information about the current status of remaining driving range estimation research for electric vehicles and suggest potential future studies in this area. Received: 31 December 2024 | Revised: 14 May 2025 | Accepted: 4 July 2025 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 Burak Caferler: Conceptualization, Investigation, Writing—review & editing, and Visualization. Aylin Ünal: Conceptualization, Investigation, Writing—review & editing, and Visualization. Sinem Bozkurt Keser: Conceptualization, Investigation, Writing—review &editing, Visualization, and Supervision. Ahmet Yazıcı: Conceptualization, Investigation, Writing—review & editing, Project administration, and Funding acquisition.
Integrating Hybrid FAHP–FRADAR Approach and the FMEA Framework for Evidence-Informed Risk Assessment in Football Player Transfers
This study established a methodology for assessing risk in football player transfers to help football club management make decisions. The proposed model is based on the Failure Mode and Effects Analysis (FMEA) framework, which is expanded by incorporating two Multi-Attribute Decision-Making (MADM) techniques: the Fuzzy Analytic Hierarchy Process (FAHP) was utilized to assign weights to risk factors, and the Fuzzy RAnking based on Distances And Range (FRADAR) method was used to rank potential football transfers. The proposed model is analogous to FMEA, but instead of the traditional risk factors severity, occurrence, and detection, risk factors tailored to the given problem are used: player market value (as severity), frequency of club changes or a player’s injury history (as occurrence), and adaptability to various tactics and playing styles (as detection). The primary objective of this study is to develop a sufficiently reliable model that is flexible enough to be applied in other sports and in various areas of management. The quality of available data and the assessments made by decision-makers, which rely on their experience, knowledge, and subjective judgment, play a crucial role in implementing the proposed model. The model was tested on a random sample of five football players, all central defenders, who are members of one of the five highest-ranked football leagues in Europe. Received: 23 January 2025 | Revised: 2 April 2025 | Accepted: 13 June 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Nikola Komatina: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Nephrolithiasis Detection and Classification Based on Supervised Machine Learning
In this paper, the author will provide an extensive exploration of the utilization of computed tomography (CT) image processing techniques for the detection of renal calculi. This is one of the most essential topics worldwide to detect the correct location of renal calculi. In the human system, the two kidneys play a crucial role in water purification and recycling. This research involves four steps: Graphic processing with a median filter, segmentation with the Otsu segmentation algorithm, nephrolithiasis detection, and discrete wavelet transform feature extraction and classification. Data from a large number of hospital patients were collected with CT scans, which diagnose renal calculi. This research studies advanced techniques to detect the extent, segment the area, and improve the detection of kidney stones or normal. This analysis helps locate the rocks through pixel analysis. The system also shows many stone patients. Specifically, the system was refined through a dataset of 1,200 X-ray images, a cubic support vector machine achieved 89.3% training accuracy with five-fold cross-validation to avoid overfitting, and an area under the curve close to 0.85, and the receiver operating characteristic curve is close to one. Its outstanding performance on unseen data led to a 90% testing accuracy, demonstrating its robustness using MATLAB and Python IDLE simulator. Received: 12 November 2024 | Revised: 3 March 2025 | Accepted: 10 June 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 GitHub at https://github.com/junaid-1013/Kidney-Stone-Detection, https://github.com/muhammedtalo/Kidney_stone_detection, and in Kaggle at https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone. Author Contribution Statement Ei Phyu Sin Win: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Using Test Trials of Processing Systems for Non-Blind Recovery of Signals and Images
The mathematical method for non-blind recovery of one-dimensional and multidimensional signals, including images, distorted during processing by linear stationary (or time-invariant) systems is considered. Instead of transfer functions, which are often difficult to determine because they belong to the class of generalized functions, the proposed method uses test trial signals of processing systems for signal recovery. Unlike the transfer functions, the test signals belong to the class of ordinary functions, which simplifies the signal reconstruction procedure considerably and makes it more accurate and stable. It also allows existing methods, such as regularization technique, to be effectively used to solve ill-posed and ill-conditioned problems for recovering nondeterministic signals with high levels of noise and uncertainty. The proposed operator approach based on the multidimensional convolution equation significantly reduces the number of operations and increases the speed of numerical computations. The considered method allows improving the quality of signal and image processing without complex and expensive modernization of the equipment. Received: 9 December 2024 | Revised: 7 March 2025 | Accepted: 10 September 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 GitHub at https://github.com/novikov-borodin/data-rec. Author Contribution Statement Andrey V. Novikov-Borodin: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration, Funding acquisition.
Quality Control and Process Optimization of Injection Molding Using a Data-Oriented Approach in an Industrial Setting
This article proposes a data-driven scheme for quality control and process optimization of injection molding in industrial plants. The suggested approach enables the operators to find optimal process parameters that enable producing high quality parts. Additionally, it allows early detection of defects through monitoring variations of process parameters. The implementation of the suggested scheme is investigated in a factory, where an on-site test is conducted, and outcomes are validated using a process capability analysis. The adopted approach and various levels of data processing are described, followed by a detailed explanation of the implementation steps and an on-site test to assess its effectiveness. The obtained results indicate an increase in production quality, reduced surface-level defects by 52.47%, a 92.5% decrease in the variation of pressure, and less than 2 ms variation in injection time. Furthermore, the process capability analysis resulted in a stabilized process with lower weight variations as reflected with the related (cpk) index, proving the approach to be capable of producing conforming products. Received: 5 November 2024 | Revised: 10 March 2025 | Accepted: 24 March 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 Oumayma Haberchad: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yassine Salih-Alj: Methodology, Validation, Writing – review & editing, Supervision, Project administration.
DCRU-Net: Dynamic Contextual Residual U-Net for Medical Image Segmentation
Accurate medical image segmentation (MIS) is crucial for computer-assisted diagnosis and treatment planning. This research proposes a deep learning (DL) architecture for accurate and efficient MIS, named Dynamic Contextual Residual U-Net (DCRU-Net). This design is a variation of the conventional U-Net that combines dynamic contextual residual block (DCRB) with a squeeze-and-excitation (SE) block. DCRU-Net combines the strengths of the DCRB and SE block. The SE block improves the feature-capture performance of the model by retuning the channel-specific feature responses. The DCRB adaptively modifies feature representations, selecting and adding significantly relevant contextual features at each network step, making DCRU-Net adaptable across multiple medical imaging modalities and to the difficulties of segmentation tasks. Experimental tests on six medical image collections show that DCRU-Net is superior to state-of-the-art (SOTA) methods in terms of Dice similarity coefficients (DSC) and intersection over union (IoU). Consistent performance is achieved across different medical imaging datasets with less annotated data because of the resilience and generalizability of the architecture. DCRU-Net is a new approach to accurate and automated MIS that can transform healthcare by improving segmentation accuracy and flexibility and becoming an invaluable instrument in computer-aided diagnosis. Received: 9 December 2024 | Revised: 20 January 2025 | Accepted: 4 July 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/competitions/data-sci-ence-bowl-2018. The data that support the findings of this study are openly available in Google Drive at https://drive.google.com/drive/folders/10QXjxBJqCf7PAXqbDvoceWmZ-qF07tFi?usp=share_link. The data that support the findings of this study are openly available in Polyp DataSet at https://doi.org/10.6084/m9.figshare.21221579.v2. Author Contribution Statement Manoj Kumar Singh: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review and editing, Visualization. Satish Chand: Validation, Formal analysis, Writing – review and editing, Visualization. Devender Kumar: Formal analysis, Writing – review and editing, Visualization, Supervision, Project administration.
Advanced Data Integration, Knowledge Extraction, and Application in Energy-Efficient Telehealth IoT Systems
This paper further studies the previously proposed energy-efficient telehealth Internet of Things (IoT) model that focuses on data integration, knowledge extraction, and application in fog-cloud hybrid architecture. Our current study concentrates on how the system uses adaptive machine learning and data mining to optimize the system operation for increased real-time data analysis and reduced energy use, thus providing more effective patient monitoring in telehealth. The simulation designed for the patients in both a fog-enabled model and a cloud-only model applies various workloads sent from patients. In this fog-enabled model, data from IoT devices is preprocessed at fog nodes by investigating anomalies, trends, or other relevant machine learning algorithms, and then this data is transmitted to the cloud. It compares key performance metrics-energy, latency, speed of processing data, and prediction accuracy—in both a fog-enabled and a cloud-only model. Results show that the fog-enabled model reduces energy consumption by 20% and latency by 50%, compared to a cloud-only configuration. This indicates the distinct advantages of localized processing. Compared to the existing system, higher speed in processing data and improved accuracy in detecting statistical anomalies, thereby demonstrating the possibility that the system offers for real-time and scalable telehealth capabilities. Meanwhile, this work presents a comprehensive model for the sustainability and scalability of telehealth infrastructures, supported by simulation data and analysis evidencing the effectiveness of the model. Received: 2 December 2024 | Revised: 4 September 2025 | Accepted: 16 October 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 Nathan Guo: Conceptualization, Investigation, Resources, Writing — original draft, Visualization. Yunyong Guo: Methodology, Validation, Writing — review & editing, Supervision, Project administration. Bryan Guo: Software, Formal analysis, Data curation.