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.
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/
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 Data are available from the corresponding author upon reasonable request. 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.
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.
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, and 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 available on request 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 on request 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.
Notary Evaluation Algorithm Adaptable to Node State Changes for Cross-Chain Notaries
Cross-chain technology has emerged as a current research hotspot in the field of blockchain. Scholars have proposed various cross-chain technologies to address the issue of "data islands." Among these technologies, the notary mechanism is one of the principal approaches. However, existing research on notary evaluation tends to favor older nodes and struggles to adapt to changes in node states. To address this challenge, this paper proposes a notary evaluation algorithm capable of adapting to node state changes. The algorithm divides the node reputation value into transaction scores and trust scores. The reputation value is then calculated based on the actual transaction cycle, thereby bringing the node reputation value closer to the node's current performance level. The experimental results show that the algorithm can effectively cope with node state changes and more accurately identify malicious nodes and trustworthy nodes than similar schemes, preventing the efficiency and success rate of the cross-chain system from decreasing due to the inability of the algorithm to adapt to node state changes. At the same time, the problem of low reputation value of new nodes due to the lack of trust relationship is alleviated, which is more conducive to mobilizing nodes to join the notary group. Through these improvements, the reliability and operational efficiency of the cross-chain system can be effectively improved. Received: 20 October 2024 | Revised: 3 December 2024 | Accepted: 27 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. Author Contribution Statement Chenhong Xie: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Xiaoming Hu: Conceptualization, Validation, Investigation, Resources, Writing – review and editing, Supervision, Project administration. Shuangjie Bai: Investigation, Data curation, Writing – review & editing, Visualization, Supervision. Yan Liu: Resources, Writing – review & editing, Visualization, Supervision.
Experts' Cognition-driven Safe Noisy Labels Learning for Precise Segmentation of Residual Tumor in Breast Cancer
Precise segmentation of residual tumor in breast cancer (PSRTBC) after neoadjuvant chemotherapy is a fundamental key technique in the treatment process of breast cancer. However, achieving PSRTBC is still a challenge, since the breast cancer tissue and tumor cells commonly have complex and varied morphological changes after neoadjuvant chemotherapy, which inevitably increases the difficulty to produce a predictive model that has good generalization with usual supervised learning (SL). To alleviate this situation, in this paper, we propose an experts’ cognition-driven safe noisy label learning (ECDSNLL) approach. In the concept of safe noisy label learning, which is a typical type of safe weakly SL, ECDSNLL is constructed by integrating the pathology experts’ cognition about identifying residual tumor in breast cancer and the artificial intelligence experts’ cognition about data modeling with provided data basis. Experimental results show that, compared with usual SL, ECDSNLL can significantly improve the lower bound of a number of UNet variants with 2.42% and 4.1% respectively in recall and fIoU for PSRTBC, while being able to achieve improvements in mean value and upper bound as well. Received: 21 November 2024 | Revised: 10 January 2025 | Accepted: 23 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Yongquan Yang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Visualization, Supervision, Project administration. Jie Chen: Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing. Yani Wei: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing. Mohammad Alobaidi: Software, Validation, Formal analysis, Investigation, Writing – review & editing. Hong Bu: Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.
iACP-SEI: An Anticancer Peptide Identification Method Incorporating Sequence Evolutionary Information
Anticancer peptides (ACPs) are a promising focus in clinical oncology due to their ability to inhibit tumor cell proliferation with minimal side effects. Nevertheless, large-scale, expeditious and efficacious identification of ACPs is hindered by the high cost and time demands of conventional wet-lab experiments. Therefore, we introduced a new method called iACP-SEI to identify ACPs using sequence evolution information. iACP-SEI method utilizes the ESM2 protein language model, based on Transformer architecture, to extract feature vectors that encapsulate evolutionary information from peptide sequences. These vectors underwent feature selection via the light gradient boosting machine and used in an ensemble learning approach. Using the AntiCP2.0 main and alternate datasets, iACP-SEI model achieved independent test accuracies of 77.78% and 94.82%, respectively. Furthermore, it outperformed current methods on an unbalanced dataset, achieving a cross-validation accuracy of 90.39%, demonstrating improved robustness in handling imbalanced class samples. Although iACP-SEI demonstrated higher predictive performance and robustness than other methods, some limitations of it are also discussed. Received: 18 November 2024 | Revised: 8 January 2025 | Accepted: 23 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Bowen Zheng: Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Rujun Li: Investigation, Data curation, Visualization. Haotian Wang: Validation, Investigation, Visualization. Sheng Wang: Investigation. Shiyu Peng: Data curation. Mingxin Li: Formal analysis. Liangzhen Jiang: Writing – review & editing. Zhibin Lv: Conceptualization, Methodology, Software, Supervision, Project administration, Funding acquisition.
The Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models
This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT gates for creating correlation among the qubits is applied as a feature extraction filter prior to passing data into the fully connected NN architecture. By applying the QPF approach, the results show that the image classification accuracy based on the MNIST (handwritten 10 digits) and the EMNIST (handwritten 47 class digits and letters) datasets can be improved, from 92.5% to 95.4% and from 68.9% to 75.9%, respectively. These improvements were obtained without introducing extra model parameters or optimizations in the machine learning process. However, tests performed on the developed QPF approach against a relatively complex German Traffic Sign Recognition Benchmark dataset with 43 distinct class real-life traffic sign images showed a degradation in the classification accuracy. Considering this result, further research into the understanding and the design of a more suitable quantum circuit approach for image classification NNs could be explored utilizing the baseline method proposed in this paper. Received: 2 September 2024 | Revised: 13 January 2025 | Accepted: 7 April 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 GitHub at https://github.com/hajimesuzuki999/qpf. Author Contribution Statement Farina Riaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Shahab Abdulla: Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Supervision. Hajime Suzuki: Conceptualization, Validation, Formal analysis, Investigation, resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Srinjoy Ganguly: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Ravinesh C. Deo: Investigation, Resources, Data curation, Writing - review & editing, Supervision. Susan Hopkins: Writing - review & editing, Supervision.
Analytical Solution for Parameter Estimation of Weibull Distributions with Interval-Censored Data
This paper addresses the challenge of parameter estimation for Weibull distributions with continuous interval-censored data, a critical issue in reliability engineering where failures are observed only at predetermined inspection intervals. Traditional estimation methods often struggle with the uncertainty of failure times, leading to suboptimal results. To overcome this limitation, we propose a novel analytical approach that directly fits the probability density function to the frequency histogram, offering an alternative to conventional numerical algorithms. This method not only improves estimation accuracy but also enhances computational efficiency. Theoretical validation is established using the dual least squares method, and extensive Monte Carlo simulations further confirm its robustness. Comparative analysis with existing approaches highlights the superiority of our method in terms of both precision and stability. To demonstrate its practical applicability, we apply the proposed approach to Hong Kong casualty data from the World Health Organization, effectively estimating the age distribution of unidentified casualties. The results underscore the method’s potential for broader applications in reliability analysis and risk assessment. Received: 27 October 2024 | Revised: 12 March 2025 | Accepted: 3 April 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 World Health Organization at https://platform.who.int/mortality/themes/theme-details/mdb/injuries. Author Contribution Statement Yang Yu: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Jianguo Gong: Resources, Funding acquisition. Kunping Zhu: Conceptualization, Methodology, Investigation, Supervision, Project administration.
Identifying Purchasing Factors in Online Flea Markets Considering Thumbnail Images
In recent years, the market for online flea markets, which are consumer-to-consumer (C2C) services where goods are bought and sold among users, has been expanding. In such services, sellers (individuals that offer goods or services for sale) create product details, including price, condition, shipping method, and images, when listing their items for sale. We consider the product image to be the first thing users see when selecting a product as a thumbnail, significantly impacting their purchase decisions. In this study, we proposed a discriminant model of purchase decisions for online flea market data to clarify the factors influencing these decisions based on product details. Specifically, we used metadata such as price and delivery method, along with image labels for product thumbnails, as features. We created and compared models with three patterns: metadata only, image labels only, and a combination of metadata and image labels. We created four types of models in this study: logistic regression, decision tree, gradient boosting, and random forest. We selected the models based on their accuracy evaluations. Our analysis revealed that the model using both metadata and image labels as features, combined with the gradient boosting method, had the highest accuracy. The partial dependence plots of the selected models highlighted the features important for users' purchase decisions. Received: 10 August 2024 | Revised: 8 February 2025 | Accepted: 14 March 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 Emi Iwanade: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Yoshihisa Shinozawa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing–original draft, Writing – review & editing, Visualization. Kohei Otake: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
A Machine Learning Based Physical Exercise and Meditation Recommendation System for Patients with Chronic Health Conditions
Chronic health conditions are long-term medical issues that degrade a person's life quality. These disorders frequently require ongoing medical care and can severely limit daily activities. Managing chronic health issues usually requires regular physical exercise, meditation, and stress-minimization technique. Previous research has focused primarily on recommending physical exercise or meditation for patients with chronic health conditions, rather than combining the two. They also did not look into proper machine learning (ML) model selection, accuracy results in improvement, dataset development, or hyperparameter tuning techniques for the physical exercise and meditation recommendation systems. To eradicate these issues, this paper delivers an ML-based physical exercise and meditation recommendation system for patients with chronic health conditions based on 9 ML classifiers. This paper created a dataset based on patient data that includes characteristics such as person's habits, alcohol consumption information, exercise information, chronic health information, stress level, anxiety symptoms, sleeping issues, exercise, and medication recommendations. Among the ML models tested, the K-nearest neighbors (KNN) model had the highest accuracy of 96% and f1 score of 98% for predicting appropriate physical exercise and medication recommendations. According to the important performance metric value comparison results, the proposed KNN-based prediction scheme outperforms previous works by 6.6 percent in accuracy and 5 percent in recall value. This paper brings a mobile application that initiates features like physical exercise and medication recommendations, information about physical exercise and medication, and doctor appointments. The mobile app evaluation revealed that more than 83% of reviewers provided acceptable feedback on the app's design and necessity. Received: 1 November 2024 | Revised: 27 December 2024 | Accepted: 7 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The dataset used in this paper is collected by using Google Forms and can be available on request from the corresponding author. Author Contribution Statement Rakib Uddin Chowdhury: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation. Mahfuzulhoq Chowdhury: Conceptualization, Methodology, Validation, Resources, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration.
Novel Text Emotion Classification Model Using Long Short-Term Memory Network
Emotion has a great influence on human cognition, behavior, and communication. In last several years, there is a tremendous rise of users in social media platforms, due to which large amount of textual data got generated. Users continuously share their thoughts and sentiments, regardless of time or location. Furthermore, the growth of emotion detection mechanism for the social media platforms becomes truly remarkable. In this paper, one novel deep learning (DL)-based classification model is proposed which is underlined on the concepts of long short-term memory (LSTM) network for the classification of six different types of emotions such as sadness, anger, love, surprise, fear, and joy from English-based texts. The model is compared with nine distinctive state-of-the-art supervised learning models. Utilizing the 80%–20% train-test split, the proposed model has shown the higher classification accuracy of 90.28% in comparison to other compared models. Practically, this model can be used in chatbots, virtual assistants, and personalized applications during the interaction with users. Received: 5 September 2024 | Revised: 20 February 2025 | Accepted: 4 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 Kaggle at https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp. Author Contribution Statement Rubul Kumar Bania: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization.
Drug Review Sentiment Analysis: Applying Transformer-Based Models for Enhanced Healthcare
Analyzing patient feedback on drug reviews is crucial in the healthcare sector as it determines the efficacy of treatment and patient experiences. Amidst the exponential growth in patient-generated data, the method of sentiment analysis has emerged as a key means of interpreting text-based reviews. In this research, the use of various machine learning and transformer-based approaches to analyze sentiments in drug reviews and gain meaningful insights from patient reviews or opinions is outlined. It juxtaposes traditional machine learning models such as Logistic Regression, Random Forest, and Support Vector Machines with deep neural networks such as Long Short-Term Memory and transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT). Various models' performance is tested using the UC Irvine drug review dataset, and data preprocessing, feature extraction, and cross-validation are used in the study. Transformers, more precisely BERT, perform better than conventional approaches at 0.96 accuracy based on findings, as they can read into intricate patterns of language and contextual hints undetectable by basic models. The research reveals how transformer-based sentiment analysis can enhance healthcare decision-making through better and context-based information. Received: 30 September 2024 | Revised: 14 January 2025 | Accepted: 4 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 UC Irvine Machine Learning Repository at https://archive.ics.uci.edu/dataset/461/drug+review+dataset+druglib+com. The data that support the findings of this study are openly available in GitHub at https://github.com/imabhi01/sentiment-analysis-dissertation. Author Contribution Statement Abhishek Chaudhary: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—original draft, Writing—review & editing, Visualization, Project administration. Sangita Pokhrel: Supervision, Writing—review & editing. Swathi Ganesan: Writing—review & editing. Prashant Bikram Shah: Investigation, Writing—review & editing. Nalinda Somasiri: Writing—review & editing.
Enhancing Cardiac Health Diagnoses Through Machine Learning Analysis of Phonocardiograms (PCG)
Phonocardiograms (PCG) provide a non-invasive approach to analyzing heart sounds, making them vital for the early detection of cardiac issues. However, identifying the most effective machine learning models and feature extraction techniques for classifying PCG signals remains a challenge. This study aims to determine the most efficient and accurate combinations of machine learning models and feature engineering techniques for classifying PCG signals, with the overarching goal of enhancing diagnostic capabilities in heart health. Seven machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Naive Bayes, AdaBoost, XGBoost, and Support Vector Machine (SVM)—were evaluated. Feature extraction methods such as Mel-frequency cepstral coefficients (MFCC), Linear Predictive Coding (LPC), and Short-Time Fourier Transform (STFT) were applied. Model performance was assessed using metrics including accuracy, precision, recall, and F1-score. The study found that advanced models like XGBoost and Random Forest, particularly when combined with STFT and MFCC features, consistently outperformed others. These models demonstrated superior accuracy and F1-scores, although they also introduced higher computational complexity. The results suggest that sophisticated model-feature combinations, particularly involving XGBoost and Random Forest with STFT and MFCC, hold promise for improving cardiac diagnostics. Received: 5 July 2024 | Revised: 18 October 2024 | Accepted: 23 January 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 Popal Khan Popalzai: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation. Khurram Shehzad Khattak: Conceptualization, Methodology, Formal analysis, Writing – review & editing, Supervision. Anwar Mehmood Sohail: Conceptualization, Writing – original draft. Zawar Hussain Khan: Methodology, Supervision.
A Dynamic Trapdoor Redactable Blockchain Schemes
The permanent storage of blockchain data exposes certain security risks in practical applications, necessitating the ability to edit data in specific scenarios. The chameleon hash function, as a trapdoor one-way hash function, has emerged as a key technology for achieving redactable blockchains. However, existing redactable blockchain schemes based on chameleon hash function face security vulnerabilities related to trapdoor misuse and leakage. This paper proposes a dynamic trapdoor redactable blockchain schemes, utilizing a dynamic trapdoor chameleon hash (DTCH) that allows for the dynamic update of trapdoors while overcoming the inherent immutability of blockchain, thus reducing the risk of malicious nodes retaining the trapdoor long-term. The proposed scheme employs controllable randomness, where the trapdoor key automatically updates after a certain period or upon use. In this scenario, authorized nodes cannot successfully find hash collisions with old keys, effectively revoking their authorization. Consequently, only users with valid trapdoor keys can modify the data on the chain. Relevant security tests and experimental results indicate that the proposed scheme offers advantages in security and maintains good efficiency at various stages, achieving higher security guarantees with minimal computational cost. Received: 27 September 2024 | Revised: 5 November 2024 | Accepted: 4 December 2024 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 Shuqiao Li: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Xiaoming Hu: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yan Liu: Formal analysis, Resources, Writing – review & editing, Visualization, Supervision. Shuangjie Bai: Validation, Formal analysis, Writing – review & editing, Visualization.
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
The Application of Quantum Pre-processing Filter for Binary Image Classification with Small Samples
Over the past few years, there has been significant interest in quantum machine learning (QML) among researchers, as it has the potential to transform the field of machine learning. Several models that exploit the properties of quantum mechanics have been developed for practical applications. In this study, we investigated the application of our previously proposed quantum pre-processing filter (QPF) to binary image classification. We evaluated the QPF on four datasets: MNIST (handwritten digits), EMNIST (handwritten digits and alphabets), CIFAR-10 (photographic images), and GTSRB (real-life traffic sign images). Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98.9% to 99.2%, 97.8% to 98.3%, and 71.2% to 76.1%, respectively, but degraded it against GTSRB from 93.5% to 92.0%. We then applied QPF in cases using a smaller number of training and testing samples, i.e., 80 and 20 samples per class, respectively. In order to derive statistically stable results, we conducted the experiment with 100 trials choosing randomly different training and testing samples and averaging the results. The result showed that the application of QPF did not improve the image classification accuracy against MNIST and EMNIST but improved it against CIFAR-10 and GTSRB from 65.8% to 67.2% and 90.5% to 91.8%, respectively. Further research will be conducted as part of future work to investigate the potential of QPF to assess the scalability of the proposed approach to larger and complex datasets. Received: 2 September 2024 | Revised: 15 October 2024 | Accepted: 4 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/hajimesuzuki999/qpf-bic. Author Contribution Statement Farina Riaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Shahab Abdulla: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision. Hajime Suzuki: Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Srinjoy Ganguly: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Ravinesh C. Deo: Investigation, Resources, Data curation, Writing – review & editing, Supervision. Susan Hopkins: Writing – review & editing, Supervision.
The Case for Human-Like Scalable Intelligence in the Medical Field
This paper discusses the use case of applying the first working cognitive architectures, built on the independent core observer model (ICOM), to the medical field, among others. The advantages of this approach are compared to the status quo and the limitations of narrow AI systems like LLMs and RL. Noteworthy advantages covered include depth, breadth, updatedness, and fidelity of medical knowledge, the "noise" or inconsistency of diagnoses and treatment, as well as preventative care, cost, time, and ethical considerations. Potent advantages for better integrating, coordinating, and otherwise accelerating medical research, particularly in less developed, underserved, and understudied regions, as aligned with the Sustainable Development Goals (SDGs), are also highlighted. Unique opportunities waiting to be explored, including interdisciplinary advantages, as well as challenges related to the disruption of current systems and processes are covered. These conservatively offer cumulative improvements across multiple dimensions measured in orders of magnitude. The novel value of human-like systems is specifically discussed for hyper-complex knowledge domains and problems, where the effect of integrating them is greatest. Received: 13 May 2024 | Revised: 14 October 2024 | Accepted: 2 January 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 paper are entirely based upon the cited works, with simple calculations applied to illustrate the data visually, in combination, and in context. The supporting calculations, tables, and charts are openly available in Excel at https://github.com/KyrtinSilver/Norn-site/blob/main/assets/data/Medical%20Use%20Case%20Figures%20v1.1.xlsx. Author Contribution Statement Kyrtin Atreides: Conceptualization, Methodology, Investigation, Writing — original draft, Writing — review & editing, Visualization.
Discipline-Sensitive Predictive Analytics for IPA-Driven Building Maintenance Management: Material Stock Quantity Modeling
The study introduces a machine learning model for BMM (Building Maintenance Management), which utilized IPA (Intelligent Process Automation) to predict the material stock required in a period, to manage the cost efficiency. Traditional BMM approaches often prepared not efficient amount of material stock for pending maintenances required materials, that could be resolved by this machine learning model; it uses discipline-sensitive machine learning algorithms to predict material needs accurately and ensure the efficient amount of material stock levels and reducing the financial waste. A proof-of-concept is implemented in a case study from this industry that validates the machine learning model's effectiveness, showing the capability of combining Artificial Intelligence (AI) and machine learning to apply the material stock level prediction. The proposed approach not only predicts the material stock efficiency but also ensures the methodology of AI that could be utilized in BMM, which could be further attached to smart urban development. With the support of the case study, the introduction of this approach could make the BMM field a significant advancement in AI-powered material stock management. It resolved both manufacturer production waste and unnecessary finance cost, helping reduce resource waste and promoting sustainability. The outcomes of this research include improved accuracy in forecasting material stock levels for upcoming building maintenance tasks, explore in this area with more sophisticated AI algorithms, mathematic, and analytics, also considered the potential IoT integration in the material warehouse to analyze and predict in real time material stock level. In a broader context, we are introducing a new standard of material stock management that could collaborate with AI together to enhance the BMM in a smart urban environment. Received: 25 July 2024 | Revised: 31 March 2025 | Accepted: 29 April 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. Building maintenance companies could request the author for the potential implementation based on their own confidential data. Author Contribution Statement Zhimeng Huang: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Project administration. Xiaodong Liu: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – review & editing, Supervision, Project administration. Imed Romdhani: Conceptualization, Conceptualization, Supervision, Project administration.
IPA-Driven Building Maintenance Management
Vision-Based Fall Detector for Elderly Based on Sliding Window Approach and Feature Engineering
A new vision-based fall detector is proposed that uses the tsfresh tool to generate features from the bounding box motion parameters of an object and performs classification in a sliding window mode. The efficiency of the generated features is demonstrated compared to the primary ones. Using the auto-sklearn library and a generalized dataset compiled from the UR Fall Detection and CAUCAFall datasets, the best human fall detection model is found. This model based on a gradient boosting classifier achieved 96% accuracy, which is not inferior to well-known detection algorithms, but uses only two primary motion parameters to generate secondary features. A principal component analysis-based class separability study showed that for secondary features 99% of the variance is captured by the first 4 principal components, while for primary features, the first 10 principal components contain only 80% of the data variance. Furthermore, the processing time for generating secondary features and making predictions was found to be relatively short, taking only a few seconds per sequence, highlighting the practical applicability of the proposed approach in real-time fall monitoring systems. Received: 15 August 2024 | Revised: 12 October 2024 | Accepted: 7 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Boris Assanovich: Conceptualization, Methodology, Validation, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Project administration. Katsiaryna Kosarava: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - review & editing, Visualization.
NMR Phase Error Correction with New Modeling Approaches
Nuclear Magnetic Resonance (NMR) spectroscopy is a highly sensitive analytical technique essential for precise molecular identification and quantification. However, accurate results depend on effective pre-processing to correct for various types of errors. Phase error correction, in particular, is crucial for ensuring the reliability of NMR data. Current methods often rely on a single linear model, which may not adequately address all types of phase errors. As a result, this limitation frequently requires manual intervention, making the process both time-consuming and prone to errors. To address these limitations, we propose three modelling approaches for NMR phase error correction: nonlinear shrinkage, multiple models, and a new optimization function called delta absolute net minimization (DANM). Our comparison of seven methods revealed that nonlinear shrinkage outperformed others in both simulated spectra and a diabetes study, followed by multiple models with DANM. Additionally, our spike-in experiments demonstrated that DANM performed quite well in both single and multiple models. Our nonlinear shrinkage approach is a simple yet effective solution. We provide an open-source R package, NMRphasing, available on CRAN (https://cran.r-project.org/web/packages/NMRphasing/) and on GitHub (https://github.com/ajiangsfu/NMRphasing). Received: 6 August 2024 | Revised: 11 October 2024 | Accepted: 31 October 2024 Conflicts of Interest Aixiang Jiang 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 Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Aixiang Jiang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Andrée E. Gravel: Resources, Data curation, Writing - review & editing. Ethan Tse: Writing - review & editing. Sanjoy Kumar Das: Resources, Data curation. James Hanley: Conceptualization, Writing - original draft, Writing - review & editing, Supervision, Project administration. Robert Nadon: Conceptualization, Resources, Data curation, Writing - original draft, Writing - review & editing, Supervision, Project administration.