Annals of Data Science最新文献

{"title":"SHMADF: A Secure and Intelligent Framework for IoT-Enabled Healthcare Monitoring and Attack Detection","authors":"Renuka Sagar,&nbsp;N. V. Sanjay Kumar,&nbsp;Anitha S. Sastry,&nbsp;Nanditha Krishna,&nbsp;Reshma S","doi":"10.1007/s40745-025-00660-6","DOIUrl":"10.1007/s40745-025-00660-6","url":null,"abstract":"<div><p>The rapid integration of Internet of Things (IoT) devices in healthcare demands a robust framework to ensure secure patient monitoring and timely attack detection. This study proposes a Secure Healthcare Monitoring and Attack Detection Framework (SHMADF) designed to safeguard IoT-enabled healthcare environments by leveraging advanced data processing and security techniques. The framework initiates with comprehensive data collection from IoT sensors and network traffic within healthcare settings. Preprocessing steps—including removal of duplicates, handling missing data, and Z-score normalization—prepare the data for efficient analysis. Feature extraction targets low-level and protocol-specific features, Session-Level and Statistical Features. Thus, the extracted features were fused by Weighted Feature Concatenation (WFC). To optimize feature selection, a novel hybrid metaheuristic—Cuckoo-Bat Echolocation Search (CBES)—combines the strengths of Cuckoo Search and Bat Algorithm, enhancing detection accuracy. Data confidentiality during transmission is ensured through a Chaotic Map-Based Stream Cipher Encryption (CMSCE), tailored for resource-constrained IoT devices. Moreover, the encrypted data are stored in cloud and it support real-time data aggregation and low-latency decision-making. The core detection engine employs a hybrid ConvGRU-Net model, merging Convolutional Neural Networks with Gated Recurrent Units for spatial–temporal pattern recognition of cyber-attacks. Additionally, an interactive alert and feedback system offers real-time visualization and adaptive model refinement, enabling proactive healthcare management. The proposed framework demonstrates potential for comprehensive, efficient, and secure IoT healthcare monitoring. The proposed SHMADF model achieved superior performance with 99.35% accuracy, 99.3% precision, 99.4% recall, and 99.35% F1-score. It demonstrated low FPR (1.0%) and FNR (0.6%), with fast encryption (10.2 ms), decryption (9.8 ms), and key generation (2.6 ms), ensuring secure, real-time IoT healthcare monitoring and attack detection.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 1","pages":"241 - 278"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: A New Hyperbolic Tangent Family of Distributions: Properties and Applications","authors":"Shahid Mohammad,&nbsp;Isabel Mendoza","doi":"10.1007/s40745-025-00654-4","DOIUrl":"10.1007/s40745-025-00654-4","url":null,"abstract":"","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"377 - 377"},"PeriodicalIF":0.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147607025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autism Spectrum Disorder Identification Using Dual-Branch Fusion Model with Privacy-Preserving","authors":"Hezi Jing,&nbsp;Wanyi Chen,&nbsp;Qingyang Xu,&nbsp;Jianjun Yang,&nbsp;Danushka Bandara,&nbsp;Rongzhen Wang,&nbsp;Ziping Zhao,&nbsp;Chao Liu","doi":"10.1007/s40745-025-00603-1","DOIUrl":"10.1007/s40745-025-00603-1","url":null,"abstract":"<div><p>Autism Spectrum Disorder (ASD) are neurodevelopmental disorders that severely impact daily life and social interactions. According to research, early diagnosis and intervention of autism is crucial to improve the overall quality of life of patients. Although existing machine learning and deep learning methods have been applied to the identification and detection of autism, healthcare organizations often refuse to share or disclose medical data with the improvement of laws and regulations. Therefore, we propose a privacy-preserving deep learning method based on the local client using a dual-stream model to further improve the ASD recognition performance by capturing the features of functional MRI in both temporal and spatial structures, and further ensure that each client improves the performance of the local recognition task through federated learning by optimizing the two steps of the local client update and the client aggregation during federated learning. The experimental results show that our model achieves the best AUC of 0.952, which ensures the overall performance of the classification model, and the recognition accuracy is significantly improved by using federated learning compared to the results when clients are trained independently.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 1","pages":"79 - 104"},"PeriodicalIF":0.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset Distillation: Recent Advances of Methods and Challenges","authors":"Muyang Li,&nbsp;Yong Shi","doi":"10.1007/s40745-025-00651-7","DOIUrl":"10.1007/s40745-025-00651-7","url":null,"abstract":"<div><p>Dataset lightweighting is the process of constructing a small dataset from a large original dataset to reduce the pressure of data storage, distribution, and model training. Dataset distillation(DD) is an emerging dataset lightweight method in recent years. This paper mainly focuses on DD problem. The mainstream algorithms of DD can be divided into several categories: meta learning framework, parameter matching framework, distribution matching framework and generative model-based DD. In this paper, we provide a comprehensive review of the distillation methods of datasets since 2018 in the above order. At the end of the article, we present some discussions on the current state of development in this field and suggest some potential future research directions.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"1883 - 1901"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power Grid Engineering Knowledge Graph and Physical Elements Alignment Algorithm Designs","authors":"Minghong Liu,&nbsp;Muze Du,&nbsp;Wenxin Mu","doi":"10.1007/s40745-025-00650-8","DOIUrl":"10.1007/s40745-025-00650-8","url":null,"abstract":"<div><p>Traditional feasibility evaluation of power grid engineering projects primarily focuses on either individual or overall evaluations. The existing approach overlooks the identification and assessment of cluster projects spanning the feasibility and implementation phases, resulting in resource wastage and schedule stagnation throughout the project management lifecycle. To address this issue, this study proposes a methodology based on knowledge graphs to align the physical elements of completed projects and those under feasibility study. This paper (i) defines the Power Grid Engineering Knowledge Graph from the perspective of physical elements (PGKG), (ii) extracts information on physical elements from feasibility study reports and constructs the initial topology of physical element nodes, and (iii) designs two physical element alignment algorithms for node - structure and node - line alignment. Finally, the two algorithms are tested on the constructed PGKG, and ablation experiments are conducted to verify their effectiveness and rationality. This paper offers a reliable method for constructing the power grid engineering knowledge graph and a physical element alignment method to tackle the problems of information deficiency and misalignment of spatio - temporal information in power grid engineering knowledge graphs.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"2009 - 2039"},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Personal Journey in Decision Sciences and Engineering Systems","authors":"James M. Tien","doi":"10.1007/s40745-025-00639-3","DOIUrl":"10.1007/s40745-025-00639-3","url":null,"abstract":"<div><p>In retrospect and over the past eighty years, it appears that the author has tracked a somewhat consistent pattern in his educational, employment and investment pursuits - all centered on the theme of decision sciences and engineering systems (DSES). Indeed, the author’s papers, lectures, speeches, and range of academic and work activities have likewise tracked such a DSES theme. Since an early age, the author came to realize that his interest in mathematics served to help him make consistent sense of his decisions about life and his various pursuits; he further understood that life itself is intertwined in a number of different engineered systems, connected with and in support of each other. Certainly, each person adopts a somewhat different personal journey in their DSES quest; this paper, of course, documents the author’s own unique journey.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"1851 - 1855"},"PeriodicalIF":0.0,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-025-00639-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Firm Growth Dynamics: The Role of Financing Strategies, Financial Management, and Managerial Skills through Multiagent Simulation","authors":"Mourad Messaadia,&nbsp;Mohammed Mispah Said Omar,&nbsp;Fouad Ben Abdelaziz","doi":"10.1007/s40745-025-00641-9","DOIUrl":"10.1007/s40745-025-00641-9","url":null,"abstract":"<div><p>This article discusses the interconnectivity of financial management abilities, managerial abilities, and funding methods in propelling company growth and performance. This study mimics different financing options (debt, equity, internal capital, trade credit, and others) using Multi-Agent-Based Modeling (MABM) to examine their effects under dynamic market scenarios. The study shows that long-term growth and competitive resilience are facilitated by a balanced finance strategy supported by efficient financial management and effective leadership. The simulation shows how firms adjust to economic transformation and balance between risk and potential growth through various strategies. The results offer the efficacy with which MABM illustrates the complexity of financial strategy selection in uncertain markets and demonstrates emergent patterns in systemic risk, firm survival, and aggregate growth, yielding new insight into robust financing strategies.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"1825 - 1849"},"PeriodicalIF":0.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Enlightenment, Digital Humanism, and Computer-supported Collaboration in Artificial Intelligence Era","authors":"George Metakides,&nbsp;Florin Gheorghe Filip","doi":"10.1007/s40745-025-00640-w","DOIUrl":"10.1007/s40745-025-00640-w","url":null,"abstract":"<div><p>The paper addresses three concepts namely <i>enlightenment</i>, <i>humanism</i> and <i>collaboration</i> in the context of a human being’s quest for new knowledge and perennial values. Established relationships among people and their evolution are impacted by Artificial Intelligence associated with other main technology drivers of the digital transformation such as the Internet and Big Data analytics. The basic aspects that characterize the three above concepts are reviewed and a brief and up-to-date technical account of the Artificial Intelligence domain is provided together with the steps envisaged toward a trustworthy and anthropocentric schema. The current beneficial as well as detrimental impacts of the current usage of the Internet, Big Data and Artificial Intelligence are reviewed together with various opinions of influential scientists and industry leaders and the updated concepts of the enlightenment, humanism, and collaboration in the digital context are eventually presented.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 6","pages":"1799 - 1823"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Estimation of Wild Blueberry Yield via Triple Integrated Machine Learning Techniques","authors":"Xiaoming Yang","doi":"10.1007/s40745-025-00638-4","DOIUrl":"10.1007/s40745-025-00638-4","url":null,"abstract":"<div><p>Vaccinium angustifolium, commonly referred to as the wild blueberry, is a wild species native to North America and valued for both its taste and nutritional benefits. Packed with antioxidants, they further provide a variety of health benefits and help maintain biodiversity when grown within their natural habitats. Wild blueberries are considered a kind of “superfood” due to their power-packed nutrition. Wild blueberries are of crucial economic importance because they provide employment opportunities and open new markets for exportation. Machine Learning (ML) is used to model and analyze a number of parameters influencing crop production, and this makes it a major asset in the prediction of wild blueberry output. ML methods like Gaussian Process Regression Networks (GPR) Networks and Histogram Gradient Boosting Regression (HGBR) are used for this purpose. These models, in turn, predict wild blueberry production using the Marine Predators Algorithm (MPA) and Northern Goshawk Optimization (NGO). The corresponding models and optimizers are put into place to improve the accuracy of the results. Among them, HGNG is the best performer, with an R² of 0.996 in the remarkable training phase. After that, in this respect, HGMP performed well, which has an R² of 0.985 at this phase. Moreover, regarding wild blueberry yield forecasting, HGB did very well and had an R² of 0.976 during the training process.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 1","pages":"215 - 239"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147340177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Facial Expression Recognition Technology Based on Image Processing in Affective Computing Interactive System","authors":"Li Xiaoshu,&nbsp;Ji Kang","doi":"10.1007/s40745-025-00636-6","DOIUrl":"10.1007/s40745-025-00636-6","url":null,"abstract":"<div><p>Traditional emotion recognition systems suffer from some problems, such as single-modality dependence, sensitivity to environmental changes, poor real-time performance, and over-reliance on manual feature extraction, which greatly limit their accuracy and robustness. To address the aforementioned problems, this study integrates deep learning with multimodal information fusion methods to enhance the accuracy, real-time capabilities, and robustness of the affective computing interaction system. Facial images and depth information are high-definition cameras and Kinect depth cameras collect and perform image preprocessing is performed to establish a facial expression recognition model based on a convolutional neural network. The AffectNet dataset was used for training and verification. At the same time, voice and text modal data are fused. Multimodal feature fusion is performed using weighted averaging to further enhance the performance of emotion recognition. Finally, an affective computing interaction system is designed and real-time affective state recognition can be achieved, as well as personalized feedback and content recommendations. Experimental results prove that the proposed system is superior to the traditional single-modal systems and support vector machine-based methods with regard to emotion recognition accuracy, real-time responsiveness, stability, and anti-interference ability. With 1,000 pieces of data, the proposed system attained an accuracy of 97.3%, and even at 5,000 pieces of data, an accuracy of 90.6%, and there was no crash or performance degradation during 12 hours of continuous operation.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 2","pages":"355 - 374"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}