Annals of Data Science最新文献_第5页

Unit Rayleigh Half-Normal Distribution: Bayesian and Non-Bayesian Inference, Regression Model for Bounded Response Data and Application 单位瑞利半正态分布：贝叶斯和非贝叶斯推断，有界响应数据的回归模型及其应用

Annals of Data Science Pub Date : 2025-09-23 DOI: 10.1007/s40745-025-00645-5

Mahmoud M. El-Awady, Ahmed T. Ramadan

{"title":"Unit Rayleigh Half-Normal Distribution: Bayesian and Non-Bayesian Inference, Regression Model for Bounded Response Data and Application","authors":"Mahmoud M. El-Awady, Ahmed T. Ramadan","doi":"10.1007/s40745-025-00645-5","DOIUrl":"10.1007/s40745-025-00645-5","url":null,"abstract":"<div><p>This paper introduces a novel one-parameter bounded distribution, called the Unit Rayleigh Half-Normal (URHN) distribution, designed for modeling data on the unit interval (0,1), which frequently arises in many fields such as economics, actuarial science, and medicine. We examine the structural properties of the URHN distribution and derive its key statistical and reliability characteristics. Parameter estimation is carried out using a Bayesian method via Markov Chain Monte Carlo (MCMC), along with other standard techniques, including maximum likelihood, maximum product spacing, and least squares methods. The performance of these estimation methods is evaluated through simulation studies. To broaden the scope of the URHN distribution, we introduce a regression model by reparameterizing it in terms of its mean, enabling the incorporation of covariates for modeling bounded response variables. The practical utility of the proposed model is demonstrated using data representing the percentage of educational attainment in countries of the Organization for Economic Co-operation and Development (OECD), which is one of the indicators within the education dimension of the Better Life Index (BLI). We examine the relationship between educational attainment and three explanatory indicators: homicide rate, housing expenditure, and labor market insecurity. The results indicate that only labor market insecurity has a statistically significant effect on educational attainment. With its theoretical foundation, modeling flexibility, and empirical effectiveness, the URHN model emerges as a competitive alternative to established bounded distributions.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 6","pages":"1467 - 1500"},"PeriodicalIF":0.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149230696","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}

引用次数: 0

An Innovative and Effective Deep Learning Architecture for Risk and Survival Rate Prediction of Triple Negative Breast Cancer Using Modified Optimization Strategy 基于改进优化策略的三阴性乳腺癌风险和生存率预测的创新有效深度学习架构

Annals of Data Science Pub Date : 2025-09-19 DOI: 10.1007/s40745-025-00642-8

Sakunthala Kovelakonda, Ramakrishna Murty Malla

{"title":"An Innovative and Effective Deep Learning Architecture for Risk and Survival Rate Prediction of Triple Negative Breast Cancer Using Modified Optimization Strategy","authors":"Sakunthala Kovelakonda, Ramakrishna Murty Malla","doi":"10.1007/s40745-025-00642-8","DOIUrl":"10.1007/s40745-025-00642-8","url":null,"abstract":"<div><p>The prognosis of various malignant tumors, including breast cancer, can be effectively assessed using peripheral hematologic parameters obtained from patients. However, their significance is not examined across diverse breast cancer molecular subtypes. Detecting Triple-Negative Breast Cancer (TNBC) remains challenging because of its invasive nature, poor prognosis ability and high risk of distant metastasis. Immunohistochemistry (IHC) biomarker expression, clinical findings of the patients and morphological features derived from histopathology assessment are important for estimating the survival rate of breast cancer patients and are considered as significant components in diagnosis and prognosis. Recently, a two-step procedure has been suggested for diagnosing TNBC, including medical imaging and IHC, although this method can be time-consuming and operator-dependent. Therefore, the diagnostic efficiency of TNBC needs to be enhanced by adopting rapid and advanced technologies. In this research work, a new deep learning-based framework for TNBC detection and survival prediction is presented. At first, the required images to perform TNBC detection are collected from available databases. These images are used for the TNBC prediction process through Attention-embedded Adaptive Efficient Net, where several parameters are tuned using the Modified Mountaineering Team-Based Optimization to improve the TNBC detection and prediction effectiveness. Then, the risk and survival rate prediction is carried out by the Cascaded Artificial Neural Network, and the predicted survival rate is obtained. The proposed system has the potential to significantly improve the prediction of TNBC disease-affected outcomes. Finally, different experiments are carried out in developed TNBC detection and prediction models and also the effectiveness of the proposed scheme is contrasted with several baseline models to prove its enhanced performance.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 6","pages":"1433 - 1466"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149230695","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}

引用次数: 0

Power Grid Engineering Knowledge Graph and Physical Elements Alignment Algorithm Designs 电网工程知识图谱与物理元素对齐算法设计

Annals of Data Science Pub Date : 2025-09-17 DOI: 10.1007/s40745-025-00650-8

Minghong Liu, Muze Du, Wenxin Mu

{"title":"Power Grid Engineering Knowledge Graph and Physical Elements Alignment Algorithm Designs","authors":"Minghong Liu, Muze Du, 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}

引用次数: 0

A Personal Journey in Decision Sciences and Engineering Systems 决策科学与工程系统的个人旅程

Annals of Data Science Pub Date : 2025-09-14 DOI: 10.1007/s40745-025-00639-3

James M. Tien

引用次数: 0

Modeling Firm Growth Dynamics: The Role of Financing Strategies, Financial Management, and Managerial Skills through Multiagent Simulation 企业成长动态建模：融资策略、财务管理和管理技能在多智能体模拟中的作用

Annals of Data Science Pub Date : 2025-09-13 DOI: 10.1007/s40745-025-00641-9

Mourad Messaadia, Mohammed Mispah Said Omar, Fouad Ben Abdelaziz

引用次数: 0

Digital Enlightenment, Digital Humanism, and Computer-supported Collaboration in Artificial Intelligence Era 人工智能时代的数字启蒙、数字人文主义与计算机支持的协作

Annals of Data Science Pub Date : 2025-09-12 DOI: 10.1007/s40745-025-00640-w

George Metakides, Florin Gheorghe Filip

引用次数: 0

A Data-Driven Approach to IoT Security: Detecting Cyber Attacks with AEInc-BGTO 数据驱动的物联网安全方法：使用aec - bgto检测网络攻击

Annals of Data Science Pub Date : 2025-09-08 DOI: 10.1007/s40745-025-00634-8

Jeyalakshmi Shunmugiah, Sekar Sellappan, Karthikeyan Lakshmanan, Ravikumar Sethuraman

{"title":"A Data-Driven Approach to IoT Security: Detecting Cyber Attacks with AEInc-BGTO","authors":"Jeyalakshmi Shunmugiah, Sekar Sellappan, Karthikeyan Lakshmanan, Ravikumar Sethuraman","doi":"10.1007/s40745-025-00634-8","DOIUrl":"10.1007/s40745-025-00634-8","url":null,"abstract":"<div><p>Cyberattacks on the Internet of Things have significantly raised due to the extensive adoption of smart devices and the multiple security flaws in networks. It is essential to identify and categorize malicious traffic to guarantee the security of these systems. Nonetheless, the earlier research has encountered obstacles such as limited characteristics, reduced effectiveness, irrelevant attributes, and complex computational tasks. This research proposes an innovative framework called the Attention Embedded Inception-based Bidirectional Gorilla Troop Optimization strategy, which is an emerging mechanism for detecting and classifying various kinds of attacks to ensure security in Internet of Things settings. Some preprocessing procedures are performed to improve the quality of received data and enhance information security. The feature extraction process is performed by Convolutional Block Attention Embedded InceptionV4, which is developed by integrating the Convolutional Block Attention Mechanism with InceptionV4. Moreover, the suggested Bidirectional Long Short Term Memory approach effectively categorizes and detects several types of attacks in IoT, and Gorilla Troops Optimization technique is implemented for fine-tuning model’s hyperparameters, thereby enhancing detection accuracy, and security rate. The proposed strategy is compared to previous attack detection mechanisms in terms of some common assessing metrics using five attack datasets, where it is obtained that the proposed strategy yields commendable findings. The proposed strategy achieves detection accuracy of 99.27% and security rate of 99.18%, which outperforms earlier assault detection techniques. Consequently, it is claimed that the proposed strategy is the most effective for several attack identification on secure Internet of Things.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 5","pages":"1365 - 1399"},"PeriodicalIF":0.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149098803","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}

引用次数: 0

Robust Estimation of Wild Blueberry Yield via Triple Integrated Machine Learning Techniques 基于三重集成机器学习技术的野生蓝莓产量稳健估计

Annals of Data Science Pub Date : 2025-08-20 DOI: 10.1007/s40745-025-00638-4

Xiaoming Yang

{"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 R<sup>2</sup> of 0.996 in the remarkable training phase. After that, in this respect, HGMP performed well, which has an R<sup>2</sup> of 0.985 at this phase. Moreover, regarding wild blueberry yield forecasting, HGB did very well and had an R<sup>2</sup> 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}

引用次数: 0

Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships 预测加密货币价格方向的动态贝叶斯网络：揭示因果关系

Annals of Data Science Pub Date : 2025-08-08 DOI: 10.1007/s40745-025-00637-5

Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee

{"title":"Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships","authors":"Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee","doi":"10.1007/s40745-025-00637-5","DOIUrl":"10.1007/s40745-025-00637-5","url":null,"abstract":"<div><p>Cryptocurrencies have gained widespread attention, particularly in finance and investment sectors. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. This study focuses on six major cryptocurrencies, including Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. The proposed model’s performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, support vector machines, and a large language model. Results demonstrate that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 6","pages":"1401 - 1431"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-025-00637-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149230743","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}

引用次数: 0

Design of Facial Expression Recognition Technology Based on Image Processing in Affective Computing Interactive System 情感计算交互系统中基于图像处理的面部表情识别技术设计

Annals of Data Science Pub Date : 2025-08-06 DOI: 10.1007/s40745-025-00636-6

Li Xiaoshu, Ji Kang

{"title":"Design of Facial Expression Recognition Technology Based on Image Processing in Affective Computing Interactive System","authors":"Li Xiaoshu, 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}

引用次数: 0