Annals of Data Science最新文献_第8页

Modi-Weibull Distribution: Inferential and Simulation Study 莫迪-威布尔分布：推理与仿真研究

Annals of Data Science Pub Date : 2023-08-15 DOI: 10.1007/s40745-023-00491-3

Harshita Kumawat, Kanak Modi, Pankaj Nagar

引用次数: 0

Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values 记录值下物流配送位置和规模参数的收缩估计

Annals of Data Science Pub Date : 2023-08-14 DOI: 10.1007/s40745-023-00492-2

Shubham Gupta, Gajendra K. Vishwakarma, A. M. Elsawah

{"title":"Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values","authors":"Shubham Gupta, Gajendra K. Vishwakarma, A. M. Elsawah","doi":"10.1007/s40745-023-00492-2","DOIUrl":"10.1007/s40745-023-00492-2","url":null,"abstract":"<div><p>Logistic distribution (LogDis) is frequently used in many different applications, such as logistic regression, logit models, classification, neural networks, physical sciences, sports modeling, finance and health and disease studies. For instance, the distribution function of the LogDis has the same functional form as the derivative of the Fermi function that can be used to set the relative weight of various electron energies in their contributions to electron transport. The LogDis has wider tails than a normal distribution (NorDis), so it is more consistent with the underlying data and provides better insight into the likelihood of extreme events. For this reason the United States Chess Federation has switched its formula for calculating chess ratings from the NorDis to the LogDis. The outcomes of many real-life experiments are sequences of record-breaking data sets, where only observations that exceed (or only those that fall below) the current extreme value are recorded. The practice demonstrated that the widely used estimators of the scale and location parameters of logistic record values, such as the best linear unbiased estimators (BLUEs), have some defects. This paper investigates the shrinkage estimators of the location and scale parameters for logistic record values using prior information about their BLUEs. Theoretical and computational justifications for the accuracy and precision of the proposed shrinkage estimators are investigated via their bias and mean square error (MSE), which provide sufficient conditions for improving the proposed shrinkage estimators to get unbiased estimators with minimum MSE. The performance of the proposed shrinkage estimators is compared with the performances of the BLUEs. The results demonstrate that the resulting shrinkage estimators are shown to be remarkably efficient.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1209 - 1224"},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46550442","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

Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique 利用Vine Copula和模糊聚类技术改进贝叶斯分类器

Annals of Data Science Pub Date : 2023-08-10 DOI: 10.1007/s40745-023-00490-4

Ha Che-Ngoc, Thao Nguyen-Trang, Hieu Huynh-Van, Tai Vo-Van

引用次数: 0

A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data 一个新的Lindley推广:双峰右偏降水数据下的估算、风险评估与分析

Annals of Data Science Pub Date : 2023-08-08 DOI: 10.1007/s40745-023-00485-1

Majid Hashempour, Morad Alizadeh, Haitham M. Yousof

{"title":"A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data","authors":"Majid Hashempour, Morad Alizadeh, Haitham M. Yousof","doi":"10.1007/s40745-023-00485-1","DOIUrl":"10.1007/s40745-023-00485-1","url":null,"abstract":"<div><p>The objectives of this study are to propose a new two-parameter lifespan distribution and explain some of the most essential properties of that distribution. Through the course of this investigation, we will be able to achieve both of these objectives. For the aim of assessment, research is carried out that makes use of simulation, and for the same reason, a variety of various approaches are studied and taken into account for the purpose of evaluation. Making use of two separate data collections enables an analysis of the adaptability of the suggested distribution to a number of different contexts. The risk exposure in the context of asymmetric bimodal right-skewed precipitation data was further defined by using five essential risk indicators, such as value-at-risk, tail-value-at-risk, tail variance, tail mean–variance, and mean excess loss function. This was done in order to account for the right-skewed distribution of the data. In order to examine the data, several risk indicators were utilized. These risk indicators were used in order to achieve a more in-depth description of the risk exposure that was being faced.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 6","pages":"1919 - 1958"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42711550","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

Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors 基于条件指定先验的变点问题的贝叶斯分析

Annals of Data Science Pub Date : 2023-08-08 DOI: 10.1007/s40745-023-00484-2

G. Shahtahmassebi, José María Sarabia

引用次数: 0

Bayesian Learning of Personalized Longitudinal Biomarker Trajectory 个性化纵向生物标志物轨迹的贝叶斯学习

Annals of Data Science Pub Date : 2023-08-01 DOI: 10.1007/s40745-023-00486-0

Shouhao Zhou, Xuelin Huang, Chan Shen, Hagop M. Kantarjian

{"title":"Bayesian Learning of Personalized Longitudinal Biomarker Trajectory","authors":"Shouhao Zhou, Xuelin Huang, Chan Shen, Hagop M. Kantarjian","doi":"10.1007/s40745-023-00486-0","DOIUrl":"10.1007/s40745-023-00486-0","url":null,"abstract":"<div><p>This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"1031 - 1050"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46463104","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

Applications of Reliability Test Plan for Logistic Rayleigh Distributed Quality Characteristic 物流瑞利分布质量特性可靠性试验计划的应用

Annals of Data Science Pub Date : 2023-07-19 DOI: 10.1007/s40745-023-00473-5

Mahendra Saha, Harsh Tripathi, Anju Devi, Pratibha Pareek

引用次数: 0

Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network 基于患者问卷的帕金森病人工神经网络分类

Annals of Data Science Pub Date : 2023-07-15 DOI: 10.1007/s40745-023-00482-4

Tarakashar Das, Sabrina Mobassirin, Syed Md. Minhaz Hossain, Aka Das, Anik Sen, Khaleque Md. Aashiq Kamal, Kaushik Deb

{"title":"Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network","authors":"Tarakashar Das, Sabrina Mobassirin, Syed Md. Minhaz Hossain, Aka Das, Anik Sen, Khaleque Md. Aashiq Kamal, Kaushik Deb","doi":"10.1007/s40745-023-00482-4","DOIUrl":"10.1007/s40745-023-00482-4","url":null,"abstract":"<div><p>Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of artificial intelligence algorithms, there is an opportunity to develop a cost-effective system for diagnosing PD at an earlier stage. No permanent remedy has been established yet; however, an earlier diagnosis helps lead a better life. Probably, the three most responsible categories of symptoms for Parkinson’s Disease are tremors, rigidity, and body bradykinesia. Therefore, we investigate the 53 unique features of the Parkinson’s Progression Markers Initiative dataset to determine the significant symptoms, including three major categories. As feature selection is integral to developing a generalized model, we investigate including and excluding feature selection. Four feature selection methods are incorporated—low variance filter, Wilcoxon rank-sum test, principle component analysis, and Chi-square test. Furthermore, we utilize machine learning, ensemble learning, and artificial neural networks (ANN) for classification. Experimental evidence shows that not all symptoms are equally important, but no symptom can be completely eliminated. However, our proposed ANN model attains the best mean accuracy of 99.51%, 98.17% mean specificity, 0.9830 mean Kappa Score, 0.99 mean AUC, and 99.70% mean F1-score with all the features. The efficiency of our suggested technique on diverse data modalities is demonstrated by comparison with recent publications. Finally, we established a trade-off between classification time and accuracy.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1821 - 1864"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00482-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46611876","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

A New Class of Distribution Over Bounded Support and Its Associated Regression Model 一类新的有界支持上分布及其回归模型

Annals of Data Science Pub Date : 2023-07-11 DOI: 10.1007/s40745-023-00483-3

Ishfaq S. Ahmad, Rameesa Jan, Poonam Nirwan, Peer Bilal Ahmad

引用次数: 0

Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation Inception-UDet:一种改进的U-Net结构用于脑肿瘤分割

Annals of Data Science Pub Date : 2023-07-01 DOI: 10.1007/s40745-023-00480-6

Ilyasse Aboussaleh, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi

{"title":"Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation","authors":"Ilyasse Aboussaleh, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi","doi":"10.1007/s40745-023-00480-6","DOIUrl":"10.1007/s40745-023-00480-6","url":null,"abstract":"<div><p>Brain tumor segmentation is an important field and a sensitive task in tumor diagnosis. The treatment research in this area has helped specialists in detecting the tumor’s location in order to deal with it in its early stages. Numerous methods based on deep learning, have been proposed, including the symmetric U-Net architectures, which revealed great results in the medical imaging field, precisely brain tumor segmentation. In this paper, we proposed an improved U-Net architecture called Inception U-Det inspired by U-Det. This work aims at employing the inception block instead of the convolution one used in the bi-directional feature pyramid neural (Bi-FPN) network during the skip connection U-Det phase. Furthermore, a comparison study has been performed between our proposed approach and the three known architectures in medical imaging segmentation; U-Net, DC-Unet, and U-Det. Several segmentation metrics have been computed and then taken into account in these methods, by means of the publicly available BraTS datasets. Thus, our obtained results have showed promising results in terms of accuracy, dice similarity coefficient (DSC), and intersection–union ratio (IOU). Moreover, the proposed method has achieved a DSC of 87.9%, 85.5%, and 83.9% on BraTS2020, BraTS2018, and BraTS2017, respectively, calculated from the best fold in fourfold cross-validation employed in the present approach.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"831 - 853"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45364813","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