Annals of Data Science最新文献_第6页

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":null,"pages":null},"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":null,"pages":null},"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

Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations 为具有强相关性的高维基因组数据选择稳定的变量

Annals of Data Science Pub Date : 2023-06-29 DOI: 10.1007/s40745-023-00481-5

Reetika Sarkar, Sithija Manage, Xiaoli Gao

{"title":"Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations","authors":"Reetika Sarkar, Sithija Manage, Xiaoli Gao","doi":"10.1007/s40745-023-00481-5","DOIUrl":"10.1007/s40745-023-00481-5","url":null,"abstract":"<div><p>High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including the Lasso and MCP, etc. In this paper, we perform comparative study of regularization approaches for variable selection under different correlation structures and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Extensive simulation studies and high-dimensional genomic data analysis on real datasets have demonstrated the advantage of the proposed rPGBS method over some of the most used regularization methods. In particular, rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to some recent methods addressing variable selection with strong correlations: Precision Lasso (Wang et al. in Bioinformatics 35:1181–1187, 2019) and Whitening Lasso (Zhu et al. in Bioinformatics 37:2238–2244, 2021). Moreover, rPGBS has been shown to be computationally efficient across various settings.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135049935","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

On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data 关于渐进截尾广义X指数分布：（非）贝叶斯估计及其在癌症数据中的应用

Annals of Data Science Pub Date : 2023-06-15 DOI: 10.1007/s40745-023-00477-1

Kousik Maiti, Suchandan Kayal, Aditi Kar Gangopadhyay

引用次数: 0

A Survey on Differential Privacy for Medical Data Analysis 医疗数据分析中的差异隐私调查

Annals of Data Science Pub Date : 2023-06-10 DOI: 10.1007/s40745-023-00475-3

WeiKang Liu, Yanchun Zhang, Hong Yang, Qinxue Meng

引用次数: 0

Naïve Bayes Classifier Model for Detecting Spam Mails Naïve垃圾邮件检测的贝叶斯分类器模型

Annals of Data Science Pub Date : 2023-06-09 DOI: 10.1007/s40745-023-00479-z

Shrawan Kumar, Kavita Gupta, Manya Gupta

引用次数: 0

Artificial Intelligence Algorithms for Collaborative Book Recommender Systems 协同图书推荐系统的人工智能算法

Annals of Data Science Pub Date : 2023-06-08 DOI: 10.1007/s40745-023-00474-4

Clemens Tegetmeier, Arne Johannssen, Nataliya Chukhrova

引用次数: 0

On Poisson Moment Exponential Distribution with Associated Regression and INAR(1) Process 带关联回归和INAR(1)过程的泊松矩指数分布

Annals of Data Science Pub Date : 2023-06-08 DOI: 10.1007/s40745-023-00476-2

R. Maya, Jie Huang, M. R. Irshad, Fukang Zhu

{"title":"On Poisson Moment Exponential Distribution with Associated Regression and INAR(1) Process","authors":"R. Maya, Jie Huang, M. R. Irshad, Fukang Zhu","doi":"10.1007/s40745-023-00476-2","DOIUrl":"10.1007/s40745-023-00476-2","url":null,"abstract":"<div><p>Numerous studies have emphasised the significance of count data modeling and its applications to phenomena that occur in the real world. From this perspective, this article examines the traits and applications of the Poisson-moment exponential (PME) distribution in the contexts of time series analysis and regression analysis for real-world phenomena. The PME distribution is a novel one-parameter discrete distribution that can be used as a powerful alternative for the existing distributions for modeling over-dispersed count datasets. The advantages of the PME distribution, including the simplicity of the probability mass function and the explicit expressions of the functions of all the statistical properties, drove us to develop the inferential aspects and learn more about its practical applications. The unknown parameter is estimated using both maximum likelihood and moment estimation methods. Also, we present a parametric regression model based on the PME distribution for the count datasets. To strengthen the utility of the suggested distribution, we propose a new first-order integer-valued autoregressive (INAR(1)) process with PME innovations based on binomial thinning for modeling integer-valued time series with over-dispersion. Application to four real datasets confirms the empirical significance of the proposed model.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264212","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 New Compound Distribution and Its Applications in Over-dispersed Count Data 一种新的复合分布及其在过分散计数数据中的应用

Annals of Data Science Pub Date : 2023-06-07 DOI: 10.1007/s40745-023-00478-0

Peer Bilal Ahmad, Mohammad Kafeel Wani

{"title":"A New Compound Distribution and Its Applications in Over-dispersed Count Data","authors":"Peer Bilal Ahmad, Mohammad Kafeel Wani","doi":"10.1007/s40745-023-00478-0","DOIUrl":"10.1007/s40745-023-00478-0","url":null,"abstract":"<div><p>Every time variance exceeds mean, over-dispersed models are typically employed. This is the reason that over-dispersed models are such an important aspect of statistical modeling. In this work, the parameter of Poisson distribution is assumed to follow a new lifespan distribution called as Chris-Jerry distribution. The resulting compound distribution is an over-dispersed model known as the Poisson-Chris-Jerry distribution. As a result of deriving a general expression for the <i>r th</i> factorial moment, we acquired the moments about origin and the central moments. In addition to this, moment’s related measurements, generating functions, over-dispersion property, reliability characteristics, recurrence relation for probability, and other statistical qualities, have also been described. For the goal of estimating parameter of the suggested model, the maximum likelihood estimation and method of moment estimation have been addressed. The usefulness of maximum likelihood estimates has also been taken into consideration through a simulation study. We employed four real life data sets, examined the goodness-of-fit test, and considered additional standards such as the Akaike’s information criterion and Bayesian information criterion. The outcomes are compared with several potential models.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46822534","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