Annals of Data Science最新文献_第8页

Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series 尼日利亚新冠肺炎疫苗接种干预分析：天真的解决方案与中断的时间序列

Annals of Data Science Pub Date : 2023-01-19 DOI: 10.1007/s40745-023-00462-8

Desmond Chekwube Bartholomew, Chrysogonus Chinagorom Nwaigwe, Ukamaka Cynthia Orumie, Godwin Onyeka Nwafor

{"title":"Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series","authors":"Desmond Chekwube Bartholomew, Chrysogonus Chinagorom Nwaigwe, Ukamaka Cynthia Orumie, Godwin Onyeka Nwafor","doi":"10.1007/s40745-023-00462-8","DOIUrl":"10.1007/s40745-023-00462-8","url":null,"abstract":"<div><p>In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in Nigeria were collected and subjected to two models: the naïve solution and the interrupted time series (the intervention model). Based on the Alkaike Information Criterion (AIC), sigma<sup>2</sup>, and log likelihood values, the interrupted time series model outperformed the Naïve solution model. ARIMA (4, 1, 4) with exogenous variables was identified as the best model. It was observed that the intervention (vaccination) was not significant at the 5% level of significance in reducing the number of daily COVID-19 cases in Nigeria since the start of the vaccination on March 5, 2021, until March 28, 2022. Also, the ARIMA (4, 1, 4) forecasts indicated that there will be surge in the number of daily COVID-19 cases in Nigeria between January and April 2023. As a result, we recommend strict adherence to COVID-19 protocols as well as further vaccination and sensitization programs to educate people on the importance of vaccine uptake and avoid Corona virus spread in the year 2023 and beyond.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45470057","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

Exchange Rate Forecasting: Nonlinear GARCH-NN Modeling Approach 汇率预测:非线性GARCH-NN建模方法

Annals of Data Science Pub Date : 2023-01-03 DOI: 10.1007/s40745-022-00458-w

Fahima Charef

引用次数: 0

An Alternative to the Beta Regression Model with Applications to OECD Employment and Cancer Data β回归模型的替代选择及其在经合组织就业和癌症数据中的应用

Annals of Data Science Pub Date : 2022-12-27 DOI: 10.1007/s40745-022-00460-2

Idika E. Okorie, Emmanuel Afuecheta

引用次数: 0

Data Analysis by Adaptive Progressive Hybrid Censored Under Bivariate Model 双变量模型下的自适应渐进混合截尾数据分析

Annals of Data Science Pub Date : 2022-10-23 DOI: 10.1007/s40745-022-00455-z

El-Sayed A. El-Sherpieny, Hiba Z. Muhammed, Ehab M. Almetwally

引用次数: 0

On Step-Stress Partially Accelerated Life Testing with Competing Risks Under Progressive Type-II Censoring 具有竞争风险的阶跃应力部分加速寿命试验

Annals of Data Science Pub Date : 2022-10-22 DOI: 10.1007/s40745-022-00454-0

Sara O. Abd El-Azeem, Mahmoud H. Abu-Moussa, Moustafa M. Mohie El-Din, Lamiaa S. Diab

引用次数: 0

Image Steganography Using Fractal Cover and Combined Chaos-DNA Based Encryption 基于分形覆盖和混沌DNA组合加密的图像隐写术

Annals of Data Science Pub Date : 2022-10-22 DOI: 10.1007/s40745-022-00457-x

Asha Durafe, Vinod Patidar

{"title":"Image Steganography Using Fractal Cover and Combined Chaos-DNA Based Encryption","authors":"Asha Durafe, Vinod Patidar","doi":"10.1007/s40745-022-00457-x","DOIUrl":"10.1007/s40745-022-00457-x","url":null,"abstract":"<div><p>To address the need for secure digital image transmission an algorithm that fulfils all prominent prerequisites of a steganography technique is developed. By incorporating the salient features of fractal cover images, dual-layer encryption using the standard chaotic map and DNA-hyperchaotic cryptography along with DWT-SVD embedding, key aspects like robustness, better perceptual quality and high payload capacity are targeted to build a blind colour image steganography algorithm in this work. A fractal cover image is used to hide a DNA-chaotic encrypted colour image using DWT-SVD embedding method. A two-dimensional standard chaotic map, which exhibits robust chaos for a very large range of parameter, is used to generate the pseudo-random number sequences of cryptographic qualities. One of the core novelty of the proposed method is the 2 layers chaotic encryption method to generate the DNA encrypted secret image which is finally embedded in a fractal cover image using DWT-SVD transform domain technique capable of withstanding the false positive attack. The comprehensive statistical security tests and the standard evaluation benchmarks depict that this efficient yet simple hybrid steganography algorithm is highly robust as well as sustainable against removal, geometrical, image enhancement and histogram attacks, offers better perceptual image quality and also contributes high perceptual quality of the extracted image.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46333893","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 Novel G Family for Single Acceptance Sampling Plan with Application in Quality and Risk Decisions 一种新的单次验收抽样计划G族及其在质量和风险决策中的应用

Annals of Data Science Pub Date : 2022-10-20 DOI: 10.1007/s40745-022-00451-3

Basma Ahmed, M. Masoom Ali, Haitham M. Yousof

引用次数: 0

Statistical Analysis from the Generalized Inverse Lindley Distribution with Adaptive Type-II Progressively Hybrid Censoring Scheme 广义逆Lindley分布的自适应型渐进式混合滤波统计分析

Annals of Data Science Pub Date : 2022-10-20 DOI: 10.1007/s40745-022-00453-1

Intekhab Alam, Murshid Kamal, Mohammad Tariq Intezar, Saqib Showkat Wani, Imran Alam

{"title":"Statistical Analysis from the Generalized Inverse Lindley Distribution with Adaptive Type-II Progressively Hybrid Censoring Scheme","authors":"Intekhab Alam, Murshid Kamal, Mohammad Tariq Intezar, Saqib Showkat Wani, Imran Alam","doi":"10.1007/s40745-022-00453-1","DOIUrl":"10.1007/s40745-022-00453-1","url":null,"abstract":"<div><p>The key assumption in accelerated life testing is that the mathematical model concerning the lifetime of the item and the stress is known or can be assumed. In several situations, such life-stress relationships are not known and cannot be assumed, i.e. accelerated life testing information cannot be extrapolated to use situation. So, in such cases, a partially accelerated life test is a more appropriate testing method to be executed for which tested objects are subjected to both normal and accelerated circumstances. Due to continual improvement in manufacturing design, it is more difficult to obtain information about the lifetime of products or materials with high reliability at the time of testing under normal conditions. An approach to accelerate failures is the step-stress partially accelerated life test which increases the load applied to the goods in a particular discrete sequence. In this study, the maximum likelihood estimators of inverse the generalized inverse Lindley distribution parameters and the acceleration factor are investigated in a step-stress partially accelerated life test model utilizing two various types of progressively hybrid censoring systems. Furthermore, the performance of the model parameter estimators with the two progressive hybrid censoring schemes is analyzed and compared in terms of biases and mean squared errors using a Monte Carlo simulation approach.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41944192","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 Extension of the Topp–Leone-Family of Models with Applications to Real Data Topp–Leone模型族的一个新扩展及其在实际数据中的应用

Annals of Data Science Pub Date : 2022-10-18 DOI: 10.1007/s40745-022-00456-y

Mustapha Muhammad, Lixia Liu, Badamasi Abba, Isyaku Muhammad, Mouna Bouchane, Hexin Zhang, Sani Musa

{"title":"A New Extension of the Topp–Leone-Family of Models with Applications to Real Data","authors":"Mustapha Muhammad, Lixia Liu, Badamasi Abba, Isyaku Muhammad, Mouna Bouchane, Hexin Zhang, Sani Musa","doi":"10.1007/s40745-022-00456-y","DOIUrl":"10.1007/s40745-022-00456-y","url":null,"abstract":"<div><p>In this article, we proposed a new extension of the Topp–Leone family of distributions. Some important properties of the model are developed, such as quantile function, stochastic ordering, model series representation, moments, stress–strength reliability parameter, Renyi entropy, order statistics, and moment of residual life. A particular member called new extended Topp–Leone exponential (NETLE) is discussed. Maximum likelihood estimation (MLE), least-square estimation (LSE), and percentile estimation (PE) are used for the model parameter estimation. Simulation studies were conducted using NETLE to assess the MLE, LSE, and PE performance by examining their bias and mean square error (MSE), and the result was satisfactory. Finally, the applications of the NETLE to two real data sets are provided to illustrate the importance of the NETLG families in practice; the data sets consist of daily new deaths due to COVID-19 in California and New Jersey, USA. The new model outperformed many other existing Topp–Leone’s and exponential related distributions based on the real data illustrations.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44095395","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: 4

Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease 机器学习模型在阿尔茨海默病诊断中的表现

Annals of Data Science Pub Date : 2022-10-17 DOI: 10.1007/s40745-022-00452-2

Siddhartha Kumar Arjaria, Abhishek Singh Rathore, Dhananjay Bisen, Sanjib Bhattacharyya

{"title":"Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease","authors":"Siddhartha Kumar Arjaria, Abhishek Singh Rathore, Dhananjay Bisen, Sanjib Bhattacharyya","doi":"10.1007/s40745-022-00452-2","DOIUrl":"10.1007/s40745-022-00452-2","url":null,"abstract":"<div><p>In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer’s (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer’s disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance is evaluated of each classifier in terms of ROC-AUC, accuracy, F1 score, recall, and precision as well as included comparative analysis between algorithms. Our study suggests that approximately 90% classification accuracy is observed under top-rated four features CDR, SES, nWBV, and EDUC.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42300829","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