Annals of Data Science最新文献_第10页

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":"10 1","pages":"225 - 250"},"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":"11 1","pages":"307 - 335"},"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

The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance 估算存活率的林德利-冈珀兹模型:性质及其在保险中的应用

Annals of Data Science Pub Date : 2022-10-15 DOI: 10.1007/s40745-022-00450-4

Heba Soltan Mohamed, M. Masoom Ali, Haitham M. Yousof

引用次数: 15

Efficient Equalization and Carrier Frequency Offset Compensation for Underwater Wireless Communication Systems 水下无线通信系统的有效均衡和载波频偏补偿

Annals of Data Science Pub Date : 2022-10-14 DOI: 10.1007/s40745-022-00449-x

Khaled Ramadan, Mohamed S. Elbakry

{"title":"Efficient Equalization and Carrier Frequency Offset Compensation for Underwater Wireless Communication Systems","authors":"Khaled Ramadan, Mohamed S. Elbakry","doi":"10.1007/s40745-022-00449-x","DOIUrl":"10.1007/s40745-022-00449-x","url":null,"abstract":"<div><p>Underwater Acoustic (UWA) wireless communication systems are plagued by a slew of flaws that restrict their performance. This includes factors such as high attenuation in seawater, sediment type, acidity concentration, water temperature, and sound speed propagation. One of the available solutions is Orthogonal Frequency Division Multiplexing (OFDM). Unfortunately, the OFDM systems suffer from the Carrier Frequency Offset (CFO) phenomenon that causes Inter-Carrier-Interference. One of the means to overcome this problem is joint equalization and CFO compensation. In this paper, the conventional OFDM system is adapted for Multiple-Input-Multiple Output (MIMO)-OFDM communication utilizing Discrete Wavelet Transform (DWT) rather than Discrete Fourier Transform (DFT). The DWT-based OFDM system has certain benefits over the comparable DFT. The trade-off, on the other hand, is the necessity for an extra DFT/IDFT to complete the Frequency-Domain equalization procedure, which increases the total computational complexity. In addition, we present a Joint Low Regularized Linear Zero Forcing equalizer for MIMO-OFDM based on DWT that employs the banded-matrix approximation approach. The suggested approach avoids signal-to-noise ratio estimation. Simulation results show that the proposed scheme outperforms different schemes at the same UWA channel conditions spatially in the case of estimation errors.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"9 6","pages":"1201 - 1221"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48551423","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: 1

Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis 使用烧瓶环境对推特订阅源的情绪分析：数据分析的卓越应用

Annals of Data Science Pub Date : 2022-10-12 DOI: 10.1007/s40745-022-00445-1

Astha Modi, Khelan Shah, Shrey Shah, Samir Patel, Manan Shah

{"title":"Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis","authors":"Astha Modi, Khelan Shah, Shrey Shah, Samir Patel, Manan Shah","doi":"10.1007/s40745-022-00445-1","DOIUrl":"10.1007/s40745-022-00445-1","url":null,"abstract":"<div><p>In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 1","pages":"159 - 180"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46146507","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

Correction to: Guest Editor’s Introduction: COVID-19 and Data Science 更正：客座编辑简介：新冠肺炎与数据科学

Annals of Data Science Pub Date : 2022-09-28 DOI: 10.1007/s40745-022-00447-z

Aihua Li

引用次数: 0

Assessment of Two Process Capabilities by Using Generalized Confidence Intervals and its Applications 用广义置信区间评价两种过程能力及其应用

Annals of Data Science Pub Date : 2022-09-23 DOI: 10.1007/s40745-022-00448-y

Mahendra Saha

引用次数: 0

Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects 网络安全中智能数据分析和自动化的机器学习:当前和未来展望

Annals of Data Science Pub Date : 2022-09-19 DOI: 10.1007/s40745-022-00444-2

Iqbal H. Sarker

{"title":"Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects","authors":"Iqbal H. Sarker","doi":"10.1007/s40745-022-00444-2","DOIUrl":"10.1007/s40745-022-00444-2","url":null,"abstract":"<div><p>Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today’s cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially <i>machine learning</i> technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of <i>machine learning</i> algorithms, emphasizing how they can be employed for <i>intelligent data analysis</i> and <i>automation</i> in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential <i>real-world use cases</i> where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The <i>future prospects</i> of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"10 6","pages":"1473 - 1498"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-022-00444-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44667216","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: 14

Impact of COVID-19 on Stock Indices Volatility: Long-Memory Persistence, Structural Breaks, or Both? COVID-19对股指波动的影响:长期记忆持续性，结构性断裂，还是两者兼而有之?

Annals of Data Science Pub Date : 2022-09-12 DOI: 10.1007/s40745-022-00446-0

Abdinardo Moreira Barreto de Oliveira, Anandadeep Mandal, Gabriel J. Power

{"title":"Impact of COVID-19 on Stock Indices Volatility: Long-Memory Persistence, Structural Breaks, or Both?","authors":"Abdinardo Moreira Barreto de Oliveira, Anandadeep Mandal, Gabriel J. Power","doi":"10.1007/s40745-022-00446-0","DOIUrl":"10.1007/s40745-022-00446-0","url":null,"abstract":"<div><p>The onset of the COVID-19 pandemic has increased volatility in financial markets, motivating researchers to investigate its impact. Some use the GARCH family of models to focus on long-memory persistence, while others use Markov chain models to better identify structural breaks and regimes. However, no study has addressed the occurrence of these two phenomena in a unified framework. Since both are important features of the data, to ignore one or the other could lead to poorly specified models. The outcome would be incorrect risk measurement, with implications for risk management, Value at risk, portfolio decisions, forecasting, and option pricing. This paper aims to fill this gap in the literature. We assemble an international dataset for 16 stock market indices in three continents over the period from August 1, 2019 to February 18, 2022, totalling 669 business days. Using R, we estimate 80 GARCH family models, 16 pure Markov-Switching models, and 900 combined GARCH/ Markov-Switching models using daily stock market log-returns. We allow for two volatility regimes (low and high). We also measure and incorporate News Impact Curves, which show how past shocks affect contemporaneous volatility. Our main finding, across estimated models, is that COVID-19 affected both long-memory persistence and volatility regimes in most markets. To describe the specific impact in each market, we report News Impact Curves. Lastly, the first wave of COVID-19 had a much greater impact on volatility than did subsequent waves linked to the emergence of new variants.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 2","pages":"619 - 646"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43607429","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

Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain 应用信息检索方法检索法律领域相关文章

Annals of Data Science Pub Date : 2022-09-06 DOI: 10.1007/s40745-022-00442-4

Ambedkar Kanapala, Sukomal Pal, Suresh Dara, Srikanth Jannu

引用次数: 0