Annals of Data Science最新文献

{"title":"Spiral Fractal Compression in Transform Domains for Underwater Communication","authors":"A. Selim,&nbsp;Taha E. Taha,&nbsp;Adel S. El-Fishawy,&nbsp;O. Zahran,&nbsp;M. M. Hadhoud,&nbsp;M. I. Dessouky,&nbsp;Fathi E. Abd El-Samie,&nbsp;Noha El-Hag","doi":"10.1007/s40745-023-00466-4","DOIUrl":"10.1007/s40745-023-00466-4","url":null,"abstract":"<div><p>This paper presents a simplified fractal image compression algorithm, which is implemented on a block-by-block basis. This algorithm achieves a Compression Ratio (CR) of up to 10 with a Peak Signal-to-Noise Ratio (PSNR) as high as 35 dB. Hence, it is very appropriate for the new applications of underwater communication. The idea of the proposed algorithm is based on the segmentation of the image, first, into blocks to setup reference blocks. The image is then decomposed again into block ranges, and a search process is carried out to find the reference blocks with the best match. The transmitted or stored values, after compression, are the reference block values and the indices of the reference block that achieves the best match. If there is no match, the average value of the block range is transmitted or stored instead. The effect of the spiral architecture instead of square block decomposition is studied. A comparison between different algorithms, including the conventional square search, the proposed simplified fractal compression algorithm and the standard JPEG compression algorithm, is introduced. We applied the types of fractal compression on a video sequence. In addition, the effect of using the fractal image compression algorithms in transform domain is investigated. The image is transferred firstly to a transform domain. The Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are used. After transformation takes place, the fractal algorithm is applied. A comparison between three fractal algorithms, namely conventional square, spiral, and simplified fractal compression, is presented. The comparison is repeated in the two cases of transformation. The DWT is used also in this paper to increase the CR of the block domain pool. We decompose the block domain by wavelet decomposition to two levels. This process gives a CR for block domain transmission as high as 16. The advantage of the proposed implementation is the simplicity of computation. We found that with the spiral architecture in fractal compression, the video sequence visual quality is better than those produced with conventional square fractal compression and the proposed simplified algorithm at the same CR, but with longer time consumed. We found also that all types of fractal compression give better quality than that of the standard JPEG. In addition, the decoded images, in case of using the wavelet transform, are the best. On the other hand, in case of using DCT, the decoded images have poor quality.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"1003 - 1030"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135307314","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}

{"title":"IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction","authors":"Linda Joel,&nbsp;S. Parthasarathy,&nbsp;P. Venkatesan,&nbsp;S. Nandhini","doi":"10.1007/s40745-023-00489-x","DOIUrl":"10.1007/s40745-023-00489-x","url":null,"abstract":"<div><p>Stock price movement forecasting is the process of predicting the future price of a financial and company stock from chaotic data. In recent years, many financial institutions and academics have shown interest in stock market forecasting. The accurate and successful predictions of the future price of stock yield a substantial profit. However, the current approaches are a major challenge due to the dynamic, chaotic, high-noise, non-linear, highly complex, and nonparametric characteristics of stock data. Furthermore, it is not sufficient to consider only the target firms' information because the stock prices of the target firms may be influenced by their related firms. Significant profits can be made by correct forecasting of stock prices, while poor forecasts can cause huge problems. Thus, we propose a novel Island Parallel-Harris Hawks Optimizer (IP-HHO)-optimized Convolutional Long Short Term Memory (ConvLSTM) with an autocorrelation model to predict stock price movement. Then, using the IP-HHO algorithm, the hyperparameters of ConvLSTM are optimized to minimize the Mean Absolute Percentage Error (MAPE). Four different types of financial time series datasets are utilized to validate the performance of the evaluation measures such as root mean square error, MAPE, Index of Agreement, accuracy, and F1 score. The results show that the IP-HHO-optimized ConvLSTM model outperforms others by improving the prediction rate accuracy and effectively minimizing the MAPE rate by 19.62%.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 6","pages":"1959 - 1974"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00489-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42572163","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}

{"title":"Bayesian Analysis of Generalized Inverted Exponential Distribution Based on Generalized Progressive Hybrid Censoring Competing Risks Data","authors":"Amal S. Hassan,&nbsp;Rana M. Mousa,&nbsp;Mahmoud H. Abu-Moussa","doi":"10.1007/s40745-023-00488-y","DOIUrl":"10.1007/s40745-023-00488-y","url":null,"abstract":"<div><p>In this study, a competing risk model was developed under a generalized progressive hybrid censoring scheme using a generalized inverted exponential distribution. The latent causes of failure were presumed to be independent. Estimating the unknown parameters is performed using maximum likelihood (ML) and Bayesian methods. Using the Markov chain Monte Carlo technique, Bayesian estimators were obtained under gamma priors with various loss functions. ML estimate was used to create confidence intervals (CIs). In addition, we present two bootstrap CIs for the unknown parameters. Further, credible CIs and the highest posterior density intervals were constructed based on the conditional posterior distribution. Monte Carlo simulation is used to examine the performance of different estimates. Applications to real data were used to check the estimates and compare the proposed model with alternative distributions.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1225 - 1264"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48748310","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}

{"title":"Quantitative Analysis of Group for Epidemiology Architectural Approach","authors":"Dephney Mathebula","doi":"10.1007/s40745-023-00493-1","DOIUrl":"10.1007/s40745-023-00493-1","url":null,"abstract":"<div><p>Epidemiology, the aspect of research focusing on disease modelling is date intensive. Research epidemiologists in different research groups played a key role in developing different data driven model for COVID-19 and monkeypox. The requirement of accessing highly accurate data useful for disease modelling is beneficial but not without having challenges. Currently, the task of data acquisition is executed by select individuals in different research groups. This approach experiences the drawbacks associated with getting permission to access the desired data and inflexibility to change data acquisition goals due to dynamic epidemiological research objectives. The presented research addresses these challenges and proposes the design and use of dynamic intelligent crawlers for acquiring epidemiological data related to a given goal. In addition, the research aims to quantify how the use of computing entities enhances the process of data acquisition in epidemiological related studies. This is done by formulating and investigating the metrics of the data acquisition efficiency and the data analytics efficiency. The use of human assisted crawlers in the global information networks is found to enhance data acquisition efficiency (DAqE) and data analytics efficiency (DAnE). The use of human assisted crawlers in a hybrid configuration outperforms the case where manual research group member efforts are expended enhancing the DAqE and DAnE by up to 35% and 99% on average, respectively.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"979 - 1001"},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00493-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44922195","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}

{"title":"Estimation of ( P[Y<X] ) for Dependence of Stress–Strength Models with Weibull Marginals","authors":"Dipak D. Patil,&nbsp;U. V. Naik-Nimbalkar,&nbsp;M. M. Kale","doi":"10.1007/s40745-023-00487-z","DOIUrl":"10.1007/s40745-023-00487-z","url":null,"abstract":"<div><p>The stress–strength model is a basic tool used in evaluating the reliability <span>( R = P(Y &lt; X))</span>. We consider an expression for <i>R</i> where the random variables X and Y denote strength and stress, respectively. The system fails only if the stress exceeds the strength. We aim to study the effect of the dependency between X and Y on <i>R</i>. We assume that X and Y follow Weibull distributions and their dependency is modeled by a copula with the dependency parameter <span>( theta )</span>. We compute <i>R</i> for Farlie–Gumbel–Morgenstern (FGM), Ali–Mikhail–Haq (AMH), Gumbel’s bivariate exponential copulas, and for Gumbel–Hougaard (GH) copula using a Monte-Carlo integration technique. We plot the graph of <i>R</i> versus <span>(theta )</span> to study the effect of dependency on <i>R</i>. We estimate <i>R</i> by plugging in the estimates of the marginal parameters and of <span>( theta )</span> in its expression. The estimates of the marginal parameters are based on the marginal likelihood. The estimates of <span>(theta )</span> are obtained from two different methods; one is based on the conditional likelihood and the other is based on the method of moments using Blomqvist’s beta. Asymptotic distribution of both the estimators of <i>R</i> is obtained. Finally, analysis of real data set is also performed for illustrative purposes.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1303 - 1340"},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48578734","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}

{"title":"Modi-Weibull Distribution: Inferential and Simulation Study","authors":"Harshita Kumawat,&nbsp;Kanak Modi,&nbsp;Pankaj Nagar","doi":"10.1007/s40745-023-00491-3","DOIUrl":"10.1007/s40745-023-00491-3","url":null,"abstract":"<div><p>This paper presents a study on a new family of distributions using the Weibull distribution and termed as Modi-Weibull distribution. This Modi-Weibull distribution is based on four parameters. To understand the behaviour of the distribution, some statistical characteristics have been derived, such as shapes of density and distribution function, hazard function, survival function, median, moments, order statistics etc. These parameters are estimated using classical maximum likelihood estimation method. Asymptotic confidence intervals for parameters of Modi-Weibull distribution are also obtained. A simulation study is carried out to investigate the bias, MSE of proposed maximum likelihood estimators along with coverage probability and average width of confidence intervals of parameters. Two applications to real data sets are discussed to illustrate the fitting of the proposed distribution and compared with some well-known distributions.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 6","pages":"1975 - 1999"},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00491-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48772839","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}

{"title":"Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values","authors":"Shubham Gupta,&nbsp;Gajendra K. Vishwakarma,&nbsp;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}

{"title":"Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique","authors":"Ha Che-Ngoc,&nbsp;Thao Nguyen-Trang,&nbsp;Hieu Huynh-Van,&nbsp;Tai Vo-Van","doi":"10.1007/s40745-023-00490-4","DOIUrl":"10.1007/s40745-023-00490-4","url":null,"abstract":"<div><p>Classification is a fundamental problem in statistics and data science, and it has garnered significant interest from researchers. This research proposes a new classification algorithm that builds upon two key improvements of the Bayesian method. First, we introduce a method to determine the prior probabilities using fuzzy clustering techniques. The prior probability is determined based on the fuzzy level of the classified element within the groups. Second, we develop the probability density function using Vine Copula. By combining these improvements, we obtain an automatic classification algorithm with several advantages. The proposed algorithm is presented with specific steps and illustrated using numerical examples. Furthermore, it is applied to classify image data, demonstrating its significant potential in various real-world applications. The numerical examples and applications highlight that the proposed algorithm outperforms existing methods, including traditional statistics and machine learning approaches.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 2","pages":"709 - 732"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49453964","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}

{"title":"A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data","authors":"Majid Hashempour,&nbsp;Morad Alizadeh,&nbsp;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}

{"title":"Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors","authors":"G. Shahtahmassebi,&nbsp;José María Sarabia","doi":"10.1007/s40745-023-00484-2","DOIUrl":"10.1007/s40745-023-00484-2","url":null,"abstract":"<div><p>In data analysis, change point problems correspond to abrupt changes in stochastic mechanisms generating data. The detection of change points is a relevant problem in the analysis and prediction of time series. In this paper, we consider a class of conjugate prior distributions obtained from conditional specification methodology for solving this problem. We illustrate the application of such distributions in Bayesian change point detection analysis with Poisson processes. We obtain the posterior distribution of model parameters using general bivariate distribution with gamma conditionals. Simulation from the posterior are readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 6","pages":"1899 - 1918"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00484-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47402928","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}