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 Annals of Data Science   Number of Followers: 11           Hybrid journal (It can contain Open Access articles)    ISSN (Print) 2198-5804 - ISSN (Online) 2198-5812    Published by Springer-Verlag  [2350 journals]
• Modelling Under-Five Mortality among Hospitalized Pneumonia Patients in
Hawassa City, Ethiopia: A Cross-Classified Multilevel Analysis
• Authors: Tariku Tessema
Pages: 111 - 132
Abstract: Community acquired pneumonia refers to pneumonia acquired outside of hospitals or extended health facilities and it is a leading infectious disease. This study aims to model mortality of hospitalized under-5 year child pneumonia patients and investigate potential risk factors associated with child mortality due to pneumonia. The study was a retrospective study on 305 sampled under-five hospitalized patients of community acquired pneumonia. A cross-classified multilevel logistic regression was employed with resident and hospital classified at the second level. Bayesian estimation method was applied in which the posterior distribution was simulated via Markov Chain Monte Carlo. The variability attributable to hospital was found to be larger than variability attributable to residence. The odds of dying from the community acquired pneumonia was higher among patients who were; diagnosed in spring season, complicated with malaria, AGE and AFI, in a neonatal age group, diagnosed late (more than a week). The risk of mortality was also found high for lower nurse: patient and physician: patients’ ratios.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0121-4
Issue No: Vol. 5, No. 2 (2018)

• Big Data and Causality
• Authors: Hossein Hassani; Xu Huang; Mansi Ghodsi
Pages: 133 - 156
Abstract: Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0122-3
Issue No: Vol. 5, No. 2 (2018)

• Face Recognition and Human Tracking Using GMM, HOG and SVM in Surveillance
Videos
• Authors: Harihara Santosh Dadi; Gopala Krishna Mohan Pillutla; Madhavi Latha Makkena
Pages: 157 - 179
Abstract: Tracking of human and recognition in public places using surveillance cameras is the topic of research in the area computer vision. Recognition of human and then tracking completes the video surveillance system. A novel algorithm for face recognition and human tracking is presented in this article. Human is tracked using Gaussian mixture model. To track the human in specific, template of GMM is divided into four regions which are placed one above the other and tracked simultaneously. For recognizing the human, the histogram of oriented gradients features of the face region are given to the support vector machine classifier. Three experiments are conducted in taking the training faces. Every $$10{\mathrm{th}}$$ frame, every $$5{\mathrm{th}}$$ frame and every $$3{\mathrm{rd}}$$ frame of the first 100 frames are considered. The other frames in the video are considered for testing using SVM classifier. Three datasets namely AITAM1 (simple), AITAM2 (moderate) and AITAM3 (complex) are used in this work. The experimental results show that as the complexity of dataset increases the performance metrics are getting decreased. The more the number of training faces in preparing a classifier, the better is the face recognition rate. This is experimented for all types of datasets. The Performance results show that the combination of the tracking algorithm and the face recognition algorithm not only tracks the person but also recognizes the person. This unique property of both tracking and recognition makes it best suit for video surveillance applications.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0123-2
Issue No: Vol. 5, No. 2 (2018)

• Parallel String Matching with Linear Array, Butterfly and Divide and
Conquer Models
• Authors: S. Viswanadha Raju; K. K. V. V. S. Reddy; Chinta Someswara Rao
Pages: 181 - 207
Abstract: String Matching is a technique of searching a pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text mining, pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0124-1
Issue No: Vol. 5, No. 2 (2018)

• On a Weibull-Inverse Exponential Distribution
• Authors: Chandrakant; M. K. Rastogi; Y. M. Tripathi
Pages: 209 - 234
Abstract: In this paper we study various reliability properties of a Weibull inverse exponential distribution. The maximum likelihood and Bayes estimates of unknown parameters and reliability characteristics are obtained. Bayes estimates are obtained with respect to the squared error loss function under proper and improper prior situations. We use the Lindley method and the Metropolis–Hastings algorithm to compute the Bayes estimates. Interval estimation is also considered. Asymptotic and highest posterior density intervals of unknown parameters are constructed in this respect. We perform a numerical study to compare the performance of all methods and obtain comments based on this study. We also analyze two real data sets for illustration purposes. Finally a conclusion is presented.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0125-0
Issue No: Vol. 5, No. 2 (2018)

• Evaluation and Comparison of Estimators in the Gompertz Distribution
• Authors: Sanku Dey; Tanmay Kayal; Yogesh Mani Tripathi
Pages: 235 - 258
Abstract: This article addresses the different methods of estimation of the probability density function and the cumulative distribution function for the Gompertz distribution. Following estimation methods are considered: maximum likelihood estimators, uniformly minimum variance unbiased estimators, least squares estimators, weighted least square estimators, percentile estimators, maximum product of spacings estimators, Cramér–von-Mises estimators, Anderson–Darling estimators. Monte Carlo simulations are performed to compare the behavior of the proposed methods of estimation for different sample sizes. Finally, one real data set and one simulated data set are analyzed for illustrative purposes.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0126-z
Issue No: Vol. 5, No. 2 (2018)

• Equalization and Carrier Frequency Offset Compensation for Underwater
Acoustic OFDM Systems
• Authors: K. Ramadan; M. I. Dessouky; S. Elagooz; M. Elkordy; F. E. Abd El-Samie
Pages: 259 - 272
Abstract: Due to noise enhancement, conventional Zero Forcing (ZF) equalizers are not suitable for wireless Underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication systems. Furthermore, these systems suffer from increasing complexity due to the large number of subcarriers, especially in Multiple-Input Multiple-Output (MIMO) systems. On the other hand, the Minimum Mean Square Error equalizer suffers from high complexity. This type of equalizers needs an estimation of the operating Signal-to-Noise Ratio to work properly. In this paper, we propose a Joint Low-Complexity Regularized ZF equalizer for MIMO UWA-OFDM systems to cope with these problems. The main objective of the proposed equalizer is to enhance the system performance with a lower complexity by performing equalization in two steps. The co-channel interference can be mitigated in the first step. A regularization term is added in the second step to avoid the noise enhancement. Simulation results show that the proposed equalization scheme has the ability to enhance the UWA system performance with low complexity.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0127-y
Issue No: Vol. 5, No. 2 (2018)

• Exponentiated Generalized Kumaraswamy Distribution with Applications
• Authors: M. Elgarhy; Muhammad Ahsan ul Haq; Qurat ul Ain
Pages: 273 - 292
Abstract: In this article, we introduced and studied exponentiated generalized Kumaraswamy distribution. We derived mathematical properties including quantile function, moment generating function, ordinary moments, probability weighted moments, incomplete moments, and Rényi entropy. The expressions of order statistics are also derived. Here we discuss the parameter estimation by using the method of maximum likelihood. We showed resilience of the introduced distribution over existing some well-known distributions by using real dataset applications.
PubDate: 2018-06-01
DOI: 10.1007/s40745-017-0128-x
Issue No: Vol. 5, No. 2 (2018)

• Cardiopulmonary Function Monitoring Based on MEWMA Control Chart
• Authors: Hongxia Zhang; Liu Liu; Jin Yue; Xin Lai
Pages: 293 - 299
Abstract: According to the characteristics of parameters of cardiopulmonary function diversity and change slowly in pathology, we apply the multivariate exponentially weighted moving average (MEWMA) control chart to monitor the state of lungs. This paper aimed at five indicators of cardiopulmonary function, using principal component test to diagnose whether it is from the multivariate normal distribution, Clearing the relationship model of control line and weight coefficient of MEWMA control graph, and drawing the control diagram for monitoring. The process stay in control state before 103 observations, however, beyond the control limit from the 104 observation statistics and give an alarm. This means that there is a problem with the cardiopulmonary starting on the 103rd sample. Control chart has a good warning function because it can raise the alarm before cardiopulmonary function has a big problem. Using MEWMA control chart for monitoring can reduce the cost of medical examination and frequency, it can improve the hospital resource utilization rate and confirm the case. Thus we can avoid missing the best treatment time.
PubDate: 2018-06-01
DOI: 10.1007/s40745-018-0137-4
Issue No: Vol. 5, No. 2 (2018)

• Analysis of Prevalence of Malaria and Anemia Using Bivariate Probit Model
• Authors: Senayit Seyoum
Pages: 301 - 312
Abstract: Malaria and anemia are public health problems that have an impact on social and economic development. Malaria causes 70,000 deaths each year and accounts for 17% of outpatient visits to health institutions. It is one of the causes of anemia. Therefore, knowing the relation between malaria and anemia could have a great contribution to the development of prevention strategies. This study is intended to jointly model the prevalence of malaria and anemia by employing a bivariate probit model and show their relationship. The data was obtained from 384 patients visiting Alaba health center. The results of the bivariate probit model shows that sex, age, education level and marital status are significantly associated with malaria and sex and education level are significantly associated with anemia. The results of the seemingly unrelated bivariate probit model shows that sex, education level, age and marital status are significantly determining the prevalence of malaria, and malaria, sex and education level are significantly determining the prevalence of anemia.
PubDate: 2018-06-01
DOI: 10.1007/s40745-018-0138-3
Issue No: Vol. 5, No. 2 (2018)

PubDate: 2018-06-06
DOI: 10.1007/s40745-018-0164-1

• The Exponentiated Generalized Marshall–Olkin Family of Distribution: Its
Properties and Applications
• Authors: Laba Handique; Subrata Chakraborty; Thiago A. N. de Andrade
Abstract: A new generator of continuous distributions called Exponentiated Generalized Marshall–Olkin-G family with three additional parameters is proposed. This family of distribution contains several known distributions as sub models. The probability density function and cumulative distribution function are expressed as infinite mixture of the Marshall–Olkin distribution. Important properties like quantile function, order statistics, moment generating function, probability weighted moments, entropy and shapes are investigated. The maximum likelihood method to estimate model parameters is presented. A simulation result to assess the performance of the maximum likelihood estimation is briefly discussed. A distribution from this family is compared with two sub models and some recently introduced lifetime models by considering three real life data fitting applications.
PubDate: 2018-06-05
DOI: 10.1007/s40745-018-0166-z

• The Hyperbolic Sine Rayleigh Distribution with Application to Bladder
Cancer Susceptibility
• Abstract: In this paper, a new extension of the Rayleigh distribution called the Hyperbolic Sine-Rayleigh distribution is introduced and studied. The proposed model is very flexible and is capable of modeling with increasing and unimodal hazard rates. A comprehensive treatment of its mathematical properties including explicit expressions for the moments, quantiles, moment generating function, Entropy and order statistics are provided. Maximum likelihood estimates of the model parameters are obtained. Furthermore, a simulation study is conducted to access the behavior of the maximum likelihood estimators. Finally, the superiority of the subject model is illustrated empirically over the other distributions by analyzing a real-life application.
PubDate: 2018-05-28
DOI: 10.1007/s40745-018-0165-0

• Alpha-Power Transformed Lindley Distribution: Properties and Associated
Inference with Application to Earthquake Data
• Authors: Sanku Dey; Indranil Ghosh; Devendra Kumar
Abstract: The Lindley distribution has been generalized by many authors in recent years. A new two-parameter distribution with decreasing failure rate is introduced, called Alpha Power Transformed Lindley (APTL, in short, henceforth) distribution that provides better fits than the Lindley distribution and some of its known generalizations. The new model includes the Lindley distribution as a special case. Various properties of the proposed distribution, including explicit expressions for the ordinary moments, incomplete and conditional moments, mean residual lifetime, mean deviations, L-moments, moment generating function, cumulant generating function, characteristic function, Bonferroni and Lorenz curves, entropies, stress-strength reliability, stochastic ordering, statistics and distribution of sums, differences, ratios and products are derived. The new distribution can have decreasing increasing, and upside-down bathtub failure rates function depending on its parameters. The model parameters are obtained by the method of maximum likelihood estimation. Also, we obtain the confidence intervals of the model parameters. A simulation study is carried out to examine the bias and mean squared error of the maximum likelihood estimators of the parameters. Finally, two data sets have been analyzed to show how the proposed models work in practice.
PubDate: 2018-05-16
DOI: 10.1007/s40745-018-0163-2

• A New Generalized Class of Distributions: Properties and Estimation Based
on Type-I Censored Samples
Abstract: This article introduces a new generalized family of distributions, which is a generalization of the exponentiated and transmuted family of distributions. A special model of this family, namely, new generalized Weibull distribution is considered in detail. General expressions for the mathematical properties of the proposed family are derived. Maximum likelihood estimates of the unknown parameters are obtained. A simulation study is done to evaluate the performances of the maximum likelihood estimators. Furthermore, estimation based on Type-I censored samples is also discussed. Finally, the superiority of the new proposal is illustrated empirically by analyzing a real-life application.
PubDate: 2018-05-05
DOI: 10.1007/s40745-018-0160-5

• A Note on Modeling the Maxima of Lagos Rainfall
• Authors: I. E. Okorie; A. C. Akpanta; J. Ohakwe; D. C. Chikezie; C. U. Onyemachi; M. C. Ugwu
Abstract: The Lagos annual maximum rainfall is modeled by the generalized extreme value distribution. Hydrologic risk measures like the probability of exceedance or recurrence, return period, and return level is given.
PubDate: 2018-05-03
DOI: 10.1007/s40745-018-0161-4

• Histopathological Breast-Image Classification Using Concatenated R–G–B
Histogram Information
• Authors: Abdullah-Al Nahid; Yinan Kong
Abstract: Breast Cancer is a serious threat to women. The identification of breast cancer relies heavily on histopathological image analysis. Among the different breast-cancer image analysis techniques, classifying the images into Benign and Malignant classes, have been an active area of research. The involvement of machine learning for breast-cancer image classification is also an active area of research. Considering the importance of the breast-cancer image classification, this paper has classified a set of histopathological images into Benign and Malignant classes utilizing Neural Network techniques and Random Forest algorithms. As histopathological images suffer intensity variation, in this paper, we have normalized the intensity information by newly proposed intensity normalization techniques, and classify the images using Neural Network techniques and Tree-based classification tools. Investigation shows that the proposed Normalization technique gives the best performance when we use Neural Network techniques but Tree-based algorithms such as the Random Forest algorithm give better performance when we use images without normalization techniques.
PubDate: 2018-05-02
DOI: 10.1007/s40745-018-0162-3

• Exponentiated Power Lindley Logarithmic: Model, Properties and
Applications
Abstract: A new class of lifetime distributions is proposed. Closed form expressions are provided for the density, cumulative distribution, survival and hazard rate functions. Maximum likelihood estimation is discussed and formulas for the elements of the observed information matrix are provided. Simulation studies are conducted. Finally, two real data applications are given showing the flexibility and potentiality of the new distribution
PubDate: 2018-05-02
DOI: 10.1007/s40745-018-0154-3

• Power Lindley-G Family of Distributions
• Authors: Amal S. Hassan; Said G. Nassr
Abstract: In this paper, we introduce a new family of probability distributions generated from a power Lindley random variable called the power Lindley-generated family. The new family extends several classical distributions as well as generalizes the odd Lindley family which is performed by Silva et al. (Austrian J Stat 46:65–87, 2017). Some of the mathematical properties are obtained involving moments, incomplete moments, quantile function and order statistics. New four distributions are provided as special models from the family. The model parameters of the family are estimated by the maximum likelihood technique. An application to real data set and simulation study are provided to demonstrate the flexibility and interest of one special model of the suggested family.
PubDate: 2018-03-16
DOI: 10.1007/s40745-018-0159-y

• Statistical Inference and Optimum Life Testing Plans Under Type-II Hybrid
Censoring Scheme
• Authors: Tanmay Sen; Yogesh Mani Tripathi; Ritwik Bhattacharya
Abstract: This article considers estimation of unknown parameters and prediction of future observations of a generalized exponential distribution based on Type-II hybrid censored data. Bayes point and HPD interval estimates of the unknown parameters are obtained under the assumption of independent gamma priors. Different classical and Bayesian point predictors and prediction intervals are obtained in two-sample situation against squared error loss function. The optimum censoring schemes are computed under various optimality criteria. Monte Carlo simulations are performed to compare different methods and two data sets are analyzed for illustrative purposes.
PubDate: 2018-03-14
DOI: 10.1007/s40745-018-0158-z

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