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## Improved Classification of the High-Resolution Image Data Using Hoeffding Algoritm

### Annals of Data Science (2016-03-01) 3: 63-70 , March 01, 2016

With the development of the spatial data mining technologies the researcher are grouping towards using the same in various domains. Once such domain is the high resolution images of the urban land. The process includes the collection of segmented image for the various scenes and the classification technique is used to check the probability that segment belongs to the same urban cover along with the class assignment. The classifier previously make use of the random forest tree classification algorithm to develop the network model for semantic web and attribute selection process. However the attribute selection process accuracy can be further improved using the Hoeffding decision tree algorithm where the node split is controlled through the error rate. It’s an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. The leaf predicting strategy is optimized for the Hoeffding tree through Naïve Bayes adaptive process for predicting the land cover with high accuracy rate. The result were simluated using weka as an open source software.

## Computational Stochastic Modelling to Handle the Crisis Occurred During Community Epidemic

### Annals of Data Science (2016-06-01) 3: 119-133 , June 01, 2016

Crisis can strike from anywhere at anyone and at any place. The unpredictability and inevitability of a crisis make it imminent that immediate and critical attention is paid to it so that it is managed and contained at the right time. Any crisis is a red alert situation so there is a widely felt need of it being handled with topmost priority and efficiency. A crisis, it may be a natural disaster, an organizational crisis, a political crisis or a product recall, brings a sudden and deep collapse in national output and a sharp increase in the income poverty. The key to handling a crisis successfully is the time required to bring it in controllable proportion. It is very important to predict the time required to handle the crucial situation during a crisis. Stochastic calculation of time is very important as the intensity of the situation goes higher. In the present paper, the whole event occurrence is the sum of specific information. The carrier of information are human beings and machines, which carry information through established communication networks. The degree of authenticity also depends upon the means of communication through human or machine interface. The proposed model is a stochastic model which contains information to be communicated one to one or broadcast one to many. This gives us estimated time to reach from one stage to another with the percentage of authenticity. In this model, it can be judged if the situation of a crisis is controllable or not, so that important inputs can be delivered to control the worst situation in the process.

## Oriental Thinking and Fuzzy Logic, Celebration of the 50th Anniversary of Fuzzy Sets

### Annals of Data Science (2015-09-01) 2: 243-244 , September 01, 2015

## A Business Model Design for the Strategic and Operational Knowledge Management of a Port Community

### Annals of Data Science (2014-06-01) 1: 191-208 , June 01, 2014

The community modality how middle and small size ports work, consists of a group of associative enterprises and logistic chains. In order to optimize the efficiency of their activities at both strategic and operational levels, they require a continuous assessment of their knowledge management. Accordingly, this work proposes a business model design for managing a sea port community. Firstly, the mission and the strategic objectives for such a community are defined. Then, the main strategic lines are classified correspondingly to the Balance Scorecard approach, using indicators as: efficiency, operational excellence, profitability, integration, social responsibility, and added value to the environment. Strategic and operational efficiency indicators are designed not only for improving the company and port management but also to gain competitive advantages.

## A New Extension of Weibull Distribution with Application to Lifetime Data

### Annals of Data Science (2017-03-01) 4: 31-61 , March 01, 2017

The Weibull distribution has been generalized by many authors in recent years. Here, we introduce a new generalization, called alpha-power transformed Weibull distribution that provides better fits than the Weibull distribution and some of its known generalizations. The distribution contains alpha-power transformed exponential and alpha-power transformed Rayleigh distributions as special cases. Various properties of the proposed distribution, including explicit expressions for the quantiles, mode, moments, conditional moments, mean residual lifetime, stochastic ordering, Bonferroni and Lorenz curve, stress–strength reliability and order statistics are derived. The distribution is capable of modeling monotonically increasing, decreasing, constant, bathtub, upside-down bathtub and increasing–decreasing–increasing hazard rates. The maximum likelihood estimators of unknown parameters cannot be obtained in explicit forms, and they have to be obtained by solving non-linear equations only. Two data sets have been analyzed to show how the proposed models work in practice. Further, a bivariate extension based on Marshall–Olkin and copula concept of the proposed model are developed but the properties of the distribution not considered in detail in this paper that can be addressed in future research.

## Review on: Twin Support Vector Machines

### Annals of Data Science (2014-06-01) 1: 253-277 , June 01, 2014

Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.

## Phylogenetic Trees Construction with Compressed DNA Sequences Using GENBIT COMPRESS Tool

### Annals of Data Science (2017-03-01) 4: 105-121 , March 01, 2017

The data contained in the DNA atom for even basic unicellular life forms is huge and requires proficient capacity. Proficient capacity implies, expulsion of all excess from the information being put away. The Proposed Compression calculation “GENBIT Compress” is solely intended to dispense with all repetition from the DNA groupings of extensive genomes. We characterize a pressure separation, taking into account an ordinary compressor to show it is a permissible separation. Just as of late have researchers started to value the way that pressure proportions imply a lot of essential measurable data. In applying the methodology, we have utilized another DNA succession compressor “GENBIT Compress”. The NCD is universal in that it is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, is provably optimal in the sense that it minimises every computable normalized metric that satisfies a certain density requirement. However, the optimality comes at the price of using the non-computable notion of Kolmogorov complexity. We propose precise notions of similarity metric, normal compressor, and show that the NCD based on a normal compressor is a similarity metric that approximates optimality The normalized compression distance, an efficiently computable, and thus practically applicable form of the normalized information distance is used to calculate Distance Matrix The normalized compression distance, an effectively processable, and along these lines for all intents and purposes relevant type of the standardized data separation is utilized to figure Distance Matrix. In this paper this new separation framework is proposed to recreate Phylogenetic tree. Phylogeny are the fundamental device for speaking to the relationship among organic elements. Phylogenetic remaking techniques endeavor to locate the developmental history of given arrangement of species. This history is generally depicted by an edge weighted tree, where edges relate to various branches of advancement, and the heaviness of an edge compares to the measure of developmental change on that specific branch. We developed a phylogenetic tree with BChE DNA arrangements of warm blooded creatures giving new proposed separation grid by GENBIT compressor to NJ (Neighbor-Joining calculation) tree. The results in the present research confirm the existence of low compression ratios for natural DNA sequences with high repetitive DNA bases(A, C, G, T), the more repetitive bases, the less is their compression ratios. The ultimate goal is, of course, to learn the “genome organization” principles, and explain this organization using our knowledge about evolution.

## Exponentiated Generalized Kumaraswamy Distribution with Applications

### Annals of Data Science (2017-08-17): 1-20 , August 17, 2017

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.

## SMAA-AD Model in Multicriteria Decision-Making Problems with Stochastic Values and Uncertain Weights

### Annals of Data Science (2014-03-01) 1: 95-108 , March 01, 2014

The current paper considers the stochastic multicriteria decision-making (MCDM) problems with multiple alternatives, stochastic criterion values and uncertain criterion weights. We propose SMAA-AD model and illustrate how SMAA-AD model is used in such stochastic MCDM problems. In SMAA-AD model, absolute dominant method is used to turn stochastic criterion values into deterministic absolute dominant values, and stochastic multicriteria acceptability analysis (SMAA) is used to rank the alternatives without foreknowing the decision maker’ preference on criterion weights. SMAA-AD model provides three indices, i.e., rank acceptability index, holistic acceptability index and central weight vector, to support the decision in the stochastic MCDM problems. SMAA-AD model overcomes some shortcomings of traditional MCDM methods. For example, it needs not to predefine any parameters and functions. We use a case of technology competition for cleaning polluted soil in Helsinki to illustrate our method.

## Estimation of the Lomax Distribution in the Presence of Outliers

### Annals of Data Science (2016-12-01) 3: 385-399 , December 01, 2016

In this paper, we find the moment, maximum likelihood, least squares and weighted least squares estimators of the parameters of Lomax distribution in the presence of outliers. Also, the mixture estimator of these four methods is derived. Further, we discuss about the efficiency of the estimators. Analysis of a simulated data set and an actual example from an insurance company has been presented for illustrative purposes.