Remote Sensing Imagery Segmentation: A Hybrid Approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

The Type I Quasi Lambert Family

A new G family of probability distributions called the type I quasi Lambert family is defined and applied for modeling real lifetime data. Some new bivariate type G families using "Farlie-Gumbel-Morgenstern copula", "modified Farlie-Gumbel-Morgenstern copula", "Clayton copula" and "Renyi's entropy copula" are derived. Three characterizations of the new family are presented. Some of its statistical properties are derived and studied. The maximum likelihood estimation, maximum product spacing estimation, least squares estimation, Anderson-Darling estimation and Cramer-von Mises estimation methods are used for estimating the unknown parameters. Graphical assessments under the five different estimation methods are introduced. Based on these assessments, all estimation methods perform well. Finally, an application to illustrate the importance and flexibility of the new family is proposed.

A New Family of Continuous Distributions: Properties, Copulas and Real Life Data Modeling

A new family of distributions called the Kumaraswamy Rayleigh family is defied and studied. Some of its relevant statistical properties are derived. Many new bivariate type G families using the of Farlie-Gumbel-Morgenstern, modified Farlie-Gumbel-Morgenstern copula, Clayton copula and Renyi’s entropy copula are derived. The method of the maximum likelihood estimation is used. Some special models based on log-logistic, exponential, Weibull, Rayleigh, Pareto type II and Burr type X, Lindley distributions are presented and studied. Three dimensional skewness and kurtosis plots are presented. A graphical assessment is performed. Two real life applications to illustrate the flexibility, potentiality and importance of the new family is proposed.

Pseudo-Lindley Family of Distributions and its Properties

In this study, we proposed a family of distribution called the Pseudo Lindley family of distributions. The limiting behaviors of the density and hazard rate function of the new family are examined. Statistical properties of the proposed family of distributions derived include quantile function, moments, order statistics, and Renyi’s entropy. The maximum likelihood method was employed in obtaining the parameter estimates of the Pseudo Lindley family of distribution. Bivariate extension of the proposed family is discussed. Some special members of the family are obtained. The shape of the density function of special members could be unimodal, bathtub shaped, increasing and decreasing.

SCIENTIFIC BASES FOR STOCK MARKET FIASCO FORECASTING TECHNOLOGY WITH USE OF INFORMATION SPACE ENTROPY

The article contains a theoretical study and description of general algorithm for predicting a stock market fiasco caused by non-financial and other factors. Market fiasco is considered as non-periodical, sudden and random event which can arise due to the many latent reasons. Methods of technical and fundamental analysis are useless to solve this problem, therefore, the use of systems analysis methods is proposed. The author’s idea is the numerical calculation of search queries entropy as a part of global information space. Decrease in the Renyi’s entropy, associated with rapid grow search queries, containing key terms from the subject area, indicates the possible stock market fiasco in the near future. This article presents an algorithm for the dynamic calculation of Renyi’s entropy, allowing predict rare events which are not reflected in statistical data (or frequency of their realizations is too small). The method and algorithm can be realized in trade information systems and decision-making systems in economic sphere.

A New Parametric Life Family of Distributions: Properties, Copula and Modeling Failure and Service Times

A new family of probability distributions is defined and applied for modeling symmetric real-life datasets. Some new bivariate type G families using Farlie–Gumbel–Morgenstern copula, modified Farlie–Gumbel–Morgenstern copula, Clayton copula and Renyi’s entropy copula are derived. Moreover, some of its statistical properties are presented and studied. Next, the maximum likelihood estimation method is used. A graphical assessment based on biases and mean squared errors is introduced. Based on this assessment, the maximum likelihood method performs well and can be used for estimating the model parameters. Finally, two symmetric real-life applications to illustrate the importance and flexibility of the new family are proposed. The symmetricity of the real data is proved nonparametrically using the kernel density estimation method.

A New Log-Logistic Lifetime Model with Mathematical Properties, Copula, Modified Goodness-of-Fit Test for Validation and Real Data Modeling

After defining a new log-logistic model and studying its properties, some new bivariate type versions using “Farlie-Gumbel-Morgenstern Copula”, “modified Farlie-Gumbel-Morgenstern Copula”, “Clayton Copula”, and “Renyi’s entropy Copula” are derived. Then, using the Bagdonavicius-Nikulin goodness-of-fit (BN-GOF) test for validation, we proposed a goodness-of-fit test for a new log-logistic model. The modified test is applied for the “right censored” real dataset of survival times. All elements of the modified test are explicitly derived and given. Three real data applications are presented for measuring the flexibility and the importance of the new model under the uncensored scheme. Two other real datasets are analyzed for censored validation.