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Vipin Kumar,Sonajharia Minz 한국산학기술학회 2014 SmartCR Vol.4 No.3
Relevant feature identification has become an essential task to apply data mining algorithms effectively in real-world scenarios. Therefore, many feature selection methods have been proposed to obtain the relevant feature or feature subsets in the literature to achieve their objectives of classification and clustering. This paper introduces the concepts of feature relevance, general procedures, evaluation criteria, and the characteristics of feature selection. A comprehensive overview, categorization, and comparison of existing feature selection methods are also done, and the guidelines are also provided for user to select a feature selection algorithm without knowing the information of each algorithm. We conclude this work with real world applications, challenges, and future research directions of feature selection.
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Multi-Objective Particle Swarm Optimization: An Introduction
Vipin Kumar,Sonajharia Minz 한국산학기술학회 2014 SmartCR Vol.4 No.5
In the real world, reconciling a choice between multiple conflicting objectives is a common problem. Solutions to a multi-objective problem are those that have the best possible negotiation given the objectives. An evolutionary algorithm called Particle swarm optimization is used to find a solution from the solution space. It is a population-based optimization technique that is effective, efficient, and easy to implement. Changes in the particle swarm optimization technique are required in order to get solutions to a multi-objective optimization problem. Therefore, this paper provides the proper concept of particle swarm optimization and the multi-objective optimization problem in order to build a basic background with which to conduct multi-objective particle swarm optimization. Then, we discuss multi-objective particle swarm optimization concepts. Multi-objective particle swarm optimization techniques and some of the most important future research directions are also included.
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Mohd Shamsh Tabarej,Sonajharia Minz 대한공간정보학회 2022 Spatial Information Research Vol.30 No.4
Hotspot detection and the analysis for the hotspot’s footprint recently gained more attention due to the pandemic caused by the coronavirus. Different countries face the effect of the virus differently. In India, very little research has been done to find the virus transmission. The paper’s main objective is to find changing pattern of the footprint of the hotspot. The confirmed, recovered, and deceased cases of the Covid-19 from April 2020 to Jan 2021 is chosen for the analysis. The study found a sudden change in the hotspot district and a similar change in the footprint from August. Change pattern of the hotspot’s footprint will show that October is the most dangerous month for the first wave of the Corona. This type of study is helpful for the health department to understand the behavior of the virus during the pandemic. To find the presence of the clustering pattern in the dataset, we use Global Moran’s I. A value of Global Moran’s I greater than zero shows the clustering in the data set. Dataset is temporal, and for each type of case, the value Global Moran’s I > 0, shows the presence of clustering. Local Moran’s I find the location of cluster i.e., the hotspot. The dataset is granulated at the district level. A district with a high Local Moran’s I surrounded by a high Local Moran’s I value is considered the hotspot. Monte Carlo simulation with 999 simulations is taken to find the statistical significance. So, for the 99% significance level, the p-value is taken as 0.001. A hotspot that satisfies the p-value threshold is considered the statistically significant hotspot. The footprint of the hotspot is found from the coverage of the hotspot. Finally, a change vector is defined that finds the pattern of change in the time series of the hotspot’s footprint.
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