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Author

Shinde Ashok

Bio: Shinde Ashok is an academic researcher. The author has contributed to research in topics: Evolutionary computation & Support vector machine. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Papers
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01 Jan 2011
TL;DR: The present paper shows the techniques, applications and future of soft computing.
Abstract: Recently new technique is available for computation known as Soft computing. Soft computing is based on natural as well as artificial ideas. Soft Computing techniques are Fuzzy Logic, Neural Network, Support Vector Machines, Evolutionary Computation and Machine Learning and Probabilistic Reasoning. The present paper shows the techniques, applications and future of soft computing. The Soft Computing Techniques & applications is also highlighted in the paper.

9 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed recalling-enhanced recurrent neural network (RERNN) optimized with woodpecker mating algorithm for brain tumor classification (BTC) to accurately classify the four types of brain tumors, namely, glioma, meningiomas, pituitary gland, and normal.
Abstract: Brain tumors are caused by the uncontrollable division and proliferation of abnormal cell groupings inside or around the brain. This cell grouping affects the function of brain activities and destroys healthy cells. Several methods have been used to detect the brain tumor, but none of the methods present adequate accuracy and increasing computational time. To overcome these issues, this article proposes recalling-enhanced recurrent neural network (RERNN) optimized with woodpecker mating algorithm for brain tumor classification (BTC) to accurately classify the four types of brain tumors, namely, glioma, meningioma, pituitary gland, and normal. The brain MRI images are collected from Brats MRI image data set. The simulation is activated in MATLAB. From the simulation, the proposed BTC-RE-RNN–WMA achieves better accuracy 29.98%, 26.74%, 33.27%, higher precision 19.24%, 34.82%, 26.92%, when comparing to the existing models, such as efficient identification with categorization of brain tumor utilizing kernel based SVM for MRI (BTC-KSVM-HHO), combined training of two-channel deep neural network for brain tumor categorization (BTC-JT-TCDNN), improved structure for brain tumor analysis utilizing MRI depending on YOLOv2 with convolutional neural network (BTC-YOLOv2-CNN) methods.

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Journal ArticleDOI
TL;DR: This article reviews and analyzes the application of certain soft computing techniques in gene prediction and some limitations of the current research activities and future research directions are provided.

38 citations

Journal ArticleDOI
TL;DR: A theoretical review of soft computing techniques for gene prediction is presented and some limitations of the current research and future research directions are presented.
Abstract: In the past decade, various genomes have been sequenced in both plants and animals. The falling cost of genome sequencing manifests a great impact on the research community with respect to annotation of genomes. Genome annotation helps in understanding the biological functions of the sequences of these genomes. Gene prediction is one of the most important aspects of genome annotation and it is an open research problem in bioinformatics. A large number of techniques for gene prediction have been developed over the past few years. In this paper a theoretical review of soft computing techniques for gene prediction is presented. The problem of gene prediction, along with the issues involved in it, is first described. A brief description of soft computing techniques, before discussing their application to gene prediction, is then provided. In addition, a list of different soft computing techniques for gene prediction is compiled. Finally some limitations of the current research and future research directions are presented.

28 citations

Journal ArticleDOI
TL;DR: This paper demonstrate designing fully connected neural network system using four different weight calculation algorithms, observing that analytical hierarchical processing is the most promising mathematical method for finding appropriate weight in fully connected NN.
Abstract: Soft computing is used to solve the problems where input data is incomplete or imprecise. This paper demonstrate designing fully connected neural network system using four different weight calculation algorithms. Input data for weight calculation is constructed in the matrix format based on the pairwise comparison of input constraints. This comparison is performed using saaty’s method. This input matrix helps to build judgment between several individuals, forming a single judgment. Algorithm considered here are Geometric average mean, Linear algebra calculation, Successive matrix squaring method, and analytical hierarchical processing method. Based on the quality parameter of performance, it is observed that analytical hierarchical processing is the most promising mathematical method for finding appropriate weight. Analytical hierarchical processing works on structuration of the problem into sub problems, Hence it the most prominent method for weight calculation in fully connected NN.

18 citations

Journal ArticleDOI
TL;DR: The overall idea of this paper is to present, analyze, investigate, compare and discuss software reliability prediction with various CI techniques and tools and their advantages and disadvantages.
Abstract: Computational Intelligence has been known to be very useful in predicting software reliability. In this paper, two kinds of investigations are performed. First, we provide a systematic review of Software Reliability Prediction studies with consideration of various metrics, methods and CI techniques (including fuzzy logic, neural networks, genetic algorithms). Second, reliability prediction and data collection with the help of various available tools are discussed. The overall idea of this paper is to present, analyze, investigate, compare and discuss software reliability prediction with various CI techniques and tools and their advantages and disadvantages.

15 citations

Journal ArticleDOI
TL;DR: In this article, a review of various Artificial Intelligence techniques used in assessing, monitoring, management and forecasting of the effects of drought in the field of hydrology, water resources management, sustainable agriculture, etc.
Abstract: Drought is a natural hazard creating havoc on economic, social and environmental aspects. As a result of its slow and creeping nature, it is problematic to establish the onset as well as the termination of drought. Irrespective of its spatial and temporal variability, drought occurs in almost all regions. A wide range of drought studies has been conducted by many researchers over a long period of time. The damage caused by drought has a huge impact on the social, economic and agricultural sectors. Researchers have defined drought in different ways depending upon the parameters and its characteristics, and universally there is no proper definition for drought because of its complexity in nature. This review is focused mainly on various Artificial Intelligence techniques used in drought assessment, monitoring, management and forecasting. The findings from the study shows that drought prediction has become significance in the field of hydrology, Water Resources Management, sustainable agriculture, etc. by using the various AI techniques. In recent studies, AI has been used widely in analysing drought in different regions. The applications of AI techniques in the domain of drought assessing, monitoring, forecasting, etc., shows a rapid growth and that the impact of these will be increasing in future. For understanding the different concepts of drought study, it is needed to establish different system of drought management in order to monitor the different factors affecting drought and then take proper measures to mitigate the damage. Literature studies have been done to analyze the onset and other measures of drought management. Future research may be oriented towards Modeling and probabilistic analysis of climatic data for refining the drought vulnerability mapping, analysis of onset and termination, warning system and drought declaration process depending on the conditions of the region.

12 citations