Review Of Soft Computing Techniques Research Articles

Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries

The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.

A Review of Soft Computing Techniques for Time Series Forecasting

Objectives: This paper gives a brief survey analysis on time series forecasting models developed so far and how soft computing techniques are becoming popular for time series forecasting. Statistical Analysis: This paper gives an introductory study on time series models develop in literature so far. First all traditional models were developed among them ARIMA became popular but only for linear data. Then after Soft Computing models came into picture like ANN and it was proved to be best for nonlinear forecasting. But none of the individual models are capable to handle all types of datasets as real data is a mixture of linear and nonlinear both. So there arises a need for hybridization. Findings: Many traditional methods have been developed since last few decades for time series forecasting, however their performance is not up to the mark till today. Recent trends have proven that soft computing techniques like neural network, support vector machine are good alternatives to conventional methods. The accuracy of time series model is very important and difficult task to achieve, and individual models are not able to perform well for all types of time series data. So, hybridization of models is better to achieve good accurate forecasting results. Application/Improvements: Implementation of a hybrid model using soft computing techniques which can give better accurate results as real data are made linear and nonlinear both. This hybrid model can be used in applications like weather forecasting, exchange rate forecasting, etc.

A Review of Soft Computing Techniques for Gene Prediction

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.