Online ISSN: 2515-8260

Keywords : Particle Swarm Optimization

Hybrid of PSO-GSA based Clustering Approach for Predicting Structural Class Prediction using Random Forest Method

Sarneet Kaur; Ashok Sharma; Parveen Singh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 10, Pages 17-32

Protein can be classified in different classes. A lot of research is being performed for analyzing the structure and classes of protein. There are many problems associated with protein structure. Some of them are folding problem and protein structure prediction (PSP) etc. PSP is the most considerable open problem in field of biology. In the present work different algorithms like particle swarm optimization (PSO), gravitational search algorithm (GSA) and K-Mean clustering algorithms are used to classify different structures of protein. A random forest (RF) classifier is proposed for analyzing and comparing different protein classes in terms of other conventionally available algorithms in terms of various performance parameters like accuracy, recall, precision and specificity. The proposed classifier proved better than other classifiers in terms of accuracy and can be helpful in predicting the protein structures. A hybrid PSO-GSA algorithm is also proposed which provided improved performance as compared to single algorithms and can be utilized for analysis of protein structure.

A Cooperative Spectrum Sensing Scheme using Particle Swarm Optimization and Cultural Algorithm

Anilkumar Dulichand Vishwakarma; Dr. Girish Ashok Kulkarni

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 921-932

Cognitive radio is a software-based technology that provides dynamical access to unused or underused spectrum bands and enables spectrum sharing without causing any disadvantage among users. The performance of the cognitive radio in wireless communication networks depends on the accurate and fast detection of spectrum gaps.
The idea of Cognitive Radio is to share the spectrum between a so-called primary user and a so-called secondary user. The main objective of this spectrum management is to obtain a maximum rate of exploitation of the radio spectrum, for this cooperation between users is necessary. This paper provides a spectrum sensing approach using cooperation and competition to solve the spectrum allocation problem and thus ensure better management. The aim of spectrum detection is to detect spectrum gaps accurately and quickly. Therefore, the performance of cognitive radio networks largely depends on the spectrum sensing function. Particle Swarm Optimization (PSO) and Cultural Algorithm (CA) are proposed to increase spectrum detection performance in cognitive radio networks. The collaborative spectrum detection performance analysis of the proposed method was performed in Rayleigh fading channel in addition to the non-damped AWGN channel. As a result of simulation studies, it has been shown that a more effective perception can be made in cognitive radio networks by optimizing the threshold value expression from the historical data