Santwana Sagnika

A Multi-objective Cat Swarm Optimization Algorithm for Workflow Scheduling in Cloud Computing Environment

As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with cat swarm-based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared with multi-objective particle swarm optimization (MOPSO) technique.

A Comparative Study on Approaches to Speckle Noise Reduction in Images

Noise refers to the random variation of intensity of a pixel, which modifies the actual information of the image. As a result, pixels which appear in the image are not the actual pixels. Addition of extraneous values to the image causes the occurrence of noise. Noise is categorized into impulse (salt-and-pepper) noise, uniform noise, Gaussian noise, exponential noise, Erlang (gamma) noise, photon noise, speckle noise, etc. Speckle noise is the noise that arises due to the effect of environmental conditions on the imaging sensor during image acquisition. Speckle noise is mostly detected in case of medical images, active Radar images and Synthetic Aperture Radar (SAR) images. Various researchers have performed experiments to overcome this kind of noise using different filtering techniques based on soft computing approaches, such as Fuzzy Filter, Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Neural Networks, etc. In this paper, we present a brief analysis of different techniques used for speckle noise reduction, along with their advantages and disadvantages, in a comparative manner.

Workflow Scheduling in Cloud Computing Environment Using Bat Algorithm

The data handling and processing capabilities of current computing systems are increasing, owing to applications involving the bigger size of data. Hence, the services have become more expensive. To maintain the popularity of cloud environment due to less cost for such requirements, an appropriate scheduling technique is essential, which will decide what task will be executed on which resource in a manner that will optimize the overall costs. This paper presents an application of the Bat Algorithm (BA) for scheduling a workflow application (i.e., a data intensive application), in cloud computing environment. The algorithm is successfully implemented and the results compared with two popular existing algorithms, namely Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The proposed BA algorithm gives an optimal processing cost with better convergence and fair load distribution.

Parallel GA in Big Data Analysis

As the scope of computation is extending to domains where large and complex datasets are needed to be dealt with, it has become a very useful approach to sub-divide the tasks and perform them in parallel, which leads to a significant reduction in the processing time. On the other hand, evolutionary and swarm-based algorithms are rapidly gaining popularity to solve complex problems. However, these methods consume a lot of time in solving problems. Hence, parallelization of evolutionary algorithms proves to be beneficial in solving intensive tasks within a feasible execution time. This chapter describes the parallelization issues in Genetic Algorithms (GA) and use of various Big data mechanisms over parallel GA models.

Image Change Detection Using Particle Swarm Optimization

Image change detection can be expressed as a function of time period, whose main objective is to find the changes on the same area at different time intervals, which is a complex and intractable one. Due to large search space, general optimization algorithm fails to give the solution in a promising amount of time. So particle swarm optimization (PSO), one of the swarm-based approaches, can be used as an efficient tool, which the authors have explored in this paper. This mechanism aims to find a change mask that performs partitioning of image into changed and unchanged areas so that the weighted sum of mean square errors of both areas is minimized. This leads to accurate change detection with less noise in a feasible time period.

Sentiment Analysis Using N-gram Technique

Dramatic growth of social media has created remarkable interest among Internet users nowadays. Information from these Web sites in the form of reviews, feedbacks, ratings, etc., can be utilized for various purposes like to find out users’ taste or interest to develop a proper marketing strategy, maybe for a survey about the product by using sentiment analysis. Twitter is generally used for posting long comments in short status. Twitter offers organizations a fast and powerful approach to investigate customers’ viewpoints toward the critical to success in the open market. Previously we calculate sentiment of each word for the sentiment, which may or may not be accurate because may be the same word used in past for negative review, but presently it is used for positive sense. We propose a method by applying both log function and N-gram techniques to find out the sentiment of the Twitter data in R to build a robust engine to achieve more accuracy.