International Conference on Computational Intelligence and Computing Research 

About: International Conference on Computational Intelligence and Computing Research is an academic conference. The conference publishes majorly in the area(s): Feature extraction & Image segmentation. Over the lifetime, 1503 publications have been published by the conference receiving 10430 citations.

Improved cost-based algorithm for task scheduling in cloud computing

Abstract: Cloud computing has been build upon the development of distributed computing, grid computing and virtualization. Since cost of each task in cloud resources is different with one another, scheduling of user tasks in cloud is not the same as in traditional scheduling methods. The objective of this paper is to schedule task groups in cloud computing platform, where resources have different resource costs and computation performance. Due to job grouping, communication of coarse-grained jobs and resources optimizes computation/communication ratio. For this purpose, an algorithm based on both costs with user task grouping is proposed. The proposed scheduling approach in cloud employs an improved cost-based scheduling algorithm for making efficient mapping of tasks to available resources in cloud. This scheduling algorithm measures both resource cost and computation performance, it also improves the computation/communication ratio by grouping the user tasks according to a particular cloud resource's processing capability and sends the grouped jobs to the resource.

Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques

Abstract: Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor detection. In this paper an intelligent system is designed to diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization tools, such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The detection of tumor is performed in two phases: Preprocessing and Enhancement in the first phase and segmentation and classification in the second phase

Automated attendance management system based on face recognition algorithms

Abstract: In this paper we propose an automated attendance management system This system, which is based on face detection and recognition algorithms, automatically detects the student when he enters the class room and marks the attendance by recognizing him The system architecture and algorithms used in each stage are described in this paper Different real time scenarios are considered to evaluate the performance of various face recognition systems This paper also proposes the techniques to be used in order to handle the threats like spoofing When compared to traditional attendance marking this system saves the time and also helps to monitor the students

A new adaptive mutation technique for genetic algorithm

Abstract: Genetic algorithm is a promising heuristic search algorithm, which searches the solution space for optimal solution using the genetic operations Mutation is one among the genetic operators that plays a vital role in searching the solution space This paper proposes a new adaptive mutation technique to improve the performance of genetic algorithm The proposed technique intends to mutate the genes in such a way that the mutation aids both global and local searching options This leads to faster convergence rather than the conventional techniques The comparative results show that the proposed mutation technique exhibits a drastic performance improvement over the conventional static mutation techniques

Web Data Mining research: A survey

Abstract: Web Data Mining is an important area of Data Mining which deals with the extraction of interesting knowledge from the World Wide Web, It can be classified into three different types i.e. web content mining, web structure mining and web usages mining. The aim of this paper is to provide past, current evaluation and update in each of the three different types of web mining i.e. web content mining, web structure mining and web usages mining and also outlines key future research directions. This paper also reports the comparisons and summary of various methods of web data mining with applications, which gives the overview of development in research and some important research issues.