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Different experiment has been advertised that the processor work load distributing equitably with the processors of a distributed system decidedly enhance framework execution and improves system management. Fuzzy logic has been implemented in numerous areas of industry and science to manage susceptibility. Proposed work with the intent of load balancing has been focused on using fuzzy logic to interpret processor's load and task execution length. This work introduces a new dynamic fuzzy-based load balancing algorithm for homogeneous dispersed frameworks. The proposed techniques use fuzzy logic to manage improper data load i.e., overloaded and under loaded, deciding on load distribution choices and preserve general framework strength. For accurately evaluating the load status of a host, proposed algorithm uses CPU utilization, CPU queue length and distance upon its present load as linguistic inputs while framing fuzzy set. Method proposes Hybrid Genetic Algorithm (HGA) that is blended with stochastic development process in order to designate and schedule real-time tasks with priority requirements. The work randomly generates the tasks using random wheel approach, once the tasks are generated then encoding tasks to chromosome is carried out. Height of each task is obtained through DAG and according to the root node, the height of each taskis updated in the chromosome. Proposed fuzzylogicbased load balancing and hybrid genetic algorithm based task scheduling (FLLBHGATS) algorithm has been evaluated with similar existing methods in order to prove its efficiency. The results prove that FLLBHGATS performs better than other techniques as far as the solution quality. [ABSTRACT FROM AUTHOR]

Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expressiondata. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches. [ABSTRACT FROM AUTHOR]

Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expressiondata. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches. [ABSTRACT FROM AUTHOR]

The sucesssive growth of collabrative applications producing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented. [ABSTRACT FROM AUTHOR]