Problem statement: Mobile Ad Hoc Network (MANET) is a collection of wireless mobile nodes that dynamically forms a network. Most of the existing ad-hoc routing algorithms select the shortest path using various resources. However the selected path may not consider all the network parameters and this would result in link instability in the network. The problems with existing methods are frequent route change with respect to change in topology, congestion as result of traffic and battery limitations since it's an infrastructure less network. Approach: To overcome these problems an optimal path management approach called path vector calculation based on fuzzy and rough set theory were addressed. The ultimate intend of this study is to select the qualified path based on power consumption in the node, number of internodes and traffic load in the network. Simple rules were generated using fuzzy and rough set techniques for calculating path vector and to remove irrelevant attributes (resources) for evaluating the best routing. The set of rules were evaluated with proactive and reactive protocols namely DSDV, AODV and DSR in the NS-2 simulation environment based on metrics such as total energy consumed, throughput, packet delivery ratio and average end-to-end delay. Results:The results have shown that in MANET, decision rules with fuzzy and rough set technique has provided qualified path based best routing. Conclusion: The network life time and performance of reactive and proactive protocols in MANET has improved with fuzzy and rough set based decision rules.

IntroductionCurrently, the phrase Big Data has become most fashionable in IT region. It refers to a broad area of dataset which are tough to be maintained by traditional functions [1]. Big Data can be used in Economics and Commerce, Finance, Electronic shopping, Medicare, Astrophysics, Oceanology, Manufacturing and numerous different areas. These datasets are most difficult. The data size is increasing exponentially day by day in extremely huge quantity. As information is rising in capacity, in variety and with huge speed, it also increases the complexities in handling it. Big Data is a developing area. It has a lot of investigation problems and objections to address. The major problems in Big Data are: i) Managing data quantity, ii) Analysis of Big Data, iii) Privacy of information, iv) Holding of massive quantity of information, v) Information visualization, vi) Job scheduling in Big Data, vii) Fault tolerance. Research Methodology Managing information quantityThe huge quantity of information/data imminent from various areas of education such as genetics, astrophysics, weather forecasting, etc makes it extremely hard for the biologists to handle [1,2]. Analysis of big dataIt is hard to diagnose Big Data due to inhomogeneous and incompleteness of information. Composed information can be in various methods, diversity and structure [3]. Privacy of information in the context of big dataThere is a general fear regarding to the improper utilization of individual information, mainly through connecting the information from numerous resources. Handling secrecy is both a scientific and a Sociological issue [3]. Storage of massive quantity of informationIt constitutes the issue of how to identify and cache main data which are separated from unorganized information, proficiently [1,3]. AbstractA huge amount of information (Information in the unit of Exabyte or Zettabyte) is called Big Data. To quantify such a large amount of data and store electronically is not easy. Hadoop system is used to handle these large data sets. To collect Big Data according to the request, Map Reduce program is used. In order to achieve greater performance, Big Data requires proper scheduling. To reduce starvation and increase the use of resource and also to assign the jobs for available resources, the scheduling technique is used. The Performance can be increased by implementing deadline constraints on jobs. The goal of the paper is to study and analyze various scheduling algorithms for better performance.

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Cognitive reframing is a useful technique for understanding unhappy feelings and moods and for challenging the sometimes wrong. It is a psychological technique that allows you to actively reprogram your brain. In short, if you modify your beliefs, you create a real, change in your brain. With cognitive reframing, you can change the way you see at something and also experience the change. It enables you to implement the ancient knowledge that you can't always control what happens to you, but you can certainly control how you react to different situations-no matter how tough your situation might be.

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