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Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

An exponential rise has been observed in the data volume over the time when considering a real time environment. A phenomenal feature termed as 'Predictability' helps in predicting and portraying related data to the user according to their needs. Moreover, classification of Big Data is usually a tedious and lengthy task. The technique of MapReduce Framework performs the data processing that being paralleled by data distribution in small chunks through the clusters. This Map Reduce technique is being proposed which is employed to process heterogeneous data items. Few issues that are being targeted in the existing paper include associating climatologically and meteorological information with large variety of farming decisions. Using the well-known MapReduce framework the above issues and challenges can be resolved. The existing paper proposes empirical techniques of climate classification and prediction by adopting Co-EANFS (Co-Effective and Adaptive Neuro-Fuzzy System) approach for data handling. Furthermore, the paper examines association rule mining too, which is being implemented for examining the best crop production by relying upon the soil and weather condition. Lastly, a technique is proposed for managing various levels such as preprocessing, clustering, classification and prediction. First, the weather dataset is being collected which undergoes processing; thereafter the proposed model is implemented which results in formation of cluster data sets linked to each season. For evaluating the performance, accuracy predictions generated by Co-EANFS is used which being formulated with varying no: of inputs and variables. The proposed framework acquires least execution time. [ABSTRACT FROM AUTHOR]