In recent years, the number of cell phones in society has increased drastically and they are getting popular due to their computational ability and adaptability. Resource provisioning is important, but still remains NP-hard problem in mobile computational grid (MCG). Once the jobs are assigned to the MCG, the main challenge is how to identify the correct resource according to the job's requirement and use them to execute the sub-jobs. The heuristic methods such as Min-Min, Max-Min, and HEFT can be used to select appropriate resources from the MCG that is assigned for job execution. Since the computational nodes are static and mobile in nature, the performance of such heuristics is not as expected. Such heuristics suffers from low throughput and low speedup. The process of localization is used in a wireless sensor network with good results. The proposed model uses heuristics and localization process for optimizing the quality of service parameter localization, normalized speedup, and throughput in MCG, with the concept of grid nodes available in MCG. The observation shows significant improvement in the quality of service parameter localization, normalized speedup, and throughput in MCG. The proposed model HGLA and MIN-MIN, MAX-MIN, and HEFT are compared with respect to localization, speedup, and throughput. The results reveal that the proposed model shows better performance over MIN-MIN, MAX-MIN, and HEFT.

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The Bioinformatics (BI) is a sequence alignment, usually three sequences which can be RNA, DNA and proteins. Because the three or more given sequences can be of large lengths, aligning them by hand can be time consuming and in some cases traditionally impossible. Thus BI comes into use thereby aligning each sequence, and revealing the similar part of the given gene. BI finds great use in bioinformatics where it is used to predict the protein structure, its function, family or its domain. These problems are related to AI and can be classified in the NP complete domain of problems. Thus with the goal of identifying maximum similarities among the sequences, we can use various approaches and techniques like Genetic Algorithms (GA) and its variant in BI.

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Voting is a widely used fault masking techniques for safety-critical systems to enhance the overall reliability of the system. Researchers over the period have proposed numerous advanced techniques in order to improve on the drawback of the existing methods. In this paper a fuzzy voting scheme has been survey and a generalized improved fuzzy voting scheme has been proposed. A comparative study of these schemes has also been carried out. It is found that proposed model is better than existing models. Single objective, multi-objective objective and many objective will be applied in future.