Sequence analysis paves way for structural and functional analysis in Bioinformatics. The preliminary step for this sequence analysis is aligning the molecular sequences. This paper introduces parallelism in aligning multiple sequences by parallelizing a bio-inspired algorithm called Grey Wolf Optimizer (GWO) technique. Owing to the tradeoff between accurate solutions and less computational time, many heuristic algorithms are developed. The GWO algorithm involves search agents, which are treated as initial solutions for the optimization problem. Data parallelism is employed in the initialization phase and generation phase. This technique is implemented in Quadro 4000 a CUDA based GPU using threads. The results show that the proposed algorithm reduces the computational time than other existing ones.

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Flame monitoring and categorization become important in efficient power generation and energy conservation sectors. Flicker Frequency Range (FFR) is considered as the key factor to monitor flames and fuel identification. The fluctuation of combustion arises due to the varying quality of fuel, which causes the overlapping of FFR for different fuels leading to misclassification. In this work, we attempt to classify fuels based on flame characteristics output from a Digital Flame Scanner. A new model is proposed by combining Time Series Analysis with Fuzzy Support Vector Machine (TSA-FSVM) for training, classification, and prediction of fuel types. With the help of 12,000 real-time data collected from the Bharat Heavy Electricals Limited (BHEL), Tuticorin branch (a public sector company, which manufactures power plant equipment), a comparative analysis is performed using different Machine Learning algorithms with the proposed technique. From the results, it is found that TSA-FSVM outperforms existing as well as other Machine Learning methods by increasing the accuracy of predicting the right fuel. Thus, it helps to avoid the boiler system explosion/OFF state, which leads to conserving the energy, increases electricity production, and cost-efficiency.

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In Bioinformatics, Motif Finding is one of the most popular problems, which has many applications. Generally, it is to locate recurring patterns in the sequence of nucleotides or amino acids. As we can't expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NPcomplete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. The most of the algorithms suffer with local optima. Particle swarm optimization (PSO) is a new global optimization technique which has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have adopted the features of the PSO to solve the Planted Motif Finding Problem and have designed a sequential algorithm. We have performed experiments with simulated data it outperforms MbGA and PbGA. The PMbPSO also applied for real biological data sets and observe that the algorithm is also able to detect known TFBS accurately when there are no mutations.