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Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.

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RESUMEN

An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is l s and the pattern length is l p occurs in O (l s ∗l p ). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named "optimized pattern similarity identification" (OPSI). This methodology constructs a table, entitled "shift beyond for avoiding redundant comparison" (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (l s . è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient.

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