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Multiple Sequence Alignment Using Optimization Algorithms
TLDR
A framework with combination of Genetic Algorithm and Simulated Annealing is presented to solve Multiple Sequence Alignment problem and can be considered as an alignment improver for any near optimal solution produced by GAs.
Multiple sequence alignment using genetic algorithm and simulated annealing
TLDR
The combination of genetic algorithm and simulated annealing to solve multiple sequence alignment (MSA) assignment and significant results were produced from the prealignment and genetic algorithm phase.
Fast Hybrid String Matching Algorithm based on the Quick-Skip and Tuned Boyer-Moore Algorithms
TLDR
This study aims to produce an efficient hybrid exact string matching algorithm called Sinan Sameer Tuned Boyer Moore-Quick Skip Search (SSTBMQS) algorithm by blending the best features that were extracted from the two selected original algorithms which are Tuned boyer-Moore and Quick-Skip Search.
Data Mining for Medical Systems: A Review
TLDR
This paper aims to allow the readers to understand about data mining and its importance in health care and the reliance of health care on data is increasing.
Parameter Identification and Selection for Childhood Obesity Prediction Using Data Mining
The accurate identification and selection of useful parameters for childhood obesity prediction are very important. This study aims to identify childhood obesity prediction parameters for children in
Supporting system development by novice software engineers using a tutor-based software visualization (TubVis) approach
TLDR
TubVis is proposed, a tutor-based software visualization approach in SoVis tool that analyses software artefacts pertaining to software engineering best practices inputted by the experts and generate a set of recommendations regarding the design and coding for a novices.
A Fast Hybrid Algorithm for the Exact String Matching Problem
TLDR
The study shows how the integration of two algorithms gives better results than the original algorithms even the same data size and pattern lengths are applied as test evaluation on each of the algorithms.
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