BOOSTING AND COMPARING PERFORMANCE OF MACHINE LEARNING CLASSIFIERS WITH META-HEURISTIC TECHNIQUES TO DETECT CODE SMELL

Boosting and Comparing Performance of Machine Learning Classifiers with Meta-heuristic Techniques to Detect Code Smell

Background: Continuous modifications, suboptimal software design practices, and stringent project deadlines contribute to the proliferation of code smells.Detecting and refactoring these code smells are pivotal to maintaining complex and essential software systems.Neglecting them may lead to future software defects, rendering systems challenging to

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SARS-CoV-2 variant evolution in the United States: High accumulation of viral mutations over time likely through serial Founder Events and mutational bursts.

Since the first case of COVID-19 in December 2019 in Wuhan, China, SARS-CoV-2 has spread worldwide and within a year and a half has caused 3.56 million deaths globally.With dramatically increasing infection numbers, and the arrival of new variants with increased infectivity, tracking the evolution of its genome is crucial for effectively controllin

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