A compression-complexity distance measure was proposed to identify the novel SARS-CoV-2 virus that caused global pandemic of virus disease (COVID-19) and distinguishing it from SARS-CoV-1 virus using only short contiguous fragments of nucleotide sequences. The proposed algorithm could potentially be used in vaccine research by enabling rapid matching of genomic sequences and could avoid the need for sequence assembly.
Neuro-chaos Inspired AI: ChaosNet, a chaos based artificial neural network architecture for classification has been proposed mimicking the chaotic firing of neurons in the brain (which conventional algorithms do not). ChaosNet produces state-of-the art performance on classification tasks on publicly available datasets. With as low as 0.05% of the total data used for training, the proposed method reports accuracies ranging 73% to 98% while also claiming to be robust to external noise.