Playing games always been interest in child and adolescent. Technology specially internet given new dimension to gaming and over the decade gaming over internet attracted major percentage of player. Excessive gaming when not controlled also lead to the behavioral dysfunction along with physiological issues. World Health Organization (WHO) has added Internet Gaming Disorder (IGD) as potential mental disorder recently in Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as research field to include in main manual. Research in this field gained interest, but about diagnostic criteria debate is still on in research group. For diagnosis of IGD, not only subjective symptoms but its underlying neurobiology needs to be included. Major behavioral addiction shares its symptoms with different addiction, which can be seen into IGD and Alcohol Use Disorder (AUD) also. In this paper, we aim to identify potential biomarkers to explore relationships between AUD and IGD symptoms using EEG signals recordings. Noise and other artifacts are removed during pre-processing and bands splitted in intermediate steps of proposed method. Spectral features are potential indicator in EEG data, for all five EEG bands power spectral density is computed and visible difference found in mean power on most of the channels for all bands. Across theta and beta bands, mean absolute power is greatly reduced in the frontal and prefrontal brain region. The results indicate symptoms are matching with impulsive behavior. Based on statistical analysis across we found similarities in EEG features in terms of emotional imbalance, high arousal, slow response inhibition and our study indicate Alcohol Use Disorder and IGD are correlated.

Comparison of protein sequence similarity is a significant study. By virtue of this method, we can expose the evolutionary relationship among protein sequences. So, it is required to design effective computational algorithms that can compare the similarities among the colossal amount of sequences. The aim of this research is to develop efficient tools in the field of protein sequences comparison and phylogenetic study. The proposed method performs a feature generation process based on the physio-chemical properties of amino acids that best describes the revolutionary relationship among the species in a protein family. The protein sequences are transferred into an Eighty dimensional feature vector among the group of amino acids. Finally, four different datasets were used to validate the accuracy of the proposal and a correlation coefficient of 0.94417 of ND5 dataset using ClustalW has been found. This is much higher than some of the methods. At last the result explains the effectiveness in the similarity analysis among genome sequences.