Corona VIrus Disease abbreviated as COVID-19 is a novel virus which is initially identified in Wuhan of China in December of 2019 and now this deadly disease has spread all over the world. According to World Health Organization (WHO), a total of 3,124,905 people died from 2019 to 2021, April. In this case, many methods, AI base techniques, and machine learning algorithms have been researched and are being used to save people from this pandemic. The SARS-CoV and the 2019-nCoV, SARS-CoV-2 virus invade our bodies, causing some differences in the structure of cell proteins. Protein-protein interaction (PPI) is an essential process in our cells and plays a very important role in the development of medicines and gives ideas about the disease. In this study, we performed clustering on PPI networks generated from 92 genes of the Covi-19 dataset. We have used three graph-based clustering algorithms to give intuition to the analysis of clusters.

The capacity to identify and analyze protein-protein interactions, along with their internal modular organization, plays a crucial role in comprehending the intricate mechanisms underlying biological processes at the molecular level. We can learn a lot about the structure and dynamics of these interactions by using network analysis. We can improve our understanding of the biological roots of disease pathogenesis by recognizing network communities. This knowledge, in turn, holds significant potential for driving advancements in drug discovery and facilitating personalized medicine approaches for disease treatment. In this study, we aimed to uncover the communities within the variant B.1.1.529 (Omicron virus) using two proposed novel algorithm (ABCDE and ALCDE) and four widely recognized algorithms: Girvan-Newman, Louvain, Leiden, and Label Propagation algorithm. Each of these algorithms has established prominence in the field and offers unique perspectives on identifying communities within complex networks. We also compare the networks by the global properties, statistic summary, subgraph count, graphlet and validate by the modulaity. By employing these approaches, we sought to gain deeper insights into the structural organization and interconnections present within the Omicron virus network.

Text Classification is the process of categorizing text into the relevant categories and its algorithms are at the core of many Natural Language Processing (NLP). Term Frequency-Inverse Document Frequency (TF-IDF) and NLP are the most highly used information retrieval methods in text classification. We have investigated and analyzed the feature weighting method for text classification on unstructured data. The proposed model considered two features N-Grams and TF-IDF on the IMDB movie reviews and Amazon Alexa reviews dataset for sentiment analysis. Then we have used the state-of-the-art classifier to validate the method i.e., Support Vector Machine (SVM), Logistic Regression, Multinomial Naive Bayes (Multinomial NB), Random Forest, Decision Tree, and k-nearest neighbors (KNN). From those two feature extractions, a significant increase in feature extraction with TF-IDF features rather than based on N-Gram. TF-IDF got the maximum accuracy (93.81%), precision (94.20%), recall (93.81%), and F1-score (91.99%) value in Random Forest classifier.