Engagement Analysis of Students in Online Learning Environments -- Application of Artificial Intelligence to predict the Degradation of Potential mRNA Vaccines Developed To Treat SARS-CoV-2 -- An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification -- MMAP : A Multi-Modal Automated Online Proctor -- Applying Extreme Gradient Boosting for Surface EMG based Sign Language recognition -- Review of Security Aspects of 51 Percent Attack on Blockchain -- Integrated Micro-video Recommender based on Hadoop and Web-Scrapper -- Automated Sleep Staging System based on Ensemble Learning Model using Single-Channel EEG signal -- Segregation and User Interactive Visualization of Covid- 19 Tweets using Text Mining Techniques -- Software Fault Prediction using Data Mining Techniques on Software Metrics.

This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets-i.e., big data-to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.