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.
Cuprins
Engagement Analysis of Students in Online Learning Environments.- Application of Artificial Intelligence to predict the Degradation of Potential m RNA Vaccines Developed To Treat SARS-Co V-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.
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