Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) / / edited by Rajiv Misra, Rudrapatna K. Shyamasundar, Amrita Chaturvedi, Rana Omer

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (372 pages)
• Disciplina: 006.31
• Soggetto topico: Engineering—Data processing; Computational intelligence; Machine learning; Big data; Data Engineering; Computational Intelligence; Machine Learning; Big Data
• Persona (resp. second.): MisraRajiv
• Note generali: Includes index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Machine learning and big data analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)
• ISBN: 3-030-82469-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910522952603321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems, . 2367-3389 ; ; 256