UID:
almahu_9949226758002882
Format:
XI, 362 p. 226 illus., 147 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783030824693
Series Statement:
Lecture Notes in Networks and Systems, 256
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030824686
Additional Edition:
Printed edition: ISBN 9783030824709
Language:
English
DOI:
10.1007/978-3-030-82469-3
URL:
https://doi.org/10.1007/978-3-030-82469-3
URL:
Volltext
(URL des Erstveröffentlichers)
