Proceedings of the 4th International Conference on Data Science, Machine Learning and Applications ICDSMLA 2022, 26–27 December, Hyderabad, India /
Proceedings of the 4th International Conference on Data Science, Machine Learning and Applications ICDSMLA 2022, 26–27 December, Hyderabad, India / [electronic resource] :
edited by Amit Kumar, Vinit Kumar Gunjan, Yu-Chen Hu, Sabrina Senatore.
- 1st ed. 2023.
- VIII, 441 p. 254 illus., 189 illus. in color. online resource.
- Lecture Notes in Electrical Engineering, 1038 1876-1119 ; .
- Lecture Notes in Electrical Engineering, 1038 .
Chapter 1: Mr. Bot – A Survey on Arduino Based Autonomous Robotic Vehicle -- Chapter 2: Opinion Mining based Fake Review Detection using Deep Learning Technique -- Chapter 3: Analysis of the Seer dataset for lung cancer diagnosis for Stage classification and survival Analysis -- Chapter 4: Data Analytics for Athlete Safety in Training -- Chapter 5: Social Media + Machine Learning to Offer Clues on Suicide Ideation Concerns -- Chapter 6: Analysis of Various Techniques of Fetal Growth Detection.
This book includes peer reviewed articles from the 4th International Conference on Data Science, Machine Learning and Applications, 2022, held at the Hyderabad Institute of Technology & Management on 26-27th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of Data Science & Machine Learning offering in-depth information on the latest developments in Artificial Intelligence, Machine Learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.
9789819920587
10.1007/978-981-99-2058-7 doi
Artificial intelligence.
Computational intelligence.
Business information services.
Neural networks (Computer science) .
Internet of things.
Quantitative research.
Artificial Intelligence.
Computational Intelligence.
IT in Business.
Mathematical Models of Cognitive Processes and Neural Networks.
Internet of Things.
Data Analysis and Big Data.
Q334-342 TA347.A78
006.3
Chapter 1: Mr. Bot – A Survey on Arduino Based Autonomous Robotic Vehicle -- Chapter 2: Opinion Mining based Fake Review Detection using Deep Learning Technique -- Chapter 3: Analysis of the Seer dataset for lung cancer diagnosis for Stage classification and survival Analysis -- Chapter 4: Data Analytics for Athlete Safety in Training -- Chapter 5: Social Media + Machine Learning to Offer Clues on Suicide Ideation Concerns -- Chapter 6: Analysis of Various Techniques of Fetal Growth Detection.
This book includes peer reviewed articles from the 4th International Conference on Data Science, Machine Learning and Applications, 2022, held at the Hyderabad Institute of Technology & Management on 26-27th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of Data Science & Machine Learning offering in-depth information on the latest developments in Artificial Intelligence, Machine Learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.
9789819920587
10.1007/978-981-99-2058-7 doi
Artificial intelligence.
Computational intelligence.
Business information services.
Neural networks (Computer science) .
Internet of things.
Quantitative research.
Artificial Intelligence.
Computational Intelligence.
IT in Business.
Mathematical Models of Cognitive Processes and Neural Networks.
Internet of Things.
Data Analysis and Big Data.
Q334-342 TA347.A78
006.3