Buch, Englisch, Band 887, 1088 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1670 g
Proceeding of The Seventh International Conference on Big Data and Internet of Things BDIoT'24, Volume 2
Buch, Englisch, Band 887, 1088 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1670 g
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-031-74490-7
Verlag: Springer Nature Switzerland
"Big Data and Internet of Things" is the latest volume in the renowned Lecture Notes in Networks and Systems series. This book compiles the latest research presented at the Seventh International Conference on Big Data and Internet of Things (BDIoT'24), showcasing innovative solutions, emerging trends, and practical applications in the fields of big data and IoT. An essential read for researchers, professionals, and students looking to stay ahead in the rapidly evolving world of technology. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems. Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems Automation, Manufacturing, Smart Grids. Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them.
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Research
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Weitere Infos & Material
Chapter 1: Using deep learning to detect cheating on TCExam platform through real-time facial emotion recognition.- Chapter 2: Comparative Analysis of Sentiment Analysis Using ML & DL Techniques for Political Issues in Malaysia Context.- Chapter 3: ICTs and smart solutions in a smart destination.- Chapter 4: Credit Card Fraud Detection Using Autoencoder Algorithm with SMOTE Technique.
