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Recent Advances in Internet of Things and Machine Learning: Real-World Applications

ISBN-13: 9783030901219 / Angielski / Miękka / 2023 / 329 str.

Valentina E. Balas; Vijender Kumar Solanki; Raghvendra Kumar
Recent Advances in Internet of Things and Machine Learning: Real-World Applications Valentina E. Balas Vijender Kumar Solanki Raghvendra Kumar 9783030901219 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Recent Advances in Internet of Things and Machine Learning: Real-World Applications

ISBN-13: 9783030901219 / Angielski / Miękka / 2023 / 329 str.

Valentina E. Balas; Vijender Kumar Solanki; Raghvendra Kumar
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This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.

This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.

Kategorie:
Technologie
Kategorie BISAC:
Technology & Engineering > Engineering (General)
Technology & Engineering > Electrical
Computers > Data Science - General
Wydawca:
Springer
Seria wydawnicza:
Intelligent Systems Reference Library
Język:
Angielski
ISBN-13:
9783030901219
Rok wydania:
2023
Wydanie:
2022
Numer serii:
000385804
Ilość stron:
329
Oprawa:
Miękka
Wolumenów:
01
Dodatkowe informacje:
Wydanie ilustrowane

Concentration Level of Learner Using Facial Expressions On E-Learning Platform Using IoT-Based Pycharm Device.- IoT-based Machine learning system for Nutritional Ingredient Analyzer for Food.- A Secured Manhole Management System Using IoT and Machine Learning.

This book covers a domain that is significantly impacted by the growth of soft computing. Internet of Things (IoT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of IoT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for IoT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to IoT and machine learning. In this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of IoT and machine learning.




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