Titlebook: Smart Sensor Networks; Analytics, Sharing a Umang Singh,Ajith Abraham,Tzung-Pei Hong Book 2022 The Editor(s) (if applicable) and The Author

DAFT

2197-6503 techniques and design and principles of smart sensor networ.This book provides IT professionals, educators, researchers, and students a compendium of knowledge on smart sensors and devices, types of sensors, data analysis and monitoring with the help of smart sensors, decision making, impact of mac

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Graph Powered Machine Learning in Smart Sensor Networks (Graph Convolutional Network) Approach, Deep Graph Convolutional Neural Network (DGCNN) and Window-Based Approach with Graphical Features. The work will also show that graphical-based features based on a smart sensor network can perform better than non-graph based features.

GROSS

2197-6503 the field of smart sensor networks and their usage in real-life applications like smart city, smart home, e-healthcare, smart social sensing networks, etc.  Chapters illustrate technological advances and trend978-3-030-77216-1978-3-030-77214-7Series ISSN 2197-6503 Series E-ISSN 2197-6511

Surgeon

An Overview of Artificial Intelligence Technology Directed at Smart Sensors and Devices from a Moderce, such as reasoning, perception of the environment and the analysis ability for decision making, which can be aimed at the industry in processes automation taking advantage of intelligent machine learning. This implies perceiving variables, making decisions, and solving problems, i.e., operating i

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CHARM

Impact of AI and Machine Learning in Smart Sensor Networks for Health Care platforms and robotic process automation. Machine Learning (ML) has been propelled as an exclusive technique for AI. ML can be demarcated as the learning procedures for enhancement of computer models that can augment the act of systems. Recently, use of ML technologies in automation of health care

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ML Algorithms for Smart Sensor Networksurther. The open issues that need to be investigated in future are addressed and future applications of SSNs are identified. This paper can be a guiding light for the new researchers entering the domain of SSNs by giving a detailed survey on the developments in the field in the last decade.

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Lobotomy

TRUST

External Threat Detection in Smart Sensor Networks Using Machine Learning Approachprediction task fully automated. A supervised machine learning approach can be applied to predict the external threats based on previous data history. Several parameters like Source Address, Source Type and Source Location etc. will be able to play the role of feature set for the supervised machine