Machine Learning Paradigm for Internet of Things Applications (Hardcover)
暫譯: 物聯網應用的機器學習範式 (精裝版)

Rani, Shalli, Maheswar, R., Kanagachidambaresan, G. R.

Machine Learning Paradigm for Internet of Things Applications (Hardcover)
  • 出版商: Wiley
  • 出版日期: 2022-03-02
  • 售價: $1,950
  • 貴賓價: 9.8 折 $1,911
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 111976047X
  • ISBN-13: 9781119760474
  • 相關分類: Machine Learning、物聯網 IoT

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商品描述

The aim of the book is to explore the benefits of deploying Machine Learning (ML)in Internet of Things (IoT) environment. As a growing number of internet-connected sensors are built into cars, planes, trains and buildings, businesses are amassing vast amounts of data. Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning (ML) -- a subset of the field of artificial intelligence (AI). In order to provide smarter environment, their needs to be implemented IoT with machine learning. Machine learning will allow these smart devices to be smarter in a literal sense. It can analyze the data generated by the connected devices and get an insight into human's behavioral pattern. Hence, it would not be wrong to say that if the IoT is the digital nervous system, then ML acts as its medulla oblongata.

This book provides the state-of-the-art applications of Machine Learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store 'contextualized marketing' and intelligent transportation systems. Readers will gain an insight into the integration of Machine Learning with IoT in various application domains.

商品描述(中文翻譯)

本書的目的是探討在物聯網 (IoT) 環境中部署機器學習 (ML) 的好處。隨著越來越多的互聯網連接感測器被嵌入到汽車、飛機、火車和建築物中，企業正在積累大量數據。利用這些數據提取有用信息是一項挑戰，而這個挑戰正開始通過機器學習 (ML) 的模式匹配能力來解決，機器學習是人工智慧 (AI) 領域的一個子集。為了提供更智能的環境，必須將物聯網與機器學習結合起來。機器學習將使這些智能設備在字面上變得更智能。它可以分析由連接設備生成的數據，並洞察人類的行為模式。因此，可以說如果物聯網是數位神經系統，那麼機器學習則充當其延髓。

本書提供了機器學習在物聯網環境中的最先進應用。機器學習和物聯網數據的最常見用例是預測性維護，其次是分析閉路電視監控、智能家居應用、智能醫療、店內「情境行銷」和智能交通系統。讀者將深入了解機器學習與物聯網在各種應用領域的整合。

作者簡介

Audience

Scholars and scientists working in artificial intelligence and electronic engineering, industry engineers, software and computer hardware specialists.

Shalli Rani, PhD is an associate professor in the Department of CSE, Chitkara University, Punjab, India.

R. Maheswar, PhD is the Dean and associate professor, School of EEE, VIT Bhopal University, Madya Pradesh, India.

G. R. Kanagachidambaresan, PhD associate professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India.

Sachin Ahuja, PhD is a professor in the Department of CSE, Chitkara University, Punjab, India.

Deepali Gupta, PhD is a professor, Department of CSE, Chitkara University, Punjab, India.

作者簡介(中文翻譯)

讀者對象

從事人工智慧和電子工程的學者與科學家、業界工程師、軟體及電腦硬體專家。

Shalli Rani, PhD 是印度旁遮普州Chitkara大學計算機科學與工程系的副教授。

R. Maheswar, PhD 是印度中央邦VIT Bhopal大學電子與電氣工程學院的院長及副教授。

G. R. Kanagachidambaresan, PhD 是印度泰米爾納德邦Vel Tech Rangarajan Dr. Sagunthala科學與技術研究所計算機科學與工程系的副教授。

Sachin Ahuja, PhD 是印度旁遮普州Chitkara大學計算機科學與工程系的教授。

Deepali Gupta, PhD 是印度旁遮普州Chitkara大學計算機科學與工程系的教授。

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