Machine learning paradigm : for internet of things applications / edited by Shalli Rani, R. Maheswar, G.R. Kanagachidambaresan, Sachin Ahuja, and Deepali Gupta.

Contributor(s): Rani, Shalli [editor.] | Maheswar, R, 1978- [editor.] | Kanagachidambaresan, G. R, 1988- [editor.] | Ahuja, Sachin [editor.] | Gupta, Deepali [editor.]
Series: Next-Generation Computing and Communication Engineering: Publisher: Hoboken, NJ : Beverly, MA : Wiley ; Scrivener, 2022Copyright date: ©2022Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119760474; 9781119763499; 1119763495; 9781119763482; 1119763487Subject(s): Machine learning | Internet of thingsGenre/Form: Electronic books.Additional physical formats: Print version :: No titleDDC classification: 006.31 LOC classification: Q325.5 | .M33 2022Online resources: Link text Full text is available at Wiley Online Library Click here to view
Contents:
Front Matter -- Machine Learning Concept-Based IoT Platforms for Smart Cities' Implementation and Requirements / M Saravanan, J Ajayan, R Maheswar, Eswaran Parthasarathy, K Sumathi -- An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan / W H Rankothge -- A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity / Mary Belinda M J Carmel, K Antonykumar, S Ravikumar, Yogesh R Kulkarni -- Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) / V Dineshbabu, Kumar V P Arul, M S Gowtham -- Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation / S Pradeep Kumar, G Kalpana -- Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning / Kumar S Pradeep, K Jayanthi, S Selvakumari -- Offline and Online Performance Evaluation Metrics of Recommender System: A Bird's Eye View / R Bhuvanya, M Kavitha -- Deep Learning-Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT / Kumar S Pradeep, R Pushpakumar, S Selvakumari -- Route Optimization for Perishable Goods Transportation System / A K Kowsalyadevi, M Megala, C Manivannan -- Fake News Detection Using Machine Learning Algorithms / M Kavitha, R Srinivasan, R Bhuvanya -- Opportunities and Challenges in Machine Learning With IoT / Sarvesh Tanwar, Jatin Garg, Medini Gupta, Ajay Rana -- Machine Learning Effects on Underwater Applications and IoUT / Mamta Nain, Nitin Goyal, Manni Kumar -- Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms / Monika Chaudhary, Nitin Goyal, Aadil Mushtaq -- Chest X-Ray for Pneumonia Detection / Sarang Sharma, Sheifali Gupta, Deepali Gupta -- Index.
Summary: As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications 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 these various application domains.
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006.31 M1843 2022 (Browse shelf) Available
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Previously issued in print: 2021.

Front Matter -- Machine Learning Concept-Based IoT Platforms for Smart Cities' Implementation and Requirements / M Saravanan, J Ajayan, R Maheswar, Eswaran Parthasarathy, K Sumathi -- An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan / W H Rankothge -- A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity / Mary Belinda M J Carmel, K Antonykumar, S Ravikumar, Yogesh R Kulkarni -- Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) / V Dineshbabu, Kumar V P Arul, M S Gowtham -- Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation / S Pradeep Kumar, G Kalpana -- Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning / Kumar S Pradeep, K Jayanthi, S Selvakumari -- Offline and Online Performance Evaluation Metrics of Recommender System: A Bird's Eye View / R Bhuvanya, M Kavitha -- Deep Learning-Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT / Kumar S Pradeep, R Pushpakumar, S Selvakumari -- Route Optimization for Perishable Goods Transportation System / A K Kowsalyadevi, M Megala, C Manivannan -- Fake News Detection Using Machine Learning Algorithms / M Kavitha, R Srinivasan, R Bhuvanya -- Opportunities and Challenges in Machine Learning With IoT / Sarvesh Tanwar, Jatin Garg, Medini Gupta, Ajay Rana -- Machine Learning Effects on Underwater Applications and IoUT / Mamta Nain, Nitin Goyal, Manni Kumar -- Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms / Monika Chaudhary, Nitin Goyal, Aadil Mushtaq -- Chest X-Ray for Pneumonia Detection / Sarang Sharma, Sheifali Gupta, Deepali Gupta -- Index.

As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.

Machine Learning Paradigm for Internet of Thing Applications 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 these various application domains.

About the Author

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.

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