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Record Nr. |
UNINA9910890172403321 |
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Autore |
Devi V. Ajantha |
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Titolo |
Sustainable IoT and Data Analytics Enabled Machine Learning Techniques and Applications / / edited by V. Ajantha Devi |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (179 pages) |
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Collana |
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Contributions to Environmental Sciences & Innovative Business Technology, , 2731-8311 |
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Disciplina |
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Soggetti |
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Sustainability |
Internet of things |
Machine learning |
Big data |
Artificial intelligence |
Internet of Things |
Machine Learning |
Big Data |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Leveraging IoT Based CNN for Streamlining Business Application -- Intelligent Electric Vehicles: Leveraging AI-IoT for Sustainable Mobility -- Internet of Things Enabled Deep Convolutional Neural Network Model for Breast Cancer Classification -- Application of Machine Learning in Cyber Security: A Technological Perceptive -- Statistical Surveillance for Host-based Intrusion Detection System (HIDS): An Intelligent System for Automation -- IoT for Healthcare: A Sustainable Approach -- A NOVEL TRUST BASED FRAMEWORK FOR SECURED VANETs IN FUTURE -- Big Data and IOT based Flood Monitoring using Deep Neural Network -- Role of Artificial Intelligence in Design & Implementation of Healthcare Web Based Application “Carefree Bharat” focusing Sustainable Development. |
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Sommario/riassunto |
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This book provides a structured presentation of machine learning related to vision, speech, and natural language processing. It addresses |
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the tools, techniques, and challenges of machine learning algorithm implementation, computation time, and the complexity of reasoning and modeling of different types of data. The book covers diverse topics such as semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, natural language processing, traffic and signaling, driverless driving, and radiology. The majority of smart applications have a need for a sustainable Internet of things (IoT) and artificial intelligence. Active research trends and future directions of machine learning under big data analytics are also discussed. Machine learning is a class of artificial neural networks that have become dominant in various computer vision tasks, attracting interest across a variety of domains as they are a type of deep neural networks efficient in extracting meaningful information from visual imagery. |
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