{"product_id":"sensor-data-analysis-and-management-isbn-9781119682424","title":"Sensor Data Analysis and Management","description":"\u003cp\u003e\u003cb\u003eDiscover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSensor Data Analysis and Management: The Role of Deep Learning\u003c\/i\u003e delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data.\u003c\/p\u003e \u003cp\u003eThe editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance.\u003c\/p\u003e \u003cp\u003eThe book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data\u003c\/li\u003e \u003cli\u003eAn exploration of the benefits of neural networks in real-time environmental sensor data analysis\u003c\/li\u003e \u003cli\u003ePractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition\u003c\/li\u003e \u003cli\u003eAn analysis of boosting with XGBoost for sensor data analysis\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.\u003c\/p\u003e \u003cp\u003eAbout the Editors vii\u003c\/p\u003e \u003cp\u003eList of Contributors ix\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eN. Vijayaraj, G. Uganya, M. Balasaraswathi, V. Sivasankaran, Radhika Baskar, and A.S. Syed Fiaz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Internet of Things for Human-Activity Recognition Based on Wearable Sensor Data 19\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDr. Vikram Rajpoot, Sudeep Ray Gaur, Aditya Patel, and Dr. Akash Saxena\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Evaluation of Feature Selection Techniques in Intrusion Detection Systems Using Machine Learning Models in Wireless Ad Hoc Networks 33\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eT.J. Nagalakshmi, M. Balasaraswathi, V. Sivasankaran, D. Ravikumar, S. Joseph Gladwin, \u003c\/i\u003eand \u003ci\u003eS. Pravin Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Neuro-Fuzzy-Based Bidirectional and Biobjective Reactive Routing Schema for Critical Wireless Sensor Networks 73\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eK.M. Karthick Raghunath and G.R. Anantha Raman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Feature Detection and Extraction Techniques for Real-Time Student Monitoring in Sensor Data Environments 97\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDr. V. Saravanan and Dr (Ms). N. Shanmuga Priya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Deep Learning Analysis of Location Sensor Data for Human-Activity Recognition 103\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHariprasath Manoharan, Ganesan Sivarajan, and Subramanian Srikrishna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A Quantum-Behaved Particle-Swarm-Optimization-Based KNN Classifier for Improving WSN Lifetime 117\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAjmi Nader, Helali Abdelhamid, and Mghaieth Ridha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Feature Detection and Extraction Techniques for Sensor Data 131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDr. L. Priya, Ms. A. Sathya, and Dr. S. Thanga Revathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Object Detection in Satellite Images Using Modified Pyramid Scene Parsing Networks 147\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAkhilesh Vikas Kakade, S Rajkumar (Corresponding Author), K Suganthi, and L Ramanathan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Coronary Illness Prediction Using the AdaBoost Algorithm 161\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Deivendran, S. Vishal Balaji, B. Paramasivan, S. Vimal (Corresponding Author)\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Geographic Information Systems and Confidence Interval with Deep Learning Techniques for Traffic Management Systems in Smart Cities 173\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePrisilla Jayanthi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA. Suresh, PhD\u003c\/b\u003e is an Associate Professor in the Department of Computer Science and Engineering in SRM Institute of Science \u0026amp; Technology, Tamil Nadu, India. With nearly two decades of experience in teaching, his areas of specializations include Data Mining, Artificial Intelligence, Image Processing, Multimedia and System Software. He has two patents and has published approximately 90 papers in International journals. He is a Senior Member of IEEE, ISTE, MCSI, IACSIT, IAENG, MCSTA and a Global Member of Internet Society (ISOC). He has hosted two special sessions for IEEE sponsored conferences in Osaka, Japan and Thailand.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eR. Udendhran\u003c\/b\u003e is an Assistant Professor Grade III in the Department of Computer Science and Engineering, at the Sri SaiRam Institute of Technology, Chennai, India. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eM. S. Irfan Ahmed\u003c\/b\u003e is Associate Professor in the Department of Computer Science and Information, Faculty of Science and Literature at Taibah University, Saudi Arabia. He is a member of ISTE, MCSI, IACSIT, and IAENG.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDiscover detailed insights into the methods, algorithms, and  techniques for deep learning in sensor data analysis\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eSensor Data Analysis and Management: The Role of Deep Learning\u003c\/i\u003e, delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. \u003c\/p\u003e\u003cp\u003eThe editors have curated a distinguished group of perceptive and concise articles that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. \u003c\/p\u003e\u003cp\u003eThe book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data\u003c\/li\u003e \u003cli\u003eAn exploration of the benefits of neural networks in real-time environmental sensor data analysis\u003c\/li\u003e \u003cli\u003ePractical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition\u003c\/li\u003e \u003cli\u003eAn analysis of boosting with XGBoost for sensor data analysis\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, \u003ci\u003eSensor Data Analysis and Management: The Role of Deep Learning\u003c\/i\u003e will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47990007169253,"sku":"NP9781119682424","price":145.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119682424.jpg?v=1761786196","url":"https:\/\/k12savings.com\/es\/products\/sensor-data-analysis-and-management-isbn-9781119682424","provider":"K12savings","version":"1.0","type":"link"}