Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated - bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and…mehr
Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated - bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL. · Comprehensive analyses and probabilistic approaches for ML and DL. · Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. · Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Ramchandra Mangrulkar have received his PhD in Computer Science and Engineering from SGBAU Amravati in 2016 and currently he is working as an Associate Professor at the department of Computing Engineering at DJSCE Mumbai, Maharashtra, India. Prior to this, he was working Associate Professor and Head, department of Computer Engineering, Bapurao Deshmukh College of Engineering Sevagram. Maharashtra, India. Dr. Ramchandra Mangrulkar has published significant number of papers and book chapters in the field related journals and conferences and have also participated as a session chair in various conferences and conducted various workshops on Network Simulator and LaTeX. He also received certification of appreciation from DIG Special Crime Branch Pune and Supretendant of Police and broadcasting media gives wide publicity for the project work guided by him on the topic "Face Recognition System". He also received 3.5 lakhs grant under Research Promotion Scheme of AICTE for the project "Secured Energy Efficient Routing Protocol for Delay Tolerant Hybrid Network". He is active member of Board of Studies in various universities and autonomous institute in India. Dr. Antonis Michalas have received his PhD in Network Security from Aalborg University, Denmark and currently he is working as an Assistant Professor at the department of computing Science at Tampere University of Technology, faculty of Computing and Electrical Engineering. Prior to this, he was working as an Assistant Professor in Cyber Security at the University of Westminster, London. Earlier, he was working as a postdoctoral researcher at the Security Lab at the Swedish Institute of Computer Science in Stockholm, Sweden. As a postdoctoral researcher at the SCE Labs, he was actively involved in National and European research projects. Dr. Antonis has published significant number of papers in the field related journals and conferences and have also participated as a speaker in various conferences and workshops. His research interest includes private and Secure e-voting system, reputation systems, privacy in decentralized environments, cloud computing, trusted computing and privacy preserving protocols in participatory sensing applications. Dr. Narendra Shekokar has received his PhD in Engineering (Network Security) from NMIMS University, Mumbai and he is working as a Professor and Head of dept. of Computer Engineering at SVKM's Dwarkadas J. Sanghvi College of Engineering, Mumbai (Autonomous college affiliated to University of Mumbai). He was a member of Board of Studies at University of Mumbai for more than 5 years and he has also been a member of various committees at University of Mumbai. His total teaching experience is 23years. Dr. Narendra Shekokar is PhD guide for 8 research fellows and more than 25 students at Post Graduation level. He has presented more than 65 papers at International & National conferences and has also published more than 25 research papers in renowned journals. He has received the Minor Research Grant twice from University of Mumbai for his research projects. He has delivered expert talk and chaired a session at numerous events and conferences. Dr. Meera Narvekar is currently the Head of Department of Computer Engineering at D.J. Sanghvi College of Engineering, Mumbai (Autonomous college affiliated to University of Mumbai). She is a member of Board of Studies at University of Mumbai. She was nominated as a Senate member of the University of Mumbai in 2008. She has a total experience of 20 years in teaching. Dr. Meera has obtained her Ph.D in Computer Science and Technology from SNDT University, Mumbai in the area of Mobile Computing. Her thesis work was on Optimization of data delivery in Mobile Networks. She has published around 50 papers in various international and national journals and conferences. She is currently guiding projects with applications in agriculture, which has also received grant from University of Mumbai. She has delivered talks in various conferences and workshops. She is also in reviewer list and has been session chair of many conferences. Dr. Pallavi Chavan has received her PhD in Computer Science and Engineering from RTM Nagpur University and he is working as Associate Professor in Information Technology Department, RAIT Nerul, Navi Mumbai, India. Prior to this, she was working Assistant Professor and department of Computer Engineering, Bapurao Deshmukh College of Engineering Sevagram. Maharashtra, India. Her area of research is visual cryptography and secret sharing. She also interestingly works with image processing and soft computing. She is the recipient of UGC Workshop Grant two time for conduction of national level workshops. She is also a recipient of CSIR seminar grant for conduction of national level seminars. Her subjects of interest Are Theory of Computation, Database Management System and Artificial Neural Network and Fuzzy Logic. She is the follower of spiritual approach of Bramhakumaris For Rajyoga Meditation.
Inhaltsangabe
1. Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications 2. Fundamental Models in Machine Learning and Deep Learning 3. Research Aspects of Machine Learning: Issues, Challenges, and Future Scope 4. Comprehensive Analysis of Dimensionality Reduction Techniques for Machine Learning Applications 5. Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network 6. Application of Neural Network and Machine Learning in Mental Health Diagnosis 7. Application of Machine Learning in Cardiac Arrhythmia 8. Advances in Machine Learning and Deep Learning Approaches for Mammographic Breast Density Measurement for Breast Cancer Risk Prediction: An Overview 9. Applications of Machine Learning in Psychology and the Lifestyle Disease Diabetes Mellitus 10. Application of Machine Learning and Deep Learning in Thyroid Disease Prediction 11. Application of Machine Learning in Fake News Detection 12. Authentication of Broadcast News on Social Media Using Machine Learning 13. Application of Deep Learning in Facial Recognition 14. Application of Deep Learning in Deforestation Control and Prediction of Forest Fire Calamities 15. Application of Convolutional Neural Network in Feather Classifications 16. Application of Deep Learning Coupled with Thermal Imaging in Detecting Water Stress in Plants 17. Machine Learning Techniques to Classify Breast Cancer 18. Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network 19. Evaluation of Intrusion Detection System with Rule-Based Technique to Detect Malicious Web Spiders Using Machine Learning 20. Application of Machine Learning to Improve Tourism Industry 21. Training Agents to Play 2D Games Using Reinforcement Learning 22. Analysis of the Effectiveness of the Non-Vaccine Countermeasures Taken by the Indian Government against COVID-19 and Forecasting Using Machine Learning and Deep Learning 23. Application of Deep Learning in Video Question Answering System 24. Implementation and Analysis of Machine Learning and Deep Learning Algorithms 25. Comprehensive Study of Failed Machine Learning Applications Using a Novel 3C Approach
1. Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications 2. Fundamental Models in Machine Learning and Deep Learning 3. Research Aspects of Machine Learning: Issues, Challenges, and Future Scope 4. Comprehensive Analysis of Dimensionality Reduction Techniques for Machine Learning Applications 5. Application of Deep Learning in Counting WBCs, RBCs, and Blood Platelets Using Faster Region-Based Convolutional Neural Network 6. Application of Neural Network and Machine Learning in Mental Health Diagnosis 7. Application of Machine Learning in Cardiac Arrhythmia 8. Advances in Machine Learning and Deep Learning Approaches for Mammographic Breast Density Measurement for Breast Cancer Risk Prediction: An Overview 9. Applications of Machine Learning in Psychology and the Lifestyle Disease Diabetes Mellitus 10. Application of Machine Learning and Deep Learning in Thyroid Disease Prediction 11. Application of Machine Learning in Fake News Detection 12. Authentication of Broadcast News on Social Media Using Machine Learning 13. Application of Deep Learning in Facial Recognition 14. Application of Deep Learning in Deforestation Control and Prediction of Forest Fire Calamities 15. Application of Convolutional Neural Network in Feather Classifications 16. Application of Deep Learning Coupled with Thermal Imaging in Detecting Water Stress in Plants 17. Machine Learning Techniques to Classify Breast Cancer 18. Application of Deep Learning in Cartography Using UNet and Generative Adversarial Network 19. Evaluation of Intrusion Detection System with Rule-Based Technique to Detect Malicious Web Spiders Using Machine Learning 20. Application of Machine Learning to Improve Tourism Industry 21. Training Agents to Play 2D Games Using Reinforcement Learning 22. Analysis of the Effectiveness of the Non-Vaccine Countermeasures Taken by the Indian Government against COVID-19 and Forecasting Using Machine Learning and Deep Learning 23. Application of Deep Learning in Video Question Answering System 24. Implementation and Analysis of Machine Learning and Deep Learning Algorithms 25. Comprehensive Study of Failed Machine Learning Applications Using a Novel 3C Approach
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
