Uma N. Dulhare – författare

This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations.The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.Subjects covered in detail include: Mathematical foundations of machine learning with various examples.An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.Hands-on machine leaning open source tools viz. Apache Mahout, H2O.Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

This volume demonstrates the diverse state-of-the-art applications that combine artificial intelligence with soft computing, which has great potential for creating smart personalized healthcare services. The book showcases the myriad uses of AI and computer techniques in healthcare that employ deep learning, robotics, machine learning, blockchain, emerging cloud, edge computing, Practical Byzantine Fault Tolerance consensus, CNN architecture, Splunk, genetic algorithms (GA), DurBhashan, and many more. These technologies can be used in healthcare for enhanced data sharing, remote health monitoring, tele-rehabilitation, connecting rural populations with healthcare services, identifying diseases and health issues, automated medical diagnosis, analyzing information in surgical videos, ensuring timely communication and transportation during health disasters and emergencies, for optimizing expenditures, and more.

This book provides awareness of methods used for functional encryption in the academic and professional communities. The book covers functional encryption algorithms and its modern applications in developing secure systems via entity authentication, message authentication, software security, cyber security, hardware security, Internet of Thing (IoT), cloud security, smart card technology, CAPTCHA, digital signature, and digital watermarking. This book is organized into fifteen chapters; topics include foundations of functional encryption, impact of group theory in cryptosystems, elliptic curve cryptography, XTR algorithm, pairing based cryptography, NTRU algorithms, ring units, cocks IBE schemes, Boneh-Franklin IBE, Sakai-Kasahara IBE, hierarchical identity based encryption, attribute based Encryption, extensions of IBE and related primitives, and digital signatures.Explains the latest functional encryption algorithms in a simple way with examples;Includes applications of functional encryption in information security, application security, and network security;Relevant to academics, research scholars, software developers, etc.

This book provides awareness of methods used for functional encryption in the academic and professional communities. The book covers functional encryption algorithms and its modern applications in developing secure systems via entity authentication, message authentication, software security, cyber security, hardware security, Internet of Thing (IoT), cloud security, smart card technology, CAPTCHA, digital signature, and digital watermarking. This book is organized into fifteen chapters; topics include foundations of functional encryption, impact of group theory in cryptosystems, elliptic curve cryptography, XTR algorithm, pairing based cryptography, NTRU algorithms, ring units, cocks IBE schemes, Boneh-Franklin IBE, Sakai-Kasahara IBE, hierarchical identity based encryption, attribute based Encryption, extensions of IBE and related primitives, and digital signatures.Explains the latest functional encryption algorithms in a simple way with examples;Includes applications of functional encryption in information security, application security, and network security;Relevant to academics, research scholars, software developers, etc.

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision.  Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision.  Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

This book takes a balanced approach between theoretical understanding and real time applications. All topics show how to explore, build, evaluate and optimize deep learning models with computer vision.  Deep learning is integrated with computer vision to enhance the performance of image classification with localization, object detection, object recognition, object segmentation, image style transfer, image colorization, image reconstruction, image super-resolution, image synthesis, motion detection, pose estimation, semantic segmentation in biomedical field. Huge number of efficient approaches/applications and models support medical decisions in the fields of cardiology, dermatology, and radiology. The content of book elaborates deep learning models such as convolution neural networks, deep learning, generative adversarial network, long short-term memory networks (LSTM), autoencoder (AE), restricted Boltzmann machine (RBM), self-organizing map (SOM), deep belief network (DBN), etc.

The reader will learn how to build a machine-learning model and analyze the data which is generated by the practical use cases of Blockchain in various smart cities sectors like Smart Health, Smart food, agriculture, smart transport, smart water management, smart waste management, smart energy management, etc.

The reader will learn how to build a machine learning model and analyze the data which is generated by the practical use cases of Blockchain in various smart cities sectors like Smart Health, Smart food, and agriculture, smart transport, smart water management, smart waste management, smart energy management, etc.

Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.