B. K. Tripathy – författare

Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains.Features:Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”.Reviews adept handling with respect to existing software and evaluation issues of interpretability.Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression.Focuses on interpreting black box models like feature importance and accumulated local effects.Discusses capabilities of explainability and interpretability.This book is aimed at graduate students and professionals in computer engineering and networking communications.

Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains.Features:Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”.Reviews adept handling with respect to existing software and evaluation issues of interpretability.Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression.Focuses on interpreting black box models like feature importance and accumulated local effects.Discusses capabilities of explainability and interpretability.This book is aimed at graduate students and professionals in computer engineering and networking communications.

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

This edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.