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Connects four contemporary areas of research: Artificial
Intelligence, big data analytics, knowledge modelling, and
healthcare Covers a list of diverse topics related to healthcare
and knowledge modelling Summarizes the most important recent and
valuable research related to big data analytics in the healthcare
sector Includes case studies related to the application of big data
in healthcare Highlights modern developments, challenges,
opportunities, and future research directions in healthcare
This book presents an overview and several applications of
explainable artificial intelligence (XAI). It covers different
aspects related to explainable artificial intelligence, such as the
need to make the AI models interpretable, how black box
machine/deep learning models can be understood using various XAI
methods, different evaluation metrics for XAI, human-centered
explainable AI, and applications of explainable AI in health care,
security surveillance, transportation, among other areas. The book
is suitable for students and academics aiming to build up their
background on explainable AI and can guide them in making
machine/deep learning models more transparent. The book can be used
as a reference book for teaching a graduate course on artificial
intelligence, applied machine learning, or neural networks.
Researchers working in the area of AI can use this book to discover
the recent developments in XAI. Besides its use in academia, this
book could be used by practitioners in AI industries, healthcare
industries, medicine, autonomous vehicles, and security
surveillance, who would like to develop AI techniques and
applications with explanations.
This book presents an overview of how machine learning and data
mining techniques are used for tracking and preventing diseases. It
covers several aspects such as stress level identification of a
person from his/her speech, automatic diagnosis of disease from
X-ray images, intelligent diagnosis of Glaucoma from clinical eye
examination data, prediction of protein-coding genes from big
genome data, disease detection through microscopic analysis of
blood cells, information retrieval from electronic medical record
using named entity recognition approaches, and prediction of
drug-target interactions. The book is suitable for computer
scientists having a bachelor degree in computer science. The book
is an ideal resource as a reference book for teaching a graduate
course on AI for Medicine or AI for Health care. Researchers
working in the multidisciplinary areas use this book to discover
the current developments. Besides its use in academia, this book
provides enough details about the state-of-the-art algorithms
addressing various biomedical domains, so that it could be used by
industry practitioners who want to implement AI techniques to
analyze the diseases. Medical institutions use this book as
reference material and give tutorials to medical experts on how the
advanced AI and ML techniques contribute to the diagnosis and
prediction of the diseases.
This book presents an overview of how machine learning and data
mining techniques are used for tracking and preventing diseases. It
covers several aspects such as stress level identification of a
person from his/her speech, automatic diagnosis of disease from
X-ray images, intelligent diagnosis of Glaucoma from clinical eye
examination data, prediction of protein-coding genes from big
genome data, disease detection through microscopic analysis of
blood cells, information retrieval from electronic medical record
using named entity recognition approaches, and prediction of
drug-target interactions. The book is suitable for computer
scientists having a bachelor degree in computer science. The book
is an ideal resource as a reference book for teaching a graduate
course on AI for Medicine or AI for Health care. Researchers
working in the multidisciplinary areas use this book to discover
the current developments. Besides its use in academia, this book
provides enough details about the state-of-the-art algorithms
addressing various biomedical domains, so that it could be used by
industry practitioners who want to implement AI techniques to
analyze the diseases. Medical institutions use this book as
reference material and give tutorials to medical experts on how the
advanced AI and ML techniques contribute to the diagnosis and
prediction of the diseases.
This book presents new trends to optimize e-Government in various
contexts. It aims to highlight new methods and approaches that
unveil the potential of data for public services. The book also
illustrates how public services can be mathematically modeled with
many case studies. Then, algorithms are proposed to optimize their
functioning and to better contribute to the general interest, such
as education, health care, safety, security, or culture. The book
also focuses on protecting citizens' personal data and obtaining
their explicit consent. The book is suitable for students and
academics aiming to build up their background on the usage of data
and algorithms through various techniques, including artificial
intelligence. The book is used as a reference book for teaching a
graduate course on e-Government, Process Modeling, or Artificial
Intelligence. Besides its use in academia, this book is used by
civil servants of every domain and citizens who aim to understand
the ongoing modernization of public services.
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