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Blockchain is new-age technology used to track every transaction
using cryptocurrency across servers linked in a peer-to-peer
network, enabling transactions to be secure, transparent and
reliable. Retaining an efficient, secure and patient-centric
healthcare industry has never been so important, especially due to
the damaging effects of the Covid-19 pandemic. The applicability of
Blockchain in the healthcare domain can be seen as a remarkable
opportunity for researchers and scientists to solve real-world
problems. This book focuses on the fundamentals of Blockchain
technology along with the methods of its integration with the
healthcare industry. It also provides an enhanced understanding of
Blockchain technology, AI and IoT across the various application
areas of the healthcare industry. Furthermore, throughout the book,
areas of relevant applications, such as patient data privacy
protection, pharmaceutical supply chains and genomics are
discussed.
This book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2020) held at the University of Engineering &
Management, Kolkata, India, during July 2020. The book is organized
in three volumes and includes high-quality research work by
academicians and industrial experts in the field of computing and
communication, including full-length papers, research-in-progress
papers, and case studies related to all the areas of data mining,
machine learning, Internet of things (IoT), and information
security.
This book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2020) held at the University of Engineering &
Management, Kolkata, India, during July 2020. The book is organized
in three volumes and includes high-quality research work by
academicians and industrial experts in the field of computing and
communication, including full-length papers, research-in-progress
papers and case studies related to all the areas of data mining,
machine learning, Internet of things (IoT) and information
security.
This book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2020) held at the University of Engineering &
Management, Kolkata, India, during July 2020. The book is organized
in three volumes and includes high-quality research work by
academicians and industrial experts in the field of computing and
communication, including full-length papers, research-in-progress
papers and case studies related to all the areas of data mining,
machine learning, Internet of things (IoT) and information
security.
This book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2018) held at the University of Engineering &
Management, Kolkata, India, on February 23-25, 2018. It comprises
high-quality research work by academicians and industrial experts
in the field of computing and communication, including full-length
papers, research-in-progress papers, and case studies related to
all the areas of data mining, machine learning, Internet of Things
(IoT) and information security.
The book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2018) held at the University of Engineering &
Management, Kolkata, India, on February 23-25, 2018. It comprises
high-quality research by academics and industrial experts in the
field of computing and communication, including full-length papers,
research-in-progress papers, case studies related to all the areas
of data mining, machine learning, IoT and information security.
This book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2020) held at the University of Engineering &
Management, Kolkata, India, during July 2020. The book is organized
in three volumes and includes high-quality research work by
academicians and industrial experts in the field of computing and
communication, including full-length papers, research-in-progress
papers, and case studies related to all the areas of data mining,
machine learning, Internet of things (IoT), and information
security.
The book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2018) held at the University of Engineering &
Management, Kolkata, India, on February 23-25, 2018. It comprises
high-quality research by academics and industrial experts in the
field of computing and communication, including full-length papers,
research-in-progress papers, case studies related to all the areas
of data mining, machine learning, IoT and information security.
This book features research papers presented at the International
Conference on Emerging Technologies in Data Mining and Information
Security (IEMIS 2022) held at Institute of Engineering &
Management, Kolkata, India, during 23-25 February 2022. The book is
organized in three volumes and includes high-quality research work
by academicians and industrial experts in the field of computing
and communication, including full-length papers,
research-in-progress papers, and case studies related to all the
areas of data mining, machine learning, Internet of Things (IoT)
and information security.
In a distributed computing system (DCS), we need to allocate a
number of tasks to different processors for execution. The problem
of task assignment in heterogeneous computing systems has been
studied for many years with many variations and to accomplish
various objectives, such as throughput maximization, reliability
maximization, and cost minimization. There are also exists a set of
system constraints related to memory and communication link
capacity. Most of the existing approaches for task allocation deal
with a single objective only. In this project we construct the task
allocation problem as a multi-objective optimization problem to
consider system constraints. The goal programming technique is used
with pre-emptive priority structure to find the optimal allocation
that not only optimize system reliability but also optimize memory
as well as path load. The genetic algorithm is used to find the
optimal allocations. Genetic algorithm is used to find the optimal
allocations.
This book introduces in a systematic manner a general nonparametric
theory of statistics on manifolds, with emphasis on manifolds of
shapes. The theory has important and varied applications in medical
diagnostics, image analysis, and machine vision. An early chapter
of examples establishes the effectiveness of the new methods and
demonstrates how they outperform their parametric counterparts.
Inference is developed for both intrinsic and extrinsic Frechet
means of probability distributions on manifolds, then applied to
shape spaces defined as orbits of landmarks under a Lie group of
transformations - in particular, similarity, reflection similarity,
affine and projective transformations. In addition, nonparametric
Bayesian theory is adapted and extended to manifolds for the
purposes of density estimation, regression and classification.
Ideal for statisticians who analyze manifold data and wish to
develop their own methodology, this book is also of interest to
probabilists, mathematicians, computer scientists, and
morphometricians with mathematical training.
This book introduces in a systematic manner a general nonparametric
theory of statistics on manifolds, with emphasis on manifolds of
shapes. The theory has important and varied applications in medical
diagnostics, image analysis, and machine vision. An early chapter
of examples establishes the effectiveness of the new methods and
demonstrates how they outperform their parametric counterparts.
Inference is developed for both intrinsic and extrinsic Frechet
means of probability distributions on manifolds, then applied to
shape spaces defined as orbits of landmarks under a Lie group of
transformations - in particular, similarity, reflection similarity,
affine and projective transformations. In addition, nonparametric
Bayesian theory is adapted and extended to manifolds for the
purposes of density estimation, regression and classification.
Ideal for statisticians who analyze manifold data and wish to
develop their own methodology, this book is also of interest to
probabilists, mathematicians, computer scientists, and
morphometricians with mathematical training.
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