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The book explores the concepts and challenges in developing novel
approaches using the Internet of Things, intelligent systems,
machine intelligence systems, and data analytics in various
industrial sectors such as manufacturing, smart agriculture, smart
cities, food processing, environment, defense, stock market and
healthcare. Further, it discusses the latest improvements in the
industrial sectors using machine intelligence learning and
intelligent systems techniques, especially robotics. Features: *
Highlights case studies and solutions to industrial problems using
machine learning and intelligent systems. * Covers applications in
smart agriculture, smart healthcare, intelligent machines for
disaster management, and smart manufacturing. * Provides the latest
methodologies using machine intelligence systems in the early
forecasting of weather. * Examines the research challenges and
identifies the gaps in data collection and data analysis,
especially imagery, signal, and speech. * Provides applications of
digitization and smart processing using the Internet of Things and
effective intelligent agent systems in manufacturing. * Discusses a
systematic and exhaustive analysis of intelligent software effort
estimation models. It will serve as an ideal reference text for
graduate students, post-graduate students, IT Professionals, and
academic researchers in the fields of electrical engineering,
electronics and communication engineering, computer engineering,
and information technology.
The book is a collection of high-quality peer-reviewed research
papers presented at International Conference on Information System
Design and Intelligent Applications (INDIA 2017) held at Duy Tan
University, Da Nang, Vietnam during 15-17 June 2017. The book
covers a wide range of topics of computer science and information
technology discipline ranging from image processing, database
application, data mining, grid and cloud computing, bioinformatics
and many others. The various intelligent tools like swarm
intelligence, artificial intelligence, evolutionary algorithms,
bio-inspired algorithms have been well applied in different domains
for solving various challenging problems.
Predictive Intelligence in Biomedical and Health Informatics
focuses on imaging, computer-aided diagnosis and therapy as well as
intelligent biomedical image processing and analysis. It develops
computational models, methods and tools for biomedical engineering
related to computer-aided diagnostics (CAD), computer-aided surgery
(CAS), computational anatomy and bioinformatics. Large volumes of
complex data are often a key feature of biomedical and engineering
problems and computational intelligence helps to address such
problems. Practical and validated solutions to hard biomedical and
engineering problems can be developed by the applications of neural
networks, support vector machines, reservoir computing,
evolutionary optimization, biosignal processing, pattern
recognition methods and other techniques to address complex
problems of the real world.
This book will focus on the involvement of data mining and
intelligent computing methods for recent advances in Biomedical
applications and algorithms of nature-inspired computing for
Biomedical systems. The proposed meta heuristic or nature-inspired
techniques should be an enhanced, hybrid, adaptive or improved
version of basic algorithms in terms of performance and convergence
metrics. In this exciting and emerging interdisciplinary area a
wide range of theory and methodologies are being investigated and
developed to tackle complex and challenging problems. Today,
analysis and processing of data is one of big focuses among
researchers community and information society. Due to evolution and
knowledge discovery of natural computing, related meta heuristic or
bio-inspired algorithms have gained increasing popularity in the
recent decade because of their significant potential to tackle
computationally intractable optimization dilemma in medical,
engineering, military, space and industry fields. The main reason
behind the success rate of nature inspired algorithms is their
capability to solve problems. The nature inspired optimization
techniques provide adaptive computational tools for the complex
optimization problems and diversified engineering applications.
Tentative Table of Contents/Topic Coverage: - Neural Computation -
Evolutionary Computing Methods - Neuroscience driven AI Inspired
Algorithms - Biological System based algorithms - Hybrid and
Intelligent Computing Algorithms - Application of Natural Computing
- Review and State of art analysis of Optimization algorithms -
Molecular and Quantum computing applications - Swarm Intelligence -
Population based algorithm and other optimizations
This book provides comprehensive details of all Swarm Intelligence
based Techniques available till date in a comprehensive manner
along with their mathematical proofs. It will act as a foundation
for authors, researchers and industry professionals. This monograph
will present the latest state of the art research being done on
varied Intelligent Technologies like sensor networks, machine
learning, optical fiber communications, digital signal processing,
image processing and many more.
In this book, application-related studies for acoustic biomedical
sensors are covered in depth. The book features an array of
different biomedical signals, including acoustic biomedical signals
as well as the thermal biomedical signals, magnetic biomedical
signals, and optical biomedical signals to support healthcare. It
employs signal processing approaches, such as filtering, Fourier
transform, spectral estimation, and wavelet transform. The book
presents applications of acoustic biomedical sensors and bio-signal
processing for prediction, detection, and monitoring of some
diseases from the phonocardiogram (PCG) signal analysis. Several
challenges and future perspectives related to the acoustic sensors
applications are highlighted. This book supports the engineers,
researchers, designers, and physicians in several interdisciplinary
domains that support healthcare.
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