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Wireless Sensor Networks: Evolutionary Algorithms for Optimizing
Performance provides an integrative overview of bio-inspired
algorithms and their applications in the area of Wireless Sensor
Networks (WSN). Along with the usage of the WSN, the number of
risks and challenges occurs while deploying any WSN. Therefore, to
defeat these challenges some of the bio-inspired algorithms are
applied and discussed in this book. Discussion includes a broad,
integrated perspective on various challenges and issues in WSN and
also impact of bio-inspired algorithms on the lifetime of the WSN.
It creates interdisciplinary theory, concepts, definitions, models
and findings involved in WSN and Bio-inspired algorithms making it
an essential guide and reference. It includes various WSN examples
making the book accessible to a broader interdisciplinary
readership. The book offers comprehensive coverage of the most
essential topics, including: Evolutionary algorithms Swarm
intelligence Hybrid algorithms Energy efficiency in WSN Load
balancing of gateways Localization Clustering and routing Designing
fitness functions according to the issues in WSN. The book explains
about practices of shuffled complex evolution algorithm, shuffled
frog leaping algorithm, particle swarm optimization and dolphin
swarm optimization to defeat various challenges in WSN. The author
elucidates how we must transform our thinking, illuminating the
benefits and opportunities offered by bio-inspired approaches to
innovation and learning in the area of WSN. This book serves as a
reference book for scientific investigators who shows an interest
in evolutionary computation and swarm intelligence as well as
issues and challenges in WSN.
Diabetes Mellitus (DM, commonly referred to as diabetes, is a
metabolic disorder in which there are high blood sugar levels over
a prolonged period. Lack of sufficient insulin causes presence of
excess sugar levels in the blood. As a result the glucose levels in
diabetic patients are more than normal ones. It has symptoms like
frequent urination, increased hunger, increase thirst and high
blood sugar. There are mainly three types of diabetes namely
type-1, type-2 and gestational diabetes. Type-1 DM occurs due to
immune system mistakenly attacks and destroys the beta-cells and
Type-2 DM occurs due to insulin resistance. Gestational DM occurs
in women during pregnancy due to insulin blocking by pregnancy
harmones. Among these three types of DM, type-2 DM is more
prevalence, and impacting so many millions of people across the
world. Classification and predictive systems are actually reliable
in the health care sector to explore hidden patterns in the
patients data. These systems aid, medical professionals to enhance
their diagnosis, prognosis along with remedy organizing techniques.
The less percentage of improvement in classifier predictive
accuracy is very important for medical diagnosis purposes where
mistakes can cause a lot of damage to patient's life. Hence, we
need a more accurate classification system for prediction of type-2
DM. Although, most of the above classification algorithms are
efficient, they failed to provide good accuracy with low
computational cost. In this book, we proposed various
classification algorithms using soft computing techniques like
Neural Networks (NNs), Fuzzy Systems (FS) and Swarm Intelligence
(SI). The experimental results demonstrate that these algorithms
are able to produce high classification accuracy at less
computational cost. The contributions presented in this book shall
attempt to address the following objectives using soft computing
approaches for identification of diabetes mellitus. Introuducing an
optimized RBFN model called Opt-RBFN. Designing a cost effective
rule miner called SM-RuleMiner for type-2 diabetes diagnosis.
Generating more interpretable fuzzy rules for accurate diagnosis of
type2 diabetes using RST-BatMiner. Developing accurate cascade
ensemble frameworks called Diabetes-Network for type-2 diabetes
diagnosis. Proposing a Multi-level ensemble framework called
Dia-Net for improving the classification accuracy of type-2
diabetes diagnosis. Designing an Intelligent Diabetes Risk score
Model called Intelli-DRM estimate the severity of Diabetes
mellitus. This book serves as a reference book for scientific
investigators who need to analyze disease data and/or numerical
data, as well as researchers developing methodology in soft
computing field. It may also be used as a textbook for a graduate
and post graduate level course in machine learning or soft
computing.
The Volume of "Advances in Machine Learning and Data Science -
Recent Achievements and Research Directives" constitutes the
proceedings of First International Conference on Latest Advances in
Machine Learning and Data Science (LAMDA 2017). The 37 regular
papers presented in this volume were carefully reviewed and
selected from 123 submissions. These days we find many computer
programs that exhibit various useful learning methods and
commercial applications. Goal of machine learning is to develop
computer programs that can learn from experience. Machine learning
involves knowledge from various disciplines like, statistics,
information theory, artificial intelligence, computational
complexity, cognitive science and biology. For problems like
handwriting recognition, algorithms that are based on machine
learning out perform all other approaches. Both machine learning
and data science are interrelated. Data science is an umbrella term
to be used for techniques that clean data and extract useful
information from data. In field of data science, machine learning
algorithms are used frequently to identify valuable knowledge from
commercial databases containing records of different industries,
financial transactions, medical records, etc. The main objective of
this book is to provide an overview on latest advancements in the
field of machine learning and data science, with solutions to
problems in field of image, video, data and graph processing,
pattern recognition, data structuring, data clustering, pattern
mining, association rule based approaches, feature extraction
techniques, neural networks, bio inspired learning and various
machine learning algorithms.
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