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After an introduction to renewable energy technologies, the authors
present computational intelligence techniques for optimizing the
manufacture of related technologies, including solar concentrators.
In particular the authors present new applications for their neural
classifiers for image and pattern recognition. The book will be of
interest to researchers in computational intelligence, in
particular in the domain of neural networks, and engineers engaged
with renewable energy technologies.
Social media data contains our communication and online sharing,
mirroring our daily life. This book looks at how we can use and
what we can discover from such big data: Basic knowledge (data
& challenges) on social media analytics Clustering as a
fundamental technique for unsupervised knowledge discovery and data
mining A class of neural inspired algorithms, based on adaptive
resonance theory (ART), tackling challenges in big social media
data clustering Step-by-step practices of developing unsupervised
machine learning algorithms for real-world applications in social
media domain Adaptive Resonance Theory in Social Media Data
Clustering stands on the fundamental breakthrough in cognitive and
neural theory, i.e. adaptive resonance theory, which simulates how
a brain processes information to perform memory, learning,
recognition, and prediction. It presents initiatives on the
mathematical demonstration of ART's learning mechanisms in
clustering, and illustrates how to extend the base ART model to
handle the complexity and characteristics of social media data and
perform associative analytical tasks. Both cutting-edge research
and real-world practices on machine learning and social media
analytics are included in the book and if you wish to learn the
answers to the following questions, this book is for you: How to
process big streams of multimedia data? How to analyze social
networks with heterogeneous data? How to understand a user's
interests by learning from online posts and behaviors? How to
create a personalized search engine by automatically indexing and
searching multimodal information resources? .
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