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Stochastic processes have a wide range of applications ranging from
image processing, neuroscience, bioinformatics, financial
management, and statistics. Mathematical, physical, and engineering
systems use stochastic processes for modeling and reasoning
phenomena. While comparing AI-stochastic systems with other
counterpart systems, we are able to understand their significance,
thereby applying new techniques to obtain new real-time results and
solutions. Stochastic Processes and Their Applications in
Artificial Intelligence opens doors for artificial intelligence
experts to use stochastic processes as an effective tool in
real-world problems in computational biology, speech recognition,
natural language processing, and reinforcement learning. Covering
key topics such as social media, big data, and artificial
intelligence models, this reference work is ideal for
mathematicians, industry professionals, researchers, scholars,
academicians, practitioners, instructors, and students.
This book explores computational engineering techniques and
applications in agriculture development. Recent technologies such
as cloud computing, IoT, big data, and machine learning are focused
on for smart agricultural engineering. This book provides practical
and use case oriented approaches for IOT-based agricultural
systems. Predictive Analysis in Smart Agriculture deals with all
aspects of smart agriculture with state-of-the-art predictive
analysis in the complete 360-degree view spectrum. The book
includes the concepts of urban and vertical farming using Agro IoT
systems and renewable energy sources for modern agriculture trends.
It discusses the real-world challenges, complexities in Agro IoT,
and advantages of incorporating smart technology. It also presents
the rapid advancement of the technologies in the existing Agri
model by applying the various techniques. Novel architectural
solutions in smart agricultural engineering are the core aspects of
this book. Several predictive analysis tools and smart agriculture
are also incorporated. This book can be used as a textbook for
students in predictive analysis, agriculture engineering, precision
farming, and smart agriculture. It can also be a reference book for
practicing professionals in cloud computing, IoT, big data, machine
learning, and deep learning working on smart agriculture
applications.
Stochastic processes have a wide range of applications ranging from
image processing, neuroscience, bioinformatics, financial
management, and statistics. Mathematical, physical, and engineering
systems use stochastic processes for modeling and reasoning
phenomena. While comparing AI-stochastic systems with other
counterpart systems, we are able to understand their significance,
thereby applying new techniques to obtain new real-time results and
solutions. Stochastic Processes and Their Applications in
Artificial Intelligence opens doors for artificial intelligence
experts to use stochastic processes as an effective tool in
real-world problems in computational biology, speech recognition,
natural language processing, and reinforcement learning. Covering
key topics such as social media, big data, and artificial
intelligence models, this reference work is ideal for
mathematicians, industry professionals, researchers, scholars,
academicians, practitioners, instructors, and students.
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