Enabling AI Applications in Data Science - Aboul-Ella Hassanien Mohamed Hamed N. Taha Nour Eldeen M. Khalifa

Enabling AI Applications in Data Science

Aboul-Ella Hassanien (Redaktør) ; Mohamed Hamed N. Taha (Redaktør) ; Nour Eldeen M. Khalifa (Redaktør)

Serie: Studies in Computational Intelligence 911

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. Les mer
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Vår pris: 2363,-

(Innbundet) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.

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Innholdsfortegnelse

Part I: Artificial Intelligence and Optimization.- Stochastic Proximal Gradient Algorithm with Minibatches Application to Large Scale Learning Models.- The Human Mental Search Algorithm for Solving Optimisation Problems.- Reducing Redundant Association Rules Using Type-2 Fuzzy Logic.- Identifiability of Discrete Concentration Graphical Models with a Latent Variable.- An Automatic Classification of Genetic Mutations by Exploring Different Classifiers.- Towards Artificial Intelligence: Concepts, Applications, and Innovations.- Part II: Big Data and Artificial Intelligence Applications.- In Depth Analysis, Applications and Future Issues of Artificial Neural Network.- Big Data and Deep Learning in Plant Leaf Diseases Classification for Agriculture.- Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities.