Cognitive Computing in Human Cognition
Perspectives and Applications
Published on 19. June 2020
Book
Hardback
XIV, 125 pages
978-3-030-48117-9 (ISBN)
Description
This edited book designs the Cognitive Computing in Human Cognition to analyze to improve the efficiency of decision making by cognitive intelligence. The book is also intended to attract the audience who work in brain computing, deep learning, transportation, and solar cell energy. Due to this in the recent era, smart methods with human touch called as human cognition is adopted by many researchers in the field of information technology with the Cognitive Computing.
More details
Series
Edition
2020 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
37 s/w Abbildungen, 38 farbige Abbildungen
XIV, 125 p. 75 illus., 38 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 14 mm
Weight
383 gr
ISBN-13
978-3-030-48117-9 (9783030481179)
DOI
10.1007/978-3-030-48118-6
Schweitzer Classification
Other editions
Additional editions
Pradeep Kumar Mallick | Prasant Kumar Pattnaik | Amiya Ranjan Panda
Cognitive Computing in Human Cognition
Perspectives and Applications
Book
06/2021
Springer
€160.49
Shipment within 7-9 days
Pradeep Kumar Mallick | Prasant Kumar Pattnaik | Amiya Ranjan Panda
Cognitive Computing in Human Cognition
Perspectives and Applications
E-Book
06/2020
1st Edition
Springer
€149.79
Available for download
Content
Chapter 1: Improved Steganography using Odd Even substitution.- Chapter 2: A Tags Mining Approach for Automatic Image Annotation Using Neighbor Images Tree.- Chapter 3: A Survey: Implemented Architectures of 3D Convolutional Neural Networks.- Chapter 4: An approach for detection of dust on solar panels using CNN from RGB dust image to predict power loss.- Chapter 5: A Novel Method of Data Partitioning Using Genetic Algorithm Work Load Driven Approach Utilizing Machine Learning.- Chapter 6: Virtual Dermoscopy using Deep Learning Approach.- Chapter 7: Evaluating Robustness for Intensity Based Image Registration Measures Using Mutual Information and Normalized Mutual Information.- Chapter 8: A New Contrast Based Degraded Document Image Binarization.