Reuse in Intelligent Systems
1st Edition
Published on 27. March 2020
Book
Hardback
252 pages
978-0-367-47338-9 (ISBN)
Description
The book is based on the best papers of IEEE IRI 2018 and IEEE FMI 2018, Salt Lake City, July, 2018. They have been enhanced and modified suitably for publication. The book comprises recent works covering several aspects of reuse in intelligent systems - including Scientific Theory and Technology-Based Applications. New data analytic algorithms, technologies, and tools are sought to be able to manage, integrate, and utilize large amounts of data despite hardware, software, and/or bandwidth constraints; to construct models yielding important data insights, and to create visualizations to aid in presenting and understanding the data. Furthermore, it addresses the representation, cleansing, generalization, validation, and reasoning strategies for the scientifically-sound and cost-effective advancement of all kinds of intelligent systems - including all software and hardware aspects. The book addresses problems such as, how to optimally select the information/data sets for reuse and how to optimize the integration of existing information/knowledge with new, developing information/knowledge sources!
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
12 farbige Abbildungen, 50 s/w Abbildungen, 58 s/w Tabellen, 50 s/w Zeichnungen, 7 farbige Zeichnungen, 5 Farbfotos bzw. farbige Rasterbilder
58 Tables, black and white; 7 Line drawings, color; 50 Line drawings, black and white; 5 Halftones, color; 12 Illustrations, color; 50 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
540 gr
ISBN-13
978-0-367-47338-9 (9780367473389)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Stuart H. Rubin | Lydia Bouzar-Benlabiod
Reuse in Intelligent Systems
Book
06/2021
1st Edition
CRC Press
€85.60
Shipment within 15-20 days
Stuart H. Rubin | Lydia Bouzar-Benlabiod
Reuse in Intelligent Systems
E-Book
03/2020
1st Edition
CRC Press
€78.99
Available for download
Stuart H. Rubin | Lydia Bouzar-Benlabiod
Reuse in Intelligent Systems
E-Book
03/2020
1st Edition
CRC Press
€78.99
Available for download
Persons
Stuart H. Rubin has a PhD in Information Science from Lehigh University and a certificate for Embodied Intelligence from MIT. He was an ONT Post-Doctoral Fellow and a tenured professor of computer science at CMU. He has over 39 Navy patents, over 332 publications - including five book chapters plus five books, and has received five publication awards. He is a Fellow of the IEEE, SIRI, the NAI, and the AAAS.
Lydia Bouzar-Benlabiod is an assitant professor at Laboratoire de Communication des Systemes Informatiques (LCSI), Ecole Nationale Superieure d'Informatique (ESI, Algeria). She received a PhD degree in Computer Science from ESI and from Universite d'Artois (France) in 2015. She received a magisterial degree from ESI in 2010. She is a member of The International Society of Applied Intelligence. She has co-edited a book (published by Springer) and a special issue of the ISF Springer journal with Dr. Stuart Rubin.
Lydia Bouzar-Benlabiod is an assitant professor at Laboratoire de Communication des Systemes Informatiques (LCSI), Ecole Nationale Superieure d'Informatique (ESI, Algeria). She received a PhD degree in Computer Science from ESI and from Universite d'Artois (France) in 2015. She received a magisterial degree from ESI in 2010. She is a member of The International Society of Applied Intelligence. She has co-edited a book (published by Springer) and a special issue of the ISF Springer journal with Dr. Stuart Rubin.
Content
Experimental Studies on the Impact of Data Sampling with Severely Imbalanced big data. How to Optimally Combine Univariate and Multivariate Feature Selection with Data Sampling for Classifying Noisy, High Dimensional and Class Imbalanced DNA Microarray Data. Big Data and Class Imbalance in Medicare Fraud Detection. Movie Recommendations Based on a Recurrent Neural Network Model. A Recommendation System Enhanced by Topic Modeling for Knowledge Reuse in MOOCs Ecosystems. Toward a Computer Vision Based Approach for Developing Algorithms for Soccer Playing Robots. Context-dependent Reachability Analysis for Hybrid Systems. Netflow Feature Evaluation for the Detection of Slow Read HTTP Attacks. Predictive Analysis of Server Log Data for Forecasting Events.