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Reuse in Intelligent Systems

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Abnormal Behavior Patterns
advanced data cleansing
ARIMA Model
Average AUC
Big Data
Bioinformatics
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Class Distributions
Class Imbalance
Collaborative Filtering
Data Quality Level
Data Sampling
DDos Attacks
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Feature Selection
Feature Selection Methods
Feature Selection Techniques
Gene Selection
HA
HCPCS Code
Holt-Winters
hybrid systems analysis
Imbalanced Datasets
intelligent data integration
Logs Behavior
Long Short-Term Memory
Machine Learning
Medicare Fraud detection
Ml Model
MOOCs Provider
Multiple Linear Regression
Noise Injection
Non-linear ODEs
optimal knowledge reuse strategies
PN
predictive modeling techniques
Prim
Random Forest
Random Undersampling
Recommendation Systems
Recurrent Layer
RMSE Score
Root Mean Square Error
scientific data validation
Sequence of Events
server log forecasting
Soccer Ball
Subset Evaluation
Tukey's HSD
Tukey’s HSD
User Item Matrix
Wrapper Feature Selection

Product details

  • ISBN 9780367510077
  • Weight: 367g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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!
About

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 Systèmes Informatiques (LCSI), Ecole Nationale Supérieure d’Informatique (ESI, Algeria). She received a PhD degree in Computer Science from ESI and from Université 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.