Reuse in Intelligent Systems

Rubin, Stuart H (EDT)/ Bouzar-Benlabiod, Lydia (EDT)

CRC Press（2021/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 242 p.
• 言語 ENG
• 商品コード 9780367510077
• DDC分類 003.54

Full 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!

Contents

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.