Computational network application tools for performance management /

Computational network application tools for performance management /

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Bibliographic Details
Imprint:Singapore : Springer, 2020.
Description:1 online resource (xii, 267 pages) : illustrations (some color)
Language:English
Series:Asset analytics, 2522-5162
Asset analytics,
Subject:
Computer networks -- Management.
Artificial intelligence.
Artificial intelligence.
Computer networks -- Management.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12602280
Hidden Bibliographic Details
Other authors / contributors:Pant, Millie, 1979- editor.
Sharma, Tarun K., editor.
Basterrech, Sebastián, editor.
Banerjee, Chitresh, editor.
ISBN:9789813295858
9813295856
9789813295865
9813295864
9789813295841
9813295848
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed October 23, 2019).
Summary:This book explores a range of important theoretical and practical issues in the field of computational network application tools, while also presenting the latest advances and innovations using intelligent technology approaches. The main focus is on detecting and diagnosing complex application performance problems so that an optimal and expected level of system service can be attained and maintained. The book discusses challenging issues like enhancing system efficiency, performance, and assurance management, and blends the concept of system modeling and optimization techniques with soft computing, neural network, and sensor network approaches. In addition, it presents certain metrics and measurements that can be translated into business value. These metrics and measurements can also help to establish an empirical performance baseline for various applications, which can be used to identify changes in system performance. By presenting various intelligent technologies, the book provides readers with compact but insightful information on several broad and rapidly growing areas in the computation network application domain. The books twenty-two chapters examine and address current and future research topics in areas like neural networks, soft computing, nature-inspired computing, fuzzy logic and evolutionary computation, machine learning, smart security, and wireless networking, and cover a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book was written to serve a broad readership, including engineers, computer scientists, management professionals, and mathematicians interested in studying tools and techniques for computational intelligence and applications for performance analysis. Featuring theoretical concepts and best practices in computational network applications, it will also be helpful for researchers, graduate and undergraduate students with an interest in the fields o f soft computing, neural networks, machine learning, sensor networks, smart security, etc.
Other form:Print version: Computational network application tools for performance management. Singapore : Springer, 2020 9813295848 9789813295841
Standard no.:10.1007/978-981-32-9585-8
10.1007/978-981-32-9
Table of Contents:
  • Performance Enhanced Hybrid Memetic Framework for Effective Coverage Based Test Case Optimization
  • An Optimization Procedure for Quadratic Fractional Transportation Problem
  • A Nature Inspired PID like Fuzzy Knowledge Based Fractional Order Controller for Optimization
  • Neuro-Fuzzy-Rough Classification for Increasing Efficiency and Performance in Case-Based Reasoning Retrieval
  • Better Performance of Human Action Recognition from Spatiotemporal Depth Information Features Classification
  • Selecting Appropriate Multipath Routing In Wireless Sensor Networks for Improvisation of Systems Efficiency and Performance
  • A Classification of ECG Arrhythmic Analysis Based on Performance Factors using Machine Learning Approach
  • A Time Efficient Semi Automatic Active Contour Model of Liver Tumor Segmentation from CT Images
  • Denoising 1d Signal Using Wavelets for Signal Quality Enhancement.

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