Computational Network Application Tools for Performance Management / Asset Analytics (PDF)
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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 book's 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 of soft computing, neural networks, machine learning, sensor networks, smart security, etc.
Dr. Tarun Kumar Sharma is an Associate Professor at Amity University Rajasthan, India. He holds a Ph.D. in Soft Computing from IIT, Roorkee, and has published over 90 research papers. He has served as General Chair, Program Chair, Track Chairin the SoCTA, SoCPros Conference Series. He has edited a number of Springer Conference Proceedings volumes. He is Associate Editor, Guest Editor and Reviewer for various Springer and Inderscience journals and IEEE Transactions. He has delivered guest lectures at various leading national and international institutions. He is member of IET, IANEG, CSTA, and MIRS Lab.
Dr. Sebastian Basterrech is an Associate Professor at the Department of CS, Faculty of Electrical Engineering, Czech Technical University, Prague, He has 70+ research publications to his credit. He is Associate Editor, Guest Editor and Reviewer Springer and Inderscience journals and IEEE Transactions. He has acted as Program Chair and Technical Chair at numerous national & international conferences, and has made valuable contributions in areas related to quasi-Newton optimization, random neural networks, reservoir computing, neural computation & soft-computing techniques.
Dr. Chitresh Banerjee is an Assistant Professor at Amity
- 2019, 1st ed. 2020, 267 Seiten, Englisch
- Herausgegeben: Millie Pant, Tarun K. Sharma, Sebastián Basterrech, Chitresh Banerjee
- Verlag: Springer-Verlag GmbH
- ISBN-10: 9813295856
- ISBN-13: 9789813295858
- Erscheinungsdatum: 18.10.2019
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 9.21 MB
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