Modern Computational Techniques for Engineering ApplicationsКНИГИ » ПРОГРАММИНГ
Название: Modern Computational Techniques for Engineering Applications Автор: Krishan Arora, Vikram Kumar, Deepak Prashar Издательство: CRC Press Год: 2024 Страниц: 229 Язык: английский Формат: pdf (true) Размер: 42.3 MB
Modern Computational Techniques for Engineering Applications presents recent computational techniques used in the advancement of modern grids with the integration of non-conventional energy sources like wind and solar energy. It covers data analytics tools for smart cities, smart towns, and smart computing for sustainable development.
This book:
Discusses the importance of renewable energy source applications wind turbines and solar panels for electrical grids. Presents optimization-based computing techniques like fuzzy logic, neural networks, and genetic algorithms that enhance the computational speed. Showcases cloud computing tools and methodologies such as cybersecurity testbeds and data security for better accuracy of data. Covers novel concepts on artificial neural networks, fuzzy systems, machine learning, and artificial intelligence techniques. Highlights application-based case studies including cloud computing, optimization methods, and the Industrial Internet of Things.
Data analytics tools are software and programs that collect and analyze data about a business, its customers, and its competition to help improve processes and uncover insights for making data-driven decisions. Some of the data analytics tools are listed below:
• R and Python • Tableau • Apache Spark • QlikView • Splunk • KNIME • Microsoft Excel • RapidMiner • Power BI • Talend
R and Python are popular programming languages used in the field of data analytics. R is an open-source tool used for statistics and analysis, whereas Python is a high-level, descriptive language with simple syntax and dynamic semantics.
The book comprehensively introduces modern computational techniques, starting from basic tools to highly advanced procedures, and their applications. It further highlights artificial neural networks, fuzzy systems, Machine Learning, and Artificial Intelligence techniques and how they form the basis for algorithms. It presents application-based case studies on cloud computing, optimization methods, blockchain technology, fog and edge computing, and the Industrial Internet of Things. It will be a valuable resource for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical engineering, electronics and communications engineering, and computer engineering.
Chapter 1 Paralysis Support System Using IoT Chapter 2 Blockchain and its Applications: a review Chapter 3 Data Analytics Tools for Smart Cities and Smart Towns Chapter 4 Industrial Internet of Things and its applications in Industry 4.0 through sensor integration fro a process parameter monitor and control Chapter 5 Automated Computer-Aided Diagnosis of COVID-19 and Pneumonia Based on Chest X-Ray Images Using Deep Learning: Classification and Segmentation Chapter 6 Fuzzy logic and applications Chapter 7 IoT-based smart monitoring topologies for energy-efficient smart buildings Chapter 8 Soft Computing Techniques for Renewable Energy Systems Chapter 9 Maize Diseases Diagnosis based on Computer Intelligence: A Systematic Review Chapter 10 Low-power Architectural Design and Implementation of reconfigurable data converters for biomedical application Chapter 11 Sign Language and Hand Gesture Recognition Using Machine Learning Techniques - A Comprehensive Review
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Smart Computing and Self-Adaptive Systems Название: Smart Computing and Self-Adaptive Systems (Computational Intelligence Techniques) Автор: Edited By Simar Preet Singh, Arun Solanki, Anju...
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Modern Computational Techniques for Engineering Applications 
Название: Modern Computational Techniques for Engineering Applications
