Neural Networks, Machine Learning, and Image Processing: Mathematical Modeling and Applications

This book discusses the latest mathematical modeling techniques and their applications in various areas, including fuzzy modelling, signal processing, neural network, machine learning, image processing, and numerical analysis. It covers image processing techniques like Viola-Jones Method for face detection and fuzzy approach for person video emotion. It is an ideal reference text for graduate students and academic researchers in mechanical engineering, electronics, communication engineering, computer engineering, and mathematics.

Format: Hardback
Length: 202 pages
Publication date: 15 December 2022
Publisher: Taylor & Francis Ltd

The book discusses applications of neural networks,machine learning,image processing,and mathematical modeling. It provides simulations techniques in machine learning and image processing-based problems. It also highlights artificial intelligence and machine learning techniques in the detection of diseases. The book includes real-life problems to enhance understanding.

Mathematical Modeling Techniques and Their Applications

Mathematical modeling has become an essential tool in various fields, enabling scientists and researchers to understand complex systems and phenomena. In this comprehensive text, we will explore the latest mathematical modeling techniques and their applications in diverse areas such as fuzzy modeling, signal processing, neural network, machine learning, image processing, and their numerical analysis.

Fuzzy Modeling:
Fuzzy modeling is a powerful technique that allows for the representation of uncertain and ambiguous information. It involves the use of fuzzy sets, which are a mathematical framework for dealing with vague or incomplete data. Fuzzy modeling has applications in various fields, including control systems, decision-making, and pattern recognition.

Signal Processing:
Signal processing is a fundamental aspect of communication systems, where signals are processed to extract meaningful information. Mathematical modeling plays a crucial role in signal processing, enabling the development of efficient algorithms for signal processing tasks such as filtering, compression, and detection. Signal processing techniques are widely used in fields such as telecommunications, audio and video processing, and medical imaging.

Neural Network:
Neural networks are artificial intelligence algorithms that mimic the workings of the human brain. They are composed of interconnected nodes that process information and make decisions based on patterns. Neural networks have applications in various fields, including image recognition, speech recognition, and autonomous vehicles.

Machine Learning:
Machine learning is a subset of artificial intelligence that involves the development of algorithms that can learn from data and make predictions. It involves the use of statistical models, computational algorithms, and machine learning techniques to analyze data and make predictions. Machine learning has applications in fields such as finance, healthcare, and natural language processing.

Image Processing:
Image processing is a technique used to manipulate and analyze digital images. It involves tasks such as image enhancement, restoration, segmentation, and object detection. Image processing techniques are widely used in fields such as medical imaging, surveillance, and computer vision.

Numerical Analysis:
Numerical analysis is a branch of mathematics that deals with the analysis and solution of mathematical problems using numerical methods. It involves the development of efficient algorithms for solving complex equations, optimization problems, and differential equations. Numerical analysis techniques are widely used in fields such as engineering, physics, and economics.

Image Processing Techniques:
In addition to mathematical modeling, image processing techniques have also played a significant role in various applications. One such technique is the Viola-Jones Method for face detection, which is widely used in facial recognition systems. Another technique is the fuzzy approach for person video emotion recognition, which has applications in human-computer interaction and emotion analysis.

Conclusion:
Mathematical modeling techniques have revolutionized the way we understand and analyze complex systems. They have applications in diverse fields, from engineering to healthcare, and have the potential to improve our lives in numerous ways. This comprehensive text provides an in-depth exploration of the latest mathematical modeling techniques and their applications, making it an ideal reference text for graduate students and academic researchers in the fields of mechanical engineering, electronics, communication engineering, computer engineering, and mathematics.

Weight: 570g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032300146