MACHINE LEARNING ALGORITHMS FOR SIGNAL AND IMAGE PROCESSING

Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing

Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks.

Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as:

• Speech recognition, image reconstruction, object classification and detection, and text processing
• Healthcare monitoring, biomedical systems, and green energy
• How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time
• Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection

Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.