Название: Applications of Optimization and Machine Learning in Image Processing and IoTАвтор: Nidhi Gupta
Издательство: CRC Press
Год: 2024
Страниц: 236
Язык: английский
Формат: pdf (true)
Размер: 10.8 MB
This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals. The applications of machine learning and IoT are explored, with topics including optimization, algorithms and Machine Learning in image processing and IoT.
Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source, providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss the fields of Machine Learning, IoT and image processing.
The field of Computer Science with the fastest growth right now is Machine Learning, which has applications in fields as varied as marketing, health-care, production, cybersecurity and mobility. Three elements are readily available and combined: (1) faster and more potent part of a computer, like multiple cores and broad sense GPU; (2) a computer program that utilizes these computational structures; and (3) essentially unlimited training data sets for a certain issue, like digital photos, digitalized files. Posts on social media or even other types of information are the primary cause of this literal “explosion” of the technique. ML is a type of AI that is capable of carrying out tasks without being particularly trained to do so. Instead, it employs a process known as “training to learn” from prior samples of the assigned task. Inference is the technique through which the task can be carried out on fresh data after training. It is notably helpful for instances where the data is challenging to analyze, such as reviewing image and video recordings, and it especially helps in getting data from enormous amounts of continuously increasing data. Computer vision is the science of analyzing and drawing conclusions from digital images and movies. It aims to automate tasks from an engineering standpoint that the human visual interface can do. The two stages of a computer-vision-based Machine Learning process are feature extraction and classification. Additional Machine Learning models, including ANN, CNN, RNN and others, can be applied for system training and optimization.
Key features:
• Includes fundamental concepts towards advanced applications in machine learning and IoT.
• Discusses potential and challenges of machine learning for IoT and optimization
• Reviews recent advancements in diverse researches on computer vision, networking and optimization field.
• Presents latest technologies such as machine learning in image processing and IoT
This book has been written for readers in academia, engineering, IT specialists, researchers, industrial professionals and students, and is a great reference for those just starting out in the field as well as those at an advanced level.
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