Название: Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Автор: Vaishali Mehta, Dolly Sharma, Sergio Marquez Sanchez
Издательство: Bentham Books
Год: 2022
Страниц: 229
Язык: английский
Формат: pdf (true), epub
Размер: 16.2 MB

The competence of Deep Learning (DL) for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep Learning enables detection of meaningful features that are difficult to perform using traditional methods.

The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of Deep Learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications.

Chapters introduce the reader to a broad range of topics in Deep Learning and Machine Learning. Several Deep Learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing (NLP), speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

The book also discusses prospective research directions that focus on the theory and practical applications of Deep Learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0.

Machine Learning can prove to be an essential tool and optimize the production process, respond quickly to the changes and market demand respectively, predict certain aspects of the particular industry to improve performance, maintain machine health and other aspects. Machine Learning technology can prove its effectiveness when applied to a specific issue in the sector— such as filtering out the primary use cases of Machine Learning manufacturing specifically, 'Predictive quality and yield' and 'Predictive maintenance.' Supervised Machine Learning and Unsupervised Machine Learning may provide the accuracy to predict the outputs and the underlying patterns.

The field of cryptocurrency has witnessed exponential growth in popularity in recent years. Almost ten years ago, the release of Bitcoin marked the beginning of a new era of innovation in the financial sector. Building on this knowledge, we examine the infamous volatility of cryptocurrency prices, analyzing pricing data and the likelihood of these currencies, specifically Bitcoin, being in the midst of a financial bubble. We examine the prediction of prices, or rather the inability to do so, before introducing the Currency Analyzer web application developed as part of this work. Containing up to date prices, this application predicts the prices of Bitcoin using Machine Learning. The research, planning methodologies, technologies, and design and evaluation of this application are described in detail in this chapter, followed by a conclusion and future scope.








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