Methods and Applications of Artificial Intelligence: Dynamic Response, Learning, Random Forest, Linear Regression v.2КНИГИ » ПРОГРАММИНГ
Название: Methods and Applications of Artificial Intelligence: Dynamic Response, Learning, Random Forest, Linear Regression, Interoperability, Additive Manufacturing and Mechatronics, Volume 2 Автор: Abdelkhalak El Hami Издательство: Wiley-ISTE Год: 2025 Страниц: 245 Язык: английский Формат: pdf (true) Размер: 21.4 MB
Artificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation.
Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs.
A significant number of advances in the field of AI rely on Machine Learning, an essential branch of this field. Machine Learning involves the development of algorithms and computer models that can learn from data. Unlike standard programs that follow strict instructions defined by programmers, Machine Learning systems can analyze data, detect diagrams and consequently adjust their behavior, while adapting to new information. AI and Machine Learning are closely interrelated. Indeed, Machine Learning is often the underlying driver that powers many AI applications. By using it, AI systems enhance their performance in time, by relying on experience and available data.
The random forest method is one of the most popular and efficient techniques used in Machine Learning for classification and regression. This method relies on the use of a set of decision trees, each tree being randomly constructed based on a subset of training data. By combining the predictions of multiple decision trees, the random forest manages to reduce overlearning and enhance the robustness of the global model. Moreover, it offers high flexibility and the capacity to manage complex data, which explains its frequent selection for many applications in fields such as medicine, finance, biology and many others.
In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc.
Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.
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Methods and Applications of Artificial Intelligence: Dynamic Response, Learning, Random Forest, Linear Regression v.2 
