- Добавил: literator
- Дата: 26-04-2023, 05:10
- Комментариев: 0
Название: Artificial Intelligence in Models, Methods and Applications
Автор: Olga Dolinina, Igor Bessmertny, Alexander Brovko, Vladik Kreinovich
Издательство: Springer
Год: 2023
Страниц: 694
Язык: английский
Формат: pdf (true)
Размер: 21.3 MB
This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence. The Chapter "Development of a Program Code Review System Using Machine Learning Methods" presents a description of the developed approach and service for analyzing source code in Python. The service reduces the time for code review due to partial automation. The FastText algorithm is used to obtain vector representations of source code texts. A pre-trained neural network language model based on the transformer architecture was used to derive a possible natural language function assignment.
Автор: Olga Dolinina, Igor Bessmertny, Alexander Brovko, Vladik Kreinovich
Издательство: Springer
Год: 2023
Страниц: 694
Язык: английский
Формат: pdf (true)
Размер: 21.3 MB
This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence. The Chapter "Development of a Program Code Review System Using Machine Learning Methods" presents a description of the developed approach and service for analyzing source code in Python. The service reduces the time for code review due to partial automation. The FastText algorithm is used to obtain vector representations of source code texts. A pre-trained neural network language model based on the transformer architecture was used to derive a possible natural language function assignment.