Harnessing Automation Machine Learning (8 results)

Condition: As New. Unread book in perfect condition.

Condition: New. In English.

Condition: As New. Unread book in perfect condition.

Condition: New. Provides insights into the potential of automation and machine learning in waste management inspiring readers to adopt sustainable waste management practicesOffers a comprehensive understanding of how waste management can be transformed int.

Condition: New.

Condition: New.

Taschenbuch. Condition: Neu. Neuware - Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - AI-Driven Software Engineering: Harnessing Machine Learning for Smarter Development and Automation explores the transformative role of AI in modern software engineering. It begins by introducing AI's impact on software development, detailing its integration into the Software Development Lifecycle (SDLC) for enhancing requirements gathering, design, coding, and refactoring. Machine learning models and their applications in software engineering, including predictive analysis, testing automation, and bug detection, are covered extensively. The book delves into AI's contributions to project management, from resource allocation to risk mitigation, and its automation of DevOps workflows through intelligent CI/CD pipelines and self-healing infrastructure. It also highlights Natural Language Processing (NLP) applications, like automating code documentation and analyzing requirements. Ethical challenges, such as bias and privacy, are addressed alongside AI's role in software maintenance and future trends like quantum computing integration. With practical tools, case studies, and a forward-looking approach, this book is a comprehensive guide to AI-driven software engineering.…