The Role Of Artificial Intelligence In Advancing Applied Life Sciences

Download The Role Of Artificial Intelligence In Advancing Applied Life Sciences PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The Role Of Artificial Intelligence In Advancing Applied Life Sciences book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
The Role of Artificial Intelligence in Advancing Applied Life Sciences

The transformative role of artificial intelligence (AI) is revolutionizing the life sciences sector. AI is being used to accelerate drug discovery, personalize treatments, and improve patient outcomes. AI has demonstrated its potential in optimizing crop yields, enhancing food safety, and addressing global food security challenges. Additionally, AI has applications in climate modeling, species conservation, and pollution monitoring. Discussion of AI implementation in life sciences may stimulate further research and development in AI-driven life science solutions. The Role of Artificial Intelligence in Advancing Applied Life Sciences equips readers with a solid understanding of technology's potential to address complex life science problems. It also discusses the ethical implications and challenges associated with AI implementation in this field. Covering topics such as biomanufacturing, disease identification, and climate change patters, this book is an excellent resource for life scientists, computer scientists, healthcare practitioners, environmentalists, agriculturalists, professionals, researchers, scholars, academicians, and more.
Artificial Intelligence in Medicine

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.