Sass Machine Learning
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Machine Learning in Healthcare
This new volume explores the integration of machine learning in healthcare, which has transformed technology for disease diagnosis, treatment, and management. The book shows the enormous possibilities made possible by computational technologies, ranging from analyzing electronic health information to predicting, detecting, and treating cancer, cardiovascular disease, thyroid disorders, and diabetes. The exploration extends beyond conventional domains, discussing topics such as wearable devices and mental health management through the use of machine learning technology.
Artificial Intelligence and the New World Order
This book discusses the implications of artificial intelligence (AI) on post-COVID-19 international relations. With the decline and fall of U.S. global leadership and the emergence of new powerful actors, as hastened by the global pandemic, new arms are now used in new forms of wars with new players. The balance of power swings between geostrategic interests and those linked to the global governance of virtual space and the race to technological sovereignty. Chapters focus on the challenges imposed by these changes on different parts of the international system—law, governance, diplomacy, international psychological security—and articulate new strategies and ethical policies as possible solutions. The volume is interdisciplinary and will appeal to researchers, students, and professionals across fields interested in the ethics of AI in the international system.
Digitally improving the identification of aquatic macroinvertebrates for indices used in biomonitoring
Author: Koen, R. C. J.
language: en
Publisher: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation
Release Date: 2023-12-31
This report provides an overview of the mini Stream Assessment Scoring System (miniSASS) and South African Scoring System Version 5 (SASS5) as biomonitoring techniques for assessing the ecological condition of streams and rivers based on the identification of aquatic macroinvertebrates. While miniSASS relies on minimally trained citizen scientists to identify macroinvertebrates at the Order-level, SASS5 utilizes expertly accredited practitioners for finer resolution, even up to the family-level. However, the reliance on citizen scientists for miniSASS identification introduces limitations in terms of precision, accuracy, and reliability. To address these limitations, ongoing developments within the CGIAR Initiative on Digital Innovation include the creation of a miniSASS smartphone application, an upgraded website, an interactive online course, and a machine-learning identification algorithm to assist with photo identification. Additionally, a revised dichotomous key has been developed to improve operator identification during miniSASS surveys. Furthermore, the potential for upscaling the machine-learning identification algorithm to assist in identifying the 91 family-level taxa used in SASS5 assessments has been explored. The outcomes of these developments and explorations presented in this paper aim to enhance the overall effectiveness and reliability of both the miniSASS and SASS5 techniques. By leveraging digital innovation and incorporating machine-learning technology, we anticipate the efficiency, accuracy, and accessibility of biomonitoring assessments will significantly improve, ultimately contributing to a better understanding and management of our aquatic ecosystems.