Machine Learning It S Algorithms Models And Applications In Finance And Biomedical Fields And Challenges
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MACHINE LEARNING IT’S ALGORITHMS, MODELS AND APPLICATIONS IN FINANCE AND BIOMEDICAL FIELDS AND CHALLENGES
Author: Mr. Pavan Patel
language: en
Publisher: Xoffencerpublication
Release Date: 2023-02-22
A classic example of a complicated system and one of the most fiercely argued and discussed topics is the monetary and economic system. It is made up of a very diverse group of participants, such as banks, mutual funds, hedge funds, insurance firms, pension funds, and shadow banks. Every one of them not only communicates directly with one another but also with the actual economy, which is in and of itself an extremely complicated system. The worldwide financial crisis that started in 2008 is the perfect illustration of a developing phenomena, which is one of the defining characteristics of a complex system. A confluence of events served as the catalyst for the catastrophe. Although there is no complete consensus on the causes of the crisis, there is a widespread belief that the crisis can be summarized as follows: Mortgage-backed securities were a financial market innovation that brought greater convenience to lenders. This led to an increase in the number of loans extended to households and the purchase of large amounts of overdraft securities. Mortgage-backed securities were one of the factors that contributed to the 2008 financial crisis. Credit liberalization helped develop a housing bubble, and when it burst, multiple global large corporations held substantial mortgage-backed securities in the portfolios of most financial institutions. caused great losses. The loosening of lending standards was another factor that led to the expansion of the housing market. As a result, credit was suspended, which prevented the core activities of the real economy from obtaining funding. As a result, there was a global recession that caused an estimated fifty trillion dollars in losses in the global economy. This is equivalent to half of one year's world GDP.
Artificial Intelligence, Machine Learning and User Interface Design
Author: Abhijit Banubakode
language: en
Publisher: Bentham Science Publishers
Release Date: 2024-05-10
Artificial Intelligence, Machine Learning and User Interface Design is a forward-thinking compilation of reviews that explores the intersection of Artificial Intelligence (AI), Machine Learning (ML) and User Interface (UI) design. The book showcases recent advancements, emerging trends and the transformative impact of these technologies on digital experiences and technologies. The editors have compiled 14 multidisciplinary topics contributed by over 40 experts, covering foundational concepts of AI and ML, and progressing through intricate discussions on recent algorithms and models. Case studies and practical applications illuminate theoretical concepts, providing readers with actionable insights. From neural network architectures to intuitive interface prototypes, the book covers the entire spectrum, ensuring a holistic understanding of the interplay between these domains. Use cases of AI and ML highlighted in the book include categorization and management of waste, taste perception of tea, bird species identification, content-based image retrieval, natural language processing, code clone detection, knowledge representation, tourism recommendation systems and solid waste management. Advances in Artificial Intelligence, Machine Learning and User Interface Design aims to inform a diverse readership, including computer science students, AI and ML software engineers, UI/UX designers, researchers, and tech enthusiasts.
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data