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Artificial Intelligence (AI) is a branch of computer sciences that uses learning algorithms that calculate probability of outcome using Bayes theorem and other statistical methods given certain input. When the chance of an event occurring is calculated over and over again adding new data or evidence at each step, the probability can reach a level of near certainty for given outcome with certain inputs. Thousands, even millions of data points are incorporated in calculating posterior probability for predictive analytics. The analytics are input neutral as programs predict the future events irrespective of the source of the data. AI has, thus, blurred the boundaries between the physical, digital, and biological worlds. AI is a branch of computer Sciences. This initial learning process is considered training where inputs are given to the program already marked for the expected outcome. This training information can be highly precise or very vague allowing different degree of freedom to the program but also increasing burden of training. Once trained an AI algorithm is able to predict or analyze given input to suggest the required outcome with some certainty, that improves with continued training or feedback. Artificial Intelligence (AI) is ubiquitous, affecting virtually every aspect of life either directly or indirectly. We are aware of some of the Ai applications , others we are not. The term AI was first introduced by McCarthy at Dartmouth college, New Hampshire, in 1956. But recently, AI has become a commonly used modality in science, business, education, aeronautics, arts, defense and government affairs to name a few. In the last several years, there has been exponential growth in AI applications. In 2016, AI added $ 600 million to the global economy, this is projected to grow to $ 15 Trillion by the year 2030. As a comparison, the size of the US economy was 22 Trillion in 2021.

Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.

Chapter 1.Comprehensive Claims of AI for Healthcare Applications-Coherence towards COVID-19 -- Chapter 2. Artificial Intelligence based systems for combating COVID-19 -- Chapter 3. Artificial intelligence mediated medical diagnosis of COVID-19 -- Chapter 4. AI combined with medical imaging enables rapid diagnosis for COVID-19 -- Chapter 5. Role of Artificial Intelligence in COVID-19 prediction based on Statistical Methods -- Chapter 6. Data Driven symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Development for COVID 19 -- Chapter 7. A decision support System using Rule based Expert System For COVID -19 Prediction and Diagnosis -- Chapter 8. A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 through Unsupervised Learning -- Chapter 9. AI-enabled prognosis technologies for SARS Co-2. Chapter 10. Intelligent agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 analysis of Recovery, Infectious Patients -- Chapter 11. Epidemic Analysis of COVID 19 Using Machine Learning -- Chapter 12. Machine learning application in COVID-19 drug development -- Chapter 13. COVID 19 Epidemic Analysis Using Linear and Polynomial Regression Approach -- Chapter 14. Prediction & Analysis of outbreak of COVID-19 Pandemic Using Machine Learning -- Chapter 15. Predictive Risk Analysis by using Machine Learning during Covid-19 -- Chapter 16. Analysis and Validation of Risk Prediction by Stochastic Gradient Boosting Along With Recursive Feature Elimination for COVID-19 -- Chapter 17. Artificial intelligence in mental healthcare during COVID-19 pandemic -- Chapter 18. Effect of Covid-19 on Autism Spectrum Disorder: Prognosis, diagnosis and therapeutics based On AI -- Chapter 19. Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: A Literature Review -- Chapter 20. Role of IoT and Social Networking in Mental Healthcare of Transgender Community in Covid-19 Pandemic -- Chapter 21. TECHNOLOGY ACCEPTANCE AND USE OF IOT DURING COVID 19 PANDEMIC-CASE STUDY OF HEALTH SECTOR IN INDIA. Chapter 22. Artificial Intelligence – The Strategies used in COVID-19 for Diagnosis -- Chapter 23. Impact of Isolation and Quarantine on Covid-19 Patients and Potential Role of Technology in Mitigation -- Chapter 24. Impact of loneliness and Quarantine on COVID-19 patients with artificial intelligence applications -- Chapter 25. Can Technology fight the loneliness Lockdown: A study of factors Affecting Loneliness in NCR during COVID 19 -- Chapter 26. Psycho-economic Impact of Obligatory Job Switching during Covid-19 Pandemic: A Study of Hawkers in Bhubaneswar (India) -- Chapter 27. AI's Role in Essential Commodities during a Pandemic Situation -- Chapter 28.Impact of COVID-19 on Manufacturing and Operational Ecosystem in India -- Chapter 29. Impact of Repatriated Migrants on the Production Possibility of Agricultural Sector owing to Covid: A Study on the basis of Inferential Statistics -- Chapter 30. Nicotine in Covid-19: Friend or Foe";? -- Chapter 31. Artificial Intelligence in Covid'19: Application and Legal Conundrums.

Statistical analysis of genetic algorithms in discovering technical trading strategies / Chueh-Yung Tsao, Shu-Heng Chen -- Co-evolving neural networks with evolutionary strategies : a new application to divisia money / Jane M. Binner, Graham Kendall, Alicia Gazely -- Forecasting the EMU inflation rate : linear econometric vs. non-linear computational models using genetic neural fuzzy systems / Stefan Kooths, Timo Mitze, Eric Ringhut -- Finding or not finding rules in time series / Jessica Lin, Eamonn Keogh -- A comparison of var and neural networks with genetic algorithm in forecasting price of oil / Sam Mirmirani, Hsi Cheng Li -- Searching for divisia/inflation relationships with the aggregate feedforward neural network / Vincent A. Schmidt, Jane M. Binner -- Predicting housing value : genetic algorithm attribute selection and dependence modelling utilising the gamma test / Ian D. Wilson, Antonia J. Jones, David H. Jenkins, J.A. Ware -- A genetic programming approach to model international short-term capital flow / Tina Yu, Shu-Heng Chen, Tzu-Wen Kuo -- Tools for non-linear time series forecasting in economics : an empirical comparison of regime switching vector autoregressive models and recurrent neural networks / Jane M. Binner, Thomas Elger, Birger Nilsson, Jonathan A. Tepper -- Using non-parametric search algorithms to forecast daily excess stock returns / Nathan Lael Joseph, David S. Br(c)♭e, Efstathios Kalyvas

Abstract
Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers' unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.

University of Central Florida College of Engineering Thesis; This report is a survey of Artificial Intelligence (AI) technology contributions to military training. It provides an overview of military training simulation and a review of instructional problems and challenges which can be addressed by AI. The survey includes current as well as potential applications of AI, with particular emphasis on design and system integration issues. Applications include knowledge and skills training in strategic planning and decision making, tactical warfare operations, electronics maintenance and repair, as well as computer-aided design of training systems. The report describes research contributions in the application of AI technology to the training world, and it concludes with an assessment of future research directions in this area. ; 1987-08-01 ; M.S. ; Engineering ; Masters ; This record was generated from author submitted information. ; Electronically reproduced by the University of Central Florida from a book held in the John C. Hitt Library at the University of Central Florida, Orlando.

1. --Application of drone and sensors in advanced farming: the future smart farming technology --2. --Development and research of a greenhouse monitoring system --3. --A cloud-computing model for implementing smart agriculture --4. --Application of conversational artificial intelligence for farmer's advisory and communication --5. -- Theuse of an intelligent fuzzy logic controller to predict the global warming effect on agriculture: the case of the chickpea cicer arietinum L. --6. --Using machine learning algorithms for mapping soil macronutrient elements variability with digital environmental data in an alluvial plain --7. -- Asmart IoT framework for soil fertility enhancement assisted via deep neural networks --8. --Plant disease detection with the help of advanced imaging sensors --9. --Artificial intelligence-aided phenomics in high throughput stress phenotyping of plants --10. --Plant disease detection using hybrid deep learning architecture in smart agriculture application --11. --Classification of coffee leaf diseases through image processing techniques --12. -- Theuse of artificial intelligence to model oil extraction yields from seeds and nuts --13. --Applications of artificial intelligence in pest management --14. --Applying clustering technique for rainfall received by different district of Maharashtra state --15. --Predicting rainfall for Aurangabad division of Maharashtra by applying auto-regressive moving average model (ARIMA) using Python programming.

The term 'AI' is not a new term but the actual meaning of ai is still hidden. Artificial intelligence is a branch of computer science that aims to create machines which are as intelligent as human beings. AI mainly focus on some questions like knowledge required while thinking, the way knowledge can be presented and the way knowledge can be used in other field's viz. Robotics. Scope of AI is much wider than our thinking. It is not limited to only one or two areas rather in coming future everything will be directly or indirectly linked to AI. Much research has been done on artificial intelligence which has shown that by the end of 2020 many works which was not possible by human beings will be efficiently and accurately can be carried out by the help of robots. Robotics is a branch of engineering that deals with formation, designing, manufacturing, operation of robots. Artificial intelligence is being applied to many areas which are capable to solve many problems like in robotics, e-commerce, domestic chores, medical treatment, gaming, mathematics, military planning etc. The main idea behind the merging of artificial intelligence and robotics is to optimize the level of autonomy through learning. In the coming future we can surely overcome the disadvantages of robots like misuse of it with the help of facial recognition. Or we can use AI in other fields like in cyber security to prevent the systems from being hacked. The applications of AI and how we can implement other applications in coming time are discussed adding to it how we can overcome the disadvantages of using robots in regular life are also discussed.

The proliferated use of medical algorithms in health care has brought about medical innovation and improvements; however, there is a cost to a prevalent use of artificial intelligence in health care—exacerbating racism in the medicine. This Article examines the use of race in medical algorithms. Part II describes the development of medical AI, including its advantages and disadvantages. Part III focuses on how the use of medical algorithms may embed racism into our health care system. Part IV describes the FDA's current regulations on medical AI. The Article concludes by advocating for legislation and regulations that work to reduce racial bias in medical algorithms.