scholarly journals Artificial intelligence in healthcare

2021 ◽  
Vol 8 (2) ◽  
pp. 102-115
Author(s):  
Yamini D Shah ◽  
Shailvi M Soni ◽  
Manish P Patel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Social Insurance ◽  
Healthcare Professionals ◽  
Medical Services ◽  
Future Scenarios ◽  
Science And Engineering ◽  
Human Interactions ◽  
Computing Capacity

Artificial Intelligence (AI) is described as a field of science and engineering that is concerned with the artificial appreciation of what is generally referred to as prudent behavior and the formation of fascinations that demonstrate such conduct. AI is an expansive concept that encloses a series of advances (a considerable lot of which have been being worked on for quite a few years) that are expected to use human-like insight to handle the problems. Right now in combination with enhanced AI developments like extreme or significantly more engaged, we are experiencing a renewed enthusiasm for AI, energized by a tremendous increase in computing capacity and a significantly greater increase in knowledge. AI, along with machine learning, can be used in computer vision. More advantages in the field of engineering as well as in medicine can be accomplished based on these future scenarios worldwide. Healthcare is seen as the next domain that is said to be altered by the use of the concept of artificial intelligence. The AI process is used for critical diseases such as cancer, neurology, cardiology and diabetes. The review includes the ongoing flow status of medical services for AI applications. A few progressive explorations of AI applications in medicinal services that provide a perspective on future where human interactions are gradually brought together by social insurance conveyance. Likewise, this review will discuss how AI and machine learning can save the life of someone. It is also a guide for healthcare professionals to see how, when, and where AI can be more efficient and have the desired outcomes.

scholarly journals Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽  
2018 ◽  
Vol 2018 (6) ◽  
Author(s):  
Yiannis Ampatzidis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Agricultural Systems ◽  
Disease Identification ◽  
Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

scholarly journals Image Classification Using Reinforcement Learning

2020 ◽  
Vol 23 (6) ◽  
pp. 1172-1191
Author(s):  
Artem Aleksandrovich Elizarov ◽  
Evgenii Viktorovich Razinkov
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Reinforcement Learning ◽  
Image Classification ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Further Development

Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence. The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of ​​the developed method comes down to solving the problem of a contextual multi-armed bandit using various strategies for achieving a compromise between exploitation and research and reinforcement learning algorithms. Strategies such as -greedy, -softmax, -decay-softmax, and the UCB1 method, and reinforcement learning algorithms such as DQN, REINFORCE, and A2C are considered. The analysis of the influence of various parameters on the efficiency of the method is carried out, and options for further development of the method are proposed.

scholarly journals Artificial Intelligence in News Media: Current Perceptions and Future Outlook

2021 ◽  
Author(s):  
Mathias-Felipe de-Lima-Santos ◽  
Wilson Ceron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Science Fiction ◽  
Language Processing ◽  
News Media ◽  
Future Research ◽  
Production And Distribution ◽  
News Industry ◽  
Journalistic Field

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

Computational Healthcare System With Image Analysis

2019 ◽  
pp. 89-127 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Vision ◽  
Image Analysis ◽  
Social Insurance ◽  
Valuable Data ◽  
Clinical Notes ◽  
Information Reporting ◽  
Segmentation Methods ◽  
Crucial Part

The quickly extending field of huge information examination has begun to assume a crucial part in the advancement of human services practices and research. In this chapter, challenges like gathering information from complex heterogeneous patient sources, utilizing the patient/information relationships in longitudinal records, understanding unstructured clinical notes in the correct setting and efficiently dealing with expansive volumes of medicinal imaging information, and removing conceivably valuable data is shown. Healthcare and IoT and machine learning along with data mining are also discussed. Image analysis and segmentation methods comparative study is given for the examination of computer vision, imaging handling, and example acknowledgment has gained considerable ground amid the previous quite a few years. Examiners have distributed an abundance of essential science and information reporting the advance and social insurance application on medicinal imaging.

We Want Robots to See and Understand the World

2017 ◽  
Author(s):  
Antonio Torralba ◽  
Adolfo Plasencia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Present Situation ◽  
Digital Cameras ◽  
Vision Systems ◽  
Image Recording ◽  
Vision Processing ◽  
The World ◽  
Driverless Cars

Antonio Torralba, member of MIT CSAIL, opens the dialogue by describing the research he performs in the field of computer vision and related artificial intelligence (AI). He also compares the conceptual differences and the context of the early days of artificial intelligence—where hardly any image recording devices existed—with the present situation, in which an enormous amount of data is available. Next, through the use of examples, he talks about the huge complexity faced by research in computer vision to get computers and machines to understand the meanings of what they “see” in the scenes, and the objects they contain, by means of digital cameras. As he explains afterward, the challenge of this complexity for computer vision processing is particularly noticeable in settings involving robots, or driverless cars, where it makes no sense to develop vision systems that can see if they cannot learn. Later he argues why today’s computer systems have to learn “to see” because if there is no learning process, for example machine learning, they will never be able to make autonomous decisions.

Robot Process Automation (RPA) and Its Future

2022 ◽  
pp. 35-58
Author(s):  
Ozge Doguc
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Customer Satisfaction ◽  
Human Behavior ◽  
The Other ◽  
Human Intervention ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Human Agent

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

scholarly journals Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis

2021 ◽  
Vol 3 (4) ◽  
pp. 284-304
Author(s):  
Seyed Omid Mohammadi ◽  
Ahmad Kalhor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Online Shopping ◽  
Fashion Industry ◽  
Rapid Progress ◽  
Research Directions ◽  
Accelerated Evolution ◽  
Evolution Speed

The rapid progress of computer vision, machine learning, and artificial intelligence combined with the current growing urge for online shopping systems opened an excellent opportunity for the fashion industry. As a result, many studies worldwide are dedicated to modern fashion-related applications such as virtual try-on and fashion synthesis. However, the accelerated evolution speed of the field makes it hard to track these many research branches in a structured framework. This paper presents an overview of the matter, categorizing 110 relevant articles into multiple sub-categories and varieties of these tasks. An easy-to-use yet informative tabular format is used for this purpose. Such hierarchical application-based multi-label classification of studies increases the visibility of current research, promotes the field, provides research directions, and facilitates access to related studies.

scholarly journals Artificial Intelligence in News Media: Current Perceptions and Future Outlook

2021 ◽  
Vol 3 (1) ◽  
pp. 13-26
Author(s):  
Mathias-Felipe de-Lima-Santos ◽  
Wilson Ceron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Science Fiction ◽  
Language Processing ◽  
News Media ◽  
Future Research ◽  
Production And Distribution ◽  
News Industry ◽  
Journalistic Field

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, and planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We made conclusions by providing examples of how these subfields are being developed in journalism and presented an agenda for future research.

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2018 ◽  
pp. 2025-2041
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

scholarly journals Artificial intelligence: A cutting edge technology in agriculture

2021 ◽  
Vol 17 (1) ◽  
pp. 114-120
Author(s):  
Sidhant Allawadi ◽  
Jayaty ◽  
Parmod Sharma ◽  
Kapil Rohilla ◽  
Gopal Deokar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Computer Vision ◽  
Cutting Edge ◽  
High Quality ◽  
High Expectations ◽  
Advanced Technologies ◽  
The Common ◽  
Smart Technologies

Attention is currently being paid to the use of smart technologies. Agriculture has provided an important source of food for humans over thousands of years, including the development of appropriate farming methods for the cultivation of different crops. The emergence of new advanced technologies has the potential to monitor the agricultural environment to ensure high-quality produce. In this context, a systematic review that aimsto study the application of various technologies and algorithms in Artificial Intelligence (AI) with the latest solutions to make the farming more efficient remains one of the greatest imperatives. Artificial intelligence can be applied directly in the field of agriculture for various operations. Amid high expectations about how AI will help the common personand transform his mindset, thoughts and attitude towards the benefits that it may bring. There are certain concerns about the ill effects of such sophisticated technologies as well.This review also focuses on the activation of perceptive technologies and application of computer vision and machine learning in agriculture.

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