scholarly journals The Scope and Applications of Artificial Intelligence in the Medical Sector

2021 ◽  
pp. 18-25
Author(s):  
Madhuri Kumar ◽  
John Alen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Mind ◽  
Major Research ◽  
Medical Field ◽  
Computing Systems ◽  
Medical Practices ◽  
Medical Sector ◽  
Effective Delivery ◽  
Smart Machines

The terminology Artificial Intelligence (AI) describes the application computing systems and technology to effectively simulate smart actions and smart thinking compared to the human mind. The concept of AI was introduced as the engineering and science of making smart machines that can operate without the engagement of humans using Machine Learning (ML). This research provides a wider scope of the concept of AI in the medical field, handling the various concepts and terms associated with the concept, including the present and future implementation of the concept. The major research materials applied are Google and PubMed searches, which were conducted using the “Artificial Intelligence” as the basic keyword. More references were retrieved by cross-referencing major publications. The advancements in AI technology in recent times and the present application of medicine have been analyzed critically. This paper ends with an assumption that AI focuses on implementing changes in the medical practices in previously unidentified ways. However, many of the application are still in the initial stages and require exploration and development. In addition, clinical experts have to comprehend and adapt with development for effective delivery of medical services.

scholarly journals Machine Learning at the Network Edge: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-37
Author(s):  
M. G. Sarwar Murshed ◽  
Christopher Murphy ◽  
Daqing Hou ◽  
Nazar Khan ◽  
Ganesh Ananthanarayanan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Major Research ◽  
Sensors And Actuators ◽  
Computing Systems ◽  
Privacy Concerns ◽  
Iot Devices ◽  
Cloud Servers ◽  
Typical Solution ◽  
Operational Aspects

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

scholarly journals Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial Intelligence

2021 ◽  
Author(s):  
J. Eric T. Taylor ◽  
Graham Taylor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Neural Networks ◽  
Experimental Approach ◽  
Well Being ◽  
Human Mind ◽  
Black Box ◽  
Psychological Science ◽  
Black Boxes ◽  
Explainable Artificial Intelligence

Artificial intelligence powered by deep neural networks has reached a levelof complexity where it can be difficult or impossible to express how a modelmakes its decisions. This black-box problem is especially concerning when themodel makes decisions with consequences for human well-being. In response,an emerging field called explainable artificial intelligence (XAI) aims to increasethe interpretability, fairness, and transparency of machine learning. In thispaper, we describe how cognitive psychologists can make contributions to XAI.The human mind is also a black box, and cognitive psychologists have overone hundred and fifty years of experience modeling it through experimentation.We ought to translate the methods and rigour of cognitive psychology to thestudy of artificial black boxes in the service of explainability. We provide areview of XAI for psychologists, arguing that current methods possess a blindspot that can be complemented by the experimental cognitive tradition. Wealso provide a framework for research in XAI, highlight exemplary cases ofexperimentation within XAI inspired by psychological science, and provide atutorial on experimenting with machines. We end by noting the advantages ofan experimental approach and invite other psychologists to conduct research inthis exciting new field.

scholarly journals Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers

2022 ◽  
Vol 2161 (1) ◽  
pp. 012015
Author(s):  
V Sai Krishna Reddy ◽  
P Meghana ◽  
N V Subba Reddy ◽  
B Ashwath Rao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Cardiovascular Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Correct Decision ◽  
Medical Field

Abstract Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.

scholarly journals Governance and Ethics of AI

2021 ◽  
Vol 9 (11) ◽  
pp. 1467-1471
Author(s):  
Atharv Jangam
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer System ◽  
Human Mind ◽  
Privacy And Security ◽  
Autonomous Machine

Abstract: In this article we discuss ways AI can be fruitful and inimical at the same time and consider hurdles in implementing ethics and governance of AI. We conclude with presenting solutions to overcome this issue. Artificial intelligence (AI) is a technology that allows a computer system to mimic the human mind. AI, like humans, is capable of learning and developing itself through doing tasks such as planning, organizing, and executing numerous activities. However, as we develop and expand our understanding of AI, there are a few advantages and downsides that should be addressed. Privacy and security are vital, but they conflict with the advancement of AI technology since computers and AI require a large quantity of data to Comprehend and anticipate outcomes. With the advancement of technology, we should be able to maximize security and eliminate the current drawbacks. Keywords: Artificial Intelligence (AI), Autonomous, Machine Learning (ML), Governance, Ethics, Deepfake

scholarly journals From Humans to Robots: Machine Learning for Healthcare

2021 ◽  
pp. 705-714
Author(s):  
Ankita Daghottra ◽  
Dr. Divya Jain
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Robotic Surgery ◽  
Patient Outcomes ◽  
Positive Patient ◽  
Positive Outcomes ◽  
Medical Practices ◽  
Applications Of Machine Learning

Machine learning is a branch of artificial intelligence (AI) through which identification of patterns in data is done and with help of these patterns, useful outcomes or conclusions are predicted. One of the most prominent or frequently studied applications of machine learning is the surgical phase or robotic surgery. This makes machine learning an important part of research in robotics. The implementation of this technology in the field of healthcare aims in improving medical practices resulting in more precise and advanced surgical assessments. This paper aims in outlining the implementation and applications of machine learning related to robotics in the field of healthcare. Machine learning aims in generating positive outcomes with assumptions. The objective of this paper is to bring light on how these technologies have become an important part of providing more effective and comprehensive strategies which eventually add to positive patient outcomes and more advanced healthcare practices.

The organization of effective multi-user operation of hybrid computing systems

2019 ◽  
Author(s):  
С.И. Смагин ◽  
А.А. Сорокин ◽  
С.И. Мальковский ◽  
С.П. Королёв ◽  
О.А. Лукьянова ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Parallel Programming ◽  
Computer Applications ◽  
Computing Systems ◽  
Scheduling System ◽  
Hybrid Computing ◽  
Wide Range ◽  
Virtualization Technology

Исследуются вопросы организации многопользовательской работы гибридных вычислительных систем. На примере кластера Центра коллективного пользования Центр данных ДВО РАН, построенного на архитектуре OpenPOWER, рассмотрены особенности функционирования систем подобного класса и предложены решения для организации их работы. С использованием механизма виртуальных узлов проведена адаптация системы диспетчеризации заданий PBS Professional, позволяющая организовать эффективное распределение аппаратных ресурсов кластера между пользовательскими задачами. Реализованное программное окружение кластера с системой комплексного планирования заданий рассчитано на работу с широким перечнем компьютерных приложений, включая программы, построенные с использованием различных технологий параллельного программирования. Для эффективного исполнения в данной среде решений на основе машинного обучения, глубокого обучения и искусственного интеллекта применены технологии виртуализации. С использованием возможностей среды контейнеризации Singularity сформирован специализированный стек программного обеспечения и реализован особый режим его работы в формате единой вычислительной цифровой платформы. Purpose. Improving the technology of machine learning, deep learning and artificial intelligence plays an important role in acquiring new knowledge, technological modernization and the digital economy development.An important factor of the development in these areas is the availability of an appropriate highperformance computing infrastructure capable of providing the processing of large amounts of data. The creation of coprocessorbased hybrid computing systems, as well as new parallel programming technologies and application development tools allows partial solving this problem. However, many issues of organizing the effective multiuser operation of this class of systems require a separate study. The current paper addresses research in this area. Methodology. Using the OpenPOWER architecturebased cluster in the Shared Services Center The Data Center of the Far Eastern Branch of the Russian Academy of Sciences, the features of the functioning of hybrid computing systems are considered and solutions are proposed for organizing their work in a multiuser mode. Based on the virtual nodes concept, an adaptation of the PBS Professional job scheduling system was carried out, which provides an efficient allocation of cluster hardware resources among user tasks. Application virtualization technology was used for effective execution of machine learning and deep learning problems. Findings. The implemented cluster software environment with the integrated task scheduling system is designed to work with a wide range of computer applications, including programs built using parallel programming technologies. The virtualization technologies were used in this environment for effective execution of the software, based on machine learning, deep learning and artificial intelligence. Having the capabilities of the container Singularity, a specialized software stack and its operation mode was implemented for execution machine learning, deep learning and artificial intelligence tasks on a unified computing digital platform. Originality. The features of hybrid computing platforms functioning are considered, and the approach for their effective multiuser work mode is proposed. An effective resource manage model is developed, based on the virtualization technology usage.

Machine Learning in Healthcare

2020 ◽  
pp. 92-109
Author(s):  
Stavros Pitoglou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Theory ◽  
Human Brain ◽  
Daily Practice ◽  
Human Mind ◽  
Academic Community ◽  
Acceptable Solution ◽  
Active Involvement ◽  
Definition Of

Machine learning, closely related to artificial intelligence and standing at the intersection of computer science and mathematical statistical theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, machine learning can find a fertile field of application. This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

When two science disciplines meet: Evaluating dynamics of conjunction. The encounter between astrophysics and artificial intelligence

2021 ◽  
pp. 053901842110258
Author(s):  
Anne Marcovich ◽  
Terry Shinn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Mind ◽  
Mathematical Tool ◽  
Reliability Of Results ◽  
Science Disciplines

This article points out some issues raised by the encounter between astrophysics (AP) and a newly emergent mathematical tool/discipline, namely artificial intelligence (AI). We suggest that this encounter has interesting consequences in terms of science evaluation. Our discussion favors an intra science perspective, both on the institutional and cognitive side. This encounter between machine learning (ML) and astrophysics points to three different consequences. (1) As a transverse tool, a same ML algorithm can be used for a diversity of very different disciplines and questions. This ambition and analytic intellectual architecture frequently identify similarities among apparently differentiated fields. (2) The perimeter of the disciplines involved in a research can lead to many and novel ways of collaboration between scientists and to new ways of evaluation of their work. And (3), the impossibility for the human mind to understand the processes involved in ML work raises the question of the reliability of results.

scholarly journals Robots at Work: Where Do We Fit?

2016 ◽  
Vol 138 (04) ◽  
pp. 32-37
Author(s):  
Alan S. Brown
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Problem Solving ◽  
Common Sense ◽  
Emerging Technologies ◽  
Cloud Robotics ◽  
Controlled Environments ◽  
Smart Machines ◽  
New Generation

This article presents a dilemma related to increasing use of robots at work. Artificial intelligence could erase jobs or create them, but economists agree that a new generation of smart machines will alter the rules of employment. Two emerging technologies that will help robots learn even faster are cloud robotics and deep learning, an advanced type of machine learning that allows robots to learn things that humans understand tacitly. However, robots require controlled environments, while humans, who are more flexible, can cope with unstructured tasks. That same adaptability is essential for medical technicians, plumbers, electricians, and many other middle-skill jobs. The experts expect pressures on middle-skill jobs to eventually reverse because these jobs combine not only knowledge, but also adaptability, problem solving, common sense, and the ability to communicate with other people. Businesses are already pairing human flexibility with mechanical precision.

Machine Learning in Healthcare, Introduction and Real World Application Considerations

2018 ◽  
Vol 7 (2) ◽  
pp. 27-36 ◽  
Author(s):  
Stavros Pitoglou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Theory ◽  
Daily Practice ◽  
Human Mind ◽  
Academic Community ◽  
Acceptable Solution ◽  
Active Involvement ◽  
Real World Application ◽  
Definition Of

Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.

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