Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences

Physiotherapy is the trending medication for curing bone-related injuries and pain. In many cases, due to sudden jerks or accidents, the patient might suffer from severe pain. Therefore, it is the miracle medication for curing patients. The aim here is to build a framework using artificial intelligence and machine learning for providing patients with a digitalized system for physiotherapy. Even though various computer-aided assessment of physiotherapy rehabilitation exist, recent approaches for computer-aided monitoring and performance lack versatility and robustness. In the authors' approach is to come up with proposition of an application which will record patient physiotherapy exercises and also provide personalized advice based on user performance for refinement of therapy. By using OpenPose Library, the system will detect angle between the joints, and depending upon the range of motion, it will guide patients in accomplishing physiotherapy at home. It will also suggest to patients different physio-exercises. With the help of OpenPose, it is possible to render patient images or real-time video.

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Needs and Performance Analysis for Changes in Higher Education and Implementation of Artificial Intelligence, Machine Learning, and Extended Reality

Education Sciences ◽

10.3390/educsci11100568 ◽

2021 ◽

Vol 11 (10) ◽

pp. 568

Author(s):

Milena P. Ilić ◽

Dan Păun ◽

Nevenka Popović Šević ◽

Aleksandra Hadžić ◽

Anca Jianu

Keyword(s):

Artificial Intelligence ◽

Higher Education ◽

Machine Learning ◽

Performance Analysis ◽

Higher Education Institutions ◽

Building Blocks ◽

Research Problem ◽

Learning By Doing ◽

The Republic ◽

And Performance

Higher education in the Republic of Serbia needs to be reformed. This paper presents a performance analysis of the changes that the authors assume are mandatory, presenting the research problem this article addresses. Cabinet research, performed by analyzing the theoretical building blocks of available knowledge and experience, is underway. Articles and studies from various publications, such as academic journals and institutes, were used as sources. In addition, academic articles and papers and studies about artificial intelligence, machine learning, and extended reality were also consulted. The authors consider that these technologies could be of great assistance in developing a new higher education strategy. Further, this research is exploratory given that information from the 100 Serbian students from selected higher education institutions was used to better understand if these technologies are welcomed by students. Based on SmartPls software, the research analysis proved that artificial intelligence (AI) and machine learning (ML) are appropriate technologies implemented in higher education institutions (HEI) to develop skills among students, a collaborative learning environment, and an accessible research environment. Additionally, extended reality (XR) facilitates increased motivation, engagement, and learning-by-doing activities between students, offering a realistic environment for learning.

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Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031117 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1117

Author(s):

Tarik Alafif ◽

Abdul Muneeim Tehame ◽

Saleh Bajaba ◽

Ahmed Barnawi ◽

Saad Zia

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

State Of The Art ◽

Diagnosis And Treatment ◽

Future Directions ◽

Health Community ◽

The World ◽

The Status ◽

And Performance

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

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Implementation of machine learning to increase productivity in the manufacturing industry: a literature review.

Operations Excellence Journal of Applied Industrial Engineering ◽

10.22441/oe.2021.v13.i2.026 ◽

2021 ◽

Vol 13 (2) ◽

pp. 267

Author(s):

Yulio Agefa Purmala

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Literature Review ◽

Manufacturing Industry ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Increase Productivity ◽

And Performance ◽

Goals And Objectives

Industry 4.0 is currently developing quite rapidly, one of the technologies that is currently very popular in the industry is artificial intelligence, where an event can be diagnosed and predicted more quickly and accurately. One of the branches of artificial intelligence that can do this is Machine Learning, and its application can now be found in daily activities. In the manufacturing industry, the application of Machine Learning is one of them is to increase productivity through the results of analysis and predictions given based on the experience gained. This study uses a systematic literature review method, in which several articles were collected from several journal databases such as Elsevier, IEE, Springer, Taylor & Francis and ACM, with the publication period of the articles from 2015 to 2020. A total of 100 articles were collected, then re-validated. suitability based on the main goals and objectives of the research. There were 36 articles that were validated and used as a reference for a more in-depth review and analysis of their boundaries, so that there was a gap for further research. In this literature review study, its application is very helpful in making decisions in improving the quality, efficiency, and performance of companies in the manufacturing industry. The most popular algorithms used in this study include random forest, support vector machine, neural network, linear regression, and k-nearest neighbor. Finally, in this study it was found that the application of Machine Learning in diagnosing or predicting an event is suggested by modeling more than one algorithm to find and determine which algorithm is the most accurate and suitable to be applied to the phenomenon that occurs.

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Machine Learning in Agriculture

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i6.713 ◽

2018 ◽

Vol 8 (6) ◽

pp. 26 ◽

Cited By ~ 1

Author(s):

Matthew N. O. Sadiku ◽

Chandra M. M Kotteti ◽

Sarhan M. Musa

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Disease Diagnosis ◽

Automated Detection ◽

Disease Prediction ◽

Paper Briefly ◽

Agriculture Sector ◽

Modern Agriculture ◽

Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data. Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change. Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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Development of innovative hygienic technologies for preservation of the workers’ reproductive health

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-59-9-792-793 ◽

2020 ◽

pp. 792-792

Author(s):

M. A. Fesenko ◽

G. V. Golovaneva ◽

A. V. Miskevich

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reproductive Health ◽

Reproductive System ◽

Preventive Measures ◽

Learning Algorithms ◽

Reproductive Function ◽

Machine Learning Algorithms ◽

New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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