Supervised Machine Learning: Algorithms and Applications

Shetty, Shruthi; Shetty, Sumiksha; Singh, Chandra Veer; Rao, Ashwath

doi:10.1002/9781119821908.ch1

Cited by 28 publications

(10 citation statements)

References 5 publications

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“…But in education, historical student data with grades already assigned provides ideal training data for supervised learning. Overall, supervised learning powers many important real-world applications like medical diagnosis, speech recognition, credit risk assessment and more -all situations where historical data with known outcomes exists (Shetty et al, 2022). In the education vertical it helps optimize student recruitment approaches, identify at-risk students needing intervention, improve personalized education and more.…”

Section: Literature Reviewmentioning

confidence: 99%

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Demir

2023

Journal of Research in Didactical Sciences

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Machine learning (ML) techniques hold promise for innovating teacher preparation and development programs. However, the current state of research leveraging AI in teacher-focused contexts remains unclear. This study undertook a systematic bibliometric analysis to characterize the emerging domain investigating ML applications for enhancing teacher effectiveness. Using the bibliographic R tool Bibliometrix, metadata of 740 English-language articles published during 2019–2023 extracted from Web of Science educational databases were examined to determine performance metrics, science mapping, citation networks, and research trends situating at the intersection of machine learning and teacher education. Document growth averaged 39.57% annually, with collaborations involving 87% of publications and 21.62% engaging international co-authorships. The USA led productivity metrics, though opportunities exist to expand geographical diversity. Analyses revealed research activity presently concentrates around employing ML for student analytics, assessment frameworks, and online learning environments. Highly cited works dealt with ML systems for evaluation and competency modeling of teachers rather than directly supporting pedagogical practice. Significant gaps persist exploring intelligent recommendation engines and affective computing chatbots tailored to teachers’ dynamic training needs and emotional responses. This bibliometric review synthesizes the contours and trends in investigating ML applications for augmenting teachers’ capabilities. Findings inform stakeholders to mobilize efforts strategically advancing this domain for enriching classrooms.

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Section: Literature Reviewmentioning

confidence: 99%

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Demir

2023

Journal of Research in Didactical Sciences

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“…Several types of machine learning algorithms are employed in mammogram interpretation, each offering unique capabilities. Machine learning algorithms can be divided into supervised learning and unsupervised learning [47]. The differences between the two different learning methods are shown in Table 2.…”

Section: Machine Learning Algorithms In Mammogram Interpretationmentioning

confidence: 99%

Artificial Intelligence-based Mammogram Analysis for Early Detection

2023

Preprint

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The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

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“…A plethora of ML approaches have been investigated and are contributed by the research community for different use cases enabling a higher level of autonomous driving. For a ready reference, readers are directed to some recent surveys [ 81 , 82 , 83 ] for a detailed discussion. However, this section analyzes the two most popular ML paradigms for planning and decision-making in AD.…”

Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

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The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

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Supervised Machine Learning: Algorithms and Applications

Cited by 28 publications

References 5 publications

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Artificial Intelligence-based Mammogram Analysis for Early Detection

How Do Autonomous Vehicles Decide?

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