Results 241 to 250 of about 279,520 (289)

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions. [PDF]

PLoS One
Chaudhary S, Sharma A, Singh K, Khichar S, Malhotra J.   +4 more
europepmc   +1 more source

Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening. [PDF]

Health Inf Sci Syst
Shrivastava T, Singh V, Agrawal A.
europepmc   +1 more source

Reproducible comparison and interpretation of machine learning classifiers to predict autism on the ABIDE multimodal dataset

Dong Y, Batalle D, Deprez M.
europepmc   +1 more source

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Printed Machine Learning Classifiers

2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2020
A large number of application domains have requirements on cost, conformity, and non-toxicity that silicon-based computing systems cannot meet, but that may be met by printed electronics. For several of these domains, a typical computational task to be performed is classification.
Mubarik, Muhammad Husnain, Weller, Dennis D., Bleier, Nathaniel, Tomei, Matthew, Aghassi-Hagmann, Jasmin, Tahoori, Mehdi B., Kumar, Rakesh   +6 more
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Tracking strategy changes using machine learning classifiers

Behavior Research Methods, 2021
In complex tasks, high performers often have better strategies than low performers, even with similar amounts of practice. Relatively little research has examined how people form and change strategies in tasks that permit a large set of strategies. One challenge with such research is identifying strategies based on behavior.
Jarrod, Moss, Aaron Y, Wong, Jaymes A, Durriseau, Gary L, Bradshaw   +3 more
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Machine Learning Classifiers in Glaucoma

Optometry and Vision Science, 2008
Machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn" patterns in data using iterative processes. Such processes can be supervised (guided by a priori group membership information) or unsupervised (guided by patterns within the data).
Christopher, Bowd, Michael H, Goldbaum
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Machine Learning for Classifying Learning Objects

2006 Canadian Conference on Electrical and Computer Engineering, 2006
Building an ontology for learning objects can be useful for translating such objects between learning contexts. Such translations are important because they afford learners and educators with the opportunity to a survey a wide selection of learning and teaching material.
Girish Ranganathan, Yevgen Biletskiy, Dawn MacIsaac   +2 more
openaire   +1 more source

Classifying osteosarcoma patients using machine learning approaches

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017
Metabolomic data analysis presents a unique opportunity to advance our understanding of osteosarcoma, a common bone malignancy for which genomic and proteomic studies have enjoyed limited success. One of the major goals of metabolomic studies is to classify osteosarcoma in early stages, which is required for metastasectomy treatment.
, Zhi Li, S M, Reza Soroushmehr, , Yingqi Hua, , Min Mao, , Yunping Qiu, Kayvan, Najarian   +5 more
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Applying Machine Learning Classifiers in Argumentation Context

2020
Group decision making is an area that has been studied over the years. Group Decision Support Systems emerged with the aim of supporting decision makers in group decision-making processes. In order to properly support decision-makers these days, it is essential that GDSS provide mechanisms to properly support decision-makers. The application of Machine
Luís Conceição, João Carneiro, Goreti Marreiros, Paulo Novais   +3 more
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Mitigating Preconception in Machine Learning Classifiers

2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021
Modern Machine Learning (ML) approaches are aimed at enhancing model performance (behaviors and accuracy) through historical data available for the specific model. Continued use of machine learning has been witnessed in the real-world business including self-driving cars, health diagnosis systems, fraud detection, and customer churn predictions among ...
Henry Mutisya Ngie, Lawrence Nderu, Leah Mutanu, Dorcas Mwigereri Gicuku   +3 more
openaire   +1 more source