An Automated Machine Learning Platform for Non-experts

2020 ◽  
Author(s):  
Jin Han ◽  
Ki Sun Park ◽  
Keon Myung Lee
Keyword(s):  
Machine Learning ◽  
Learning Platform ◽  
Automated Machine Learning

Automated Machine Learning Platform

2021 ◽  
Author(s):  
Gozde Selvi ◽  
Goknur Dag ◽  
Ennur Gaye Dirican ◽  
Tevfik Aktay ◽  
Sevgi Merve Aksu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Platform ◽  
Automated Machine Learning

A web-based automated machine learning platform to analyze liquid biopsy data

Lab on a Chip ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 2166-2174
Author(s):  
Hanfei Shen ◽  
Tony Liu ◽  
Jesse Cui ◽  
Piyush Borole ◽  
Ari Benjamin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liquid Biopsy ◽  
Learning Models ◽  
Learning Tool ◽  
Web Based ◽  
Learning Platform ◽  
Automated Machine Learning ◽  
Machine Learning Tool ◽  
Machine Learning Models

We have developed a web-based, self-improving and overfitting-resistant automated machine learning tool tailored specifically for liquid biopsy data, where machine learning models can be built without the user's input.

scholarly journals Automated machine learning for endemic active tuberculosis prediction from multiplex serological data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hooman H. Rashidi ◽  
Luke T. Dang ◽  
Samer Albahra ◽  
Resmi Ravindran ◽  
Imran H. Khan
Keyword(s):  
Machine Learning ◽  
Emerging Infectious Diseases ◽  
Machine Intelligence ◽  
Active Tuberculosis ◽  
Clinical Implementation ◽  
Learning Platform ◽  
Out Of Sample ◽  
Automated Machine Learning ◽  
Model Training ◽  
Serological Data

AbstractSerological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Clinical interpretation of these complex datasets requires development of suitable algorithms, a time consuming and tedious undertaking addressed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). MILO seamlessly integrates data processing, feature selection, model training, and model validation to simultaneously generate and evaluate thousands of models. These models were then further tested for generalizability on out-of-sample secondary and tertiary datasets. Out of 31 antigens evaluated, a 23-antigen model was the most robust on both the secondary dataset (TB vs healthy) and the tertiary dataset (TB vs COPD) with sensitivity of 90.5% and respective specificities of 100.0% and 74.6%. MILO represents a user-friendly, end-to-end solution for automated generation and deployment of optimized models, ideal for applications where rapid clinical implementation is critical such as emerging infectious diseases.

Bandit-Based Automated Machine Learning

2018 ◽  
Author(s):  
Silvia Cristina Nunes das Dores ◽  
Carlos Soares ◽  
Duncan Ruiz
Keyword(s):  
Machine Learning ◽  
Automated Machine Learning

scholarly journals Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1370
Author(s):  
Igor Vuković ◽  
Kristijan Kuk ◽  
Petar Čisar ◽  
Miloš Banđur ◽  
Đoko Banđur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Intelligent Support ◽  
Course Success ◽  
States Of Emergency ◽  
Learning Platform ◽  
Rapid Spread ◽  
E Learning ◽  
Multi Agent ◽  
Observer System

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

scholarly journals Automated Machine-Learning Radiotherapy Planning for Pediatric and Adult Brain Tumours

2021 ◽  
Vol 52 (2) ◽  
pp. S3
Author(s):  
Grace Tsui ◽  
Derek S. Tsang ◽  
Chris McIntosh ◽  
Thomas G. Purdie ◽  
Glenn Bauman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumours ◽  
Adult Brain ◽  
Radiotherapy Planning ◽  
Automated Machine Learning
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