scholarly journals Overview on Open Source Machine Learning Platforms-TensorFlow

2020 ◽  
Author(s):  
Muqthadar Ali Syed
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Learning Platforms

scholarly journals Building A Platform for Machine Learning Operations from Open Source Frameworks

2020 ◽  
Vol 53 (5) ◽  
pp. 704-709
Author(s):  
Yan Liu ◽  
Zhijing Ling ◽  
Boyu Huo ◽  
Boqian Wang ◽  
Tianen Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source

scholarly journals Do machine learning platforms provide out-of-the-box reproducibility?

2021 ◽  
Author(s):  
Odd Erik Gundersen ◽  
Saeid Shamsaliei ◽  
Richard Juul Isdahl
Keyword(s):  
Machine Learning ◽  
Learning Platforms

scholarly journals The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques

2021 ◽  
Vol 1804 (1) ◽  
pp. 012133
Author(s):  
Mahmood Shakir Hammoodi ◽  
Hasanain Ali Al Essa ◽  
Wial Abbas Hanon
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

scholarly journals An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206409 ◽  
Author(s):  
Stephen Solis-Reyes ◽  
Mariano Avino ◽  
Art Poon ◽  
Lila Kari
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Learning Tool ◽  
Hiv 1 ◽  
Machine Learning Tool

scholarly journals Machine Learning Boosted Docking (HASTEN): An Open-Source Tool To Accelerate Structurebased Virtual Screening Campaigns

2021 ◽  
Author(s):  
Tuomo Kalliokoski
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Open Source ◽  
Learning Models ◽  
Open Source Tool ◽  
The Mean ◽  
Machine Learning Models

The software macHine leArning booSTed dockiNg (HASTEN) was developed to accelerate<br>structure-based virtual screening using machine learning models. It has been validated using<br>datasets both from literature (12 datasets, each containing three million molecules docked<br>with FRED) and in-house sources (one dataset of four million compounds docked with<br>Glide). HASTEN showed reasonable performance by having the mean recall value of 0.78 of<br>the top one percent scoring molecules after docking 10 % of the dataset for the literature data,<br>whereas excellent recall value of 0.95 was achieved for the in-house data. The program can be<br>used with any docking- and machine learning methodology, and is freely available from<br>https://github.com/TuomoKalliokoski/HASTEN.

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