Automatic Detection and Classification of Enhanced Brain Tumor Using Machine Learning Algorithm

2021 ◽  
pp. 36-42
Author(s):  
Poulomi Das ◽  
Arpita Das
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Machine Learning Algorithm

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

scholarly journals Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 75 ◽  
Author(s):  
Ole Rindal ◽  
Trine Seeberg ◽  
Johannes Tjønnås ◽  
Pål Haugnes ◽  
Øyvind Sandbakk
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Automatic Classification ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Micro Sensor ◽  
Cross Country Skiing ◽  
Cross Country ◽  
Classical Cross

An Analysis of Machine Learning Algorithm for the Classification of Emotion Recognition

2021 ◽  
pp. 399-408
Author(s):  
Aditi Sakalle ◽  
Pradeep Tomar ◽  
Harshit Bhardwaj ◽  
Divya Acharya ◽  
Arpit Bhardwaj
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

Chemometric Classification of Crude Oils in Complex Petroleum Systems Using t-Distributed Stochastic Neighbor Embedding Machine Learning Algorithm

2020 ◽  
Vol 34 (5) ◽  
pp. 5884-5899
Author(s):  
Keyu Tao ◽  
Jian Cao ◽  
Yuce Wang ◽  
Julei Mi ◽  
Wanyun Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Crude Oils ◽  
Machine Learning Algorithm ◽  
Petroleum Systems
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