A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco

2019 ◽  
Vol 24 (6) ◽  
pp. 3577-3589 ◽  
Author(s):  
Aimad Qazdar ◽  
Brahim Er-Raha ◽  
Chihab Cherkaoui ◽  
Driss Mammass
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Framework

Twitter Users' Classification Based on Interest

2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Knowledge Sharing ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Software System ◽  
Web Based ◽  
Twitter Users

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.

scholarly journals Accessing and applying molecular history

2015 ◽  
Author(s):  
Joshua G Stern ◽  
Eric A Gaucher
Keyword(s):  
Machine Learning ◽  
Molecular Evolution ◽  
Real World ◽  
Evolutionary History ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
History Of ◽  
Evolution And Medicine ◽  
Real World Problems

Studying the evolutionary history of life’s molecules - DNA, RNA, and protein - reveals nature-based solutions to real-world problems. We discuss an approach to applied molecular evolution that is well-known within the field but may be unfamiliar to a wider audience. Using a case study at the intersection of molecular evolution and medicine, we introduce the fundamental concepts of orthology and paralogy. We also explain a practical entry point to molecular evolution named STORI: Selectable Taxon Ortholog Retrieval Iteratively. STORI is a machine learning algorithm designed to clear a bottleneck that researchers encounter when studying evolution.

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