H
Hassan Al Moatassime
Researcher at Cadi Ayyad University
Publications - 27
Citations - 1323
Hassan Al Moatassime is an academic researcher from Cadi Ayyad University. The author has contributed to research in topics: Big data & Crash. The author has an hindex of 11, co-authored 27 publications receiving 745 citations.
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Journal ArticleDOI
Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis
TL;DR: A performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer datasets is conducted and Experimental results show that SVM gives the highest accuracy with lowest error rate.
Journal ArticleDOI
Data quality in internet of things
TL;DR: Techniques for enhancing DQ in IoT are presented with a special focus on data cleaning techniques which are reviewed and compared using an extended taxonomy to outline their characteristics and their fitness for use for IoT.
Journal ArticleDOI
Predictive modeling of wildfires: A new dataset and machine learning approach
TL;DR: The method used in this paper combines Big Data, Remote Sensing and Data Mining algorithms to process data collected from satellite images over large areas and extract insights from them to predict the occurrence of wildfires and avoid such disasters.
Journal ArticleDOI
The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review
TL;DR: A conceptual framework is outlined whereby DB is viewed in terms of different dimensions established within the Driver–Vehicle–Environment (DVE) system, and an interpretive framework incorporating multiple dimensions influencing the driver’s conduct is identified.
Proceedings ArticleDOI
Big data in healthcare: Challenges and opportunities
TL;DR: The potential benefits of big data to healthcare are explained and how it improves treatment and empowers patients, providers and researchers are explored and the ability of reality mining in collecting large amounts of data to understand people's habits, detect and predict outcomes is described.