Amri Samir

TL;DR: A genome-wide association study meta-analysis of up to 125,584 cases and over 2.5 million control individuals across 60 studies from 25 countries reveals compelling insights regarding disease susceptibility and severity.

TL;DR: Stemming and lemmatization are two language modeling techniques used to improve the document retrieval precision performances.

TL;DR: Amazigh is a morphologically rich language, which presents a challenge for Part of Speech tagging, which is an important component for almost all Natural Language Processing application areas.

TL;DR: A new tagging algorithm with a Machine Learning algorithm that combines decision trees model and HMM model to tag Amazigh unknown words.

TL;DR: In this article , the authors compare the accuracy of crowd-sourced and medical expert annotation on point-of-care ultrasound clips, and show that the crowd-source annotation has substantial potential to augment medical imaging analysis and clinical decision-making.