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Data Science

Sunday, May 26, 2024
Holder of the UNESCO Chair in Data Science for Sustainable Development, Dr. Majdi Owda is Hosted as a Guest Speaker at the University of Essex in the United Kingdom

The University of Essex in the United Kingdom has hosted Dr. Majdi Owda, Dean of the Faculty of Artificial Intelligence and Data Science at the Arab American University and holder of the “UNESCO Chair in Data Science for Sustainable Development,” to deliver a lecture titled: Open Science for Sustainable Development: An International Perspective .

Dr. Owda's lecture touched upon the importance of open science in sustainable development. Thus, he provided an international perspective on the collaborative efforts needed to address global challenges. He also discussed the main areas of open science and relevant actors.

Sunday, May 19, 2024
Two AAUP Researchers from the Faculty of Graduate Studies Publish a Peer-Reviewed Research Article in Cooperation with the Higher School of Technology (ETS) at the Canadian University of Quebec

Students Mohammad Abu Tame’, researcher in the Master’s program in Data Science and Business Analysis, and Sari Al-Masry, researcher in the Master’s program in Artificial Intelligence at the Faculty of Graduate Studies at the Arab American University, have published a peer-reviewed research article in the Information - MDPI Journal with an impact factor of 3.1 titled “Transformer-Based Approach to Pathology Diagnosis Using Audio Spectrogram”

The research article was part of the machine learning course, under the supervision of Dr. Ahmed Al-Hassasneh, and in cooperation with researchers at the Ecole Supérieure de Technologie (ETS) at the University of Quebec in Montreal - Canada.

Tuesday, March 19, 2024
Defense of a Master’s Thesis by Muhannad Amarneh in the Data Science and Business Analytics Program

Researcher Muhannad Ahmed Amarneh, a student in the Master’s program in Data Science and Business Analytics, has defended his thesis titled “Predicting the Incidence of a Psychological Disorder (Anxiety, Depression and Stress) Using Machine Learning Algorithms.”

The present study aims to use machine learning algorithms to predict diagnoses of stress, anxiety and depression as the most common psychiatric disorders, using the dataset collected as part of this work. The dataset consisted of approximately 700 records using an online survey, which was based on the Depression, Anxiety and Stress International Scale (DASS21). The data was collected from Palestinian community participants and university students. To ensure the effectiveness of applying artificial intelligence algorithms, the data was processed before analyzing it.


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