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Monday, March 18, 2024
Defense of a Master’s Thesis by Ahmad Al Khatib in the Data Science and Business Analytics Program

Researcher Ahmad Hassan Al Khatib, a student in the Master’s program in Data Science and Business Analytics, has defended his thesis titled "Explainable Deep Learning Methods for Neuroscience Data to Analyze the Extracted Features in The Hidden Layers"

In recent years, deep learning models have provided various applications in various fields, especially in the medical fields such as neuroscience. Thus, ensuring the interpretation of the results predicted by deep learning models has been an important challenge, especially exploring the hidden layers in these models, and this is called black box interpretation.

Sunday, March 17, 2024
Defense of a Master’s Thesis by Mohammad Shubaita in the Data Science and Business Analytics Program

Researcher Mohammad Zuhdi Shubaita, a student in the Master’s program in Data Science and Business Analytics, has defended his thesis titled “Two-Steps Approach for Breast Cancer Detection and Classification Using Convolutional Neural Networks and mammography images”.

This thesis presents an integrated two-step learning framework that uses deep learning to simplify and increase the accuracy of the breast cancer diagnosis process. This innovative methodology differs from traditional methods by using raw, unmarked X-ray images, and this would facilitate the pre-treatment phase.

Wednesday, March 6, 2024
Defense of a Master’s Thesis by Ayser To’mi in the Data Science and Business Analytics Program

Researcher Ayser Maysara To’mi, a student in the Master’s program in Data Science and Business Analytics, has defended his thesis titled: “A Machine Learning-Based Analysis of the Impact of University Specialization on the Unemployment Rate.”

This research presented two models for classifying data on graduates in general, and data on some specializations in particular, by employment status (employed, unemployed). This was through applying machine learning algorithms to data from the Labor Force Survey carried out by the Palestinian Central Bureau of Statistics.

The researcher created a classification model using five different algorithms - Random Forest, DT, XGBoost, KNN, and AdaBoost for graduate data in general, and three algorithms were applied to classify the data according to university specialization.

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