Dr. Sahar Selim

Assistant Professor

Faculty Office Ext.

1782

Faculty Building

UB1

Office Number

213

Biography

Dr. Sahar Selim received her bachelor's and M.Sc. degrees from the Faculty of Computer Science and Information Systems at Ain Shams University. She obtained her Ph.D. degree in Brain-Computer Interface (BCI) from the Faculty of Computers and Information at Cairo University. Prior to starting her academic career, Selim worked as a software developer for three years.

Before joining Nile University, where she currently holds the position of assistant professor, Selim was a research associate at the Faculty of Media Engineering & Technology at the German University in Cairo. Her research focuses on using machine learning to analyze biological signals for developing applications that enable disabled subjects to interact with computer devices using their brain electroencephalography (EEG).

Dr. Sahar Selim is an active member of multiple research groups at the Center for Informatics Sciences at Nile University, where she has contributed to numerous projects leveraging machine learning for medical applications. Her profound technical expertise is complemented by her interest in Machine Learning and Artificial Intelligence. Her research interests primarily revolve around Medical Imaging, Brain Computer Interface, and Neural Engineering.

Achievements
  1. Nominated for the Prof. Abdelaziz Hegazy Award for Outstanding Teaching by the ITCS School for the Academic Year 2022.
  2. Recipient of the ITCS Teaching Award for the Academic Year 2022.
  3. Principal Investigator (PI) for the KUKA Innovation Award 2022, with the proposal "Human-Brain Robot Interaction for Mobility Rehabilitation Control" being selected among the top 10 applications.
  4. Led a project focused on the rehabilitation of post-stroke aphasia patients using machine learning, funded by the NU research office and in collaboration with the Neuromodulation lab at El-Demerdash Hospital, Ain Shams University.

  5. Co-PI for a project on lung cancer detection in Chest X-Ray images, funded by ITAC.

Recent Publications

Detection of COVID-19 from Chest X-Ray Images Using Deep Neural Network with Fine-Tuning Approach

The coronavirus (COVID-2019) quickly spread throughout the world and came to be a pandemic. To avoid further spreading this epidemic and treat affected patients rapidly, it is important to recognize the positive cases as early as possible. In this paper, deep learning techniques are employed to detect COVID-19 from chest X-ray images quickly. The images of the two classes, COVID and No-findings

Artificial Intelligence
Healthcare

Generic Library Mapping Approach for Trans-Compilation

Cross-platform mobile development is a widely used framework due to its nature of building an app using one development life cycle and deploying it to multiple platforms like Android and iOS. Many cross-platform solutions were recently developed to convert from one platform to another using Trans-compilation approach as Trans-Compiler Android to IOS Conversion (TCAIOSC) and Trans-Compiler Based

Artificial Intelligence

Native Mobile Applications UI Code Conversion

With the widespread use of mobile applications in daily life, it has become crucial for software companies to develop the applications for the most popular platforms like Android and iOS. Using a native development is time consuming and costly. Cross-platform mobile development like Xamarin and React native emerged as a solution to the mentioned problem of native development for the time and cost

Artificial Intelligence
Software and Communications
Innovation, Entrepreneurship and Competitiveness

Using CNN-XGBoost Deep Networks for COVID-19 Detection in Chest X-ray Images

At the time of writing, the COVID-19 pandemic is one of the lead causes of death worldwide and has caused significant changes to everyone's lives. While a vaccine is still unavailable, early screenings and detection of the disease can significantly help in managing the healthcare system's capacity as well as allow radiologists and clinicians better assign their priorities. With deep learning's

Artificial Intelligence
Research Tracks
  • Biomedical Informatics
  • Medical Imaging and Image Processing MIIP
  • Software and Systems
  • Data Mining and Natural Language Processing
Projects
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Research Project

Lung Cancer Detection in Chest X-Ray Images Empowered by 3D Computed Tomography Deep Convolutional Radiomics (CXRClear)

Objective/Contributions: Cancer is treatable if it is discovered at an early stage, and lung cancer screening is a critical component in a preventive care protocol. Although CT imaging affords higher spatial resolution and 3D density information than digital chest X-rays, there are still limitations to having it as a cheap and fast method for rural areas outreach. These limitations are outlined in
3
Research Project

Smart Agricultural Clinic: Egyptian Farmer Electronic Platform for the Future

Objective/Contributions: Smart agricultural clinic (SAC) aims to: 1) Provide an integrated end-to-end digital system to effectively deliver personalized agriculture extension and veterinary services, including best cultivation, fertilization and breeding practices, to farmers and animal producers through the use of mobile/handheld devices. 2) Use advanced computer vision and deep learning