Dr. Walaa M. Medhat

Dr. Walaa Medhat

Program Director of Computer Science (CS)

Faculty Office Ext.

3044

Faculty Building

UB2

Office Number

F12

Biography

Program Director of Computer Science (CS) Walaa Medhat is an assistant professor at Nile University. Walaa Medhat is an associate professor at Nile University. She got her Ph.D., M.Sc., and BA degree in computer systems from faculty of engineering, Ain Shams University. She worked at the Faculty of Computers and Artificial Intelligence, Benha University. She was a consultant in MIS unit, the Supreme Council of Universities. She is an expert in Natural Language Processing, Data Mining and Software Engineering. She is a member in the Egyptian Society of Language Engineering.

Recent Publications

Hands-on analysis of using large language models for the auto evaluation of programming assignments

The increasing adoption of programming education necessitates efficient and accurate methods for evaluating students’ coding assignments. Traditional manual grading is time-consuming, often inconsistent, and prone to subjective biases. This paper explores the application of large language models (LLMs) for the automated evaluation of programming assignments. LLMs can use advanced natural language

Artificial Intelligence
Circuit Theory and Applications

Formal Verification of Code Conversion: A Comprehensive Survey

Code conversion, encompassing translation, optimization, and generation, is becoming increasingly critical in information systems and the software industry. Traditional validation methods, such as test cases and code coverage metrics, often fail to ensure the correctness, completeness, and equivalence of converted code to its original form. Formal verification emerges as a crucial methodology to

Circuit Theory and Applications

Exploring State-of-the-Art Models in Arabic NLP: Insights into Multi-Label Text Classification

This study addresses the challenge of multi-label text classification in the Arabic language, focusing on movie genre categorization using plot summaries. Even though over 400 million people speak Arabic, its natural language processing (NLP) advances are not keeping up with those of other languages because of data shortages and quality difficulties. Three key contributions are made by this

Artificial Intelligence
Circuit Theory and Applications

Trans-Compiler-Based Conversion from Cross-Platform Applications to Native Applications

Cross-platform mobile application development is emerging widely in the mobile applications industry. Cross-platform Frameworks (CPFs) like React Native, Flutter, and Xamarin are used by many developing companies. The technology these frameworks use faces performance and resource use efficiency limitations compared to native applications. The native applications are written in the native languages

Artificial Intelligence
Circuit Theory and Applications
Innovation, Entrepreneurship and Competitiveness

Trans-Compiler-Based Database Code Conversion Model for Native Platforms and Languages

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler

Artificial Intelligence
Circuit Theory and Applications
Software and Communications

A Core Ontology to Support Agricultural Data Interoperability

The amount and variety of raw data generated in the agriculture sector from numerous sources, including soil sensors and local weather stations, are proliferating. However, these raw data in themselves are meaningless and isolated and, therefore, may offer little value to the farmer. Data usefulness is determined by its context and meaning and by how it is interoperable with data from other

Artificial Intelligence
Circuit Theory and Applications
Software and Communications
Agriculture and Crops
Mechanical Design

Blockchain Application on Big Data Security

In recent years, advances in technology in several industries have resulted in massive data collections on the web. It raises worries about large data security and protection. The advent of Blockchain technology has caused a revolution in the security field for different applications. The distributed ledger is stored on each Blockchain node, which enhances security and data transparency. On the

Artificial Intelligence
Healthcare
Circuit Theory and Applications
Software and Communications
Innovation, Entrepreneurship and Competitiveness

Blockchain in Healthcare for Achieving Patients' Privacy

Heath data are sensitive and valuable for individuals. The patients need to integrate and manage their medical data continuously. Personal Health Record (PHR) is introduced as a solution for managing their health information. It gives patients ownership over their medical data and provides physicians with realignment data. However, it does not achieve reliability, traceability, trust, nor security

Artificial Intelligence
Healthcare
Circuit Theory and Applications
Software and Communications

Industrial Practitioner Perspective of Mobile Applications Programming Languages and Systems

The growth of mobile application development industry made it crucial for researchers to study the industry practices of choosing mobile applications programming languages, systems, and tools. With the increased attention of cross-platform mobile applications development from both researchers and industry, this paper aims at answering the question of whether most of the industries are using cross

Artificial Intelligence
Circuit Theory and Applications
Software and Communications
Innovation, Entrepreneurship and Competitiveness
Research Tracks
  • Natural Language Processing (NLP)
  • Data Mining
  • Software Engineering
  • Big Data Analytics
Projects
1
Research Project

AgriSem: Semantic Web Technologies for Agricultural Data Interoperability

Objective/Contributions: The amount and types of raw data generated within the agriculture domain are dramatically growing. However, these raw data in themselves are meaningless and isolated, and therefore may offer little value to the farmer. The Agricultural Research Center (ARC)and the Central Lab for Agricultural Expert Systems (CLAES) was established to enhance the productivity of knowledge