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Face Recognition Using SNN and DNN Together
CS-E-29
Oria Yakov; oria974@gmail.com Roei Azriel; roeiazriel@gmail.com
Advisor: Prof. Shlomo Greenberg
SCE - Shamoon College of Engineering, Be’er-Sheva
In today’s digital world, face recognition plays a key role in enhancing security, enabling personalized services, and improving user authentication in everyday applications. As the demand for more accurate and reliable face recognition systems increases, traditional methods often fall short when facing challenges such as changes in lighting, facial angles, and different expressions. This project developed a deep learning-based face recognition system that can receive and extract meaningful facial features directly from data. The system was trained on a balanced dataset to support fairness and improve its ability to generalize across different conditions. By leveraging deep neural networks (DNNs) together with spiking neural networks (SNNs), the project built a foundation for a more robust and adaptable face recognition solution.
Keywords: deep learning, face recognition, image processing, neural networks
Improving Information Retrieval in Semantic Search Engines
CS-E-30
Yossi Elbaz; yossi5196@gmail.com Eliron Barel; elironbarel123@gmail.com
Advisor: Dr. Aviad Elyashar
SCE - Shamoon College of Engineering, Be’er-Sheva
In today's digital world, how we search for information has evolved significantly. Semantic-based tools are becoming increasingly prominent, replacing traditional keyword-based search engines. The shift requires search engines to understand the context and meaning behind words, rather than simply matching the words. In this project, we developed an innovative AI-based algorithm to enhance information retrieval by addressing it as a Semantle challenge (a popular online word-guessing game). Using unsupervised learning techniques and network science, the project bridged the gap between user queries and search results through advanced techniques, paving the way for more accurate and meaningful search outcomes.
Keywords: information retrieval, network science, search engines, semantics, unsupervised learning