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“PlaceMe”: A Smart Seating Management System for Events
SE-B-11
Neria Atias; neria126@gmail.com Nativ Levi; nativl12345@gmail.com
Advisor: Dr. Lior Aronshtam
SCE - Shamoon College of Engineering, Ashdod
“PlaceMe” is a seating management system designed to create optimal improvement and streamlining of seating arrangements at various types of events. Our system is built upon the integration of advanced technologies, such as “Flutter” and “Firebase”, and using advanced algorithms, data analysis, and semantic image processing; it offers a comprehensive solution to personalized seating management. The goal is to enable optimal seating planning, tailored to individual preferences and logistical needs, while ensuring that the process is efficient, accessible, and intelligent. This project is currently in its early developmental stages and includes a review of existing models, towards construction of a tool that will enhance the planning process for seating arrangements in the future.
Keywords: algorithm, events, optimization, seating management, smart technology
Artificial Intelligence (AI) for Next Generation Insect Tracking
SE-B-12
Sergei Yakima; sergeya@ac.sce.ac.il Yehuda Harush; yehudha2@ac.sce.ac.il
Advisors: Dr. Hadassa Daltrophe1, Dr. Tammar Shrot1 1SCE - Shamoon College of Engineering, Ashdod
Tracking small, fast-moving insects is a challenge with broad research implications. This study presents an AI-based multi-object tracking system for fruit flies, developed for a study of the relationship between aging and geotaxis by researchers at the Hebrew University. Using a labelled video database, a deep-learning model was trained to track multiple flies within test vials, addressing challenges like movement overlap, individual trajectory isolation, and sub-optimal lighting conditions. Our AI system improves tracking accuracy and enables automated data collection. Automated tracking and data collection enhance research efficiency, enable large-scale behavioral analyses, and provide precise movement data—ultimately supporting deeper investigations into insect cognition and life history traits.
Keywords: AI tracking, automation, geotaxis, insect behavior, machine learning, multi-object tracking