RoboRumble is an interactive game where players can use their creativity to design, build and program their robot. The robots can be programmed using visual blocks of code. Each round player's robot is put against a unique enemy robot, demanding different strategic approaches. At its core, our game seeks to transform the experience of learning robotics and programming, making it engaging and accessible, particularly for the younger audience.
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SummarEyes is a cutting-edge solution for enhancing online meetings and lectures. Leveraging the power of machine learning and computer vision, we aim to detect and track instances when participants become distracted, ensuring that the valuable content is not missed. Our language model then generates comprehensive summaries, complete with topic titles and timestamps, for all the key discussions during those distracted moments. These summaries are invaluable for attendees to catch up and for speakers to gain insights into audience engagement. By providing feedback on when distractions occur most frequently, we empower speakers to deliver more engaging and impactful presentations. This innovation will revolutionize how companies and educational institutions approach online communication, making meetings and lectures more productive and engaging for everyone involved.
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vendAR is an online shopping platform that utilizes Augmented Reality/Hyper Reality. Primary goal of utilizing AR/HR is to let buyers visualize the object in their houses to shorten the measurement process. Users will be able to manipulate the transform of the AR objects to place it wherever they need to. vendAR's innovative use of Augmented Reality and Hyper Reality not only simplifies the measurement process but also enhances the overall shopping experience. By allowing users to interact with and manipulate AR objects within their own living spaces, vendAR bridges the gap between online and physical shopping, encouraging customers to make more informed purchasing decisions.
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The main aim in this project is to create driving profiles of drivers so that companies such as Trendyol, Migros, MNG Kargo can have detailed information about the drivers. ın this way, companies will be able to evaluate their drivers' driving and see which route the vehicle is traveling on. We aim to provide users/customers with information about the driving of company vehicles through parameters by which vehicle usage can be evaluated, such as tracking vehicle fleets, driving analysis and driver profiling using Arduino or other embedded systems. By using ready-made modules that assist GSM/GPRS signals and measure acceleration/inclination/G force, we aim to obtain the necessary information about driving from the embedded system and establish the necessary connections with the remote computer to send this information to the data processing stage. The received driving information is analyzed and classified by artificial intelligence algorithms on the remote computer. The new driving analysis is reused during the artificial intelligence algorithm training phase to be used in later analyses, thus making the online learning cycle continuous. Analysis results are included in the database to be displayed in the mobile application. Past driving and instant driving information analyzed as a result of the processed data can be followed by the user with the mobile application we will develop. More than one vehicle and driving information can be observed on a single screen at the same time.
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Our project aims to enhance the customer experience and to increase the profits of stores and markets by deploying cutting-edge technology and with the help of computer vision. We're developing a system that not only detects foot traffic but delves deeper into understanding customer preferences. From tracking their focus areas and time spent in-store to capturing demographic data such as age, gender, we're creating a comprehensive profile. Our goal is to empower retailers with invaluable insights, enabling them to tailor their offerings and services to match the ever-evolving needs of their customer base. Beyond just customer demographics, we're exploring the potential for financial advice based on spending patterns, adding a personalized touch to the shopping experience. Forecasting future trends is another dimension of our project. By analyzing the data collected, we will provide retailers with forecasting capabilities, allowing them to stay ahead of market trends and make informed decisions. However, we are aware of the ethical and privacy considerations in this project. Our commitment extends to addressing privacy concerns related to camera-based data collection and ensuring strict compliance with data protection regulations. The ethical use of facial recognition technology is a priority, and robust data security measures are embedded throughout our system. In essence, our project is not just about tracking customers-it's about empowering retailers with actionable insights, fostering a symbiotic relationship between businesses and their clientele.
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Strada is a Recruitment and ınterview Helper, a cutting-edge revolutionary solution redefining the hiring process. Powered by advanced technologies, our system efficiently matches candidates' resumes with job descriptions, ranking them based on relevance and skills. We extract precise resume information using Optical Character Recognition (OCR), refined further by a Language Learning Model (LLM) for thorough evaluation. Our state-of-the-art models analyze facial expressions, voice tones, and spoken words during interviews, generating real-time, tailored questions. Timestamped candidate reactions provide a holistic view of performance, culminating in a final LLM analysis. This pioneering approach offers efficiency and profound insights, enabling data-driven decisions in recruitment. With a streamlined process, businesses can expect a significant reduction in time-to-hire while also ensuring a higher caliber of candidates, ultimately leading to more successful hires and a stronger, more competitive workforce.
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