Isolated Sign Language Recognition [Google - Isolated Sign Language Recognition | Kaggle ]
Around 90% of which are born to hearing parents many of which may not know American Sign Language. (kdhe.ks.gov, deafchildren.org) Without sign language, deaf babies are at risk of Language Deprivation Syndrome. This syndrome is characterized by a lack of access to naturally occurring language acquisition during their critical language-learning years. It can cause serious impacts on different aspects of their lives, such as relationships, education, and employment. The goal of this competition is to classify isolated American Sign Language (ASL) signs.
Dataset: Google - Isolated Sign Language Recognition | Kaggle
Mentor: Mrugendrasinh Rahevar
Project Status: Open
Team-size: 03
Technology: Python and Pytorch or Keras
CT-Cervical spine fracture detection
Bone fracture is increasing rapidly nowadays due to traumatic injuries and this may also lead to death. so, it becomes important to detect the exact location of the fracture as soon as possible this can help the radiologist to find the location of the fracture so they can treat the patient as soon as possible. There cannot be a generalized method for the detection of bone fracture in humans as every body part of a human being consist of different shapes and sizes of bones and there is less research done on cervical spine fracture detection. In this, we will be using CT scan images in DICOM format and apply Attention on we need to find a fracture that occurred only cervical spine (C1-C7). In the medical field up till now only CNN has applied its extensive models. So, we are planning to apply the attention model on it.
Dataset: RSNA 2022 Cervical Spine Fracture Detection | Kaggle
Mentor: Mrugendrasinh Rahevar
Technology: Python Pytorch/Keras
Project Status:
Team-Size: 02 (6th Semester) (02 8th Semester)
Technology: Python and Pytorch or Keras
Small Object Detection Challenge for Spotting Birds
The task of this challenge is to detect relatively small birds with few pixel features on images, which is categorized to the Small Object Detection (SOD) problem. Distant-bird detection is an important function for unmanned aerial vehicles (UAVs) such as drones to avoid bird attacks, or drive away harmful birds that destroy fields and rice paddies. Thus, this challenge not only raises academic issues in Computer Vision but also promotes practical technology developments that are expected to be employed in real-world applications. The images in our dataset are captured from drones. In addition to the general difficulties of SOD (e.g., lack of geometrical information for small objects), the annotated birds are of different types (e.g., hawk, crow, and wild bird), and have different parallax, postures, and different degrees of motion blur.
Dataset: Small Object Detection Challenge for Spotting Birds 2023 - MVA2023 (mva-org.jp)
Mentor: Mrugendrasinh Rahevar
Technology: Python, Pytorch/Keras
Team-Size: 03
Predicting 6 Vital Plant Traits from Plant Images for Ecosystem Health
Think of plants as the superheroes of our ecosystems. Their traits hold the key to understanding ecosystems, e.g., in terms of their diversity, productivity, or how these green heroes face the challenges brought on by climate change. By solving this competition effectively, you become a superhero, too — contributing to our understanding of how plants navigate the complexities of climate change. Our goal is to predict a broad set of 6 plant traits (e.g. leaf area, plant height) from crowd-sourced plant images and some ancillary data.
Dataset: PlantTraits2024 - FGVC11 | Kaggle
Mentor: Mrugendrasinh Rahevar
Technology: Python, Pytorch/Keras
Team-Size: 03
BirdClef Challenge 2025
Description:BirdCLEF+ 2025 is a Kaggle competition focused on developing machine learning models for species identification from audio recordings in the Middle Magdalena Valley of Colombia. The goal is to recognize birds, amphibians, mammals, and insects in soundscape data, even with limited labeled samples. This initiative supports biodiversity monitoring and ecological restoration by automating passive acoustic analysis, aiding conservation efforts in El Silencio Natural Reserve.
Dataset: BirdCLEF+ 2025
Mentor: Mrugendrasinh Rahevar
Technology: Python, Pytorch/Keras
Team-Size: 03
Smart Campus Assistant (Chat + Automation)
Smart Campus Assistant is an AI-powered chatbot system designed to simplify campus life for students by providing instant access to important academic and administrative information. It acts as a virtual assistant that operates 24/7, automating tasks such as schedule reminders, assignment tracking, event notifications, and fee alerts — all through popular chat platforms like WhatsApp, Telegram, or a dedicated web portal.
The assistant uses natural language processing (NLP) to understand student queries and integrates with college systems like Moodle, Google Calendar, and Student Information Systems (SIS) to fetch real-time data.
Mentor: Mrugendrasinh Rahevar / Martin Parmar
Technology: Fullstack (Any Technology)
Team-Size: 04
AI Quiz Generator for Moodle
AI Quiz Generator for Moodle is an intelligent system designed to automate the creation of quizzes and assessments on the Moodle Learning Management System (LMS). It leverages the power of OpenAI’s GPT models to generate contextually relevant multiple-choice, short-answer, and true/false questions from course material such as lecture notes, PDFs, or topics entered by the instructor.
The tool drastically reduces the manual effort involved in quiz creation while ensuring a variety of question types and levels of difficulty. It integrates directly with Moodle via its REST API to auto-upload and publish quizzes for selected courses and categories.
Mentor: Mrugendrasinh Rahevar / Martin Parmar
Technology: Fullstack (Any Technology)
Team-Size: 04
AdmissionCRM – Smart Admission Management System
AdmissionCRM is a Customer Relationship Management (CRM) system specifically designed for educational institutions to streamline and automate the student admission process. The platform empowers admission teams with tools to efficiently manage inquiries, track applicant journeys, communicate with prospective students, and improve enrollment conversions.
The system provides an end-to-end solution for handling admission-related activities, from capturing leads through various channels (website, walk-ins, social media, etc.) to nurturing them with timely follow-ups and converting them into enrolled students.
Lead Management: Centralized dashboard to capture, assign, and track leads from multiple sources.
Communication Tools: Integrated email, SMS, and WhatsApp messaging to stay connected with applicants.
Follow-up Scheduling: Automated reminders and notes to ensure timely engagement with prospects.
Application Tracking: Monitor application progress across different stages of the admission funnel.
Analytics and Reports: Insights into lead sources, counselor performance, and conversion rates to make data-driven decisions.
Multi-User Access: Role-based access for counselors, admin staff, and management.
Custom Workflows: Configurable admission process tailored to institutional policies.
Mentor: Mrugendrasinh Rahevar / Martin Parmar
Technology: Fullstack (Any Technology)
Team-Size: 04