Python
Analysis of Smart helmets and Designing an IoT based Smart helmet: A cost effective solution for Riders
Android Malware Detection Using Informative Syscall Subsequences
Application of IoT and Artificial Intelligence in Road Safety
Embedded, Artificial Intelligence(AI), Internet of Things(IoT), Artificial Intelligence, IoT, Machine Learning
Approximate DCT Design for Video Encoding Based on Novel Truncation Scheme
AR Interactive Story Telling for Children Book
The goal of this project is to develop an AR (Augmented Reality) Interactive Storytelling system for children's books. The primary aim is to create an engaging and educational experience that immerses children into the story through visual and interactive elements, making the reading experience more enjoyable and educational.
Arbitrary-Oriented Ship Detection Framework in Optical Remote-Sensing Images
AREA DETERMINATION OF DIABETIC FOOT ULCER IMAGES USING A CASCADED TWO-STAGE SVM BASED CLASSIFICATION
Artificial Intelligence in Agriculture: A Systematic Review of Crop Yield Prediction and Optimization
Assisted Vision: a Transformative Web App for the Visually Impaired
Aim:
Ā Ā Ā Ā Ā Ā The primary aim of this project is to detect, monitor, and provide mental health support for pregnant women by analyzing free conversational speech in naturalistic settings. The system leverages cloud-based communication, AI-driven audio analysis, and humanāAI hybrid interaction to screen for depression and anxiety while providing emotional support through chat and voice-based systems.
ATHYNOS: Helping Children with Dyspraxia Through an Augmented Reality Serious Game
ATT Squeeze U-Net A Lightweight Network for Forest Fire Detection and Recognition
Automated Brain Tumor Segmentation and Classification in MRI using YOLO-based Deep Learning
Python, Deep Learning, Generative AI, Projects, Artificial Intelligence, Deep Learning, Generative AI
The aim of this research is to develop a more effective and efficient brain tumor segmentation system using the YOLOv11 architecture. The focus is on enhancing the accuracy and reliability of tumor identification in brain imaging, specifically through advanced segmentation techniques. By leveraging deep learning models, the study seeks to provide an automated solution for real-time tumor segmentation, assisting in clinical decision-making and early diagnosis.