Ā Ā Ā Ā Ā Ā Ā Ā Ā The aim of this work is to develop an accurate and interpretable machine learning framework for early-stage detection of Autism Spectrum Disorder (ASD) by integrating explainable artificial intelligence techniques to enhance clinical trust and decision transparency.
Ā Ā Ā Ā Ā To develop an improved dangerous goods detection system using YOLOv11 that achieves higher accuracy and real-time performance in identifying prohibited items in X-ray baggage images.
To develop a robust and interpretable AI system for ovarian cancer diagnosis using multiclassification techniques and advanced deep learning models, including ResNet152V2, EfficientNetV2B3, and ResNet50V2.
Ā Ā Ā Ā Ā To improve the accuracy and efficiency of cyberbullying detection in social media text by utilizing an advanced machine learning model (DistilBERT) that overcomes ambiguity and classification challenges.
Aim:
The aim of this study is to develop a robust and accurate traffic accident risk prediction model by leveraging deep learning techniques such as CNN (Convolutional Neural Network), BiLSTM (Bi-directional Long Short-Term Memory), and GRU (Gated Recurrent Unit) models.