“Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning” has been added to your cart. View cart
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.
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.
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.