Advancing Ovarian Cancer Diagnosis Through Deep Learning and Explainable AI: A Multiclassification Approach

5,500.00
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.

Road Traffic Accident Risk Prediction and Key Factor Identification Framework Based on Explainable Deep Learning

5,500.00
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.