Object Detection and Explainability with PyTorch Model Using Co-Execution Example 1: Object Detection and Explainability with Imported TensorFlow Model This example shows how to import a TensorFlow ...

Recently, it has been observed that deep learning models can be very useful in handling healthcare facilities mostly medical diagnosis and management. However, they are not interpretable and ...

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved ...

Many believe that XAI promotes model transparency and trust, making people more comfortable with the risk of improper learning and incorrect predictions that can occur with machine learning models.

Additionally, most of the ML models to which XAI is applied are traditional ML algorithms, while deep learning models are still very rarely addressed. The application of XAI to ML models in the ...

In response to the escalating threat of Android malware, this research proposes a hybrid model for malware detection and classification using a combination of machine learning (ML) and deep learning ...

Deep learning models have demonstrated exceptional efficacy in brain tumor detection, ... MRI, classification, deep learning, explainability. Citation: Rasool N, Wani NA, Bhat JI, Saharan S, Sharma VK ...

Scientists in Malaysia have developed a novel deep-learning method for PV suitability mapping. Applying the new approach to the Middle East, they found that approximately 5.8% of the region has very ...