For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

Moreover, our method also outperforms the state-of-the-art multi-label methods in both computational efficiency and classification accuracy on two publicly available multi-label image datasets: ...

The functionalities of the screening platform are demonstrated in a case study for the hydroformylation of decene. The image detection is performed with a Mask R-CNN model achieving a ±1.5% accuracy ...

Welcome to Learn with Jay – your go-to channel for mastering new skills and boosting your knowledge! Whether it’s personal development, professional growth, or practical tips, Jay’s got you ...

Matsuyama, E. , Watanabe, H. and Takahashi, N. (2024) Performance Comparison of Vision Transformer- and CNN-Based Image Classification Using Cross Entropy: A Preliminary Application to Lung Cancer ...

After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection ...

Therefore, improving the interpretability of DCNNs is crucial for their adoption in medical diagnoses. This paper proposes a novel three-tier self-interpretable DCNN (TS-CNN) architecture for ...

In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases.