Abstract: Utilizing Deep Convolution Strategies to Perceive Human Platelets (Hematological Pictures): Deep Learning is rapidly becoming seen as a preferable method for learning over conventional ML ...

Abstract: Semantic segmentation of remote sensing imagery plays an important role in applications such as environmental monitoring and disaster response. However, challenges such as complex spatial ...

Abstract: Recent medical image segmentation methods extract the characteristics of anatomical structures only from the spatial domain, ignoring the distinctive patterns present in the spectral ...

Abstract: Hypercomplex graph convolutions with higher hypercomplex dimensions can extract more complex features in graphs and features with varying levels of complexity are suited for different ...

Abstract: Convolutional neural networks (CNNs) accelerated by photonic computing have attracted significant attention due to its potential to overcome the speed, scalability, and energy limitations of ...

Abstract: In multichannel electroencephalograph (EEG) emotion recognition, most graph-based studies employ shallow graph model for spatial characteristics learning due to node over-smoothing caused by ...

Abstract: Tensor factorization is an effective tool that has been successfully applied in the field of context-aware recommendation. However, most existing factorization models assume a multilinear ...

Abstract: Graph convolutional neural networks (GCNs) have demonstrated effectiveness in processing graph structure. Due to the diversity and complexity of real-world graph data, heterogeneous GCN have ...

Abstract: The effectiveness and efficiency of modeling complex spectral–spatial relations are crucial for hyperspectral image (HSI) classification. Most existing methods based on convolution neural ...

QUAKERTOWN, Pa. - Community members in Quakertown are demanding answers a day after multiple arrests were made during a student protest. A group of people gathered outside the borough's police ...