The architecture of this model combines each user's features and his historical event lists by sequence-to-sequence (Seq2Seq) structure and make predictions based on his recent event lists. We also ...

Therefore, this study develops wide-area subsidence calculation model integrating Convolutional Neural Network (CNN) and Transformer. Initially, a training sample set for mining subsidence ...

Abstract: This paper addresses the challenge of anti-jamming in orthogonal time frequency space (OTFS) modulation systems by proposing a novel anti-jamming decoder. The design of this decoder presents ...

Convolutional Neural Networks (CNN) are widely used for image classification and VMMR problems. Complex model structures and more internal parameters are needed to improve classification accuracy with ...

This paper presents a neural network model based on a hybrid CNN-LSTM architecture to detect several attacks in the network traffic at the Edge of IIoT using only features from the transport and ...

In this paper, we propose a waste segmentation method using Convolutional Neural Network based on the Encoder-Decoder approach of SegNet architecture ... then evaluate our model using TrashNet ...

Abstract: The breast cancer based image classification and division is proposed by utilizing a Deep learning (DL) technique. A few DL models is used to classify Mammographic information to forecast ...