How are CNNs adapted for sequence-to-sequence tasks?

Mar 9, 2024 ... A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart ... Nov 28, 2022 ... In this study, different deep CNN models with multiple machine learning classifiers are investigated for monkeypox disease diagnosis using ... Mar 25, 2025 ... We introduce CNN-TumorNet, an innovative method for brain tumor classification utilizing a deep convolutional neural network (CNN) for the ... This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles ... We have proposed a deep learning method called BCM-SegNet, an optimized Mask R-CNN-based algorithm for segmenting BSE images of bronze casting moulds and cores. Mar 19, 2022 ... What is R-CNN? R-CNN or RCNN, stands for Region-Based Convolutional Neural Network. It is a type of machine learning model for computer vision ... Jan 23, 2023 ... Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN ... Feb 1, 2023 ... A new bio-inspired deep learning model that enables generalization in dynamic lighting conditions. Feb 1, 2023 ... Convolutional Neural Networks (CNNs) is a powerful deep learning architecture well-suited to image classification and object recognition tasks. Dec 7, 2017 ... While people usually tend to simply resize any image into a square while training a CNN (for example, resnet takes a 224x224 square image), that looks ugly to ...