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521por Fang, Jiangxiong, Jiang, Houtao, Zhang, Shiqing, Sun, Lin, Hu, Xudong, Liu, Jun, Gong, Meng, Liu, Huaxiang, Fu, Youyao“…To address the issue, we propose a bidirectional attention fusion network combing the convolution neural network (CNN) and Swin Transformer, called BAF-Net, to segment the pepper leaf image. …”
Publicado 2023
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522“…The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN). …”
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523“…In this study, we present initial efforts for a new speech recognition approach aimed at producing different input images for convolutional neural network (CNN)-based speech recognition. We explored the potential of the tympanic membrane (eardrum)-inspired viscoelastic membrane-type diaphragms to deliver audio visualization images using a cross-recurrence plot (CRP). …”
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524por Retta, Ephrem Afele, Sutcliffe, Richard, Almekhlafi, Eiad, Enku, Yosef Kefyalew, Alemu, Eyob, Gemechu, Tigist Demssice, Berwo, Michael Abebe, Mhamed, Mustafa, Feng, JunEnlace del recurso
Publicado 2023
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526“…CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 − 4) features. …”
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527Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes“…The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. …”
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528“…The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). …”
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529por He, Jinghu, Yang, Xiaohong, Zhang, Chuansen, Li, Ang, Wang, Wei, Xing, Junjie, E, Jifu, Xu, Xiaodong, Wang, Hao, Yu, Enda, Shi, Debing, Wang, Hantao“…Furthermore, EGR1 was identified as a downstream of CNN2, forming a complex with CNN2 and YAP1 and playing an essential role in the CNN2-induced regulation of CRC development. …”
Publicado 2023
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530“…We created a convolutional neural network (CNN) model from scratch for this study. Combining Local Binary Patterns (LBP) and deep learning features resulted in the creation of the ensemble features vector, which was then optimized using the Binary Dragonfly Algorithm (BDA) and the Sine Cosine Algorithm (SCA). …”
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533Publicado 2023“…The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.…”
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535“…Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.…”
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536“…To address these issues, this paper proposes a new instance segmentation network, RTS R-CNN, for road surface traffic sign detection tasks based on Mask R-CNN. …”
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537“…Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. …”
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538por Lu, Yaozhi, Aslani, Shahab, Zhao, An, Shahin, Ahmed, Barber, David, Emberton, Mark, Alexander, Daniel C., Jacob, Joseph“…In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. …”
Publicado 2023
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539por Tang, Can, Chen, Du, Wang, Xin, Ni, Xindong, Liu, Yehong, Liu, Yihao, Mao, Xu, Wang, Shumao“…The method is divided into three stages: In the first stage, self-calibrated convolutions are added to the Mask R-CNN backbone network to improve the model performance, and then the model is used to extract the strawberry target in the image. …”
Publicado 2023
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