Underwater Sound Speed Profile Inversion Based on Res-SACNN from Different Spatiotemporal Dimensions by Jiru Wang, Fangze Xu, Yuyao Liu, Yu Chen, Shu Liu

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Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks by Zhanhao Mo, He Sui, Zhongwen Lv, Xiaoqian Huang, Guobin Li, Dinggang Shen, Lin Liu, Shu Liao

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CNN Convolutional layer optimisation based on quantum evolutionary algorithm by Tzyy-Chyang Lu

“…In the simulation part, CIFAR-10 (including 50k training images and 10k test images in 10 classes) is used to train VGG-19 and 20-layer, 32-layer, 44-layer and 56-layer CNN networks, and compare the difference between the optimal and non-optimal convolutional layer networks. …”
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Activation function cyclically switchable convolutional neural network model by İsmail Akgül

“…This study presents a different approach apart from fixed or trainable AF approaches. …”
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Improving deep convolutional neural networks with mixed maxout units by Hui-zhen ZHAO, Fu-xian LIU, Long-yue LI, Chang LUO

“…The maxout units have the problem of not delivering non-max features， resulting in the insufficient of pooling operation over a subspace that is composed of several linear feature mappings,when they are applied in deep convolutional neural networks.The mixed maxout (mixout) units were proposed to deal with this constrain.Firstly,the exponential probability of the feature mappings getting from different linear transformations was computed.Then,the averaging of a subspace of different feature mappings by the exponential probability was computed.Finally,the output was randomly sampled from the max feature and the mean value by the Bernoulli distribution,leading to the better utilizing of model averaging ability of dropout.The simple models and network in network models was built to evaluate the performance of mixout units.The results show that mixout units based models have better performance.…”
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Unsupervised intrusion detection model based on temporal convolutional network by LIAO Jinju, DING Jiawei, FENG Guanghui

“…In addition, UDMT can adopt different privacy layer modes, and the configuration was flexible to meet the requirements of different detection rates and detection delays. …”
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Rice Plant Disease Detection using Convolutional Neural Networks by A. Bala Ayyappan, T. Gobinath, M. Kumar, A. Sivaramakrishnan

“…In this paper, we use Convolutional Neural Networks (CNNs) and deep learning approaches to identify various rice plant diseases like blast, brown spot and bacterial blight. …”
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Dental Arch Development of Chinese Adolescents: A Retrospective Cross-Sectional Study by Chao Liu, Jieyi Chen, Mengjuan Cui, Zhengliang Wang, Wentao Shi, Siyuan Du, Yang Feng, Manfei Zhang, Zhenxia Li, Shufei Ge, Bing Fang

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Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network by Xiuzhi You, Junqian Zhang, Bingyang Chen, Ke Zhang, Xiaodong Liu, Bin Yan, Wanhua Zhu

“…This paper proposes a magnetic moment estimation algorithm that combines a scalar magnetic field sensor and the three components of the local geomagnetic field with a convolutional neural network (CNN). The simulation results demonstrate that the proposed algorithm performs well in noise environments with a signal-to-noise ratio (SNR) greater than −5 dB. …”
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CSC-GCN: Contrastive semantic calibration for graph convolution network by Xu Yang, Kun Wei, Cheng Deng

“…Graph convolutional networks (GCNs) have been successfully applied to node representation learning in various real-world applications. …”
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Convolutive blind source separation method based on tensor decomposition by Baoze MA, Tianqi ZHANG, Zeliang AN, Pan DENG

“…A convolutive blind source separation algorithm was proposed based on tensor decomposition framework, to address the estimation of mixed filter matrix and the permutation alignment of frequency bin simultaneously.Firstly, the tensor models at all frequency bins were constructed according to the estimated autocorrelation matrix of the observed signals.Secondly, the factor matrix corresponding to each frequency bin was calculated by tensor decomposition technique as the estimated mixed filter matrix for that bin.Finally, a global optimal permutation strategy with power ratio as the permutation alignment measure was adopted to eliminate the permutation ambiguity in all the frequency bins.Experimental results demonstrate that the proposed method achieves better separation performance than other existing algorithms when dealing with convolutive mixed speech under different simulation conditions.…”
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Battery Life Evaluation Method Based on Temporal Convolution Network by SUN Yushu, AN Juan, HUANG Cunqiang, ZHANG Shunzhen, DANG Yanyang, PEI Wei, TANG Xisheng

“…ObjectiveTo improve the technical economy of battery system applications, a temporal convolutional network (TCN) is employed to evaluate battery life from two perspectives: State of health (SOH) and remaining useful life (RUL). …”
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Improved Convolutional Neural Networks for Course Teaching Quality Assessment by Yun Liu

“…In this paper, a separate long-term recursive convolutional network (SLRCN) microexpression recognition algorithm is proposed using deep learning technology for building a course teaching effectiveness evaluation model. …”
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Application of convolutional neural networks in the intelligence security system subsystem by V. S. Demeshko, A. I. Фёдоров

“…Training and verification of the recognition quality of architecture data was carried out on an experimentally created data set with a human image on a contrasting background and at different ranges. The results obtained indicate the possibility of using a convolutional neural network in the security system and its ability to work in real time.…”
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Frame points attention convolution for deep learning on point cloud by Luyang Li, Ligang He, Jinjin Gao, Xie Han

“…FPAC then combines the quantified correlations with the weights of the frame points to generate spatially continuous filters. The convolution weights for different local areas in the filters are calculated dynamically, without relying on generative models or probabilistic assumptions. …”
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AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK by LAWRENCE OMOTOSHO, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, JOSHUA OYENIYI

“…In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. …”
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Compression of Marine Environmental Data Using Convolutional Attention Autoencoder by Xuehai Sun, Peiyu Wang, Yanxia Zhou, Kedi Wu, Limin Huang, Xuewen Ma

“…This study proposes a convolutional attention autoencoder (CAAE) to compress and reconstruct three-dimensional temperature fields and evaluates its performance across different depths and compression ratios. …”
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Spectral-Spatial Convolutional Hybrid Transformer for Hyperspectral Image Classification by Haixin Sun, Jingwen Xu, Fanlei Meng, Mengdi Cheng, Qiuguang Cao

“…First, the spectral pyramid 3D convolution and 2D convolution are combined to extract joint and detailed spectral-spatial features. …”
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Classification of Some Barley Cultivars with Deep Convolutional Neural Networks by Fatih Bayram, Mustafa Yıldız

“…In this research, a novel image database consisting of 2800 images were created to classify 14 barley cultivars. Six different deep convolutional neural network models were designed based on a transfer learning method with pretrained DenseNet-121, DenseNet-169, DenseNet-201, InceptionResNetV2, MobileNetV2 and Xception networks. …”
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Explainable classification of goat vocalizations using convolutional neural networks. by Stavros Ntalampiras, Gabriele Pesando Gamacchio

“…This paper advances classification of goat vocalizations leveraging a publicly available dataset recorded at diverse farms breeding different species. We developed a Convolutional Neural Network (CNN) architecture tailored for classifying goat vocalizations, yielding an average classification rate of 95.8% in discriminating various goat emotional states. …”
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