SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…This step involves image resizing along with the augmentation step. The proposed convolutional neural network (CNN) model is comprised of six blocks placed at different layers. …”
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A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network by Ying Lu, Naiwei Yao

“…Second, it designs a cross-modal alignment mechanism to better connect information across different data types. Third, it optimizes the feature fusion structure for more effective integration. …”
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Week-Ahead Water Demand Forecasting Using Convolutional Neural Network on Multi-Channel Wavelet Scalogram by Adithya Ramachandran, Hatem Mousa, Andreas Maier, Siming Bayer

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Effects of scale on segmentation of Nissl–stained rat brain tissue images via convolutional neural networks by Alexandro Arnal, Olac Fuentes

“…In this work, we test a fully convolutional architecture, U–Net, with Nissl–stained rat brain tissue images of different scales. …”
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CLASSIFICATION OF THE NUTRITIONAL CONDITION OF BEAN PLANTS (Phaseolus Vulgaris) USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE ANALYSIS by Julia Couto, Jamile Regazzo, Murilo Baesso, Adriano Tech, Thiago Silva

“…The images were processed and used to train and test different CNN configurations. The results indicated that larger sets of images and smaller blocks (10x10 pixels) increased accuracy, especially at 37 DAE. …”
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Fault Diagnosis Method of Planetary Gearbox based on Wavelet Time-frequency Diagram and Convolutional Neural Network by Jianhua Zhou, Pan Zheng, Shuaixing Wang, Shijing Wu, Xiaosun Wang

“…Experimental results show that the proposed method has better diagnostic accuracy and robustness than the BP neural network when the speed of training set data and test set data is different. This approach provides a reference for planetary gearbox fault diagnosis.…”
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Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory by Dazhang You, Linbo Chen, Fei Liu, YePeng Zhang, Wei Shang, Yameng Hu, Wei Liu

“…Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. …”
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From data to dynamics: Reconstructing soliton collision phenomena in optical fibers using a convolutional autoencoder by Qibo Xu, Jifang Rong, Qilin Zeng, Xiaofang Yuan, Longnv Huang, Hua Yang

“…In this study, a convolutional autoencoder is constructed to extract and reconstruct the dynamical processes of soliton collisions in optical fibers. …”
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Finding the Optimal Convolutional Kernel Size for Semantic Segmentation of Pole-like Objects in Lidar Point Clouds by Z. Zhang, D. Shojaei, K. Khoshelham

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Three Dimensional Image Reconstruction of Electrical Capacitance Tomography Based on Improved ALEXNET Convolutional Neural Network by LI Yan, WANG Lu, LI Jiaqi

“…A method is proposed that the corresponding AlexNet neural network is trained according to the data of different flow patterns for the problem of slow sample training and low imaging accuracy for the threedimensional image reconstruction algorithm of convolutional neural networks. …”
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Few shot object detection for headdresses and seats in Thangka Yidam based on ResNet and deformable convolution by Hu Wenjin, Xue Panpan, He Guoyuan, Tang Huiyuan, Song Huafei, Yue Chaoyang

“…By introducing the offset of deformable convolution, the receptive field can adapt to the different sizes and shapes of the detection target of Thangka Yidam. …”
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Dynamic Tracking and Real-Time Fall Detection Based on Intelligent Image Analysis with Convolutional Neural Network by Ching-Bang Yao, Cheng-Tai Lu

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Enhanced Adaptive Wiener Filtering for Frequency-Varying Noise with Convolutional Neural Network-Based Feature Extraction by Chun-Lin Liao, Jian-Jiun Ding, De-Yan Lu

“…Noise appears in various forms, such as additive white Gaussian noise (AWGN) and Poisson noise across different frequencies. This study aims to denoise images without prior knowledge of the noise distribution. …”
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MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks. by Yan Miao, Xuan Tang, Chunyu Wang, Zhenyuan Sun, Guohua Wang, Shan Huang

“…MVHGCN first constructs a heterogeneous graph and generates feature descriptors by integrating multiple databases. Then it extracts different connection views of circRNA and diseases through meta-paths, maximizing the utilization of known association information, and aggregates deep feature information through graph convolutional networks. …”
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Algorithm development for recognizing human emotions using a convolutional neural network based on audio data by V. V. Semenuk, M. V. Skladchikov

“…To validate the neural network different set of audio data, not participating in the training, was selected. …”
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Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention by Qibing Wang, Chuanjie Yin, Kun She, Qinfeng Tong, Guoxiong Lu, Hongbing Zhang, Jiawei Lu

“…This is achieved by capturing feature differences and utilising non-local(NL) operations, thereby enhancing the feature representation ability under different working conditions. …”
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Overseas short video recommendations: A multimodal graph convolutional network approach incorporating cultural preferences by Xishi Liu, Haolin Wang, Dan Li

“…Due to the effectiveness of the proposed method on TikTok and MovieLens dataset with a recall of 0.590 and video label classification accuracy more than 94.9%, The approach demonstrates effective use of resources with a maximum CPU utilization of only 44% whilst maintaining high user satisfaction across different age groups. Overall, the results have an implication that the proposed approach can lead to better user interaction and satisfaction in a culturally diverse environment.…”
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Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification by Aytuğ Onan

“…In addition, such models value different features equally. To solve these issues, we propose a bidirectional convolutional recurrent neural network architecture, which utilizes two separate bidirectional LSTM and GRU layers, to derive both past and future contexts by connecting two hidden layers of opposite directions to the same context. …”
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Robust Classification of Encrypted Network Services Using Convolutional Neural Networks Optimized by Information Bottleneck Method by Zhijiong Wang, Wei Lin, Yu Chen, Mang I. Vai

“…Additionally, we analyze the impact of different IB parameters on the classification performance and provide insights into the optimal configuration for practical deployment. …”
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RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms by Hameda A. Sennary, Ghada Abozaid, Ashraf Hemeida, Alexey Mikhaylov

“…In our study, we’re discussing a way to solve this phenomenon by using Term Frequency-Inverse Document Frequency (TF-IDF) based approach to feature engineering on eleven classifiers for machine and deep learning that can automatically identify hate speech. Three different databases were used, the first of which “Hate speech offensive tweets by Davidson et al.”, the second called "Twitter hate speech" and finally we merged the second data with (Cyberbullying dataset (toxicity_parsed_dataset)". …”
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