MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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Data driven assessment of built environment impacts on urban health across United States cities by Siavash Ghorbany, Ming Hu, Siyuan Yao, Matthew Sisk, Chaoli Wang, Kai Zhang, Quynh Camthi Nguyen

“…Yet, a comprehensive analysis across multiple U.S. cities covering different geographical conditions has been missing. …”
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Papillary thyroid carcinoma whole-slide images as a basis for deep learning by M. V. Fridman, A. A. Kosareva, E. V. Snezhko, P. V. Kamlach, V. A. Kovalev

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Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models by Zishuai Wang, Wangchang Li, Zhonglin Tang

“…Deep learning (DL) methods like multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. …”
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HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species by Huimin Li, Wei Gao, Yi Tang, Xiaotian Guo

“…Methods We proposed HD-6mAPred, a hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN) and attention mechanism, along with various DNA sequence coding schemes. …”
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WiCNNAct: Wi-Fi-Based Human Activity Recognition Utilizing Deep Learning on the Edge Computing Devices by Venkata Raghava Shashank Viswanathuni, Rakesh Reddy Yakkati, Sreenivasa Reddy Yeduri, Linga Reddy Cenkeramaddi

“…The proposed approach utilizes the channel state information (CSI) measurements (complex values) from Wi-Fi and processes the different combinations of the real, imaginary, and absolute values using multi-channel 1D convolutional neural networks (1D-CNN). …”
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Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

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Formation permeability estimation using mud loss data by deep learning by Yaser Abdollahfard, Seyed Morteza Mirabbasi, Mohammad Ahmadi, Abdolhossein Hemmati-Sarapardeh, Sefatallah Ashoorian

“…This can be used to estimate the formation permeability values. One-dimensional convolutional neural networks(1D-CNN), a type of convolutional neural network, is utilized to be trained with data to perform a regression problem based on the contribution of flattening, dropout, and fully connected layers to estimate permeability with high accuracy (training data R2 = 0.970, testing data R2 = 0.964). …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. …”
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A Hybrid Method Combining Variational Mode Decomposition and Deep Neural Networks for Predicting PM2.5 Concentration in China by Senlin Li, Bo Tang, Xiaowu Deng

“…The deep neural structures used include recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs). To demonstrate the effectiveness of VDPS, we conducted comparative evaluations of different models’ performance on many experimental datasets of PM2.5 concentrations in four cities: Beijing, Shanghai, Guangzhou, and Chengdu. …”
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Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study by Mingxiu He, Mingxiu He, Yiyang Shi, Fangfang Han, Fangfang Han, Fangfang Han, Yongming Cai, Yongming Cai, Yongming Cai

“…The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.Results and DiscussionThe performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. …”
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SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification by Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang, Zongze Zhao

“…After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. …”
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SPCB-Net: A Multi-Scale Skin Cancer Image Identification Network Using Self-Interactive Attention Pyramid and Cross-Layer Bilinear-Trilinear Pooling by Xin Qian, Tengfei Weng, Qi Han, Chen Wu, Hongxiang Xu, Mingyang Hou, Zicheng Qiu, Baoping Zhou, Xianqiang Gao

“…Deep convolutional neural networks have made some progress in skin lesion classification and cancer diagnosis, but there are still some problems to be solved, such as the challenge of small inter-class feature differences and large intra-class feature differences, which might limit the classification performance of the model as high-level and low-level features are not properly utilized. …”
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CNN-ELM-BASED DEEP LEARNING FRAMEWORK FOR KNEE OSTEOARTHRITIS CLASSIFICATION FROM RADIOGRAPHIC IMAGES by V Srividhya, P Jega Juliet, N Neelima, Senthil Kumar Seeni

“…The CNN-ELM model system integrates Contrast Limited Adaptive Histogram Equalisation (CLAHE) in the preprocessing stage to enhance image quality and highlight subtle structural differences associated with KOA. The custom CNN composed of three convolutional layers extracts deep spatial features from the enhanced X-ray images and these features are passed to the ELM classifier, which performs fast, non-iterative learning using pseudo-inverse computations. …”
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Reducing overfitting in vehicle recognition by decorrelated sparse representation regularisation by Wanyu Wei, Xinsha Fu, Siqi Ma, Yaqiao Zhu, Ning Lu

“…Abstract Most state‐of‐the‐art vehicle recognition methods benefit from the excellent feature extraction capabilities of convolutional neural networks (CNNs), which allow the models to perform well on the intra‐dataset. …”
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Human-Annotated Label Noise and Their Impact on ConvNets for Remote Sensing Image Scene Classification by Longkang Peng, Tao Wei, Xuehong Chen, Xiaobei Chen, Rui Sun, Luoma Wan, Jin Chen, Xiaolin Zhu

“…Human-labeled training datasets are essential for convolutional neural networks (ConvNets) in satellite image scene classification. …”
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Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco, Lucian-Teodor Grigorie

“…In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. …”
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TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification by Nelly Elsayed, Constantinos L. Zekios, Navid Asadizanjani, Zag ElSayed

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Salt and pepper denoising filters for digital images: A technical review by Kumar Abhishek, Kumar Sanjeev, Kar Asutosh

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Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification by Nana Jiang, Wenbo Zhao, Jiao Guo, Qiang Zhao, Jubo Zhu

“…We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. …”
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