Characteristics Analysis of Mental Health Data of College Students Based on Convolutional Neural Network and TOPSIS Evaluation Model by Lanfeng Zhou

“…This paper develops a feature analysis method of the mental health data of students in different colleges and regions and of different ages based on a convolutional neural network and TOPSIS evaluation model and studies the college students’ mental health analysis model based on convolutional neural network. …”
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Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. …”
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Cross-species AI: shifting a convolutional neural network from pigs to lambs to detect pneumonia at slaughter by Anastasia Romano, Antonio De Camillis, Domenico Sciota, Simona Baghini, Andrea Di Provvido, Alfonso Rosamilia, Andrea Capobianco Dondona, Nicola Bernabò, Francesca Vaccarelli, Attilio Corradi, Giuseppe Marruchella

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Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes by Gabriel L. Zanellato, Gabriel A. Pagnossin, Mauricio Failla, Juan F. Masello

“…Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. …”
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MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit by Chenxi Wang, Huizhen Zhang, Shuilin Yao, Wenlong Yu, Ming Ye

“…However, the spatiotemporal relationship between stations not only is limited to geospatial adjacency, but also lacks different perspectives of station correlation analysis. …”
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Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm by Puneet Kumar, Dalwinder Singh, Mamoona Humayun, Ali Alqazzaz, Arun Malik, Ibrahim Alrashdi, Isha Batra, Ghadah Naif Alwakid

“…Initially, Newman and Girvan proposed traditional algorithms for community detection from social networks in 2004, but with the growth of social networks, Convolutional Neural Network (CNN) based algorithms are proposed by different researchers in recent years due to the inefficiency of traditional methods. …”
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Hybrid Convolutional Neural Network-Transformer Model for End-to-End Binaural Sound Source Localization in Reverberant Environments by Xinyi Chen, Lijia Zhao, Jie Cui, Hua Li, Xiaodong Wang

“…The WavLocT model has a unique feature extraction block that incorporates a convolutional neural network (CNN) and transformer structure, in which binaural localization features are extracted from different subband signal waveforms via the CNN structure. …”
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Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods by Stephen B. Lee

“…The study uses various poisoned datasets of different compositions (2%, 16.7% and 100% ratios of poisoned data) and two different test sets (a normal set of test data and one that contained poisoned images) to study the effects of BadNets. …”
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A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network by Xiaodong Yu, Peng Zhang, Xi Yu

“…In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. …”
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A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network by Hend S. Saad, John F. W. Zaki, Mohamed M. Abdelsalam

“…MHD diseases are difficult to be distinguished from each other because they come in many forms with different severity of symptoms and different periods of suffering. …”
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Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention Mechanism by Jiacheng Sun, Hua Ding, Ning Li, Xiaochun Sun, Xiaoxin Dong

“…Subsequently, to effectively utilize the feature information of different frequencies, the HAM is integrated into the 1DCNN to form the MA-MS1DCNN. …”
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Vibration Signal-Based Fault Diagnosis of Rotary Machinery Through Convolutional Neural Network and Transfer Learning Method by Chirag Mongia, Shankar Sehgal

“…To address this limitation, a transfer learning (TL)-based methodology has been developed for bearing fault detection, so that the model trained under some specific training conditions can perform accurately under significantly different real-time working conditions, thereby significantly improving diagnostic efficiency while reducing training time. …”
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A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects by Rui Li, Xuanwen Yang, Jun Lou, Junsong Zhang

“…To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). To capture high-level information distributed in time, spatial, and frequency domains, TSGCN considers both neural oscillation changes in different time windows and topological structures between different brain regions. …”
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Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning by Lingyu Shen, Jianfeng Li, Jingjing Pan, Junpeng Shi, Rui Xu, Hao Wang, Weiming Deng

“…By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. …”
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Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images by Shadman Q. Salih, Hawre Kh. Abdulla, Zanear Sh. Ahmed, Nigar M. Shafiq Surameery, Rasper Dh. Rashid

“…Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. …”
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Prediction of Residual Life of Rolling Bearings Based on Multi-Scale Improved Temporal Convolutional Network (MITCN) Model by Keru Xia, Qi Li, Luyuan Han, Zhaohui Ren, Hengfa Luo

“…By introducing a multi-scale expanded causal convolution residual structure, improved temporal convolutional network (ITCN) modules with different expansion factors capture information on different time scales and combine soft threshold functions and channel attention mechanisms to adaptively generate thresholds and eliminate redundant information. …”
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Real-Time Series Arc Fault Detection and Appliances Classification in AC Networks Based on Competing Convolutional Kernels by Francesco Ferracuti, Riccardo Felicetti, Luca Cavanini, Patrick Schweitzer, Andrea Monteriu

“…The proposed method is validated using a public database, where data from 13 different types of loads is collected according to the IEC 62606 standard. …”
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Detection of degraded forests in Guinea, West Africa, using convolutional neural networks and Sentinel-2 time series by An Vo Quang, An Vo Quang, Nicolas Delbart, Gabriel Jaffrain, Camille Pinet

“…Altogether, the results show that the method is transferable and applicable across different years and among the different Guinean forest regions, such as the Ziama, Diécké, and Nimba massifs. …”
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Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect by Maria Rugenstein, Senne VanLoon, Elizabeth A. Barnes

“…Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.…”
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Multi-step prediction of coal mine adit deformation based on time convolutional long short-term memory by Wenli JI, Xin DAN, Chenyang MA, Jing CHAI, Yuyi WU, Fengqi QIU, Wentao LIU, Wulin LEI, Yongliang LIU

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