Diagnosis of trigeminal neuralgia based on plain skull radiography using convolutional neural network by Jung Ho Han, So Young Ji, Myeongju Kim, Ji Eyon Kwon, Jin Byeong Park, Ho Kang, Kihwan Hwang, Chae-Yong Kim, Tackeun Kim, Han-Gil Jeong, Young Hwan Ahn, Hyun-Tai Chung

“…Abstract This study aimed to determine whether trigeminal neuralgia can be diagnosed using convolutional neural networks (CNNs) based on plain X-ray skull images. …”
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Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks by Masoud Geravanchizadeh, Amir Shaygan Asl, Sebelan Danishvar

“…Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. …”
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A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes by S. M. Nuruzzaman Nobel, S. M. Masfequier Rahman Swapno, Md Babul Islam, AKM Azad, Salem A. Alyami, Md Alamin, Pietro Liò, Mohammad Ali Moni

“…Glioblastoma is the most common adult brain tumor, significantly impacts disability and mortality. …”
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Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning by Hongyu Han, Shengjie Wang, Baojun Qiao, Lanxue Dang, Xiaomei Zou, Hui Xue, Yingqi Wang

“…The proposed model utilizes dependency trees combined with self-attention mechanisms to generate new weight matrices, emphasizing the locational information of aspect terms, and optimizes the graph convolutional network (GCN) to extract aspect terms more efficiently. …”
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Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks by José Miguel Valiente, Juan José Martín-Osuna, Ana María Peral, Isabel Escriche

“…This Ground Truth termed POLLEN24_SP, comprises 32,285 pollen/particle images (captured by an expert using optical microscopy), covering the 24 most prevalent types of pollen grains found in Spanish honeys. …”
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Recognition and classification techniques of marine mammal calls based on LSTM and expanded causal convolution by Wanlu Cheng, Wanlu Cheng, Hao Chen, Jiaming Jiang, Jiaming Jiang, Shuang Li, Shuang Li, Jingjing Wang, Jingjing Wang, Yanping Zhou

“…However, traditional machine learning methods struggle to capture complex acoustic patterns, while most existing deep learning approaches rely solely on frequency-domain features and require large datasets, which limits their performance on small-scale marine mammal datasets. …”
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MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia by Haiyuan Wang, Runlin Peng, Yuanyuan Huang, Liqin Liang, Wei Wang, Baoyuan Zhu, Chenyang Gao, Minxin Guo, Jing Zhou, Hehua Li, Xiaobo Li, Yuping Ning, Fengchun Wu, Kai Wu

“…The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. …”
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Automatic image segmentation using Region-Based convolutional networks for Melanoma skin cancer detection by Karen Dayana Tovar-Parra, Luis Alexander Calvo-Valverde, Ernesto Montero-Zeledón, Mac Arturo Murillo-Fernández, Jose Esteban Perez-Hidalgo, Dionisio Alberto Gutiérrez-Fallas

“… Melanoma is one of the most aggressive skin cancers, however, its early detection can significantly increase probabilities to cure it. …”
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A network traffic classification method based on random forest and improved convolutional neural network by Bensheng YUN, Xiaoya GAN, Yaguan QIAN

“…In order to improve the efficiency and reduce the complexity of network traffic classification model, a classification method based on random forest and improved convolutional neural network was proposed.Firstly, the random forest was used to evaluate the importance of each feature of network traffic, and the feature was selected according to the importance ranking.Secondly, AdamW optimizer and triangular cyclic learning rate were adopted to optimize the convolutional neural network classification model.Then, the model was built on Spark cluster to realize the parallelization of model training.Adopting triangular cyclic learning rate with constant cycle amplitude, the experimental results of selecting 1 024, 400, 256 and 100 most important features as input show that the model accuracy is improved to 97.68%, 95.84%, 95.03% and 94.22%, respectively.The 256 most important features were selected and the experimental results based on adopting different learning rates show that the learning rate with half the cycle amplitude works best, the accuracy of the model is improved to 95.25%, and training time of the model is reduced by nearly half.…”
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Medical Image Retrieval Based on Ensemble Learning using Convolutional Neural Networks and Vision Transformers by Ahmed Yahya, Dalya Khaled, Waleed Al-Azzawi, Tawfeeq Alghazali, H. Sabah Jabr, R. Madhat Abdulla, M. Kadhim Abbas Al-Maeeni, N. Hussin Alwan, S. Saad Najeeb, Kh. T. Falih

“…Our method is trained to encode the images in the database and outputs a ranking list containing the most similar image to the least similar one to the query. …”
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Canned Apple Fruit Freshness Detection Using Hybrid Convolutional Neural Network and Transfer Learning by Mudasar Iqbal, Syed Tahseen Haider, Rana Saud Shoukat, Saif Ur Rehman, Khalid Mahmood, Santos Gracia Villar, Luis Alonso Dzul Lopez, Imran Ashraf

“…Apple fruit is one of the most important traditional table fruits in the temperate zone besides being the most commonly consumed fruit in the world. …”
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Deep Learning with Convolutional Neural Networks: A Compact Holistic Tutorial with Focus on Supervised Regression by Yansel Gonzalez Tejeda, Helmut A. Mayer

“…In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. …”
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Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm by Hassan Ghaedi, Seyed Reza Kamel Tabbakh, Reza Ghaemi

“…Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). …”
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Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion by Canghong Shi, Xin Qiu, Min Wu, Xianhua Niu, Xiaojie Li, Sani M. Abdullahi

“…Abstract One of the most common forms of audio forgery is copying and moving certain audible segments of audio to other locations in the same audio. …”
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PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network by Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu, Boce Chu

“…Most of the classical PolSAR superpixel generation approaches use the features extracted manually and even only consider the pseudocolor images. …”
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Using deep convolutional networks combined with signal processing techniques for accurate prediction of surface quality by Mohammad Zangane, Mohammad Shahbazi, Seyed Ali Niknam

“…SSPC and SSSC were the most noise-resistant approaches, maintaining testing accuracy above 90% at high noise. …”
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Application Research on Deep Convolution Neural Network Based Fault Diagnosis Technology for Traction Converter by LI Chen, ZHANG Huiyuan, LIU Yong, YANG Weifeng

“…The fault of converter can easily lead to the paralysis of train operation and is one of the most dangerous failures of electric locomotive. In order to avoid poor generalization of feature selection in expert experience and simulation mode in traction converter fault diagnosis, this paper proposes a fault diagnosis method based on deep convolution neural network. …”
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Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. by Mehran Ghafari, Justin Clark, Hao-Bo Guo, Ruofan Yu, Yu Sun, Weiwei Dang, Hong Qin

“…The capsule networks had the most robust performance in detecting one specific category of cell images. …”
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Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification by Bo Yuan, Yudie Hu, Yan Liang, Yutong Zhu, Lingyu Zhang, Shimin Cai, Rui Peng, Xianbin Wang, Zheng Yang, Jinhui Hu

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Kinship Recognition based on Deep Scattering Wavelet Convolutional Neural Network on Wild Facial Image by Somayeh Arab Najafabadi, Sara Nazari, Nafiseh Osati Eraghi

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