WTSM-SiameseNet: A Wood-Texture-Similarity-Matching Method Based on Siamese Networks by Yizhuo Zhang, Guanlei Wu, Shen Shi, Huiling Yu

“…First, to address the issue that fixed receptive fields cannot adapt to textures of different sizes, a multi-receptive field fusion feature extraction network was designed. …”
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GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition by Adnan Saeed, Khurram Shehzad, Shahzad Sarwar Bhatti, Saim Ahmed, Ahmad Taher Azar

“…Due to the limited availability of training data, the diverse shapes of brain tumors among different patients, inter-class similarity, and intra-class variation, achieving high recognition accuracy and speed in deep learning-based brain tumor recognition remains challenging. …”
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An Improved Backbone Fusion Neural Network for Orchard Extraction by Baiyu Dong, Ziqi Wang, Chongzhi Chen, Ke Wang, Jing Zhang

“…However, different backbone networks exhibit varying capabilities and characteristics in feature extraction, limiting the performance of a single backbone model. …”
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Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module by Wei Ji, Kelong Zhai, Bo Xu, Jiawen Wu

“…Firstly, an improved DETR network main feature extraction module adopts the ResNet18 network and replaces some residual layers with deformable convolutions (DCNv2), enabling the model to better adapt to pollution-free fruit changes at different scales and angles, while eliminating the impact of microbial contamination on fruit testing; Subsequently, the extended spatial pyramid pooling model (DSPP) and multiscale residual aggregation module (FRAM) are integrated, which help reduce feature noise and minimize the loss of underlying features during the feature extraction process. …”
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Global Optical and SAR Image Registration Method Based on Local Distortion Division by Bangjie Li, Dongdong Guan, Yuzhen Xie, Xiaolong Zheng, Zhengsheng Chen, Lefei Pan, Weiheng Zhao, Deliang Xiang

“…Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). …”
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Global–Local Multigranularity Transformer for Hyperspectral Image Classification by Zhe Meng, Qian Yan, Feng Zhao, Gaige Chen, Wenqiang Hua, Miaomiao Liang

“…Specifically, we introduce a multigranularity spatial feature extraction block to extensively extract spatial information at different granularities, including multiscale local spatial features and global spatial features. …”
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Role of Biological Age in the Determination of Long‐Term Cause‐Specific Death Following Percutaneous Coronary Interventions by Mandeep Singh, Paul A. Friedman, Rajiv Gulati, Abdallah El Sabbagh, Bradley R. Lewis, Amrit Kanwar, Claire E. Raphael, Mohammed A. Al‐Hijji, Zachi I. Attia, Atta Behfar, James L. Kirkland

“…Age‐Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. …”
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Oral atogepant mitigates spreading depolarization-induced pain and anxiety behavior in mice by Melih Z. Kaya, Pradeep Banerjee, Cenk Ayata, Andrea M. Harriott

“…Following atogepant treatment, there was no difference in thigmotaxis score in SD versus sham groups (ato sham vs. ato SD: p = 0.200). …”
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Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images by Long Gao, Liyuan Li, Jianing Yu, Xiaoxuan Zhou, Lu Zou, Nan Fang, Xiaofeng Su, Fansheng Chen

“…The results demonstrated SwinCloud's generalization capability across different bands.…”
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Maize quality detection based on MConv-SwinT high-precision model. by Ning Zhang, Yuanqi Chen, Enxu Zhang, Ziyang Liu, Jie Yue

“…Concurrently, the extracted features undergo further processing through a specially designed convolutional block. The fused features, combined with those processed by the convolutional module, are fed into an attention layer. …”
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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…Within the scope of the analysis, machine learning algorithms such as K-Nearest Neighbors, XGBoost and AdaBoost are compared with the proposed Convolutional Neural Network (CNN) model. The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. …”
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Method to generate cyber deception traffic based on adversarial sample by Yongjin HU, Yuanbo GUO, Jun MA, Han ZHANG, Xiuqing MAO

“…In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.…”
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Prediction of Intraday Electricity Supply Curves by Guillermo Vivó, Andrés M. Alonso

“…This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. …”
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Analysis of baseball behavior recognition model based on Dual-GCN improved by motion weights by Ji Li

“…A motion weight improvement model based on dual-graph convolutional network is proposed. The new model takes a dual-graph convolutional network for behavior recognition and key region segmentation of baseball video images, and enhances the correlation and contribution between characters through motion weights. …”
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Enhancing the Power of CNN Using Data Augmentation Techniques for Odia Handwritten Character Recognition by Mamatarani Das, Mrutyunjaya Panda, Shreela Dash

“…This paper shows the performance of five different machine learning models that uses a convolutional neural network to identify handwritten characters in response to handwritten datasets that are manipulated and expanded using several augmentation techniques to create variation and increase the volume of the data in the given dataset. …”
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FAULT DIAGNOSIS METHOD OF DIESEL ENGINE BASED ON PCA-EDT-CNN (MT) by BAI YunJie, JIA XiSheng, LIANG QingHai, MA YunFei, WEN Liang

“…A diesel engine preset failure test bench was built to verify the effectiveness of the method, and through comparison with traditional methods, the results show that the method has high accuracy in diagnosing different fault states of diesel engines and has practical engineering application value.…”
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Quantum‐inspired Arecanut X‐ray image classification using transfer learning by Praveen M. Naik, Bhawana Rudra

“…A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. …”
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Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network. by Shaoming Qiu, Liangyu Liu, Yan Wang, Xinchen Huang, Bicong E, Jingfeng Ye

“…In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. …”
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A lightweight CNN-LSTM hybrid model for land cover classification in satellite imagery by Nowshad Hasan, Md. Saiful Islam

“…Finally, softmax is used to classify the different types of satellite images. The proposed method achieved 98.8% accuracy on the RSI-CB256 dataset and 99.9% on the EuroSAT dataset, with a training time of 3 milliseconds per epoch.…”
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