Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules by Yihao Chen, Ling Guo, Yue Pan, Hui Cai, Zhitong Bing

“…However, most of the existing network inference methods are based on large-scale gene collections, which ignore the characteristics of different tumors. Methods: In this work, weighted gene coexpression network analysis was deployed to screen key genes and gene modules. …”
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Predicting road quality using high resolution satellite imagery: A transfer learning approach. by Ethan Brewer, Jason Lin, Peter Kemper, John Hennin, Dan Runfola

“…We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as "low", "middle", or "high" quality using an open, cellphone-based measuring platform. …”
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Combining Region-Guided Attention and Attribute Prediction for Thangka Image Captioning Method by Fujun Zhang, Wendong Kang, Wenjin Hu

“…This predictor leverages feature maps from four different convolutional blocks within the region-guided module to incorporate more detailed information into the model. …”
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Enhanced Rolling Bearing Fault Diagnosis Using Multimodal Deep Learning and Singular Spectrum Analysis by Yunhang Wang, Hongwei Wang, Ruoyang Bai, Yuxin Shi, Xicong Chen, Qingang Xu

“…Based on this, a recursive gated convolutional neural network (RGCNN) is designed to process the STFT image data, while a 1D convolutional neural network (1DCNN) is specifically optimized for training with time series data. …”
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Deep Learning Forecasting Model for Market Demand of Electric Vehicles by Ahmed Ihsan Simsek, Erdinç Koç, Beste Desticioglu Tasdemir, Ahmet Aksöz, Muammer Turkoglu, Abdulkadir Sengur

“…This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. …”
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Medical Image Hybrid Watermark Algorithm Based on Frequency Domain Processing and Inception v3 by Yu Fan, Jingbing Li, Uzair Aslam Bhatti, Saqib Ali Nawaz, Yenwei Chen

“…Existing research has mostly focused on optimizing individual techniques, lacking comprehensive solutions that integrate the strengths of different methods. This article proposes a hybrid digital watermarking algorithm for medical images based on frequency domain transformation and deep learning convolutional neural networks. …”
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Cross modal recipe retrieval with fine grained modal interaction by Fan Zhao, Yuqing Lu, Zhuo Yao, Fangying Qu

“…Preceding a hierarchical recipe Transformer for encoding individual recipe components, we introduce the cross-component multiscale recipe enriching (CCMRE) module, which enhances the components of the recipe through fully convolutional operations with convolutional kernels of different lengths. …”
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End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines by Karthikeyan Velayuthapandian, Natchiyar Murugan, Saranya Paramasivan

“…The experiment results for different individuals demonstrate an accuracy rate of around 99.84% in authenticating test samples. …”
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Cotton Weed-YOLO: A Lightweight and Highly Accurate Cotton Weed Identification Model for Precision Agriculture by Jinghuan Hu, He Gong, Shijun Li, Ye Mu, Ying Guo, Yu Sun, Tianli Hu, Yu Bao

“…The Receptive Field Enhancement (RFE) module is proposed to enable the feature pyramid network to adapt to the feature information of different receptive fields. A Scale-Invariant Shared Convolutional Detection (SSCD) head is proposed to fully utilize the advantages of shared convolution and significantly reduce the number of parameters in the detection head. …”
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YOLO-GML: An object edge enhancement detection model for UAV aerial images in complex environments. by Zhihao Zheng, Jianguang Zhao, Jingjing Fan

“…Finally, we propose a Lightweight layered Shared Convolutional BN(LLSCB) Detection Head based on LSCD, so that the detection heads share the convolutional layer, and the BN is calculated independently, which improves the detection accuracy and reduces the number of parameters. …”
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HUMAN EMOTION RECOGNITION SYSTEM USING DEEP LEARNING ALGORITHMS by Kateryna Yuvchenko, Valentyn Yesilevskyi, Olena Sereda

“…They can be expressed in different ways: facial expressions, posture, motor reactions, voice. …”
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EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement by Hu Haoyan, Tong Jinwu, Wang Haibin, Lu Xinyun

“…Finally, the designed C2f_EMSCP method is integrated into the backbone and neck networks, effectively merging multi-scale convolutional networks and position-aware modules to enhance the model’s sensitivity and detection capability for multi-scale targets by fusing feature maps of different scales. …”
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Optimizing the automated recognition of individual animals to support population monitoring by Tijmen A. deLorm, Catharine Horswill, Daniella Rabaiotti, Robert M. Ewers, Rosemary J. Groom, Jessica Watermeyer, Rosie Woodroffe

“…To evaluate intraspecific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat coloration patterns. The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. …”
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Multi‐data classification detection in smart grid under false data injection attack based on Inception network by H. Pan, H. Yang, C. N. Na, J. Y. Jin

“…Abstract During operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. …”
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Terrain and Atmosphere Classification Framework on Satellite Data Through Attentional Feature Fusion Network by Antoni Jaszcz, Dawid Połap

“…The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. …”
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Towards Automatic Detection of Pneumothorax in Emergency Care with Deep Learning Using Multi-Source Chest X-ray Data by Santiago Ibañez Caturla, Juan de Dios Berná Mestre, Oscar Martinez Mozos

“…In particular, we use datasets which contain chest X-ray images corresponding to different conditions (including pneumothorax). A convolutional neural network (CNN) with an EfficientNet architecture is trained and optimized to identify radiographic signs of pneumothorax using those public datasets. …”
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Image data-driven intelligent recognition of permafrost strength and feature visualization based analysis by Zhaoming YAO, Xun WANG, Hang WEI, Xiaolong WANG

“…By comparing the training processes and test results of different models, it was found that the ResNet-34 model performed the best, achieving an accuracy of 92.8% with no signs of overfitting. …”
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Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). …”
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Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management by Meysam Alizamir, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim, Jeong Eun Lee

“…Based on the results of this study, various deep learning models demonstrated optimal performance for soil temperature prediction at different depths across the two stations. At Chamchamal station, the hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN), denoted as BiGRU-CNN achieved the best result for the 05 cm depth (RMSE = 1.298°C), while the hybrid model based on gated recurrent unit (GRU) and convolutional neural network (CNN), referred to as GRU–CNN yielded the best performance at 10 cm (RMSE = 1.333°C). …”
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Detection of <i>Aspergillus flavus</i> in Figs by Means of Hyperspectral Images and Deep Learning Algorithms by Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas Cortez

“…The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). …”
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