geodl: An R package for geospatial deep learning semantic segmentation using torch and terra. by Aaron E Maxwell, Sarah Farhadpour, Srinjoy Das, Yalin Yang

“…Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. …”
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YOLO-DKM: A Flame and Spark Detection Algorithm Based on Deep Learning by Linpo Shang, Xufei Hu, Zijian Huang, Qiang Zhang, Zhiyu Zhang, Xin Li, Yanzuo Chang

“…Improvements have been made to the backbone network and neck network. Firstly, the SKAttention attention mechanism has been introduced into the backbone network, which can adaptively adjust the attention weights at different scales, effectively improving the detection of small flame targets or tiny granular sparks. …”
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A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

“…Secondly, a lightweight neck network is constructed using Grouped Shuffle Convolution (GSConv) to simplify computational complexity. …”
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Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation by J. M. Torres, R. M. Aguilar

“…An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.…”
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Research on graph-based heterogeneous data integration method by HUANG Yuezhen, YANG Fen, TIAN Feng, ZHANG Chengye, LI Yuchan

“…Then, input the constructed graph into the graph neural network, and the vector representation of each node in the graph was obtained through graph convolution. …”
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SEPDNet: simple and effective PCB surface defect detection method by Du Lang, Zhenzhen Lv

“…SEPDNet uses RepConv (Re-parameterizable Convolution) to improve the backbone representation ability, and FPN (Feature Pyramid Network) is used in the neck part to simplify the model. …”
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Research on graph-based heterogeneous data integration method by HUANG Yuezhen, YANG Fen, TIAN Feng, ZHANG Chengye, LI Yuchan

“…Then, input the constructed graph into the graph neural network, and the vector representation of each node in the graph was obtained through graph convolution. …”
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Application and prospects of machine learning for rockfalls, landslides and debris flows by Jiazhu WANG, Yongbo TIE, Yongjian BAI, Yanchao GAO, Donghui WANG, Mingzhi ZHANG

“…Deep learning architectures, including autoencoders, deep belief networks, convolutional neural networks, and recurrent neural networks, are instrumental in hazard identification, susceptibility assessment, and displacement prediction. …”
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Challenge of the performance management of trust control systems with deep learning by A. A. Zelensky, T. K. Abdullin, M. M. Zhdanova, V. V. Voronin, A. A. Gribkov

“…The necessity of using convolutional artificial neural networks for deep machine learning is determined. …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…When used together, models like the Convolutional Neural Networks (CNN) and LSTM networks have significant high-performance results for text feature extraction and semantic relationship of the word. …”
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Kans-Unet Model and Its Application in Image Patch-Shaped Detection by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Kexin Zhu, Bo Hao, Junjie Song, Yumeng Huo

“…The module applies a learnable activation function at the edge of the network, which not only reduces the number of model parameters but also significantly improves the generalization performance of the network. …”
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Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD by Chao Chen, Zhuo Chen, Hao Li, Yawen Wang, Guangzhou Lei, Lingling Wu

“…By introducing the FasterNet network to replace the original backbone network, computational complexity and memory access are reduced. …”
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YOLO-WAD for Small-Defect Detection Boost in Photovoltaic Modules by Yin Wang, Wang Yun, Gang Xie, Zhicheng Zhao

“…Firstly, we replace C2f (CSP bottleneck with two convolutions) with C2f-WTConv (CSP bottleneck with two convolutions–wavelet transform convolution) in the backbone network to enlarge the receptive field and better extract the features of small-target defects (hot spots). …”
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Traffic sign detection method based on improved YOLOv8 by Gaihua Wang, Peng Jin, Zhiwei Qi, Xiaohuan Li

“…Secondly, a lightweight convolution module, LWConv, is designed, based on which the Bottleneck structure of Cross-convolution with two filters (C2f) in YOLOv8 is reconstructed and named LW_C2f, effectively reducing the model size and parameters. …”
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Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…This paper then integrates a bidirectional feature pyramid network (BiFPN) into the neck network to improve feature capture across different scales, enhancing the model’s ability to handle objects of varying sizes and complexities. …”
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A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods by Xinhua Liu, Kaiyi Yang, Lisheng Zhang, Wentao Wang, Sida Zhou, Billy Wu, Mengyu Xiong, Shichun Yang, Rui Tan

“…Based on the Materials Project database, auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks, which finally screen 12 lithium-ion, 6 zinc-ion, and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials. …”
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Neural signals, machine learning, and the future of inner speech recognition by Adiba Tabassum Chowdhury, Ahmed Hassanein, Aous N. Al Shibli, Youssuf Khanafer, Mohannad Natheef AbuHaweeleh, Shona Pedersen, Muhammad E. H. Chowdhury

“…We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. …”
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1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA by Wenguang Hou, Shaojie Mei, Qiuling Gui, Yingcheng Zou, Yifan Wang, Xianbo Deng, Qimin Cheng

“…After then, the 2D Convolutional Neural Network (CNN) is established to do classification. …”
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Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

“…Moreover, while visual function is the main concern for glaucoma patients, and the ability to infer future visual outcome from imaging will benefit patients by early intervention, there is currently no available tool for this. To detect glaucoma progression from ocular hypertension both structurally and functionally, and identify potential objective early biomarkers associated with progression, we developed and evaluated deep convolutional long short-term memory (CNN-LSTM) neural network models using longitudinal CFPs from the Ocular Hypertension Treatment Study (OHTS). …”
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