Real-time Detection of Imperfect Wheat Grains on Wheat Pile Surface Based on IDS-YOLO by FAN Jiawei, WU Lan, YAN Jingjing

“…At last, depthwise separable convolution was employed as the feature extraction method for the residual network of the backbone component to reduce the calculation of model parameters, optimize model deployment, and solve the issue of poor real-time performance. …”
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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…Second, the neck network employs the lightweight hybrid convolution GSConv. …”
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Research and application of mining AI video edge computing technology by ZHANG Liya, HAO Bonan, MA Zheng, YANG Zhifang

“…Currently, mining AI video systems mainly rely on ground servers for analysis and processing, which leads to issues such as high overall response latency, multi-system linkage delays, and high network bandwidth utilization. …”
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AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images. by Parsa Delavari, Gulcenur Ozturan, Eduardo V Navajas, Ozgur Yilmaz, Ipek Oruc

“…Here we examine whether DR manifests differently in male and female patients, using a dataset of retinal images and leveraging convolutional neural networks (CNN) integrated with explainable artificial intelligence (AI) techniques. …”
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Deep Learning Approach for Predicting Psychodiagnosis by Zouaoui Samia, Khamari Chahinez

“…By exploiting the power of convolutional neural networks (CNN), we propose a novel CNN-based natural language processing method without removing stop words for predicting psychiatric diagnoses capable of accurately classifying individuals based on their psychological data. …”
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Trend analysis of the application of multispectral technology in plant yield prediction: a bibliometric visualization analysis (2003–2024) by Jiahui Xu, Jiahui Xu, Jiahui Xu, Yalong Song, Yalong Song, Yalong Song, ZhaoYu Rui, ZhaoYu Rui, Zhao Zhang, Zhao Zhang, Can Hu, Can Hu, Can Hu, Long Wang, Long Wang, Long Wang, Wentao Li, Wentao Li, Wentao Li, Jianfei Xing, Jianfei Xing, Jianfei Xing, Xufeng Wang, Xufeng Wang, Xufeng Wang

“…Through comprehensive analysis, we identified that research using multispectral technology for crop yield prediction primarily focuses on key areas, such as chlorophyll content, remote sensing, convolutional neural networks (CNNs), and machine learning. …”
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Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review by Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

“…ResultsRadiographic biomarkers (n=129, 84.9%) and deep learning (DL; n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), and DenseNet-121 (n=19, 12.5%) architectures being the most common DL approach. …”
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Multi-Energy-Microgrid Energy Management Strategy Optimisation Using Deep Learning by Wenyuan Sun, Shuailing Ma, Yufei Zhang, Yingai Jin, Firoz Alam

“…Therefore, a two-stage robust optimisation model based on Bidirectional Temporal Convolutional Networks (BiTCN) and Transformer prediction for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with a carbon trading mechanism is proposed to solve this problem. …”
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SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

“…Abstract Currently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. …”
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“Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

“…At the settlement scale, the Mask Region-based Convolutional Neural Network (Mask R-CNN) deep learning model is utilized to identify architectural spatial features, categorizing three typical building types: traditional pitched-roof buildings, uniformly planned flat-roof buildings, and color steel plate-modified structures. …”
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Improved YOLOv10n model for enhanced cotton recognition in complex environments by Yutao Gong, Wenwen Ding, Nenghui Huang, Tao Li, Juntao Zhou

“…Accurate and efficient identification of cotton boll states is crucial for mechanical and intelligent cotton harvesting. Currently, target detection models struggle with multi - category cotton recognition in complex farmland environments. …”
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Oxide Phototransistor Array With Multiply-and-Accumulation Functions for In-Sensor Image Processing by Saisai Wang, Xiaotao Jing, Wanlin Zhang, Rui Wang, Hong Wang, Qi Huang

“…Moreover, photoconductive devices are incapable of achieving on-chip current summation because the regulation of photoresponsivity usually leads to inconsistent dark current, thereby impeding the practical implementation of artificial neural networks (ANNs) on the sensor array. …”
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Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis by Jiangyi Ju, Zhen Qu, Han Qing, Yunxia Ding, Lihua Peng

“…Abstract Background Currently, the application of convolutional neural networks (CNNs) in artificial intelligence (AI) for medical imaging diagnosis has emerged as a highly promising tool. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are popular deep learning architectures currently used for rapid flood simulations. …”
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Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning by Francis Loignon-Houle, Nicolaus Kratochwil, Maxime Toussaint, Carsten Lowis, Gerard Ariño-Estrada, Antonio J. Gonzalez, Etiennette Auffray, Roger Lecomte

“…Deep learning, particularly convolutional neural networks (CNNs), can also enhance CTR by training with digitized waveforms. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…We experimentally demonstrate the versatility of our approach on a programmable superconducting processor, where we observe notable performance enhancements across various quantum machine learning models, including quantum neural networks and quantum convolutional neural networks. …”
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MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. …”
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University proceedings. Volga region. Technical sciences by V.I. Volchikhin, A.I. Ivanov, A.V. Bezyaev, I.A. Filipov

“…Background. Currently, single-layer networks of artificial neurons are used, which are equivalent to classical statistical criteria. …”

Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples by Anna-Katharina Meißner, Tobias Blau, David Reinecke, Gina Fürtjes, Lili Leyer, Nina Müller, Niklas von Spreckelsen, Thomas Stehle, Abdulkader Al Shugri, Reinhard Büttner, Roland Goldbrunner, Marco Timmer, Volker Neuschmelting

“…Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. …”
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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). …”
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