Regional distributed photovoltaic power forecasting considering spatiotemporal correlation and meteorological coupling by HUANG Xiaoyan, GUO Sasa, CHEN Chengyou, XU Tengchong, HAN Xiao, WANG Tao

“…First， based on an analysis of the output characteristics of distributed photovoltaic power stations， an adaptive graph convolutional neural network combined with a long short-term memory network （LSTM） is used to extract the spatiotemporal features of the photovoltaic output. …”
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CVT-HNet: a fusion model for recognizing perianal fistulizing Crohn’s disease based on CNN and ViT by Lanlan Li, Ziyue Wang, Chongyang Wang, Tao Chen, Ke Deng, Hong’an Wei, Dabiao Wang, Juan Li, Heng Zhang

“…In response, computer vision methods have been adopted to improve efficiency. Convolutional neural networks(CNNs) are the main basis for detecting anal fistulas in current computer vision techniques. …”
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The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review by Jingguo Qu, Xinyang Han, Man-Lik Chui, Yao Pu, Simon Takadiyi Gunda, Ziman Chen, Jing Qin, Ann Dorothy King, Winnie Chiu-Wing Chu, Jing Cai, Michael Tin-Cheung Ying

“…This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. …”
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Automatic detection of lucky bamboo nodes based on Improved YOLOv7 by Jing Zhang, Jing Zhang, Jing Zhang, Ruoling Deng, Ruoling Deng, Ruoling Deng, Chengzhi Cai, Erpeng Zou, Haitao Liu, Haitao Liu, Mingxin Hou, Xinzhi Chen, Huamin Lin, Zhenye Wei

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A text dataset of fire door defects for pre-delivery inspections of apartments during the construction stageFigshare by Seunghyeon Wang, Sungkon Moon, Ikchul Eum, Dongjin Hwang, Jaejun Kim

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Self-Supervised Foundation Model for Template Matching by Anton Hristov, Dimo Dimov, Maria Nisheva-Pavlova

“…As going deeper in the convolutional neural network (CNN) layers, their filters begin to react to more complex structures and their receptive fields increase. …”
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Cimiciato defect detection in hazelnuts: CNN models applied on X-ray images by Andrea Vitale, Matteo Giaccone, Antonio Gaetano Napolitano, Flavia de Benedetta, Laura Gargiulo, Giacomo Mele

“…Insect damages affect hazelnut quality, requiring post-harvest selection based on industrial quality standards which often exceed official regulations. Currently used methods for identifying insect damages (cimiciato) often rely on visual inspection, external imaging or require destructive testing.This study compared twelve different pretrained Convolutional Neural Network (CNN) architectures applied on hazelnut kernels X-ray radiographs for the automated detection of cimiciato defects.Through an extensive training and validation process, followed by testing on a separate dataset, InceptionV3 architecture showed the best overall balance across all performance metrics, including accuracy, sensitivity, and precision, while Xception demonstrated superior specificity and the lowest false positive rate. …”
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Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks by Mahmut Türk, Heybet Kılıç, Cem Haydaroglu

“…In order to reduce these losses, we propose an artificial intelligence-based approach that utilizes deep learning architectures in the detection of different types of leakage (voltage leakage, current leakage and voltage-current leakage). Unlike the studies in the literature, the data set is converted into two-dimensional matrices and analyzed with today's popular approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models; CNN surpassed LSTM's 64.17% accuracy rate with 97.50% accuracy rate. …”
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Small Object Detection Algorithm Based on Partial Convolution and Attention Fusion Detection Head by Peng Sheng, Zhu Fenghua, Zhou Jin, Zhu Gaofeng, Wang Yingxu, Chen Yuehui

“…To improve spatial feature extraction and control network computing time, a more efficient FasterNet backbone network is introduced along with partial convolution (PConv) to reduce memory access and redundant calculations during deep convolution. …”
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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

“…A Lightweight Asymmetric Dual Convolution module (LADC) was devised to diminish the detrimental effects of extraneous features on the model’s precision, thereby enhancing the feature extraction network. …”
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DDoS-MSCT: A DDoS Attack Detection Method Based on Multiscale Convolution and Transformer by Bangli Wang, Yuxuan Jiang, You Liao, Zhen Li

“…In order to provide an effective method for detecting abnormal traffic, this paper proposes a novel network architecture called DDoS-MSCT, which combines a multiscale convolutional neural network and transformer. …”
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GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer by Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Solomon Atnafu, Zahid Akhtar, Glenn Van Wallendael

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Enhancing wheat yellow rust detection through modified deep learning approach by Shant Kumar, Rohit Singh, Sudheer Kumar, Sandeep Gupta

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Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM by Lihua Zhong, Feng Pan, Yuyao Yang, Lei Feng, Haiming Shao, Jiafu Wang

“…This led to the development of an MDBO-enhanced Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) network hybrid prediction model for carbon emission prediction. …”
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Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis by Gunjan Shandilya, Sheifali Gupta, Salil Bharany, Ateeq Ur Rehman, Upinder Kaur, Hafizan Mat Som, Seada Hussen

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Online Fault Tolerant RUL Prediction Strategy for Lithium-Ion Batteries Using Machine Learning by Brahim Zraibi, Mohamed Mansouri, Chafik Okar, Hicham Chaoui

“…This study introduces a highly available fault-tolerant prediction framework designed to forecast the RUL of lithium-ion batteries in challenging scenarios where key features such as voltage, current, and temperature are unavailable. The framework utilizes an Improved Convolutional Long Short-Term Memory Deep Network (Imp-CLD), a hybrid machine learning algorithm integrating Deep Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory networks. …”
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Application of Seq2Seq models for predicting the development of thunderstorm activity to enhance the pilot’s situational awareness in flight by G. V. Kovalenko, I. A. Yadrov

“…The results showed that convolutional recurrent neural networks (ConvRNN, ConvLSTM, ConvGRU) outperform classical recurrent models and improve the thunderstorm forecast by 25–30% in terms of RMSE (root mean square error) metric compared to the baseline model, which always selects the most recent radar image available at the time of prediction. …”
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Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS by Shuyan Pan, Mingzhu Tang, Na Li, Jiawen Zuo, Xingpeng Zhou

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MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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Revolutionizing Periodontal Care: The Role of Artificial Intelligence in Diagnosis, Treatment, and Prognosis by Giacomo Spartivento, Viviana Benfante, Muhammad Ali, Anthony Yezzi, Domenico Di Raimondo, Antonino Tuttolomondo, Antonio Lo Casto, Albert Comelli

“…Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), hybrid networks, generative adversarial networks (GANs), and transformer networks. …”
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