The Application of 3D Virtual Technology in the Teaching of Clinical Medicine by Haifeng Zhang, Juan Kong, Yan Tong, Chunmei Gao, Wei Huang

“…Therefore, this paper addresses the shortcomings of previous 3D virtual technology in 3D modeling of organs, introduces deep learning, and then proposes a pyramidal shape perception network with the ability to generate samples on point clouds. …”
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A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods by Meng Meng, Kun Zhu, Keqin Chen, Hang Qu

“…With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. …”
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Research on OPF Control of Three-Phase Four-Wire Low-Voltage Distribution Network considering Uncertainty by Rui Wang, Xiaoqing Bai, Shengquan Huang, Shoupu Wei

“…Using historical data and deep learning classification methods, the proposed method simulates optimal system behaviour without requiring communication infrastructure. …”
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Histopathological domain adaptation with generative adversarial networks: Bridging the domain gap between thyroid cancer histopathology datasets. by William Dee, Rana Alaaeldin Ibrahim, Eirini Marouli

“…Deep learning techniques are increasingly being used to classify medical imaging data with high accuracy. …”
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Metric-based learning approach to botnet detection with small samples by Honggang LIN, Junjing ZHU, Lin CHEN

“…Botnets pose a great threat to the Internet, and early detection is crucial for maintaining cybersecurity.However, in the early stages of botnet discovery, obtaining a small number of labeled samples restricts the training of current detection models based on deep learning, leading to poor detection results.To address this issue, a botnet detection method called BT-RN, based on metric learning, was proposed for small sample backgrounds.The task-based meta-learning training strategy was used to optimize the model.The verification set was introduced into the task and the similarity between the verification sample and the training sample feature representation was measured to quickly accumulate experience, thereby reducing the model’s dependence on the labeled sample space.The feature-level attention mechanism was introduced.By calculating the attention coefficients of each dimension in the feature, the feature representation was re-integrated and the importance attention was assigned to optimize the feature representation, thereby reducing the feature sparseness of the deep neural network in small samples.The residual network design pattern was introduced, and the skip link was used to avoid the risk of model degradation and gradient disappearance caused by the deeper network after increasing the feature-level attention mechanism module.…”
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Application of deep residual networks to predict the effective properties of fiber-reinforced composites with voids by Mahdi Karimian, Seyed Ali Hosseini Kordkheili

“…A novel deep-learning method is adopted to predict effective mechanical properties of epoxy-based fiber-reinforced composites. …”
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Micro-Doppler-Based Space Target Recognition with a One-Dimensional Parallel Network by Lixun Han, Cunqian Feng

“…Moreover, we present a deep-learning (DL) model composed of a one-dimensional parallel structure and long short-term memory (LSTM). …”
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Learning Spatial-Temporal Features of Ride-Hailing Services with Fusion Convolutional Networks by Dapeng Zhang, Feng Xiao, Gang Kou, Jian Luo, Fan Yang

“…In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network ( LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. …”
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Studies on Underwater Image Processing Using Artificial Intelligence Technologies by Sugunapriya A, Markkandan S

“…The findings of this survey suggest promising directions for future research, particularly in the development of more sophisticated deep learning models that can further improve image quality and contribute to the underwater exploration and monitoring system.…”
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Predicting learning achievement using ensemble learning with result explanation. by Tingting Tong, Zhen Li

“…The proposed model outperforms traditional machine learning and deep learning model in terms of prediction accuracy. …”
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Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN by Zheng Li, Shengli Wu, Wenting Xing, Shiyong Liao

“…Aiming at the problems of insufficient samples of gear fault signals collected in practical engineering，insufficient training，low fault recognition rate and easy pattern collapse of generative adversarial networks when using common deep learning network for pattern recognition under noise interference. …”
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Research on Spam Filters Based on NB Algorithm by Su Shengyue

“…Future work may focus on improving the model’s accuracy and robustness by integrating it with other machine learning models, like Support Vector Machines (SVMs) and deep learning techniques, to enhance spam classification capabilities.…”
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Automated Diagnosis of Skin Cancer. by Nyiramugisha, Shallon, Nuwahereza, Godfrey

“…Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep-learning models to combine features from images and clinical data. …”
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A generative whole-brain segmentation model for positron emission tomography images by Wenbo Li, Zhenxing Huang, Hongyan Tang, Yaping Wu, Yunlong Gao, Jing Qin, Jianmin Yuan, Yang Yang, Yan Zhang, Na Zhang, Hairong Zheng, Dong Liang, Meiyun Wang, Zhanli Hu

“…The proposed method was compared with various deep learning-based approaches in terms of the Dice similarity coefficient, Jaccard index, precision, and recall serving as evaluation metrics. …”
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Pengembangan Auto-AI Model Generatif Analisis Kompleksitas Waktu Algoritma Untuk Data Multi-Sensor IoT Pada Node-RED Menggunakan Extreme Learning Machine by Imam Cholissodin, Dahnial Syauqy, Dwi Ady Firmanda, Ibrahim Aji, Edy Rahman, Syazwandy Harahap, Fernando Septino

“…Namun dengan perkembangan teknologi komputer untuk AI, Machine Learning maupun Deep Learning, algoritma dengan basis AI tersebut, dalam penelitian ini dikembangkan untuk menemukan solusi general persamaan model T(n) secara otomatis dari desain algoritma sederhana atau kompleks. …”
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Intelligent Construction, Operation, and Maintenance of a Large Wastewater-Treatment Plant Based on BIM by Jiu-Lin Li, Li-Min Chen, Hao Xu

“…According to this case example, we also offer suggestions on how deep learning and intelligent control techniques can be used to enhance intelligent O&M in WTPs.…”
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Clinical Treatment and Nursing Intervention Study of Clipping Treatment of Cerebral Aneurysm under the Health Model of Data Analysis by Yuyou Huang, Liping Huang

“…The model combines the characteristics of cerebral aneurysms for targeted analysis, and then through the understanding of the clipping treatment of cerebral aneurysms, this paper combines the deep learning in the neural network to train the treatment plan under the data analysis health model. …”
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Geographical patterns of intraspecific genetic diversity reflect the adaptive potential of the coral Pocillopora damicornis species complex. by M Carr, C Kratochwill, T Daly-Engel, T Crombie, R van Woesik

“…We focused on detecting genetic diversity hotspots, wherein some individuals are likely to possess gene variants that tolerate marine heatwaves. A deep-learning, multi-layer neural-network model showed that geographical location played a major role in intraspecific diversity, with mean sea-surface temperature and oceanic regions being the most influential predictor variables differentiating diversity. …”
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Intelligent phase imaging guided by physics models by Zhen LIU, Hao ZHU, You ZHOU, Zhan MA, Xun CAO

“…Implicit neural representation characterizes the mapping between the signal’s coordinate to its attributes, and has been widely used in the optimization of inverse problems by embedding the physics process into the loss function.However, the implicit neural representation is suffering the low convergence speed and accuracy from the random initialization of the network parameters.The meta-learning algorithm for providing implicit neural representation with a strong prior of network parameters was proposed, thus enhancing the optimization efficiency and accuracy for solving inverse problems.To address the important issue of lens less phase imaging, an intelligent method on phase imaging was proposed based on the snapshot lens less sensing model.By embedding the optical diffraction propagation theory into the design of loss function for implicit neural representation, the dependency of large-scale labelled dataset in traditional deep learning-based methods was eliminated and accurate phase image from a single diffraction measurement image was provided.Furthermore, the meta-learning model was introduced for initializing network to further improve the efficiency and accuracy of network training.Numerical simulation results show that the proposed method can achieve a PSNR improvement of more than 11 dB compared to the conventional method.The experimental results in real data show that the phase image reconstructed by the proposed method is clearer and has fewer artifacts.…”
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YOLOv10-Enabled IoT Robot Car for Accurate Disease Detection in Strawberry Cultivation by Bellout Abdelaaziz, Zarboubi Mohamed, Dliou Azzedine, Latif Rachid, Saddik Amine

“…Our system merges deep learning, IoT, and precision agriculture techniques to enable real-time monitoring of strawberry fields. …”
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