Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Analysis of the criteria selection problem in diversification models by Анна Бакурова, Алла Савранська, Еліна Терещенко, Дмитро Широкорад, Марк Шевчук

“…To formalize the problem, five models are proposed that differ in vector objective functions, both in the quantity and quality of the selected criteria. …”
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Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques by Arifa Zahir, Zulfiqar Ali, Ahmad Sami Al-Shamayleh, Syed Raza Ab bas, Basharat Mahmood, Abdullah Hussein Al-Ghushami, Rubina Adnan, Adnan Akhunzada

“…Various Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3 are implemented to classify the records and extract various patterns. …”
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BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information by Lun Zhu, Hao Sun, Sen Yang

“…Meanwhile, adopting data augmentation to solve the problem of data imbalance. We combine convolutional networks of different scales and Bi-LSTM layers to obtain high-level feature vectors of different features. …”
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Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition by Osamah Y. Fadhil, Bashar S. Mahdi, Ayad R. Abbas

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Ulcer detection in Wireless Capsule Endoscopy images using deep CNN by Vani V, K.V. Mahendra Prashanth

“…In this paper, we propose deep Convolutional Neural Network(CNN) for automatic discrimination of ulcers on different ratios of augmented datasets ranging from 1000 to 10000 WCE images comprising of ulcer and non-ulcer images. …”
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Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU by Abuda Chad Ferrino, Tae Young Choe

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Clothing classification method based on attention mechanism and transfer learning by CHEN Jinguang, HUANG Xiaoju, MA Lili

“…Image dataset was processed by data augmentation of geometric transform and color jitter to improve the generalization ability of the model. Convolutional block attention module (CBAM) was added to the ResNet50-based network, and attention of different region of clothing was improved from both channel and spatial dimensions in turn. …”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…China is a global leader in fruit production, and fruit picking mainly relies on manual labor, which helps to select fruits according to fruit size and quality to reduce loss in this way. Different techniques and tools can be adopted according to the characteristics and picking needs of each fruit crop. …”
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DETERMINATION OF THE BEST OPTIMIZER FOR A NEURONETWORK IN THE DEVELOPMENT OF AUTOMATIC IMAGE TAGGING SYSTEMS by Andrian Kozynets

“…The neural networks were trained on two different datasets with significantly different characteristics. …”
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Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network by Haofeng ZHENG, Guohua YANG, Wenjun KANG, Zhiyuan LIU, Shitao LIU, Hong WU, Honghao ZHANG

“…To improve the prediction accuracy, a multi-load feature combination (MLFC) is proposed, and a load prediction framework is constructed by combining Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU). …”
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Research on algorithm for improving imaging accuracy of CFRP low speed impact damage by WU Xiangnan, CHENG Xiaojin, LI Qixin, SHANG Jianhua

“…To enhance the classification model’s performance，an image reconstruction model（IRM）based on convolutional neural networks was proposed to improve imaging precision. …”
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Research on the Timing of Replacing Worn Milling Cutters by Using Wear Transition Percentage Constructed Based on Spindle Current Clutter Signals by Zhihao Liu, Min Wang, Zhishan Wang, Tao Zan, Xiangsheng Gao, Peng Gao

“…Then, using convolutional neural networks (CNN) to learn the SCCS data features of severe wear and normal wear stages, a binary classification CNN model is obtained. …”
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Encrypted traffic identification method based on deep residual capsule network with attention mechanism by Guozhen SHI, Kunyang LI, Yao LIU, Yongjian YANG

“…With the improvement of users’ security awareness and the development of encryption technology, encrypted traffic has become an important part of network traffic, and identifying encrypted traffic has become an important part of network traffic supervision.The encrypted traffic identification method based on the traditional deep learning model has problems such as poor effect and long model training time.To address these problems, the encrypted traffic identification method based on a deep residual capsule network (DRCN) was proposed.However, the original capsule network was stacked in the form of full connection, which lead to a small model coupling coefficient and it was impossible to build a deep network model.The DRCN model adopted the dynamic routing algorithm based on the three-dimensional convolutional algorithm (3DCNN) instead of the fully-connected dynamic routing algorithm, to reduce the parameters passed between each capsule layer, decrease the complexity of operations, and then build the deep capsule network to improve the accuracy and efficiency of recognition.The channel attention mechanism was introduced to assign different weights to different features, and then the influence of useless features on the recognition results was reduced.The introduction of the residual network into the capsule network layer and the construction of the residual capsule network module alleviated the gradient disappearance problem of the deep capsule network.In terms of data pre-processing, the first 784byte of the intercepted packets was converted into images as input of the DRCN model, to avoid manual feature extraction and reduce the labor cost of encrypted traffic recognition.The experimental results on the ISCXVPN2016 dataset show that the accuracy of the DRCN model is improved by 5.54% and the training time of the model is reduced by 232s compared with the BLSTM model with the best performance.In addition, the accuracy of the DRCN model reaches 94.3% on the small dataset.The above experimental results prove that the proposed recognition scheme has high recognition rate, good performance and applicability.…”
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Regional distributed photovoltaic power forecasting considering spatiotemporal correlation and meteorological coupling by HUANG Xiaoyan, GUO Sasa, CHEN Chengyou, XU Tengchong, HAN Xiao, WANG Tao

“…Additionally， a neural network layer with non-shared parameters is employed to capture the coupling relationship between different photovoltaic stations and meteorological factors， enabling the forecasting of power generation across multiple stations. …”
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Research on Pork Cut and Freshness Determination Method Based on Computer Vision by Shihao Song, Qiqi Guo, Xiaosa Duan, Xiaojing Shi, Zhenyu Liu

“…To improve the precision and efficiency of pork quality assessment, an automated detection method based on computer vision technology is proposed for evaluating different parts and freshness of pork. First, high-resolution cameras were used to capture image data of Jinfen white pigs, covering three pork cuts—hind leg, loin, and belly—across three different collection times. …”
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Additive Attention for Vetting Transiting Exoplanet Candidates by Àlvar Hernàndez-Carnerero, Miquel Sànchez-Marrè, Juan Carlos Morales

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Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification by Chun-Chao Huang, Hsin-Fan Chiang, Cheng-Chih Hsieh, Bo-Rui Zhu, Wen-Jie Wu, Jin-Siang Shaw

“…<b>Background:</b> This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). …”
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