Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks by Aman Ullah, Bin wang, Jinfang Sheng, Jun Long, Nasrullah Khan

“…Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. …”
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Identification of Recurrent Congestion in Main Trunk Road Based on Grid and Analysis on Influencing Factors by Qiuxia Sun, Guoxiang Chu, Qing Li, Yu Zhang

“…Finally, the TPI data was applied to compare and evaluate the identification results of the above two models to identify frequently congested grids and main trunk roads. …”
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Identification of Mechanical Parameters of Prestressed Box Girder Bridge Based on Falling Weight Deflectometer by Yijun Chen, Wenqi Wu, Qingzhao Li, Pan Guo, Yingchun Cai, Jiandong Wei

“…The theoretical validation indicated a high modeling accuracy and inversion efficiency, with a convergence accuracy within 1%. …”
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A Novel Lightweight Framework for Non-Contact Broiler Face Identification in Intensive Farming by Bin Gao, Yongmin Guo, Pengshen Zheng, Kaisi Yang, Changxi Chen

“…Efficient individual identification is essential for advancing precision broiler farming. …”
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A feature explainability-based deep learning technique for diabetic foot ulcer identification by Pramod Singh Rathore, Abhishek Kumar, Amita Nandal, Arvind Dhaka, Arpit Kumar Sharma

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Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification by Long Li, Xiaoming Tao, Hui Song, Xiaolong Li, Zhilong Ye, Yao Jin, Qiuyu He, Shiyin Wei, Wenli Chen

“…This study addresses this challenge through three key contributions: dataset augmentation, an efficient feature representation, and a probabilistic modeling approach. …”
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Identification of thioaptamer ligand against E-selectin: potential application for inflamed vasculature targeting. by Aman P Mann, Anoma Somasunderam, René Nieves-Alicea, Xin Li, Austin Hu, Anil K Sood, Mauro Ferrari, David G Gorenstein, Takemi Tanaka

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Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network by Zhiqi Huang, Xiangjun Xin, Qi Zhang, Haipeng Yao, Feng Tian, Fu Wang

“…In the fusion layer, the two feature vectors are merged and classified through fully connected layers, thus constructing an efficient MFI model. The method enhances MFI accuracy by leveraging features of different modulation formats and representations at different neural network levels. …”
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Accurate Chemistry Identification of Lithium-Ion Batteries Based on Temperature Dynamics with Machine Learning by Ote Amuta, Jiaqi Yao, Dominik Droese, Julia Kowal

“…As the proposed approach has proven to be efficient in the chemistry identification of the electrode materials LIBs in most cases, we believe it can greatly benefit the recycling and second-life application of spent LIBs in real-life applications.…”
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YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation by Madalena de Oliveira Barbosa, Fernanda Pereira Leite Aguiar, Suely dos Santos Sousa, Luana dos Santos Cordeiro, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…This research addresses the critical need for an efficient and precise identification of <i>Capsicum</i> spp. fruit varieties within the post-harvest contexts to enhance quality control and ensure consumer satisfaction. …”
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Fluid Identification of Deep Low-Contrast Gas Reservoirs Based on Random Forest Algorithm by CAO Yuan, ZHAO Yuanliang, YUAN Xuehua, YUAN Long, RONG Junqing, ZHAO Pan, BIE Kang

“…The accuracy of the fluid identification model of low contrast gas reservoir based on random forest algorithm is 89.25%, which weakens the multiple solutions caused by a single fluid identification factor and provides a reliable basis for the efficient development of gas fields.…”
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Enhanced Potato Pest Identification: A Deep Learning Approach for Identifying Potato Pests by Amir Sohel, Md. Shahriar Shakil, Shah Md. Tanvir Siddiquee, Ahmed Al Marouf, Jon G. Rokne, Reda Alhajj

“…These findings might lead to the development of pest management strategies for potato farming that are more effective. The efficient use of VGG-16 in potato pest identification systems is demonstrated by its excellent performance. …”
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Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks by José Miguel Valiente, Juan José Martín-Osuna, Ana María Peral, Isabel Escriche

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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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A High-Resolution DEM-Based Method for Tracking Urban Pluvial–Fluvial Floods by Yongshuai Liang, Weihong Liao, Hao Wang

“…Flood models based on high-resolution digital elevation models (DEMs) are important for identifying urban land inundation during extreme rainfall events. …”
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Deep neural networks and fractional grey lag Goose optimization for music genre identification by Yuanye Tian

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Sensor Fault Detection and Identification in an Electro-pump System using Extended Kalman Filter by Monir Rezaee, Nargess Sadeghzadeh Nokhobberiz, Javad Poshtan

“…Then, the sensory soft faults are modeled and amplified to electro-pump state space model.  …”
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Load identification method based on one class classification combined with fuzzy broad learning by Wang Yi, Wang Xiaoyang, Li Songnong, Chen Tao, Hou Xingzhe, Fu Xiuyuan

“…Non-Intrusive Load Monitoring(NILM) is a key technology for smart electricity consumption, which helps strengthen load-side management and improve electricity efficiency. With the rapid increase of power load types and quantities, when unknown electrical appliances outside the training sample are connected to the model, it will cause the model to misjudge and reduce the accuracy of load identification. …”
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Advancements in Frank’s sign Identification using deep learning on 3D brain MRI by Sungman Jo, Jun Sung Kim, Min Jeong Kwon, Jieun Park, Jeong Lan Kim, Jin Hyeong Jhoo, Eosu Kim, Leonard Sunwoo, Jae Hyoung Kim, Ji Won Han, Ki Woong Kim

“…Despite its clinical significance, there lacks a standardized method for its identification. This study aimed to develop a deep learning model for automated FS detection in 3D facial images derived from MRI scans. …”
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Random uncertain motor parameters identification combining fourth-order moment and trust region by Wengui Mao, Congcong Liao, Jie Guo, Xuemei Wu, Jianhua Li

“…Abstract In random uncertain motor parameter identification field, there is low identification efficiency and ill-conditioned data coming from the second iteration involved in the uncertainty propagation and surrogate model between the motor parameters and the performance response. …”
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