A defense method against multi-label poisoning attacks in federated learning by Wei Ma, Qihang Zhao, Wenjun Tian

“…Abstract Federated learning is a distributed machine learning framework that allows multiple parties to collaboratively train models without sharing raw data. …”
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An automatic pruning method for SAR target detection based on multitask reinforcement learning by Huiyao Wan, Jie Chen, Pazlat Nurmamat, Hongcheng Zeng, Wei Yang, Tao Xu, Bocai Wu, Jie Chen, Zhixiang Huang, Paulo S.R. Diniz

“…In recent years, research on synthetic aperture radar (SAR) target detection based on deep learning methods has made substantial progress in model accuracy. …”
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Direct method in learning speaking skills in extracurricular activities: AFL student's perspectives by Alfi Nur Tazkiyah, Asep Sopian, Hikmah Maulani

“…This study aims to explore students' perceptions of the application of direct methods in learning Arabic speaking skills in the extracurricular activities of MA Persis Tarogong. …”
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The automatic segmentation of the temporomandibular joint based on MRI using deep learning method by LIU Fei, ZHANG Jiulou, JIN Ruofan, ZHANG Nan, ZHOU Weina

“…Objective To build an automatic segmentation model of temporomandibular joint(TMJ) based on magnetic resonance imaging(MRI) using deep learning method. Methods The MRI data of TMJ of 104 subjects were collected, with the articular disc, condyle and glenoid fossa marked. …”
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BBSNet: An Intelligent Grading Method for Pork Freshness Based on Few-Shot Learning by Chao Liu, Jiayu Zhang, Kunjie Chen, Jichao Huang

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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…This paper proposes an improved attention mechanism-based network for image detection and recognition of power equipment. The proposed method introduces a target feature prediction strategy tailored to power equipment: it incorporates a learning mechanism for depth variation to extract deep semantic information from images; enhances the global structure learning network module by stacking convolutional kernels and removing pooling layers in the front-end network, thereby acquiring prior information rich in detailed and correlated image features of power equipment. …”
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A Rapid Design Method for Centrifugal Pump Impellers Based on Machine Learning by Y. Chen, W. Li, Y. Luo, L. Ji, S. Li, Y. Long

“…To reduce development time and costs, this paper proposes a rapid impeller design method focused on hydraulic performance, integrating traditional similarity design theory with machine learning. …”
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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings by Chuanxiang Ren, Chunxu Chai, Changchang Yin, Haowei Ji, Xuezhen Cheng, Ge Gao, Heng Zhang

“…Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. …”
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Method and Application of Deformation Performance Evaluation for Complex Components Based on Machine Learning by Guo Dawen, Zheng Jiakai, Du Jian, Ma Yuhong, Zhang Mengyue, Wang Jian

“…ObjectiveBased on the issues of high experimental costs, difficulties in quantifying evaluation methods, and low assessment accuracy in deformation performance studies of complex structural components, a machine learning-based method for evaluating component deformation performance was proposed. …”
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Android malware detection method based on byte-code image and deep learning by Tieming CHEN, Binbin XIANG, Mingqi LV, Bo CHEN, Xie JIANG

“…A new Android malware detection method based on byte-code image and deep learning was proposed. …”
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A new machine learning method for rainfall classification: temporal random tree by Kokten Ulas Birant, Bita Ghasemkhani, Özlem Varlıklar, Derya Birant

“…These findings highlight TRT’s potential as a valuable method for spatiotemporal rainfall classification.…”
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Deep Learning-Based Invalid Point Removal Method for Fringe Projection Profilometry by Nan He, Jiachun Huang, Shaoli Liu, Sizhe Fan, Jianhua Liu, Jia Hu, Hao Gong

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A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis by Sha Du, Hailong Shen

“…In this paper, we use the Q-learning algorithm based on a convolutional neural network and add sentiment analysis to establish a prediction method for Chinese stock investment tasks. …”
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Implementation of home visit method in offline learning during the Covid-19 pandemic by Benjamin Metekohy, Novita Sahertian, David Ming

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Method based on contrastive learning for fine-grained unknown malicious traffic classification by Yifeng WANG, Yuanbo GUO, Qingli CHEN, Chen FANG, Renhao LIN

“…In order to protect against unknown threats and evasion attacks, a new method based on contrastive learning for fine-grained unknown malicious traffic classification was proposed.Specifically, based on variational auto-encoder (CVAE), it included two classification stages, and cross entropy and reconstruction errors were used for known and unknown traffic classification respectively.Different form other methods, contrastive learning was adopted in different classification stages, which significantly improved the classification performance of the few-shot and unknown (zero-shot) classes.Moreover, some techniques (e.g., re-training and re-sample) combined with contrastive learning further improved the classification performance of the few-shot classes and the generalization ability of model.Experimental results indicate that the proposed method has increased the macro recall of few-shot classes by 20.3% and the recall of unknown attacks by 9.1% respectively, and it also has protected against evasion attacks on partial classes to some extent.…”
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Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning by Laor Boongasame, Jirapond Muangprathub, Karanrat Thammarak

“…It performed optimally with a batch size of 32 and a learning rate between 0.001 and 0.01. These findings establish Laor as a robust alternative to conventional methods, suitable for moderately deep architectures. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…In this study, a machine learning (ML) method is developed to predict the damage distribution in the rear plate. …”
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Achieving Acceleration to First-in-Human: MSD’s Learnings on Platform Method Validation Strategy by Xiaoqing Hua, Jorge Quiroz, Joop Waterval, Brian Harrison, Maria DeBruin, Lynn Gennaro

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Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall by Cheng Jian, Tiancheng Xie, Xiaojian Hu, Jian Lu

“…Subsequently, the latter two stages encompass yolov5-based vehicle recognition and the employment of the virtual coil method for traffic flow parameter estimation. Following rigorous testing, the accuracy of traffic flow parameter estimation reaches 97.2% under moderate snow conditions.…”
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Water Quality Prediction Method Based on Reinforcement Learning Graph Neural Network by Mingming Yan, Zhe Wang

“…To address these issues, we propose a reinforcement learning graph neural network-based approach. Our method, an adjacency reinforcement learning, and multi-channel graph convolution autoencoder, predicts water quality by performing reinforcement learning on the adjacency of water quality indicator images. …”
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