P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s by Zhiping Tan, Zhiping Tan, Dapeng Ye, Jiancong Wang, Wenxiang Wang

“…In P4CN-YOLOv5s, the Mosaic-9 and Mixup algorithms are initially used for data augmentation to augment the training dataset and enhance data complexity. …”
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FLWD:A Webshell detection method based on federated learning by ZENG Qingpeng, CHAI Jiangli, WU Shuixiu

“…Meanwhile, the FedAvg algorithm of federated learning and the DP-SGD algorithm were employed for collaborative training across multiple participants without data leaving their domains. …”
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The Line Pressure Detection for Autonomous Vehicles Based on Deep Learning by Xuexi Zhang, Ying Li, Ruidian Zhan, Jiayang Chen, Junxian Li

“…At present, the line pressure detection algorithms mainly include algorithms based on traditional features and models and algorithms based on deep learning. …”
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Study on the Lightweighting Strategy of Target Detection Model with Deep Learning by Junli Hu

“…Experiments on the public data set AI-TOD to show that the target detection lightweight model of deep learning has stronger detection ability and higher average detection accuracy than other algorithms, which proves the applicability and effectiveness of this algorithm.…”
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Concept Drift Detection in Data Stream Mining : A literature review by Supriya Agrahari, Anil Kumar Singh

“…Hidden data contexts, which are not known to the learning algorithms, are known as concept drift. …”
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Aviation Rivet Classification and Anomaly Detection Based on Deep Learning by Xiao-bo Zhu

“…And a method of aviation rivet classification and anomaly detection based on deep learning algorithm was proposed. …”
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A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. …”
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PUE Attack Detection in CWSN Using Collaboration and Learning Behavior by Javier Blesa, Elena Romero, Alba Rozas, Alvaro Araujo, Octavio Nieto-Taladriz

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Fusing wrist pulse and ECG data for enhanced identification of coronary heart disease and its complications by Lei-Xin Hong, Wen-Jie Wu, Xia Chen, Dan-Qun Xiong, Ye-Qing Zhang, Xiang-Dong Xu, Jian-Jun Yan, Rui Guo

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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

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Hate Speech Detection Using Machine Learning: A Survey by Seble, H., Muluken, S., Edemealem Kingawa, Kafte, T., Terefe, F., Mekashaw, G., Abiyot, B., Senait, T.

“…Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions are discussed in detail.…”
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Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…The proposed approach is efficient and feasible, offering a more stable and accurate disease detection system while utilizing fewer resources. This work demonstrates the potential of AI-driven solutions in agriculture, particularly in improving disease detection and crop yield through advanced machine learning techniques.…”
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Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools by Alfredo Cuzzocrea, Abderraouf Hafsaoui, Carmine Gallo

“…Finally, we demonstrate that, when the collection of node representation vectors is incorporated into a neural network classifier, our model outperforms the state-of-the-art GNN-based algorithms in the detection of bot nodes within unknown networks.…”
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Pilot spoofing detection algorithm for edge nodes based on heterogeneous pilot energy estimation by Shiguo WANG, Shujuan TIAN, Qingyong DENG

“…For the federated learning scenarios with edge-end cooperation, edge servers and device terminals update their models and exchange gradient parameters frequently, and hence eavesdroppers can manipulate channel estimation through pilot spoofing to intercept the transmitted information and reduce the update efficiency of federated learning model.Therefore, a pilot attack detection algorithm with heterogeneous pilot energy estimation was proposed.Firstly, a federated learning pilot attack system model was constructed after the security of pilot attacks on data transmission had been analyzed.Then, a pilot attack detection method based on random segmentation and encryption methods was proposed to detect the pilot spoofing accurately and the contaminated channel could be recovered as well.Experimental results show that the proposed algorithm is more suitable for detecting pilot attacks with low transmit power and strong concealment compared to other existing algorithms.Furthermore, the data transmission rate of edge servers is improved significantly through the precoding based on the recovered channel state information.…”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The findings demonstrate that deep learning algorithms offer more accurate and earlier detection of diseases, highlighting their potential for application in plant growing. …”
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Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques by Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai, Yu-Jung Lin

“…By incorporating ensemble learning principles into oversampling techniques, our proposed methods offer distinct strategies for selecting newly generated sarcastic data. …”
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Detecting Botrytis Cinerea Control Efficacy via Deep Learning by Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov, Xiangping Cheng

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The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. …”
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Calibration and uncertainty quantification for deep learning-based drought detection by Mengxue Zhang, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Gustau Camps-Valls

“…To address this shortcoming, we introduce methodologies for model calibration and entropy-based uncertainty quantification for deep learning-based drought detection. The calibration algorithm can deal with calibration errors by reducing distributional shifts and alleviating overconfident predictions. …”
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Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

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