Temperature change is a complex atmospheric phenomenon. Even the ERA5-Land atmospheric reanalysis temperature dataset with the highest accuracy currently has errors with the actual observed temperature. This study cap...
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This paper discusses how to organically combine image retrieval with data mining, image reconstruction and other related technologies. It makes use of the prior knowledge and rich images contained in the bigdata, and...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy conce...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
Large Language Models (LLMs) have achieved significant performance in various natural language processing tasks but also pose safety and ethical threats, thus requiring red teaming and alignment processes to bolster t...
Session-based recommendation aims to generate personalized recommendations based on user behaviors within a browsing session. The traditional graph neural network (GNN) methods focus on modeling pair-wise relationship...
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Due to the ability to automatically extract phishing features without relying on expert knowledge, deep learning methods have been widely applied in the research of phishing email classification and detection. However...
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Deep learning methods, known for their powerful feature learning and classification capabilities, are widely used in phishing detection. To improve accuracy, this study proposes DPMLF (Deep Learning Phishing Detection...
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With the rapid development of drones. The traditional 'manual feature extraction + classifier-based' object detection algorithm can no longer meet the accuracy requirements. Aiming at the problem that the reas...
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Belt conveyors are common transportation equipment, and optimizing their performance is an effective measure to achieve intelligence and efficient transportation. The application of new technologies has increased the ...
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Belt conveyors are common transportation equipment, and optimizing their performance is an effective measure to achieve intelligence and efficient transportation. The application of new technologies has increased the controllable parameters and operational parameters to be optimized in belt transportation systems, making performance prediction and multi-objective optimization problems more challenging. Traditional response prediction and optimization methods have become increasingly inadequate to meet research requirements. The emergence and development of machine learning methods and modern intelligent optimization methods have provided new directions for the prediction and optimization research of belt transportation systems. Therefore, in order to improve the system prediction and performance optimization of belt transportation systems, this study proposes an interpretive machine learning approach based on improved neural networks and support vector machines, which combines the optimization of network initial weight threshold and training set test set, and establishes a high-performance predictive interpretive machine learning model. The results indicate that under different combinations of training and validation set partitioning, all evolutionary processes in the trajectory of the initial weight threshold optimization of the proposed comprehensive neural network optimization method reach their optimal state in the 17th generation. The optimization algorithm stably converged to the optimal value after only 14 generations of evolution, resulting in a mean square error of 0.011678 for the optimal network prediction, while the mean square error of all individuals in the initial population was 0.016845. If the average network prediction performance of all individuals in the initial population is taken as the basic standard, a comprehensive optimization algorithm is used to optimize the network’s prediction performance to 29.6%. After the 10th reinforcement training,
In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patie...
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In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon ***,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis *** order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant *** help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of *** results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp *** to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.
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