Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the genera...
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Using the trajectory data of moving objects to analyze and study the infection mode of viruses or germs has practical application value. The definition of infection pattern in existing works only considers one-to-one ...
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data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. H...
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We propose a community discovery method based on deep auto-encoding (DGAE_DST). Firstly, we use the pre-trained two-layer neural network and k-means algorithm to initialize the centroid vector, and then use the DNN mo...
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Existing supervised deep learning model requires large amounts of labeled training data to learn new tasks. This is a limitation for many practical applications in disaster areas as well as in many other fields such a...
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The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on ...
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Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in pr...
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Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.
SDN (Software-defined networking) are important for current network systems, such as cloud systems. The characteristics of flow in SDN and the impact of flow table entries and controllers on data packet transmission a...
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Estimating individual treatment effect (ITE) is a challenging task due to the need for individual potential outcomes to be learned from biased data and counterfactuals are inherently unobservable. Some researchers pro...
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How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering a...
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