When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes ...When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.展开更多
The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer＇s accuracy （PA） and user＇s accuracy （UA...The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer＇s accuracy （PA） and user＇s accuracy （UA）. The PA of a category indicates to what extent the reference pixels of the category are correctly classified, whereas the UA of a category represents to what extent the other categories are less misclassified into the category in question. Therefore, the UA of the various categories determines the reliability of their interpretation on the classified image and is more important to the analyst than the PA. The present investigation has been performed in order to determine if there occurs improvement in the UA of the various categories on the classified image of the principal components of the original bands and on the classified image of the stacked image of two different years. We performed the analyses using the IRS LISS Ⅲ images of two different years, i.e., 1996 and 2009, that represent the different magnitude of urbanization and the stacked image of these two years pertaining to Ranchi area, Jharkhand, India, with a view to assessing the impacts of urbanization on the UA of the different categories. The results of the investigation demonstrated that there occurs significant improvement in the UA of the impervious categories in the classified image of the stacked image, which is attributable to the aggregation of the spectral information from twice the number of bands from two different years. On the other hand, the classified image of the principal components did not show any improvement in the UA as compared to the original images.展开更多
In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors suc...In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors such as'arc','ab'(aurora but bright),'cloudy','diffuse','discrete',and'clear'.Subsequently,we utilized a state-of-the-art convolutional neural network,ConvNeXt(Convolutional Neural Network Next),deploying deep learning techniques to train the model on a dataset classified into six distinct categories.Remarkably,on the test set our methodology attained an accuracy of 99.4%,a performance metric closely mirroring human visual observation,thereby underscoring the classifier’s competence in paralleling human perceptual accuracy.Building upon this foundation,we embarked on the identification of large-scale auroral optical data,meticulously quantifying the monthly occurrence and Magnetic Local Time(MLT)variations of auroras from stations at different latitudes:RANK(high-latitude),FSMI(mid-latitude),and ATHA(low-latitude),under different solar wind conditions.This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.展开更多
Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of t...Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.展开更多
With the rapid development and popularization of new-generation technologies such as cloud computing,big data,and artificial intelligence,the construction of smart grids has become more diversified.Accurate quick read...With the rapid development and popularization of new-generation technologies such as cloud computing,big data,and artificial intelligence,the construction of smart grids has become more diversified.Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents,which is essential to ensure the normal operation of the power system,energy management and planning.Based on the distributed architecture of cloud computing,this paper designs an improved random forest residential electricity classification method.It uses the unique out-of-bag error of random forest and combines the Drosophila algorithm to optimize the internal parameters of the random forest,thereby improving the performance of the random forest algorithm.This method uses MapReduce to train an improved random forest model on the cloud computing platform,and then uses the trained model to analyze the residential electricity consumption data set,divides all residents into 5 categories,and verifies the effectiveness of the model through experiments and feasibility.展开更多
Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the...Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park, a Brazilian Atlantic rainforest area. Two classification methods were tested, including i） a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii） an object-based classification using the software eCognition version 5. In the first method, six different classes were established, while in the second method, another two classes were established in addition to the six classes in the first method. Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224. Application of the user＇s knowledge during the object-based classification process achieved the desired quality; therefore, the use of inter-relationships between objects, superelasses, subclasses, and neighboring classes were critical to improving the efficiency of land cover classification.展开更多
In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user req...In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.展开更多
Understanding the underlying goal behind a user＇s Web query has been proved to be helpful to improve the quality of search. This paper focuses on the problem of automatic identification of query types according to th...Understanding the underlying goal behind a user＇s Web query has been proved to be helpful to improve the quality of search. This paper focuses on the problem of automatic identification of query types according to the goals. Four novel entropy-based features extracted from anchor data and click-through data are proposed, and a support vector machines （SVM） classifier is used to identify the user＇s goal based on these features. Experi- mental results show that the proposed entropy-based features are more effective than those reported in previous work. By combin- ing multiple features the goals for more than 97% of the queries studied can be correctly identified. Besides these, this paper reaches the following important conclusions： First, anchor-based features are more effective than click-through-based features; Second, the number of sites is more reliable than the number of links; Third, click-distribution- based features are more effective than session-based ones.展开更多
Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a met...Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.展开更多
In order to judge whether the user reviews are relevant to App software, this paper proposed a method to judge the relevance of user reviews based on Naive Bayesian text classification and term frequency.Firstly, the ...In order to judge whether the user reviews are relevant to App software, this paper proposed a method to judge the relevance of user reviews based on Naive Bayesian text classification and term frequency.Firstly, the keywords sets of App software’s user reviews are extracted. Then, the keywords sets are optimized. Finally, the relevance score of the user reviews are calculated, and whether the user reviews are relevant is judged. Through the experiment, this method is proved that can judge the relevance of App software’s user reviews effectively.展开更多
基金supported by Phase 4,Software Engineering(Software Service Engineering)under Grant No.XXKZD1301
文摘When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.
文摘The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer＇s accuracy （PA） and user＇s accuracy （UA）. The PA of a category indicates to what extent the reference pixels of the category are correctly classified, whereas the UA of a category represents to what extent the other categories are less misclassified into the category in question. Therefore, the UA of the various categories determines the reliability of their interpretation on the classified image and is more important to the analyst than the PA. The present investigation has been performed in order to determine if there occurs improvement in the UA of the various categories on the classified image of the principal components of the original bands and on the classified image of the stacked image of two different years. We performed the analyses using the IRS LISS Ⅲ images of two different years, i.e., 1996 and 2009, that represent the different magnitude of urbanization and the stacked image of these two years pertaining to Ranchi area, Jharkhand, India, with a view to assessing the impacts of urbanization on the UA of the different categories. The results of the investigation demonstrated that there occurs significant improvement in the UA of the impervious categories in the classified image of the stacked image, which is attributable to the aggregation of the spectral information from twice the number of bands from two different years. On the other hand, the classified image of the principal components did not show any improvement in the UA as compared to the original images.
基金supported by the General Program of the National Natural Science Foundation of China(Grant No.42374212)the National Magnetic Confinement Fusion Energy Research and Development Program of China(Grant No.2024YFE03020004).
文摘In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors such as'arc','ab'(aurora but bright),'cloudy','diffuse','discrete',and'clear'.Subsequently,we utilized a state-of-the-art convolutional neural network,ConvNeXt(Convolutional Neural Network Next),deploying deep learning techniques to train the model on a dataset classified into six distinct categories.Remarkably,on the test set our methodology attained an accuracy of 99.4%,a performance metric closely mirroring human visual observation,thereby underscoring the classifier’s competence in paralleling human perceptual accuracy.Building upon this foundation,we embarked on the identification of large-scale auroral optical data,meticulously quantifying the monthly occurrence and Magnetic Local Time(MLT)variations of auroras from stations at different latitudes:RANK(high-latitude),FSMI(mid-latitude),and ATHA(low-latitude),under different solar wind conditions.This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.
文摘Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.
基金This work was partially supported by the National Natural Science Foundation of China(61876089).
文摘With the rapid development and popularization of new-generation technologies such as cloud computing,big data,and artificial intelligence,the construction of smart grids has become more diversified.Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents,which is essential to ensure the normal operation of the power system,energy management and planning.Based on the distributed architecture of cloud computing,this paper designs an improved random forest residential electricity classification method.It uses the unique out-of-bag error of random forest and combines the Drosophila algorithm to optimize the internal parameters of the random forest,thereby improving the performance of the random forest algorithm.This method uses MapReduce to train an improved random forest model on the cloud computing platform,and then uses the trained model to analyze the residential electricity consumption data set,divides all residents into 5 categories,and verifies the effectiveness of the model through experiments and feasibility.
基金Supported by the Sa o Paulo Research Foundation (FAPESP), Brazil
文摘Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park, a Brazilian Atlantic rainforest area. Two classification methods were tested, including i） a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii） an object-based classification using the software eCognition version 5. In the first method, six different classes were established, while in the second method, another two classes were established in addition to the six classes in the first method. Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224. Application of the user＇s knowledge during the object-based classification process achieved the desired quality; therefore, the use of inter-relationships between objects, superelasses, subclasses, and neighboring classes were critical to improving the efficiency of land cover classification.
基金National Natural Science Foundation of China (No. 70631003)
文摘In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.
基金the Tianjin Applied Fundamental Research Plan (07JCYBJC14500)
文摘Understanding the underlying goal behind a user＇s Web query has been proved to be helpful to improve the quality of search. This paper focuses on the problem of automatic identification of query types according to the goals. Four novel entropy-based features extracted from anchor data and click-through data are proposed, and a support vector machines （SVM） classifier is used to identify the user＇s goal based on these features. Experi- mental results show that the proposed entropy-based features are more effective than those reported in previous work. By combin- ing multiple features the goals for more than 97% of the queries studied can be correctly identified. Besides these, this paper reaches the following important conclusions： First, anchor-based features are more effective than click-through-based features; Second, the number of sites is more reliable than the number of links; Third, click-distribution- based features are more effective than session-based ones.
基金supported by the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(BK19CF002).
文摘Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.
文摘In order to judge whether the user reviews are relevant to App software, this paper proposed a method to judge the relevance of user reviews based on Naive Bayesian text classification and term frequency.Firstly, the keywords sets of App software’s user reviews are extracted. Then, the keywords sets are optimized. Finally, the relevance score of the user reviews are calculated, and whether the user reviews are relevant is judged. Through the experiment, this method is proved that can judge the relevance of App software’s user reviews effectively.
