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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Fei Gao Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

In this paper,Deep Belief Nets(DBN) model and Support Vector machine(SVM) are used to mine the features hidden in social news,which can influence the emotions of *** feature selection methods for text modeling are used to build the input vectors of DBN,with the purpose of keeping the text information to the greatest *** take advantage of the deep features abstracted by DBN to build social news text *** the same time,three optimal models are used as inputs of SVM to train and classify the social *** get a conclusion that DBN not only reduces the dimension of original features,but also makes the abstracted features with more text information and shows better performance in determining the influence on people' s emotions by social news.

关键词： Deep Belief Network(DBN) Restricted Boltzmann machine(RBM) Impacts on Emotion Social News

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NEW WORD DETECTION AND EMOTIONAL TENDENCY JUDGMENT BASED ON MIXED MODEL

NEW WORD DETECTION AND EMOTIONAL TENDENCY JUDGMENT BASED ON ...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Chongyuan Sun Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

The paper studies a new method for Chinese new word detection and emotional tendency judgment based on mixed model and proposes a new word generation *** we construct conditional random fields(CRFs) to recognize the new words,lead-in features based on character combined with the crowd sourcing network *** then express word as a word vector based on neural network language model(NNLM) to judge the new word emotional *** experimental results show that the method can improve the precision and recall of the new word detection with a good system performance,and it also provides a new way for forecasting the public mood.

关键词： New Word Detection Conditional Random Fields Emotional Tendency Judgment Neural Network Language Model

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MODELING BLOG USER FOR CHINESE MICRO-BLOG SENTIMENT CLASSIFICATION WITH FUZZY SVM

MODELING BLOG USER FOR CHINESE MICRO-BLOG SENTIMENT CLASSIFI...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiao Sun Chengcheng Li Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and InformationHefei University of Technology Faculty of Engineering University of Tokushima2-1 Minami-JosanjimaTokushima 770-8506Japan

ISBN: (纸本)9781467369541

Micro-blog is easily posted and communicated because of its short text ***,its brevity limits the feature *** paper presents a blogger modeling method for Chinese micro-blog sentiment *** paper would adopt a multidimensional strategy to accomplish the feature extraction *** with traditional feature extraction methods and combined with the interactive property of blog,the blog comment will be considered as the performance characteristic of micro-blog emotion *** comes to comments content and the relationship between commentators and micro-bloggers while it extracts the micro blogging text *** last,the paper adopts fuzzy SVM model to reduce the sensation of noise points and outliers on classification *** results demonstrate the effectiveness of the multidimensional feature extraction method on sentiment classification and the classify effect of fuzzy SVM is better than SVM.

关键词： sentiment classification multidimensional fuzzy SVM

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Feature fusion of HOG and WLD for facial expression recognition

Feature fusion of HOG and WLD for facial expression recognit...

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IEEE/SICE International Symposium on System Integration

作者： Xiaohua Wang Chao Jin Wei Liu Min Hu Liangfeng Xu Fuji Ren AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology China School of Computer and Information Hefei University of Technology China The Japan Federation of Engineering Societies University of Tokushima Shinkura-cho Tokushima Japan

A considerable amount of research work has been done for facial expression recognition using local or global feature extraction methods. Weber Local Descriptor (WLD), a simple and robust local image descriptor, is recently developed for local feature extraction. In facial expression recognition, the information contained in the local is important for the recognition result. The Histograms of Oriented Gradients (HOG) can well describe the local area information using gradient and orientation density distribution of the edge. In order to solve the lack of contour and shape information only by WLD features and to extract facial local features more efficiently, we propose a hybrid approach that combines the WLD with HOG features. We divide the images into blocks and weight each of them, then extract the two features and fuse them. At last, the weighted fused histograms are used to classify facial expressions by chi-square distance and the nearest neighbor method. The proposed method is applied on popular JAFFE and Cohn-Kanade facial expression databases and recognition rate is up to 93.97% and 95.86%. Compared with the Gabor Wavelet, LBP, and AAM and experimental results show that the proposed method achieves better performance for facial expression recognition.

关键词： Feature extraction Histograms Databases Face recognition Equations Active appearance model Data mining

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FACE RECOGNITION BASED ON ADAPTIVE WEIGHTING AND FUZZY FUSION WITH SINGLE TRAINING SAMPLE

FACE RECOGNITION BASED ON ADAPTIVE WEIGHTING AND FUZZY FUSIO...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Xiaohua Wang Chao Jin Wei Liu Min Hu Fuji Ren Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

In order to solve the robustness issues for single training sample face recognition under occlusion conditions,this paper presents a recognition method based on adaptive weighting and fuzzy *** our method,the information entropy expansion mode is introduced in sub-mode method and via adaptively assigning the weights corresponding to each sub-model can reduce the impact of occluded *** addition,through summing the similar blocks in face images can make up for the defect of the sub-model which ignores the integrity of ***,the method based on fuzzy comprehensive evaluation is utilized for decision-level fusion to the outputs by these two ways of *** results on AR face database show that this method has state-of-the-art classification accuracy and also robustness to occlusion.

关键词： Face recognition Single training sample Adaptive weighting Fuzzy comprehensive evaluation Sub-model

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ADAPTIVE FACIAL EXPRESSION RECOGNITION METHOD BASED ON MBP AND HMOG FEATURE

ADAPTIVE FACIAL EXPRESSION RECOGNITION METHOD BASED ON MBP A...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Min Hu Xiaoyin Huang Xiaohua Wang Kun Li Fuji Ren Liangfeng Xu Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

Because of the limitation of Monogenic waves’ extraction from expression texture,this thesis proposes an adaptive facial expression feature fusion method based on MBP（monogenic binary pattern） and Histogram of HMOG（monogenic orientated gradients）.To begin with our method,we get information of orientation,phase and magnitude component from filtered image by monogenic waves,besides,extract and fuse texture information from orientation and magnitude component to recognize facial *** single texture feature have some inadequacies for facial expression *** of all,this paper describes a new kind of HMOG feature inspired by thoughts of HOG（histogram of oriented gradients） to indicate shape information of facial ***,acquiring best adaptive weights of expression block through information ***,we classify the expression image using Nearest Neighbor to get the final result of effect of proposed *** order to verify the efficiency of the methods above,we do a large number of experiments and it turns out our methods have great ability to explain expression feature.

关键词： Monogenic signal Histogram of Monogenic Oriented Gradients Adaptive weights by information entropy Nearest Neighbor

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AGE ESTIMATION AND GENDER CLASSIFICATION OF FACIAL IMAGES BASED ON LOCAL DIRECTIONAL PATTERN

AGE ESTIMATION AND GENDER CLASSIFICATION OF FACIAL IMAGES BA...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Min Hu Yaona Zheng Fuji Ren He Jiang Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

In order to improve shortage of Lxical Binary Pattern(LBP) algorithm in the field of facial feature extraction,this paper uses Lxical Directional Pattern(LDP) algorithm to extract the facial features about age estimation and gender *** gradient information is more stable than gray information,LDP algorithm is not affected easily by *** preprocessing images,we use block strategy to represent facial features more *** then,we extract features of each block with LDP algorithm,storing the LDP histogram Finally,we obtain age and gender identification results by Support Vector machine(SVM).Experimental results on FG-NET face database show that this method has a good effect on age estimation and gender classification.

关键词： feature extraction,LDP,age estimation,gender classification

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An Automatic Method for Brain Tumors Segmentation Based on Deep Convolutional Neural Network

An Automatic Method for Brain Tumors Segmentation Based on D...

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IEEE International Conference on Medical Imaging Physics and Engineering

作者： Hui Lin Benjian Zhang Xin Guo Dong Guo Jia Jing Luo Huiyuan Zhao Yumeng Min Hu Fuji Ren School of Physics Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei China School of Computer and Information Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

ISBN: (纸本)9781665426091

[Purpose] Accurate outline of tumor targets is critical to a high quality radiotherapy plan. Manual segmentation is of great workload and has a strong artificial subjectivity. Using deep learning method to assist automatically segmenting of tumor target is the penetration and application of artificial intelligence in medicine. [Methods] A 6-layer model of deep Convolution Neural Network (CNN) has been constructed by taking advantage of different types of layers for brain tumor segmentation. This model is a 6 layer CNN model (6-CNN) composed of three convolution layers, two pool layers and one full connection layer. To obtain enough samples for 6-CNN model training, a patch-based technology has been adopted. That is to successively extract a local area from the whole image as a patch. And the center pixel value is taken as the pixel value of the whole patch. Similarly, the label of the center pixel is also taken as the label of the whole patch. Thus the 6-CNN model transforms the brain tumor image segmentation into patch classification based on the excellent classification characteristics of deep convolution neural network. The model combines the local features of patch, the information extracted from shallow network and the global features to predict the category label of the central pixel of patch. [Results] The model is validated on BRATS 2015 dataset and results show that the segmentation accuracy can be up to Dice Similarity Coefficient (DSC) $90\%\pm 4\%$ . [Conclusions] An automatic deep CNN segmentation model for brain tumors has been constructed based on MRI image patches, which is expected to assist or even substitute the manual segmentation of brain tumors.

关键词： Image segmentation Convolution Magnetic resonance imaging Transforms Predictive models Brain modeling Feature extraction

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FACE RECOGNITION BASED ON DOUBLE COMPLEMENTARY SPACE AND MULTI-DECISION CLASSIFICATION

FACE RECOGNITION BASED ON DOUBLE COMPLEMENTARY SPACE AND MUL...

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2014 IEEE 3rd International Conference on Cloud computing and Intelligence Systems

作者： Fuji Ren Yanqiu Li Liangfeng Xu Min Hu Xiaohua Wang Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory School of Computer and Information Hefei University of Technology Faculty of Engineering The University of Tokushima770-8506TokushimaJapan

In order to overcome the one-sidedness and limitations of a single subspace in feature extraction and classification,we propose a face recognition method that extracts features in double complementary space and utilize multi-decision for *** the feature extraction stage,we use ICA and LPE algorithm as the first layer in complementary space to extract global and local features of the face ***,for the purpose of solving the problem that features extracted by ICA are lack of classification information,we further extract classification information from the independent features extracted by ICA on the condition that the FLDA and DCV algorithm are used as the second layer in the complementary *** the classification stage,the test samples are firstly projected to the independent *** that,projected the samples which are difficult to identify to the LPE space and reclassified ***,the results are been determined *** results on ORL database show that the proposed method can effectively improve the recognition rate.

关键词： algorithms complementary space multi-decision classification face recognition

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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