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Optimal Scheme of Retinal Image Enhancement using Curvelet Transform and Quantum Genetic Algorithm
( Zhixiao Wang ),( Xuebin Xu ),( Wenyao Yan ),( Wei Wei ),( Junhuai Li ),( Deyun Zhang ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.11
A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.
An Optimization for Hybrid Semantic Similarity Computation
Zhixiao Wang,Xiaofang Ding,Ying Huang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.10
Semantic similarity computation is of great importance in many applications such as natural language processing, knowledge acquisition and information retrieval. In recent years, many concept similarity measures have been developed for ontology and lexical taxonomy. Generally speaking, ontology concepts semantic similarity computation is tedious and time-consuming. This paper puts forward an optimization algorithm to simplify semantic similarity computation. The optimization algorithm utilizes hierarchical relationship between concepts to simplify similarity computation process. Simulation experiments showed the optimization algorithm could make similarity computation simple and convenient, and similarity computation speed was improved by one time. The more complexity an ontology structure, and the bigger the maximum depth of ontology, the more significantly the performance improved.
Zhengxia Wang,Ningfei Ji,Zhongqi Chen,Zhixiao Sun,Chaojie Wu,Wenqing Yu,Fan Hu,Mao Huang,Mingshun Zhang 대한천식알레르기학회 2020 Allergy, Asthma & Immunology Research Vol.12 No.5
Purpose: CD4+T cells are essential in the pathogenesis of allergic asthma. We have previously demonstrated that microRNA-1165-3p (miR-1165-3p) was significantly reduced in T-helper type (Th) 2 cells and that miR-1165-3p was a surrogate marker for atopic asthma. Little is known about the mechanisms of miR-1165-3p in the regulation of Th2-dominated allergic inflammation. We aimed to investigate the associations between Th2 differentiation and miR-1165b-3p in asthma as well as the possible mechanisms. Methods: CD4+ naïve T cells were differentiated into Th1 or Th2 cells in vitro. MiR-1165-3p was up-regulated or down-regulated using lentiviral systems during Th1/Th2 differentiation. In vivo, the lentiviral particles with the miR-1165-3p enhancer were administered by tail vein injection on the first day of a house dust mite -induced allergic airway inflammation model. Allergic inflammation and Th1/Th2 differentiation were routinely monitored. To investigate the potential targets of miR-1165-3p, biotin-microRNA pull-down products were sequenced, and the candidates were further verified with a dual-luciferase reporter assay. The roles of a target protein phosphatase, Mg2+/Mn2+-dependent 1A (PPM1A), in Th2 cell differentiation and allergic asthma were further explored. Plasma PPM1A was determined by ELISA in 18 subjects with asthma and 20 controls. Results: The lentivirus encoding miR-1165-3p suppressed Th2-cell differentiation in vitro. In contrast, miR-1165-3p silencing promoted Th2-cell development. In the HDM-induced model of allergic airway inflammation, miR-1165-3p up-regulation was accompanied by reduced airway hyper-responsiveness, serum immunoglobulin E, airway inflammation and Th2-cell polarization. IL-13 and PPM1A were the direct targets of miR-1165-3p. The expression of IL-13 or PPM1A was inversely correlated with that of miR-1165-3p. PPM1A regulated the signal transducer and activator of transcription and AKT signaling pathways during Th2 differentiation. Moreover, plasma PPM1A was significantly increased in asthmatic patients. Conclusions: MiR-1165-3p negatively may regulate Th2-cell differentiation by targeting IL-13 and PPM1A in allergic airway inflammation.
Community-based Collaborative Filtering Recommendation Algorithm
Xiaofang Ding,Zhixiao Wang,Shaoda Chen,Ying Huang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2
Collaborative filtering recommendation technology is by far the most widely used and successful personalized recommendation technology. However, the method currently faced with some problems such as sparse matrix, affecting the accuracy of the predicted results. This paper puts forward a new community detection algorithm based on topological potential theory, and combines it with collaborative filtering recommendation algorithm. The users with similar interests are put into the same community. When searching for the user’s nearest neighbor, it target to the users in a specific community or several communities instead of all users, which narrows the search and improves the prediction accuracy. Experimental results suggest that this approach effectively reduces the impact on the prediction accuracy of the sparse matrix, and significantly improves the prediction ability and recommendation quality.
Human Face Recognition using Multi-Class Projection Extreme Learning Machine
Xu, Xuebin,Wang, Zhixiao,Zhang, Xinman,Yan, Wenyao,Deng, Wanyu,Lu, Longbin The Institute of Electronics and Information Engin 2013 IEIE Transactions on Smart Processing & Computing Vol.2 No.6
An extreme learning machine (ELM) is an efficient learning algorithm that is based on the generalized single, hidden-layer feed-forward networks (SLFNs), which perform well in classification applications. Many studies have demonstrated its superiority over the existing classical algorithms: support vector machine (SVM) and BP neural network. This paper presents a novel face recognition approach based on a multi-class project extreme learning machine (MPELM) classifier and 2D Gabor transform. First, all face image features were extracted using 2D Gabor filters, and the MPELM classifier was used to determine the final face classification. Two well-known face databases (CMU-PIE and ORL) were used to evaluate the performance. The experimental results showed that the MPELM-based method outperformed the ELM-based method as well as other methods.