姓 名：韩晓红
职 称：教授，博士，硕士生导师
Email：hanxiaohong (at) tyut (dot) edu (dot) cn
专业方向：计算机技术及应用
研究领域：大数据挖掘，模式识别，人工智能，算法优化，图像处理
个人简介:韩晓红，博士，教授，全球最大的赌钱网(中国)有限公司硕士生导师。曾赴美国德克萨斯州达拉斯分校进行为期一年的智能优化算法应用科研交流与合作研究。主要研究领域为大数据挖掘、人工智能、模式识别、图像处理等。承担和参与国家基金、国家教育部博士学科点专项科研基金、山西省科学基金、山西省留学基金、山西省国际科技合作基金、山西省工业科技攻关计划项目基金等国家省部级项目 10 余项。在《Information Sciences 》,《Knowledge and Information System》,《Journal of Optimization Theory and Application》,《Engineering Applications of Artificial Intelligence》，《Applied Soft Computing》等国际及国内同行评审的刊物发表高水平论文 30 篇，国内及国际他引次数 250 余次。其中 SCI、EI 收录 20 余篇， 12 篇 2 区高水平 SCI 检索论文，在高等教育出版社和西安交通大学出版社出版教材和专著各一部，授权专利 8项。并且被《Pattern Recognition》,《Engineering Applications of Artificial Intelligence》、《Journal of Control and Decision》等 8 个国际刊物邀请为评审专家。
承担项目
1、山西省自然科学基金项目，2014011021-1，基于分段分形的多跨段实测数据事件检测研究，2014/01-2016/12，3万元，结题，主持
2、山西省自然科学基金项目，201801D121136，基于WSIs及基因组通路深度融合的肺癌生存预测模型研究。，2018/01-2020/12，5万元，在研，主持
3、山西省回国留学人员科研教研资助项目, 基于基因组通路深度融合的肺癌生存预测模型研究, 2020/01-2022/12，5万元，在研，主持
4、横向课题：基于基因组测序数据的拷贝数变异检测系统，2019，在研，主持
5、基于深度学习的生理信息分类算法研究，2020，在研，主持
6、横向课题：工程现场人员车辆管理系统,2017, 结题，主持
7、横向课题：基于移动终端的掌纹辅助诊断模型,2017, 结题，主持
8、产学研项目：教育部普开数据教学内容和课程体系改革项目,2018, 结题，主持
9、横向课题：全球最大的赌钱网(中国)有限公司、晋中银行股份有限公司、信雅达金融大数据研究院成立金融大数据联合实验室合作项目, 在研，主持
10、横向课题 山西银行股份有限公司与集团金融大数据技术服务 2021 在研 主持
11、横向课题 文物知识图谱构建关键技术研究与应用 2021 在研 主持
学术成果
[1] Han Xiaohong, et al. A new graph-preserving unsupervised feature selection embedding LLE with low-rank constraint and feature-level representation[J]. ARTIFICIAL INTELLIGENCE REVIEW,2020,53(4): 2875-2903. (SCI检索, 2区)
[2] Han, XiaoHong; et al. A novel data clustering algorithm based on modified gravitational search algorithm[J].ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2017,61: 1-7. (SCI检索, 2区)
[3] Han Xiaohong, et al. Unsupervised feature selection via graph matrix learning and the low-dimensional space learning for classification[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2020, 87: 03283. (SCI检索, 2区)
[4] Han, Xiaohong, et al. Feature selection by recursive binary gravitational search algorithm optimization for cancer classification[J]. SOFT COMPUTING,2020,24(6): 4407-4425. (SCI检索, 3区)
[5] Han, XiaoHong, et al., A Bird Flock Gravitational Search Algorithm Based on the Collective Response of Birds[J]. THE COMPUTER JOURNAL, 2017,60(11): 1687-1716. (SCI检索, 4区)
[6] Han, XiaoHong, et al., A hybrid prediction model based on improved multivariable grey model for long-term electricity consumption [J]. Electrical Engineering, 2020. (SCI检索, 4区)
[7] Han, XiaoHong; et al. A Hybrid Cancer Classification Model Based Recursive Binary Gravitational Search Algorithm in Microarray Data[C]. Procedia Computer Science, 9th International Conference of Information and Communication Technology，2019, 154: 274-282. (EI 检索)
[8] Han X H, Chang X, Quan L, et al. Feature subset selection by gravitational search algorithm optimization[J]. Information Sciences, 2014, 281: 128-146. (SCI检索, IF: 5.524, 1区)
[9] Xiang J, Han X H*, Duan F, et al. A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method[J]. Applied Soft Computing, 2015, 31: 293-307. (SCI检索,IF: 4.873,1区)
[10] Han X H, Chai H, Liu P, et al. A new graph-preserving unsupervised feature selection embedding LLE with low-rank constraint and feature-level representation[J]. Artificial Intelligence Review, 2019: 1-29. (SCI检索,IF: 5.095, 2区).
[11] Han X H, Liu P, Wang L, et al. Unsupervised feature selection via graph matrix learning and the low-dimensional space learning for classification[J]. Engineering Applications of Artificial Intelligence, 2020, 87: 103283. (SCI检索,IF: 3.526, 2区)
[12] Han X H, Li D, Liu P, et al. Feature selection by recursive binary gravitational search algorithm optimization for cancer classification[J]. Soft Computing, 2020, 24(6): 4407-4425. (SCI检索,IF: 2.784, 3区)
[13] Han X H, Quan L, Xiong X, et al. Facing the classification of binary problems with a hybrid system based on quantum-inspired binary gravitational search algorithm and K-NN method[J]. Engineering Applications of Artificial Intelligence, 2013, 26(10): 2424-2430. (SCI检索,IF: 3.526, 2区)
[14] Han X H, Quan L, Xiong X Y, et al. A novel data clustering algorithm based on modified gravitational search algorithm[J]. Engineering Applications of Artificial Intelligence, 2017, 61: 1-7. (SCI检索,IF: 3.526, 2区)
[15] Han X H, Quan L, Xiong X Y, et al. Diversity enhanced and local search accelerated gravitational search algorithm for data fitting with B-splines[J]. Engineering with Computers, 2015, 31(2): 215-236. (SCI检索, IF: 3.551,2区)
[16] Han X H, Quan L, Xiong X. A modified gravitational search algorithm based on sequential quadratic programming and chaotic map for ELD optimization[J]. Knowledge and Information Systems, 2015, 42(3): 689-708. (SCI检索, IF: 2.397, 3区)
[17] Han X H, Qiang Y, Lan Y. A Bird Flock Gravitational Search Algorithm Based on the Collective Response of Birds[J]. The Computer Journal, 2017, 60(11): 1687-1716. (SCI检索,4区，0.98)
[18] Han X H, Li D A, Wang L. A Hybrid Cancer Classification Model Based Recursive Binary Gravitational Search Algorithm in Microarray Data[J]. Procedia Computer Science, 2019, 154: 274-282. (EI 检索)
[19] XiaoHong Han, et al. An intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting, Information Sciences, 208(1), pp 103-118, 2013.(SCI检索, IF: 4.832,2区)
[20] XiaoHong Han, et al. A chaotic digital secure communication based on a modified gravitational search algorithm filter, Information Sciences, 208(15): pp 14-27, 2012.(SCI检索, IF: 4.832, 2区)
[21] Han Xiao Hong, et al. Genetic Algorithm Assisted Wavelet Noise Reduction Scheme for Chaotic Signals. Journal of optimization theory and application, 151(3), pp 646-653, 2011.(SCI检索, IF: 1.406, 2区)
[22] Han XiaoHong, et al. Chaotic secure communication based on a gravitational search algorithm filter, Engineering applications of artificial intelligence, 25(4), pp 766-774, 2012. (SCI检索, IF: 3.177,2区)
[23] Han, Xiaohong, et al. Noise reduction method for chaotic signal based on phase space reconstruction[C]. 2011 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011, 2011.03. 28-29, Shenzhen, Guangdong, China.(EI 检索)
[24] XiaoHong Han, et al. A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping, Appl Intell, 43（4），p855-873,2015.(EI 检索) 被引频次: 18
[25] Han, Xiao Hong; Li, Deng Ao; Wang, A Hybrid Cancer Classification Model Based Recursive Binary Gravitational Search Algorithm in Microarray Data, Procedia Computer Science, v 154, p 274-282, 2018, 9th International Conference of Information and Communication Technology, ICICT 2019. (EI 检索)
授权发明专利情况
[1]韩晓红等，一种基于BFBA和ELM的乳腺X射线图像特征选择方法. 中国 发明专利 ZL201710048258.2
[2] 韩晓红等, 一种用于齿轮齿廓曲线重构的数据拟合方法, 中国, 发明专利, ZL201310409757.1, 2016
[3] 韩晓红等，一种颈部淋巴结超声图像特征选择方法, 中国，发明专利, ZL201310585163.6, 2016
[4] 强彦 韩晓红等，一种基于词缀的用于对未知词进行语义分类的方法, 中国，发明专利, ZL201210361150.6,2015
[5] 丁敏 韩晓红等， 一种环保脱渣焊条及其制备方法，中国，发明专利, ZL201210517969.7, 2015
[6] 韩晓红等 一种分叉掌纹识别方法及装置 中国 发明专利 ZL201710972828.7
著作情况
[1] 参编《JAVA语言程序设计》教材1部，并担任该教材副主编。
[2] 出版《混沌时序非线性去噪方法研究及其应用》学术专著1部。
