陈慧灵

2012年6月吉林大学博士毕业,同年7月被引入温州大学。主持国家青年科学基金项1项,浙江省自然基金1项,温州市重大科技专项1项,教育部重点实验室开放课题1项,温州市科技计划一般项目1项,以主要参加人参与国家基金项目4项、浙江省自然基金项目5项。荣获温州市第十五届自然科学优秀论文一等奖1项,2014年度温州大学优秀共产党员,2017年度温州大学优秀教师等称号。入选2016年度温州市“551人才工程”。近年来主要从事数据挖掘、机器学习方法研究及其在医学、金融等领域的应用研究。发表论文近100篇,其中ESI高被引论文一篇,多篇发表在Expert Systems with Applications、 Knowledge-based Systems、Neurocomputing、Soft computing、Applied mathematical modelling和Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 等国际主流学术期刊和会议上。当前H指数为26(Google Scholar 统计),论文被引数达2200余次。个人主页:https://www.researchgate.net/profile/Huiling_Chen/publications
个人信息
姓名:陈慧灵
性别:男
民族:汉
最后学历:博士研究生
职务/职称:校聘教授,硕导
地址:浙江省温州市茶山高教园区梅泉大街586号5号楼B421办公室
邮箱:chenhuiling.jlu@gmail.com、chenhuiling_jsj@wzu.edu.cn
电话:0577-86689125
工作及教育经历
2015年--至今,温州大学,校聘教授
2017年12月--至今,温州大学 副教授
2012年07月--2017年11月,温州大学,讲师
2008年09月--2012年06月,吉林大学,博士
目前担任Information Science、Knowledge-based Systems、Artificial Intelligence in Medicine、Neurocomputing、Future Generation Computer Systems等国际杂志的审稿人。
专业研究
研究的专业领域:
人工智能、知识工程
研究的主要方向:
数据挖掘、机器学习方法及应用
研究成果
项目基金:
1.面向医学诊断的智能决策新方法研究,国家青年自然科学基金项目(61303113,2014.01-2016.12,主持)
2.基于大规模医学数据的智能疾病诊断方法研究,浙江省自然基金面上项目,(Y17F020061,2017.01-2019.12,主持)
3.大数据驱动的肺部感染性疾病预测预警关键技术研究,温州市重大科技专项(ZG2017019,2017.01-2019.12,主持)
4.面向医学诊断决策问题的机器学习方法研究,教育部重点实验室开放课题(93K172013K01,2013.01-2014.12,主持)
5.面向企业危机预警的智能决策关键技术研究,温州市科技计划项目(G20140048,2015.06-2017.05,主持)
6.基于机器学习的企业危机预警模型研究,温州大学实验室开放项目(13SK29A,2013.04-2014.04,主持)
7.基于声音信号的帕金森病早期诊断新方法研究,温州大学实验室开放项目(15SK26A,2015.04-2016.04,主持)
8.微课驱动的“学”“练”一体化课程探索和改革--以《程序设计基础》为例,温州大学教学改革项目(15jg57,2015.11-2017.11,主持)
9.基因调控网络的鲁棒结构干预研究,国家面上自然科学基金项目(61572367,2016.01-2019.12,参加/第三)
10.贝类重金属污染的多模态融合光谱开集检测及不确定度研究,国家自然科学基金面上项目(31571920,2016.01-2019.12,参加/第三)
11.面向个性化推荐服务的社交网络数据深挖掘关键技术研究,国家青年自然科学基金项目(61402336,2015.01-2017.12,参加/第二)
12.基于动态特征的真伪笑容表达与识别研究,国家青年自然科学基金项目(31500875,2016.01-2018.12,参加/第二)
13.基于数据驱动的公交网络性能监测及影响因素分析,浙江省自然基金项目(LQ13G010007,2013.01-2015.12,参加/第二)
14.多源多模态医学数据挖掘及其在阿尔茨海默病诊断中的应用,浙江省自然基金项目(LY14F020035,2014.01-2016.12,参加/第二)
15.大数据驱动的短期公交客流量预测算法研究,浙江省自然基金项目(LQ16G010006,2015.01-2017.12,参加/第三)
专利著作:
1.一种基于改进灰狼优化算法的数据分类预测方法及系统,授权号:201711048597.7,陈慧灵、罗杰等
2.一种基于灰狼优化算法的数据分析方法及装置,受理号:201711203871.3,陈慧灵、罗杰等
3.分类预测模型的优化方法、装置及终端设备,受理号:201711249399.7,陈慧灵、王科杰等
4.一种基于核极限学习机的风险预测的方法和装置,公示,中国,201610326839.3(排名:1/9)
5.一种基于混沌灰狼优化的支持向量机方法,公示,中国,201610669347.4(排名:1/8)
6.模型参数优化的方法及装置,公示,中国,201611131726.4(排名:1/8)
软件著作:
1.陈慧灵、沈立明、张璐、王名镜,基于支持向量机的帕金森病诊断系统V1.0. 软件著作权登记号:2016SR284899,温州大学,2016年7月31日
2.沈立明、张璐、王科杰、柳建飞、陈慧灵,基于机器学习的乳腺癌诊断决策支持系统V1.0. 软件著作权登记号:2016SR382027,温州大学,2016年10月27日
3.王科杰、陈慧灵、朱俊杰、沈立明,基于机器学习的甲状腺疾病智能诊断系统V1.0. 软件著作权登记号:2017SR108395,温州大学,2017.4.10
4.柳建飞,陈慧灵,陶珂珂,王科杰,基于优化支持向量机的胸腔积液智能化诊断系统V1.0. 软件著作权登记号:2017SR294349,温州大学,2017.6.21
5.陈慧灵,朱彬磊,蔡振闹,基于机器学习的信用风险评估系统V1.0. 软件著作权登记号:2017SR619787,温州大学,2017
6.朱彬磊,陈慧灵,王科杰,朱俊杰,基于退火果蝇支持向量机的企业破产预测系统V1.0. 软件著作权登记号:2017SR622596,温州大学,2017
7.陈慧灵,罗杰,蔡振闹,李成业,基于血液样本的结核性胸膜炎智能辅助诊断系统V1.0. 软件著作权登记号:2017SR619715,温州大学,2017
8.陈慧灵,张谦,蔡振闹,李成业,基于PHP的医学数据预处理系统V1.0. 软件著作权登记号:2017SR619813,温州大学,2017
近年发表论文
1.Zhang Q, Huiling Chen(陈慧灵)*, Heidari A A, Zhao X, Xu Y, Wang P, Li Y, Li C. Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers [J]. IEEE ACCESS, 2019, 7:31243-31261.
2.Xu Y, Huiling Chen(陈慧灵)*, Heidari A A, Luo J, Zhang Q, Zhao X, Li C. An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks [J]. Expert Systems with Applications, 2019, 129:135-155.
3.Luo J, Huiling Chen(陈慧灵)*, Heidari A A, Xu Y, Zhang Q, Li C. Multi-strategy Boosted Mutative Whale-inspired Optimization Approaches [J]. Applied Mathematical Modelling, 2019, https://doi.org/10.1016/j.apm.2019.03.046
4.Heidari A A, Mirjalili S, Faris H, Aljarah I, Mafarja M, Huiling Chen(陈慧灵)*. Harris hawks optimization: Algorithm and applications [J]. Future Generation Computer Systems, 2019, 97(849-872).
5.Huiling Chen(陈慧灵), Xu Y, Wang M, Zhao X. A balanced whale optimization algorithm for constrained engineering design problems [J]. Applied Mathematical Modelling, 2019, 71(45-59).
6.Luo J, Chen H(陈慧灵)*, Zhang Q, Xu Y, Huang H, Zhao X: An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling 2018, 64:654-668.
7.Wang M, Chen H(陈慧灵)*, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C: Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing2017, 267:69-84.
8. Wang M, Chen H(陈慧灵)*, Li H, Cai Z, Zhao X, Tong C, Li J, Xu X: Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction. Engineering Applications of Artificial Intelligence 2017, 63:54-68.
9.Chen H-L(陈慧灵), Wang G, Ma C, Cai Z-N, Liu W-B, Wang S-J: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 2016, 184:131-144.
10.Shen L, Chen H(陈慧灵)*, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D: Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems 2016, 96:61-75.
11.Chen H-L (陈慧灵), Yang B, Wang S-J, Wang G, zhong Li H, bin Liu W: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy. Applied Mathematics and Computation 2014, 239:180-197.
12.Chen H-L(陈慧灵), Yang B, Wang G, Liu J, Xu X, Wang S-J, Liu D-Y: A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems 2011, 24(8):1348-1359.
13.Chen H-L(陈慧灵), Yang B, Liu J, Liu D-Y: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Systems with Applications 2011, 38(7):9014-9022.
14.Chen H-L(陈慧灵), Liu D-Y, Yang B, Liu J, Wang G: A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis. Expert Systems with Applications 2011.
15.Chen H-L(陈慧灵), Yu X-G, Xu X, Sun X, Wang G, Wang S-J: An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach. Expert Systems with Applications 2013, 40(1):263-271.
16.Chen H(陈慧灵), Hu L, Li H, Hong G, Zhang T, Ma J, Lu Z: An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes. Basic & clinical pharmacology & toxicology 2017, 120(1):86-96.
17.Chen H(陈慧灵), Yang B, Liu D, Liu W, Liu Y, Zhang X, Hu L: Using blood indexes to predict overweight statuses: an extreme learning machine-based approach. PloS one 2015, 10(11):e0143003.
18.Chen H-L(陈慧灵), Yang B, Wang G, Wang S-J, Liu J, Liu D-Y: Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence. Journal of Medical Systems 2012, 36(4):2505-2519.
19.Chen HL(陈慧灵), Yang B, Wang G, Liu J, Chen YD, Liu DY: A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis. Journal of Medical Systems 2012, 36(3):1953-1963.
20.Zhao X, Liu X, Chen H(陈慧灵)*: Network modelling and variational Bayesian inference for structure analysis of signed networks. Applied Mathematical Modelling 2018, 61:237-254.
21.Li C, Hou L, Sharma B, Li H, Chen C, Li Y, Zhao X, Huang H, Cai Z, Chen H(陈慧灵)*: Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Computer Methods & Programs in Biomedicine 2018:211-225.
22.Xia J, Chen H(陈慧灵)*, Li Q, Zhou M, Chen L, Cai Z, Fang Y, Zhou H: Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Computer Methods and Programs in Biomedicine 2017, 147:37-49.
23.Xu X, Chen H-L(陈慧灵)*: Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Computing 2014, 18(4):797-807.
24.Zuo W-L, Wang Z-Y, Liu T, Chen H-L(陈慧灵)*: Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach. Biomedical Signal Processing and Control 2013, 8(4):364-373.
25.Li Q, Chen H(陈慧灵)*, Huang H, Zhao X, Cai Z, Tong C, Liu W, Tian X: An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.Computational and Mathematical Methods in Medicine 2017, 2017:15.
26.Zhu J, Zhu F, Huang S, Chen H(陈慧灵)*, Zhao X, Zhang S: A New Evolutionary Machine Learning Approach to Identify the Pyrene Induced Rat Hepatotoxicity and Renal Dysfunction. IEEE Access 2019: DOI:10.1109/ACCESS.2018.2889151.
27.Zhu J, Zhao X, Li H, Chen H(陈慧灵)*, Wu G: An Effective Machine Learning Approach for Identifying the Glyphosate Poisoning Status in Rats Using Blood Routine Test. IEEE Access 2018, 6:15653-15662.
28.Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H(陈慧灵)*, Hu L: Chaos enhanced Grey Wolf Optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Computational Biology and Chemistry 2019, 78:481-490.
29.Xu J, Zhang X, Chen H(陈慧灵)*, Li J, Zhang J, Shao L, Wang G: Automatic Analysis of Microaneurysms Turnover to Diagnose the Progression of Diabetic Retinopathy. IEEE Access 2018, 6:9632-9642.
30.Wang X, Wang Z, Weng J, Wen C, Chen H(陈慧灵)*, Wang X: A New Effective Machine Learning Framework for Sepsis Diagnosis. IEEE Access 2018, 6:48300-48310.
31.Cai Z, Gu J, Wen C, Zhao D, Huang C, Huang H, Tong C, Li J, Chen H(陈慧灵)*: An Intelligent Parkinsons' Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach. Computational and Mathematical Methods in Medicine 2018, 2018:24.
32.Hu L, Lin F, Li H, Tong C, Pan Z, Li J, Chen H(陈慧灵)*: An intelligent prognostic system for analyzing patients with paraquat poisoning using arterial blood gas indexes. Journal of Pharmacological and Toxicological Methods 2017, 84:78-85.
33.Hu L, Li H, Cai Z, Lin F, Hong G, Chen H(陈慧灵)*, Lu Z: A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices. Plos One 2017, 12(10):e0186427.
34.Cai Z, Gu J, Chen HL(陈慧灵)*: A New Hybrid Intelligent Framework for Predicting Parkinson's Disease. IEEE Access 2017, 5:17188-17200.
35.Lufeng Hu GH, Jianshe Ma, Xianqin Wang, Huiling Chen(陈慧灵)*: An Efficient Machine Learning Approach for Diagnosis of Paraquat-Poisoned Patients. Computers in Biology and Medicine 2015, 59:116-124.
36.Liu T, Hu L, Ma C, Wang Z-Y, Chen H-L(陈慧灵)*: A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection. International Journal of Systems Science 2015, 46(5):919-931.
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