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Cross-Domain TSK Fuzzy System Based on Semi-Supervised Learning for Epilepsy Classification

Computer Modeling in Engineering & Sciences 2023年第11期137卷 1613-1633页

作者： zaihe cheng Yuwen Tao Xiaoqing Gu Yizhang Jiang Pengjiang Qian School of Internet of Things Wuxi Institute of TechnologyWuxi214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China School of Computer Science and Artificial Intelligence Changzhou UniversityChangzhou213164China

Through semi-supervised learning and knowledge inheritance,a novel Takagi-Sugeno-Kang(TSK)fuzzy system framework is proposed for epilepsy data classification in this study.The new method is based on the maximum mean discrepancy(MMD)method and TSK fuzzy system,as a basic model for the classification of epilepsy data.First,formedical data,the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe.Second,in view of the deviation in the data distribution between the real source domain and the target domain,MMD is used to measure the distance between different data distributions.The objective function is constructed according to the MMD distance,and the distribution distance of different datasets is minimized to find the similar characteristics of different datasets.We introduce semi-supervised learning to further explore the relationship between data.Based on the MMD method,a semi-supervised learning(SSL)-MMD method is constructed by using pseudo-tags to realize the data distribution alignment of the same category.In addition,the idea of knowledge dissemination is used to learn pseudo-tags as additional data features.Finally,for epilepsy classification,the cross-domain TSK fuzzy system uses the cross-entropy function as the objective function and adopts the back-propagation strategy to optimize the parameters.The experimental results show that the new method can process complex epilepsy data and identify whether patients have epilepsy.

关键词： Takagi-Sugeno-Kang fuzzy systems back propagation semi-supervised learning inheritancemechanism transfer learning

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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： zaihe cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data mining.However,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this paper.In P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce framework.Then the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset utility.In the Map phase,the P-EFIM algorithm divides the task into multiple independent subtasks.It uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure load-balancing.Furthermore,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce phase.Experiments are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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科教兴国背景下职业院校智慧育人体系构建路径研究

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中国职业技术教育 2024年第10期 79-87页

作者：徐进奚茂龙程载和无锡职业技术学院开源创新创业学院/智能制造技术研究院无锡职业技术学院党总支无锡职业技术学院无锡职业技术学院人事处

职业院校智慧育人体系是促动技能型人才培育模式重塑的新路径、驱动职业教育资源高效配置的新动能、推动区域数字经济高质量发展的新引擎。科教兴国背景下,职业院校智慧育人体系在初始融合阶段完成“人—物”双元融合以提升主动智能服... 详细信息

职业院校智慧育人体系是促动技能型人才培育模式重塑的新路径、驱动职业教育资源高效配置的新动能、推动区域数字经济高质量发展的新引擎。科教兴国背景下,职业院校智慧育人体系在初始融合阶段完成“人—物”双元融合以提升主动智能服务水平,中度融合阶段实现“人—机”双元融合拓展主体认知边界,深度融合阶段推动“人—机—物”三元融合构建虚实一体的智慧育人场域。基于此,从智慧育人制度、智慧育人目标、智慧育人服务、智慧育人模式、智慧育人生态等五方面创新,着力提升职业院校高素质技能人才的智能化培育能力,深入推进科教兴国发展战略的全面实施。

关键词：科教兴国智慧育人体系职业院校

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“双高计划”背景下高职院校实训室绩效评价

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实验技术与管理 2021年第7期38卷 261-266页

作者：程载和黄春麟无锡职业技术学院资产管理处江苏无锡214121 浙江经济职业技术学院浙江杭州310018

为提高高职院校实训室绩效,构建“确立指标—整合数据—挖掘分析”三步走的绩效评价体系及其实施路径。从项目全生命周期角度设计实训室建设和使用不同阶段的绩效评价指标。利用高校资产管理系统等数据,建立针对学校实训室和仪器设备的... 详细信息

为提高高职院校实训室绩效,构建“确立指标—整合数据—挖掘分析”三步走的绩效评价体系及其实施路径。从项目全生命周期角度设计实训室建设和使用不同阶段的绩效评价指标。利用高校资产管理系统等数据,建立针对学校实训室和仪器设备的数据中心,搭建信息化绩效平台。采用数据挖掘技术,通过横向、纵向的评价分析,实现对实训室绩效状态的实时监控和数据可视化呈现,提高实训室管理水平和使用效率。为学校决策提供科学支持。

关键词：实训室绩效双高计划大型仪器设备绩效评价体系

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