基于机器学习的开放创新社区创意识别方法研究

目的 探索基于机器学习的开放创新创意识别方法,解决创意识别过程中存在的耗时长、效率低、成本高等问题。方法 从用户特征、用户参与度和创意内容特征三个方面构建评估模型,以OpenIDEO社区为研究对象,采集数据并进行数据清洗和数据转化映射,最后进行多种机器学习算法的参数优化,并以F1值为选择标准,选择分类效果最佳的算法作为分类模型。结果 运用KNN、SVM、决策树、随机森林四种机器学习算法分析OpenIDEO数据,随机森林算法通过参数优化取得了最大的F1值（0.919 09）,同时对于验证数据,该算法同样可以取得较好的分类效果。结论 应用机器学习方法对开放式创新社区中的创意进行识别,具有较高的可行性和有效性,可以大大降低社区在创意筛选中的投入,提高创新效率,优化社区生态。     

基于机器学习与多尺度特征的金属氧化物性能预测

材料信息学是信息学技术在材料学中的应用,通过材料信息数据库和集成材料设计平台对材料的数据进行分析和预测。通过应用不同的机器学习(回归分析)方法和不同的特征选择算法,从众多的多尺度特征集中选择最优的特征子集可以预测金属氧化物的物理特性,归纳出适合材料不同特性的机器学习模型。分析结果表明,特征选择方法可以提升机器学习模型的性能,为进一步开发更有效的材料性能预测方法提供参考。     

政府奖惩激励下快递包装回收行为演化博弈与仿真分析

目的 厘清快递包装废弃物回收利益主体间的博弈关系与演化路径,解决快递包装回收率普遍较低的问题。方法 在信息不对称和有限理性的条件下,构建政府、回收商和消费者三群体间快递包装回收行为演化博弈模型。分析群体间稳定策略组合的实现条件,运用Matlab软件进行数值仿真,探究不同参数变化对三方演化路径的影响。结果 政府的奖惩力度、激励成本、收益大小是影响博弈三主体策略选择和演化稳定路径走向的关键因素。同时回收商和消费者的参与比例对政府行为选择有显著作用。结论 参与主体得益是影响其行为的关键,政府通过积极鼓励、支持和宣传,同时设立科学合理的奖惩激励政策能够明显提高快递包装回收率。     

基于功能配置下共享快递包装模块化设计研究

目的 基于模块化功能配置,探索共享快递包装从功能分解、创意重组到替换重构的迭代过程,创新共享快递包装设计方法,设计一种多功能共享快递包装,以此满足不同产品的功能需求,迎合消费多元化发展。方法 在问题导向视角下,通过共享快递包装现状,探讨功能配置应用在共享快递包装设计中的可行性。在设计过程中,对共享快递包装进行功能需求转换,分析共享快递包装功能,划分层级模块,借助图形化方式,展开功能模块组合、分解示意,创新性地提出共享快递包装设计新形式,并以共享快递包装设计实践进行验证,证明基于功能配置下共享快递包装设计方法的有效性。结论 在包装概念设计阶段,将模块化功能配置应用到共享快递包装设计中,厘清了共享快递出现的问题,引导了共享快递包装的设计路径,为共享快递包装提供了创意设计参考,有助于共享快递可持续发展。     

基于机器学习的钛合金弹性模量预测方法研究

目的 探索一种高效可行的预测方法以提高钛合金弹性模量的预测精度,采用第一性原理计算方法与机器学习相结合的方式建立高精度的预测模型。方法 通过数据挖掘获取材料数据库中钛合金的力学性质微观结构参数,结合第一性原理计算方法构建初始数据集,并对其进行预处理,包括噪音消除、归一化及标准化,以得到高质量的数据集。同时,采用随机森林特征重要性分析法对输入参数进行筛选,去除弱相关变量以降低预测模型的复杂度。在此基础上,构建随机森林模型、支持向量机模型、BP神经网络模型及优化后的GA-BP神经网络模型,综合对比各模型的回归能力,分析误差后选出最优的算法模型。结果 最终建立了钛合金弹性模量预测模型,其中随机森林模型、支持向量机模型、BP神经网络模型、GA-BP神经网络模型的预测相关系数R分别为0.836、0.943、0.917、0.986。结论 GA-BP模型对弹性模量的预测误差基本保持在5%～7%。遗传算法可以优化BP神经网络的权值和阈值,使预测精度大幅提升。说明通过该方法可以实现钛合金弹性模量的预测,大大节省研发和实验成本,加快高性能材料的筛选。     

基于模糊PID的包装覆膜自适应恒张力控制方法

目的 为提高极限条件下对包装覆膜自适应恒张力的控制效果，基于模糊PID设计了一种新的控制方法。方法 首先通过参数拟合分析建立了包装覆膜的结构力学分析模型，并采用荷载-挠度全曲线拟合方法模拟了包装覆膜力学参数，然后结合区域–合并连续估计方法得到结构参数解析模型。在此基础上，通过外荷载作用力学评估分析，在极限转动刚度作用下，构建了弹塑性力学模型。然后通过极限抗力分析和屈服应力学参数的优化评估，得到抗弯刚度软化模型，继而基于模糊PID控制实现包装覆膜的自适应控制优化。结果 应用该方法后，包装覆膜自适应恒张力的输出稳定性较强，参数寻优能力较好。结论 根据实验结果可知，上述恒张力控制方法具有较好的控制效果。     

快递包装回收网络中回收点选址研究

目的针对快递包装重复利用率低、回收难的现象,建立有效的快递包装回收网络,提高快递包装的回收效率。方法文中通过改进的K-均值聚类与集合覆盖模型相结合的方法对回收点选址进行研究。首先利用改进的K-均值聚类对快递点进行聚类分析,求解快递网点的平均差异度,并通过选定原则得到初始聚类中心,减少迭代的次数,将最终聚类中心作为候选回收点。再利用集合覆盖模型进行优化,在候选回收点的基础上选出回收点,为充分利用现有物流资源,选取最近的快递网点作为回收点。结果以最少的回收点数量覆盖了所有的快递网点,降低了网络的回收成本,提高了快递包装回收效率。结论通过2种方法的结合选择出了合适的快递网点作为回收点。利用SPSS和Matlab软件对SY市的回收点选址进行了实例分析,验证了该方法是有效的。     

设计伦理视域下的共享快递包装设计

目的 以日益增长的环境污染和资源浪费问题为切入点,从设计伦理学的角度对共享快递包装进行设计研究。方法 分析共享快递包装现阶段所存在的伦理问题,通过实地调研和用户访谈,探讨用户对共享快递包装的设计诉求,深入研究寄取件操作难、部分弱势群体出行不便、货品过重不易运输、包装物回收难度大等快递包装使用过程中所出现的一系列问题,尝试提出以快递包装箱为载体,实现共享快递租赁服务的系统解决方案。结论 新型共享包装设计从材料选择、功能设计、结构设计再到回收体系,都应遵循生态性、共享性、人性化的设计伦理原则,能够有效解决包装成本高、回收困难、资源浪费、服务体系不健全等问题。并且依靠互联网、大数据等技术搭建快递共享平台,提出伦理价值实现路径,保证共享快递包装系统的精准高效施行。     

红外光谱法结合化学计量学对快递包装纸盒的检验研究

目的 研究一种红外光谱法与化学计量学相结合的方法，以对现场提取的快递包装纸盒样品进行快速检验分类。方法 利用红外光谱法对53个快递包装纸盒样品进行检验，依据其主要填料差异进行分类，并利用系统聚类进行分组。基于该分组，训练随机森林模型、多层感知器判别、Fisher判别3种预测模型，实现对新样品组别的分类预测。结果 53个快递包装纸盒样品被分为3类，而后进一步细分为9组，训练得到的3种判别模型中的Fisher判别预测准确率较高。结论 该检验方法快速、无损、准确，依据化学计量学实现对快递包装纸盒样品的快速检验，为公安机关检验此类物证提供依据。     

基于正交实验的快递包装循环利用的优化分析

目的 分析现有的快递包装循环利用观点,优化整合快递包装循环利用形式,调查各个阶段快递回收的流程及其可行性,整理出成本因素、材料因素、促进因素、社会因素4个因素,对比各因素对快递包装循环利用的影响程度。方法 通过去重优化方法和正交实验进行分析,采用直观分析法和方差分析法进行对比观察,得出各因素的重要程度。结果 对快递包装循环利用的影响从大到小依次为成本因素、材料因素、促进因素、社会因素,特别是成本因素影响最大,而社会因素的影响效果并不明显。结论4项因素的影响度排序可以提高现有的包装循环利用效率,提高资源利用率,保护生态环境,为利用快递包装循环的行业提供更加优化的选择。     

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting the right features in the whole feature set is a complicated process, and many researchers are concentrating on it to produce a predictive model with high accuracy. In this work, a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression (L2) to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data set. Overfitting is a major problem in feature selection, where the new data are unfit to the model because the training data are small. Ridge regression is mainly used to overcome the overfitting problem. The features are selected by using the proposed feature selection method, and random forest classifier is used to classify the data on the basis of the selected features. This work uses the Pima Indians Diabetes data set, and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed algorithm. The accuracy of the proposed algorithm in predicting diabetes is 100%, and its area under the curve is 97%. The proposed algorithm outperforms existing algorithms.     

A study on credit scoring modeling with different feature selection and machine learning approaches

A bit hurdle for financial institutions is to decide potential candidates to give a line of credit identifying the right people without any credit risk. For such a crucial decision, past demographic and financial data of debtors is important to build an automated artificial intelligence credit score prediction model based on machine learning classifier. In addition, for building robust and accurate machine learning models, important input predictors (debtor's information) must be selected. The present computational work focuses on building a credit scoring prediction model. A publicly available German credit data is incorporated in this study. An improvement in the credit scoring prediction has been shown with the use of different feature selection techniques (such as Information-gain, Gain-Ratio and Chi-Square) and machine learning classifiers (Bayesian, Naïve Bayes, Random Forest, Decision Tree (C5.0) and SVM (support Vector Machine)). Further, a comparative analysis is performed between different machine learning classifiers and between different feature selection techniques. Different evaluation metrics are considered for analyzing performance of the models (such as accuracy, F-measure, false positive rate, false negative rate and training time). After analysis, a best combination of machine learning classifier and feature selection technique are identified. In this study, a combination of random forest (RF) and Chi-Square (CS) is found good, among other combinations, with respect to good performance accuracy, F-measure and low false positive and false negative rates. However, training time for this particular combination was found to be slightly higher. Result of C5.0 with chi-square was comparable with the best one. This study provides an opportunity to financial institutions to build an automated model for better credit scoring.     

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a network-wide impact. Machine learning is now widely used for fast detection of these attacks. In this paper, some important feature selection methods for machine learning on DDoS detection are evaluated. The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller. A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks. The experimental results show that the Random forest (RF) classifier trains the more accurate model with 99.97% accuracy using features subset by the Recursive feature elimination (RFE) method.     

基于随机森林的统计控制图模式识别研究

引入随机森林方法进行统计控制图模式识别的研究。提取了控制图的统计特征和形状特征,设计了5种不同的特征组合方法,利用蒙特卡洛仿真方法产生训练数据集和测试数据集,选取了常用的3种模式识别方法（支持向量机方法、人工神经网络方法、决策树方法）进行对比。实验结果表明,随机森林方法相比其他3种分类器方法,在分类准确率和消耗时间两个维度上都有明显优势,可以应用于统计过程控制图模式识别。     

改进MDSMOTE与PSO-SVM在汽车组合仪表分类预测中的应用

汽车组合仪表生产过程中质检项目多且检测时间长,这在一定程度上制约了其生产效率的进一步提升。为此,提出一种基于改进最远点合成少数类过采样技术（max distance synthetic minority over-sampling technique,MDSMOTE）的支持向量机（support vector machine, SVM）分类预测方法。首先,结合专家经验对汽车组合仪表的原始生产数据进行特征筛选,并在MDSMOTE中引入类不平衡率I_R,以对所筛选的特征数据进行扩充;然后,利用粒子群优化（particle swarm optimization, PSO）算法对SVM的误差惩罚因子C和核函数参数γ进行优化;最后,建立优化的SVM分类预测模型,并对汽车组合仪表进行分类。通过与其他分类预测模型在不同数据集上的预测结果进行对比可知,基于改进MDSMOTE的SVM分类预测模型的准确率、F值和几何平均值等评价指标均优于其他模型。所提出方法在汽车仪表产品分类上表现出较强的泛化能力和稳定性,可为仪表制造企业生产效率的提升提供有效参考。     

Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology

Patents represent the technological or inventive activity and output across different fields, regions, and time. The analysis of information from patents could be used to help focus efforts in research and the economy; however, the roles of the factors that can be extracted from patent records are still not entirely understood. To better understand the impact of these factors on patent value, machine learning techniques such as feature selection and classification are used to analyze patents in a sample industry, nanotechnology. Each nanotechnology patent was represented by a comprehensive set of numerical features that describe inventors, assignees, patent classification, and outgoing references. After careful design that included selection of the most relevant features, selection and optimization of the accuracy of classification models that aimed at finding most valuable (top-performing) patents, we used the generated models to analyze which factors allow to differentiate between the top-performing and the remaining nanotechnology patents. A few interesting findings surface as important such as the past performance of inventors and assignees, and the count of referenced patents.     

A combination of rough-based feature selection and RBF neural network for classification using gene expression data

This paper presents a novel rough-based feature selection method for gene expression data analysis. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers that approximate to the correct ones. In this paper, we attempt to introduce a prediction scheme that combines the rough-based feature selection method with radial basis function neural network. For further consider the effect of different feature selection methods and classifiers on this prediction process, we use the NaIve Bayes and linear support vector machine as classifiers, and compare the performance with other feature selection methods, including information gain and principle component analysis. We demonstrate the performance by several published datasets and the results show that our proposed method can achieve high classification accuracy rate.     

Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.