基于奇异值分解、模糊聚类和粗糙集理论的旋转机械故障诊断

对含有重复和冲突对象的离散决策表,提出了一种基于粗糙集的规则获取方法,使得获得的规则能够涵盖所有的对象。对连续条件属性值和离散决策属性值的决策表,基于矩阵的奇异值分解、模糊C均值聚类和粗糙集属性约简技术,提出连续属性最佳离散数目确定方法。在上述方法的基础上,进行旋转机械故障诊断的规则获取,获得的诊断规则具有很好的知识归纳能力和知识泛化能力。利用获得的诊断规则进行旋转机械故障诊断,建立了待诊断对象和诊断规则的弹性匹配模式,使得诊断结论的获取取决于不同的诊断要求。     

Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier

Prediction of protein secondary structures is an important problem in bioinformatics and has many applications. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). The SVM method is a comparatively new learning system which has mostly been used in pattern recognition problems. In this study, SVM is used as a machine learning tool for the prediction of secondary structure and several encoding schemes, including orthogonal matrix, hydrophobicity matrix, BLOSUM62 substitution matrix, and combined matrix of these, are applied and optimized to improve the prediction accuracy. Also, the optimal window length for six SVM binary classifiers is established by testing different window sizes and our new encoding scheme is tested based on this optimal window size via sevenfold cross validation tests. The results show 2% increase in the accuracy of the binary classifiers when compared with the instances in which the classical orthogonal matrix is used. Finally, to combine the results of the six SVM binary classifiers, a new tertiary classifier which combines the results of one-versus-one binary classifiers is introduced and the performance is compared with those of existing tertiary classifiers. According to the results, the Q/sub 3/ prediction accuracy of new tertiary classifier reaches 78.8% and this is better than the best result reported in the literature.     

Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.     

Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool

Dynamic selection of scheduling rules during real operations has been recognized as a promising approach to the scheduling of the production line. For this strategy to work effectively, sufficient knowledge is required to enable prediction of which rule is the best to use under the current line status. In this paper, a new learning algorithm for acquiring such knowledge is proposed. In this algorithm, a binary decision tree is automatically generated using empirical data obtained by iterative production line simulations, and it decides in real time which rule to be used at decision points during the actual production operations. The configuration of the developed dynamic scheduling system and the learning algorithm are described in detail. Simulation results on its application to the dispatching problem are discussed with regard to its scheduling performance and learning capability.     

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles (IoV) usually have linear and nonlinear composite characteristics. In order to accurately describe the dynamic change trend of such loads, this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV. Firstly, a chaotic analysis algorithm is implemented to process the load-time series, while some learning samples of load prediction are constructed. Secondly, a support vector machine (SVM) is used to establish a load prediction model, and an improved artificial bee colony (IABC) function is designed to enhance the learning ability of the SVM. Finally, a CloudSim simulation platform is created to select the per-minute CPU load history data in the mobile cloud computing system, which is composed of 50 vehicles as the data set; and a comparison experiment is conducted by using a grey model, a back propagation neural network, a radial basis function (RBF) neural network and a RBF kernel function of SVM. As shown in the experimental results, the prediction accuracy of the method proposed in this study is significantly higher than other models, with a significantly reduced real-time prediction error for resource loading in mobile cloud environments. Compared with single-prediction models, the prediction method proposed can build up multidimensional time series in capturing complex load time series, fit and describe the load change trends, approximate the load time variability more precisely, and deliver strong generalization ability to load prediction models for mobile cloud computing resources.     

cDNA芯片缺失值处理对基于基因表达谱的疾病分类的影响

选取了4套cDNA芯片数据,分别运用补零和K近邻的方法,对有检测缺失的基因进行了补缺失值处理,分析了不同处理对支持向量机、K近邻分类器、决策树三种分类器分类效能的影响.结果显示: 在cDNA基因表达谱数据中,对检测缺失率不高于5%的基因补缺失值是一种较好的策略,这样可以保留较多的基因供后续的功能分析,同时仍然能够保持很高的疾病分类效能.     

Partial discharge pattern classification using the fuzzy decision tree approach

Partial discharge (PD) measurement is a proven flaw detection technique for finding cavities that are defects in the insulating material. In this paper, a novel approach for the classification of cavity sizes, based on their maximum PD charge transfer-applied voltage (/spl Delta/Q-V) characteristics using a fuzzy decision tree system, is proposed. The (/spl Delta/Q-V) partial discharge patterns for different cavity sizes are represented by features extracted from their pulse shapes, and the classification rules are directly extracted from the data using the decision tree. The decision rules obtained from the decision tree are then converted to the fuzzy IF-then rules, and the back-propagation algorithm is utilized to tune the parameters of the membership functions employed in the fuzzy classifier. The neuro-fuzzy classification technique is shown to provide successful classification of void sizes in an easily interpretive fashion.     

冷壁面霜层生长的支持向量机模型

针对结霜过程因具有明显的非线性特征,采用传统方法难以精确预测的问题。建立了基于支持向量机的冷壁面霜成生长的预测模型,应用实验数据对模型进行验证、评估,并与基于最小二乘法的非线性多元回归模型进行了对比、分析。结果表明,基于支持向量机的预测模型能够很好的解决非线性预测问题。在已建立的预测模型基础上,以霜层生长过程中传热率预测为例,分别在测试集中的自变量与因变量加入不同噪声信号对模型预测性能影响进行了研究。结果表明,基于支持向量机的模型具有良好的抗干扰能力。     

基于Bagging算法构造强分类器的one class SVM导线舞动预测应用

考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方法,实现了数据的随机抽样,分成不同组数据集进行相互独立的训练,避免对舞动数据过拟合,提升机器学习算法的抗噪声能力以及泛化能力,采用k折交叉验证算法进行模型的验证,并利用F1-score描述导线舞动预警模型的性能,验证了该方法在舞动预测方面的有效性。     

基于支持向量机的现地地震预警地震动峰值预测

以0.1-10 Hz带通滤波后三分向矢量合成地震动峰值PGA 与PGV 为预测目标参数,利用日本K-net 强震台网P波触发后3 s数据,基于人工智能中的经典机器学习方法-支持向量机,选取加速度幅值Pa、速度幅值Pv、位移幅值Pd、傅里叶谱幅值AMmax、速度平方积分IV2、破坏烈度DI、累积绝对速度CAV、阿里亚斯烈...     

Learning user preferences of route choice behaviour for adaptive route guidance

As the use of navigation systems becomes more widespread, the demand for advanced functions of navigation systems also increases. In the light of user satisfaction, personalisation of route guidance by incorporating user preferences is one of the most desired features. A user model applied to personalised route guidance is presented. The user model adaptively updates route selection rules when it discovers the predicted choice differs from the actual choice of the driver. This study employs a decision tree learning algorithm, the C4.5 algorithm, which has advantages over other data mining methods in terms of its comprehensible model structure. Simulation experiments with a real-world network were conducted to analyse the applicability of the model to adaptive route guidance and the accuracy of its prediction     

Warranty and discrete preventive maintenance

In this paper two machine learning algorithms, decision trees (DT) and Hamming clustering (HC), are compared in building approximate reliability expression (RE). The main idea is to employ a classification technique, trained on a restricted subset of data, to produce an estimate of the RE, which provides reasonably accurate values of the reliability. The experiments show that although both methods yield excellent predictions, the HC procedure achieves better results with respect to the DT algorithm.     

Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.     

基于全连接神经网络的压铸件质量预测算法

目的 针对复杂压铸制造过程中高精度监控和质量预测问题,构建全连接神经网络,以提高压铸件缺陷分类和预测的准确性及高效性。方法 提出了一种基于全连接神经网络的算法,用于压铸件的质量预测。以汽车发动机下缸体为研究对象,先通过压铸岛采集关键工艺数据,后通过异常值处理和数据归一化进行数据预处理,再采用最小冗余和最大相关性的启发式算法（MRMR）进行特征处理,选出对压铸件质量影响较大的5个参数,该算法以3个压射速度、真空度、动模流量为输入层参数,以铸件质量为输出层参数。最后确定该算法的结构及各个参数,进行模型的训练与构建,并与不同算法进行性能比较。结果 与传统的决策树、SVM算法相比,该算法在相同数据集的分类和预测性能方面均更优,表明全连接神经网络在预测压铸缺陷方面具有优势。结论 该算法在实际应用中具有很大的潜力,证明全连接神经网络在预测能力和精度方面具有优势,可以为数据分类和预测提供更好的解决方案。     

基于混合鸡群算法和核极端学习机的锅炉NOx排放的预测

以某300MW亚临界循环流化床锅炉为研究对象,对锅炉的NOx排放量进行预测。利用模拟退火混合鸡群算法(SACSO)和核极端学习机(KELM)对不同工况下NOx的排放量进行建模;对比了差分进化算法,粒子群算法和原始鸡群算法,证明了改进后算法的优越性;之后,又对传统BP算法,支持向量机,极端学习机和核极端学习机模型进行对比;最终确定的SACSO-KELM模型具有更高的预测精度和稳定性以及更好的泛化能力,可选择将此模型用于锅炉NOx排放的建模预测。     

Cancers classification based on deep neural networks and emotional learning approach

In the present era, enormous factors contribute to causing cancer. So cancer classification cannot rely only on doctor''s thoughts. As a result, intelligent algorithms concerning doctor''s help are inevitable. Therefore, the authors are motivated to suggest a novel algorithm to classify three cancer datasets; colon, ALL‐AML, and leukaemia cancers. Their proposed algorithm is based on the deep neural network and emotional learning process. First of all, by applying the principal component analysis, they had a feature reduction. Then, they used deep neural as a feature extraction. Then, they implemented different classifiers; multi‐layer perceptron, support vector machine (SVM), decision tree, and Gaussian mixture model. In the end, because in the real world, especially when working on systems biology, unpredictable events, and uncertainties are undeniable, the robustness of their model against uncertainties is important. So they added Gaussian noise to the input features of the first encoder in each dataset, then, they applied the stacked denoising method. Experimental results disclosed that, generally, using emotional learning increased the accuracy. In addition, the highest accuracy was gained by SVM, 91.66, 92.27, and 96.56% for colon, ALL‐AML, and leukaemia, respectively. However, GMM led to the lowest accuracy. The best accuracy gained by GMM was 60%.Inspec keywords: cancer, learning (artificial intelligence), principal component analysis, multilayer perceptrons, feature extraction, support vector machines, pattern classification, Gaussian processes, decision trees, Gaussian noise, medical computingOther keywords: colon cancer, Gaussian noise, stacked denoising method, SVM, support vector machine, emotional learning process, cancer datasets, intelligent algorithms, cancer classification, ALL‐AML, input features, Gaussian mixture model, decision tree, multilayer perceptron, feature extraction, feature reduction, principal component analysis, deep neural network, leukaemia cancers     

Hunting for "key residues" in the modeling of globular protein folding: an artificial neural network-based approach

An approach to modeling globular protein folding based on artificial neural networks (ANNs) is presented. This approach, that can be regarded as an inverse protein folding problem, investigates whether and when a protein fragment needs a specific residue in the center of its primary structure as a necessary condition to fold as observed. To perform this analysis, an ANN has been trained on a set of 55 proteins, searching for a relation between protein fragments modeled by 13/spl alpha/ torsion angles and the residue corresponding to the central /spl alpha/ torsion angle of the fragment. The results obtained show that only Asp, Gly, Pro, Ser and Val residues are often a necessary, even though not sufficient, condition to obtain a specific folded fragment structure, playing therefore, the role of "key residue" of this fragment.     

一种新型回归支持向量机的学习算法

支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种具有很好泛化性能的回归方法,本文对标准支持向量机稍作改动,提出了一种新型回归支持向量机,并推导出它的对偶表达方式,随后利用一个优化定理设计了一个多变量更新学习算法,该算法能单调收敛于极值点,并具有简单的迭代方式,仿真实例说明所提出的回归支持向量机及其训练算法具有较好的学习性能.