Recognizing yield patterns through hybrid applications of machine learning techniques

Yield management in semiconductor manufacturing companies requires accurate yield prediction and continual control. However, because many factors are complexly involved in the production of semiconductors, manufacturers or engineers have a hard time managing the yield precisely. Intelligent tools need to analyze the multiple process variables concerned and to predict the production yield effectively. This paper devises a hybrid method of incorporating machine learning techniques together to detect high and low yields in semiconductor manufacturing. The hybrid method has strong applicative advantages in manufacturing situations, where the control of a variety of process variables is interrelated. In real applications, the hybrid method provides a more accurate yield prediction than other methods that have been used. With this method, the company can achieve a higher yield rate by preventing low-yield lots in advance.     

Minimum variance embedded auto-associative kernel extreme learning machine for one-class classification

Neural Computing and Applications - One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was...     

Dropout prediction in e-learning courses through the combination of machine learning techniques

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature.     

工业控制系统中基于单类支持向量机异常检测方法研究

Modbus TCP/IP协议作为工业控制系统中常用的通信协议,存在其自身的脆弱性。文章主要研究了Modbus TCP/IP协议的异常检测方法,首先介绍了基于单类支持向量机的异常检测模型的实现过程,对单类支持向量机选择不同的滑动窗口长度和核函数进行测试,设计与传统支持向量机、标准RBF算法、BP神经网络、异常检测模型的对比实验,并对实验结果进行分析。还设计了与基于功能码序列的异常检测模型的对比实验,验证选取功能码和寄存器地址组合对作为特征的优越性。     

Thermal infrared imaging based breast cancer diagnosis using machine learning techniques

The human’s temperature is little known and important to the diagnosis of diseases, according to most researchers and health workers.In ancient medicine, doctors may treat patients with wet mud or slurry clay. The part that would dry up first was considered the diseased part when either of these spread over the body. This can be done today with thermal cameras generating pictures with electromagnetic frequencies. Inflammation and blockage areas that predict cancer without radiation or touch may be detected by thermography. It can be used before any visible symptoms occur as a great advantage in medical testing. Machine learning (ML) is used in this paper as statistical techniques to give software programs the capacity to learn from information without being directly coded. ML can help to do so by learning these thermal scans and identifying suspected areas where a doctor needs to research more. Thermal photography is a comparatively better alternative to other methods that need sophisticated equipment, enabling machines to provide an easier and more effective approach to clinics and hospitals.

     

基于改进单类支持向量机的工业控制网络入侵检测方法

针对单类支持向量机（OCSVM）入侵检测方法无法检测内部异常点和离群点导致决策函数偏离训练样本的问题,提出了一种结合具有噪声的密度聚类（DBSCAN）方法和K-means方法的OCSVM异常入侵检测算法。首先通过DBSCAN算法,剔除训练数据中的离群点,消除离群点的影响;然后利用K-means划分数据类簇的方法筛选出内部异常点;最后利用OCSVM算法为每一个类簇建立单分类器用于检测异常数据。工控网络数据集上的实验结果表明,该组合分类器能够利用无异常数据样本检测出工控网络入侵,并且提高了OCSVM方法的检测效果。在气体管道网络数据集入侵检测实验中,所提方法的总体检测率为91.81%;而原始OCSVM算法则为80.77%。     

Energy inefficiency diagnosis for Android applications: a literature review

Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.     

Fault detection and diagnosis in process data using one-class support vector machines

In this paper, a new approach for fault detection and diagnosis based on One-Class Support Vector Machines (1-class SVM) has been proposed. The approach is based on a non-linear distance metric measured in a feature space. Just as in principal components analysis (PCA) and dynamic principal components analysis (DPCA), appropriate distance metrics and thresholds have been developed for fault detection. Fault diagnosis is then carried out using the SVM-recursive feature elimination (SVM-RFE) feature selection method. The efficacy of this method is demonstrated by applying it on the benchmark Tennessee Eastman problem and on an industrial real-time Semiconductor etch process dataset. The algorithm has been compared with conventional techniques such as PCA and DPCA in terms of performance measures such as false alarm rates, detection latency and fault detection rates. It is shown that the proposed algorithm outperformed PCA and DPCA both in terms of detection and diagnosis of faults.     

支持向量机在工业过程中的应用

支持向量机(SVM)是一种基于统计学习理论,针对小样本学习问题的通用学习算法,它采用结构风险最小化(Structural risk minimization,SRM)准则,大大提高了模型的泛化能力,成功地解决了神经网络的过学习问题。目前主要应用在模式识别领域,在工业过程中的应用相对较少。本文首先从理论研究、算法结构、参数选择和扩展SVM4个方面详细介绍了近些年来支持向量机的研究进展;然后对SVM在工业过程中的应用现状进行分析,指出进一步研究的方向。     

Application of machine learning techniques to analyse student interactions and improve the collaboration process

In e-learning environments that use the collaboration strategy, providing participants with a set of communication services may not be enough to ensure collaborative learning. It is thus necessary to analyse collaboration regularly and frequently. Using machine learning techniques is recommended when analysing environments where there are a large number of participants or where they control the collaboration process. This research studied two approaches that use machine learning techniques to analyse student collaboration in a long-term collaborative learning experience during the academic years 2006–2007, 2007–2008 and 2008–2009. The aims were to analyse collaboration during the collaboration process and that it should be domain independent. Accordingly, the intention was to be able to carry out the analysis regularly and frequently in different collaborative environments. One of the two approaches classifies students according to their collaboration using unsupervised machine learning techniques, clustering, while the other approach constructs metrics that provide information on collaboration using supervised learning techniques, decision trees. The research results suggest that collaboration can be analysed in this way, thus achieving the aims set out with two different machine learning techniques.     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Auto-adaptive robot-aided therapy using machine learning techniques

This paper presents an application of a classification method to adaptively and dynamically modify the therapy and real-time displays of a virtual reality system in accordance with the specific state of each patient using his/her physiological reactions. First, a theoretical background about several machine learning techniques for classification is presented. Then, nine machine learning techniques are compared in order to select the best candidate in terms of accuracy. Finally, first experimental results are presented to show that the therapy can be modulated in function of the patient state using machine learning classification techniques.     

A hybrid system for Parkinson’s disease diagnosis using machine learning techniques

International Journal of Speech Technology - Parkinson’s disease is a neurodegenerative disorder that progresses slowly and its symptoms appear over time, so its early diagnosis is not easy....     

基于ELM和连续过程神经网络的抽油机工况诊断

普通神经网络进行抽油机工况诊断时存在诊断精度偏低的问题,提出选用连续过程神经元网络作为诊断模型,特征输入选取能直接反映示功图几何形态特征的位移和载荷两种连续信号。为提高模型学习速度,提出过程神经网络的极限学习算法,将训练转换为最小二乘问题,根据样本输入计算隐层输出矩阵,使用SVD法求解Moore-Penrose广义逆,最后计算隐层输出权值。通过诊断实验,模型学习速度提升5倍左右,与普通神经网络进行对比,诊断精度提高8个百分点左右,验证了方法的有效性。     

Filtering artificial texts with statistical machine learning techniques

Fake content is flourishing on the Internet, ranging from basic random word salads to web scraping. Most of this fake content is generated for the purpose of nourishing fake web sites aimed at biasing search engine indexes: at the scale of a search engine, using automatically generated texts render such sites harder to detect than using copies of existing pages. In this paper, we present three methods aimed at distinguishing natural texts from artificially generated ones: the first method uses basic lexicometric features, the second one uses standard language models and the third one is based on a relative entropy measure which captures short range dependencies between words. Our experiments show that lexicometric features and language models are efficient to detect most generated texts, but fail to detect texts that are generated with high order Markov models. By comparison our relative entropy scoring algorithm, especially when trained on a large corpus, allows us to detect these “hard” text generators with a high degree of accuracy.     

Diagnosis of skin cancer using machine learning techniques

Generally, skin disease is a common one in human diseases. In computer vision application, the skin color is the powerful indication for this disease. This system identifies the skin cancer disease based on the images of skin. Initially, the skin is filtered using median filter and segmented using Mean shift segmentation. Segmented images are fed as input to feature extraction. GLCM, Moment Invariants and GLRLM features are extracted in this research work. The extracted features are classified by using classification techniques like Support vector machine, Probabilistic Neural Networks and Random forest and Combined SVM+ RF classifiers. Here combined SVM+RF classifier provided better results than other classifiers.     

Advanced machine learning techniques for microarray spot quality classification

It is well known that microarray printing, hybridization, and washing oftentimes create erroneous measurements, and these errors detrimentally impact machine microarray spot quality classification. Thus, it is crucial to identify and remove these errors if automation is to replace the still common practice of visually assessing spot quality, an extremely expensive and time-consuming procedure. A major problem in microarray spot quality classification methods proposed in the literature is the correlation among the features extracted from the spots. In this paper, we propose using a random subspace ensemble of neural networks and a feature selection algorithm to improve the performance of our microarray spot quality classification method. Our best method obtains an error under the receiver operating characteristic curve (EAUR) of 0.3 outperforming the stand-alone support vector machine EAUR of 1.7. The consistency of our proposed approach makes it a viable alternative to the labour-intensive manual method of spot quality assessment.     

Financial time series prediction using distributed machine learning techniques

Neural Computing and Applications - The financial time series is inherently nonlinear and hence cannot be efficiently predicted by using linear statistical methods such as regression. Hence,...