An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

Big news stories and longitudinal data collection: A prominent child sexual abuse case negatively affects parents’ attitudes toward male caregivers

Consumption of news media can influence attitudes toward specific groups, but the influence of news media on longitudinal data collection has not yet been researched. We present a method to index media attention on a specific topic, as well as a case study on a big child sexual abuse (CSA) story and its effect on parents’ attitudes toward male childcare professionals in a longitudinal study with fathers and mothers of 207 Dutch families. Questionnaire data on attitudes toward gender-differentiated parenting were collected in four annual waves between 2010 and 2014. NexisUni® Academic database was used to index articles on CSA to chart patterns of media attention before and during that time span. There was an immediate increase in media attention, the amount of articles on CSA doubled, as well as a prolonged increase in attention which culminated during the second wave of the study. In the first wave, 97 of the families participated before the CSA case became known, and 110 participated afterward. Parents who participated after the first news about the case came out reported a more negative attitude toward hiring a male babysitter than those who participated before it. This effect was stronger for mothers. The negative effect on attitude endured during the subsequent waves for all fathers and for those mothers who participated before the news broke. Findings indicate that big news stories influence attitudes that lasts over time and can therefore influence longitudinal data. Further analysis suggests that the influence of news stories is gendered, as mothers showed a recovery in their attitudes over time while fathers did not. We recommend further research on the effect of news on attitude and behavioral measures in longitudinal research.

A control chart pattern recognition methodology for controlling information technology-enabled service (ITeS) process customer complaints

PurposeThe purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.Design/methodology/approachA two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.FindingsThe validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.Practical implicationsThis study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.Originality/valueThe application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

Monitoring manufacturing process variation remains challenging, especially within a rapid and automated manufacturing environment. Problematic and unstable processes may produce distinct time series patterns that could be associated with assignable causes for diagnosis purpose. Various machine learning classification techniques such as artificial neural network (ANN), classification and regression tree (CART), and fuzzy inference system have been proposed to enhance the capability of traditional Shewhart control chart for process monitoring and diagnosis. ANN classifiers are often opaque to the user with limited interpretability on the classification procedures. However, fuzzy inference system and CART are more transparent, and the internal steps are more comprehensible to users. There have been limited works comparing these two techniques in the control chart pattern recognition (CCPR) domain. As such, the aim of this paper is to demonstrate the development of fuzzy heuristics and CART technique for CCPR and compare their classification performance. The results show the heuristics Mamdani fuzzy classifier performed well in classification accuracy (95.76%) but slightly lower compared to CART classifier (98.58%). This study opens opportunities for deeper investigation and provides a useful revisit to promote more studies into explainable artificial intelligence (XAI).

Working with Comparative Data

Chapter 4 develops further the methodological principles sketched in this introduction. The foundation of the subject is comparison of religious practices in different geographical areas, which alows us to chart patterns of both similarities and variations. Ths may sometimes allow to infer influence, though it is much harder to prove borrowing than has sometimes been assumed. In fact, comparison has other functions as well, not least that it allows us to understand what is distinctive about different cultures. In the last section I illustrate my approach with some examples from the history of the subject.

Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action.

Mining subsequent trend patterns from financial time series

Chart patterns are one of the important tools used by the financial analysts for predicting future price trends (subsequent trends) in stock markets. Although many works related to the descriptions of chart patterns and several effective methods to identify chart patterns from the financial time series have been proposed in recent years, there is no in-depth study about the general characteristics of the subsequent trends. In this paper, we proposed a general framework for mining subsequent trend for chart patterns. We extensively analyze the characteristics of subsequent trends of chart patterns found with the proposed framework. Based on the analysis, we propose a concept called subsequent trend pattern by mining frequently occurring shapes from these trends. The process of subsequent trend pattern mining was evaluated on a dataset containing 502 time series from S&P 500 and a test dataset containing 494 stocks from Yahoo finance. The proposed concept of subsequent trend pattern provides a solid foundation for the understanding of chart patterns in predicting future price movement and extends the formal definition of chart patterns.