One-sided Downward Control Chart for Monitoring the Multivariate Coefficient of Variation with VSSI Strategy

In recent years, control charts monitoring the coefficient of variation (CV), denoted as the ratio of the variance to the mean, is attracting significant attention due to its ability to monitor processes in which the process mean and process variance are not independent of each other. However, very few studies have been done on charts to monitor downward process shifts, which is important since downward process shifts show process improvement. In view of the importance of today's competitive manufacturing environment, this paper proposes a one-sided chart to monitor the downward multivariate CV (MCV) with variable sample size and sampling interval (VSSI), i.e. the VSSID MCV chart. This paper monitors the MCV as most industrial processes simultaneously monitor at least two or more quality characteristics, while the VSSI feature is incorporated, as it is shown that this feature brings about a significant improvement of the chart. A Markov chain approach was adopted for designing a performance measure of the proposed chart. The numerical comparison revealed that the proposed chart outperformed existing MCV charts. The implementation of the VSSID MCV chart is illustrated with an example

Adaptive Control Charts For Monitoring The Univariate And Multivariate Coefficient Of Variation

The first objective of this research is to propose a univariate adaptive CV chart using the variable sample size and sampling interval (VSSI) approach, called the VSSI CV chart, to monitor the process CV. The VSSI CV chart will be optimally designed, where two parameters, namely the sample size and sampling intervals are allowed to vary. In real life scenarios, there are many situations in which a simultaneous monitoring of two or more correlated quality characteristics is necessary. Erroneous conclusions will occur if quality practitioners use univariate control charts to monitor a multivariate process. The second objective of this study is to propose CV charts for monitoring the multivariate process CV (MCV) by adopting the adaptive procedures. Three new charts for monitoring the MCV, namely the variable sampling interval MCV (by varying the sampling interval), variable sample size MCV (by varying the sample size) and VSSI MCV (by varying both the sample size and sampling interval) charts are proposed to improve the performance of the existing MCV chart. All the charts proposed for monitoring the univariate and multivariate CVs are designed using the Markov chain approach. The implementation procedures and optimization designs of these proposed charts are enumerated in this xxiv thesis

New Hybrid Data Preprocessing Technique for Highly Imbalanced Dataset

One of the most challenging problems in the real-world dataset is the rising numbers of imbalanced data. The fact that the ratio of the majorities is higher than the minorities will lead to misleading results as conventional machine learning algorithms were designed on the assumption of equal class distribution. The purpose of this study is to build a hybrid data preprocessing approach to deal with the class imbalance issue by applying resampling approaches and CSL for fraud detection using a real-world dataset. The proposed hybrid approach consists of two steps in which the first step is to compare several resampling approaches to find the optimum technique with the highest performance in the validation set. While the second method used CSL with optimal weight ratio on the resampled data from the first step. The hybrid technique was found to have a positive impact of 0.987, 0.974, 0.847, 0.853 F2-measure for RF, DT, XGBOOST and LGBM, respectively. Additionally, relative to the conventional methods, it obtained the highest performance for prediction

Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms

We can’t escape the fact that using telecommunications has become a significant part of our everyday lives. Since the Covid-19 pandemic, the telecommunication industry has become crucial.  Hence, the industry now enjoys growth opportunities. In this study, KNN, Random Forest (RF), AdaBoost, Logistic Regression (LR), XGBoost, and Support Vector Machine (SVM) are 6 supervised machine learning algorithms that will be used in this study to predict the customer churn of a telecom company in California. The goal of this study is to identify the classifier that predicts customer churn the most effectively. As evidenced by its accuracy of 79.67%, precision of 64.67%, recall of 51.87%, and F1-score of 57.57%, XGBoost is the overall most effective classifier in this study. Next, the purpose of this study is to identify the characteristics of customers who are most likely to leave the telecom company. These characteristics were discovered based on customers’ demographics and account information. Lastly, this study also provides the company with advice on how to retain customers. The study advises company to personalize the customer experience, implement a customer loyalty program, and apply AI in customer relationship management in retaining customers

Variable Sample Size Control Charts for Monitoring the Multivariate Coefficient of Variation Based on Median Run Length and Expected Median Run Length

The monitoring of a well-functioning process system has always held significant importance. In recent times, there has been notable attention towards employing control charts to oversee both univariate and multivariate coefficients of variation (MCV). This shift is in response to the concern of erroneous outcomes that can arise when traditional control charts are applied under the condition of dependent mean and standard deviation, as highlighted by prior research. To address this, the remedy lies in adopting the coefficient of variation. Furthermore, this study underscores the application of MCV in scenarios where multiple quality attributes are simultaneously under surveillance within an industrial process. This aspect has demonstrated considerable enhancement in chart performance, especially when incorporating the variable sample size (VSS) feature into the MCV chart. Adaptive VSS, evaluated through metrics like median run length (MRL) and expected median run length (EMRL), is also integrated for MCV monitoring. In contrast to earlier studies that predominantly focused on average run length (ARL), this research acknowledges the potential inaccuracies in ARL measurement. In this study, two optimal designs for VSS MCV charts are formulated by minimizing two criteria: firstly, MRL; and secondly, EMRL, both accounting for deterministic and unknown shift sizes. Additionally, to assess the distribution's variability in run lengths, the study provides the 5th and 95th percentiles. The research delves into two VSS schemes: one with a defined small sample size (nS), and another with a predetermined large sample size (nL) for the initial subgroup (n(1)). The approach taken involves the development of a Markov chain method for designing and deriving performance measures of the proposed chart. These measures include MRL and EMRL. Moreover, a comparative analysis between the proposed chart's performance and the standard MCV chart (STD) is presented in terms of MRL and EMRL criteria. The outcomes illustrate the superiority of the proposed chart over the STD MCV chart for all shift sizes, whether they are upward or downward, and when n(1) equals nS or nL

Benchmarking electric power companies’ sustainability and circular economy behaviors : using a hybrid PLS-SEM and MCDM approach

This research examines the impact of firms’ decision-making, crisis management, and risk-taking behaviors on their sustainability and circular economy behaviors through the mediating role of their eco-innovation behavior in the energy industry in Iraq. Firms are exploring applicable mechanisms to increase green practices. This requires the industry to possess the essential skills to overcome the challenges that reduce sustainable activities. We applied a dual-stage structural equation modeling (PLS-SEM) and a multi-criteria decision-making (MCDM) approach to explore the linear relationships between variables, determine the weight of the criteria, and rank energy companies based on a circular economy. The online questionnaire was sent to 549 managers and heads of departments of Iraqi electric power companies. Out of these, 384 questionnaires were collected. The results indicate that firms’ crisis management, decision-making, and risk-taking behaviors are significantly and positively linked to their eco-innovation behavior. This study confirms the significant and positive impact of firms’ eco-innovation behavior on their sustainability and circular economy behaviors. Likewise, eco-innovation behavior has a fully mediating role. For the MCDM methods, ranking energy companies according to the circular economy can support policymakers’ decisions to renew contracts with leading companies in the ranking. Practitioners can also impose government regulations on low-ranked companies. Thus, governments can reduce the problems of greenhouse gas emissions and other environmental pollution.peer-reviewe

One-sided Downward Control Chart for Monitoring the Multivariate Coefficient of Variation with VSSI Strategy

In recent years, control charts monitoring the coefficient of variation (CV), denoted as the ratio of the variance to the mean, is attracting significant attention due to its ability to monitor processes in which the process mean and process variance are not independent of each other. However, very few studies have been done on charts to monitor downward process shifts, which is important since downward process shifts show process improvement. In view of the importance of today's competitive manufacturing environment, this paper proposes a one-sided chart to monitor the downward multivariate CV (MCV) with variable sample size and sampling interval (VSSI), i.e. the VSSID MCV chart. This paper monitors the MCV as most industrial processes simultaneously monitor at least two or more quality characteristics, while the VSSI feature is incorporated, as it is shown that this feature brings about a significant improvement of the chart. A Markov chain approach was adopted for designing a performance measure of the proposed chart. The numerical comparison revealed that the proposed chart outperformed existing MCV charts. The implementation of the VSSID MCV chart is illustrated with an example

Neural Architecture Search for Lightweight Neural Network in Food Recognition

Healthy eating is an essential element to prevent obesity that will lead to chronic diseases. Despite numerous efforts to promote the awareness of healthy food consumption, the obesity rate has been increased in the past few years. An automated food recognition system is needed to serve as a fundamental source of information for promoting a balanced diet and assisting users to understand their meal consumption. In this paper, we propose a novel Lightweight Neural Architecture Search (LNAS) model to self-generate a thin Convolutional Neural Network (CNN) that can be executed on mobile devices with limited processing power. LNAS has a sophisticated search space and modern search strategy to design a child model with reinforcement learning. Extensive experiments have been conducted to evaluate the model generated by LNAS, namely LNAS-NET. The experimental result shows that the proposed LNAS-NET outperformed the state-of-the-art lightweight models in terms of training speed and accuracy metric. Those experiments indicate the effectiveness of LNAS without sacrificing the model performance. It provides a good direction to move toward the era of AutoML and mobile-friendly neural model design

New adaptive control charts for monitoring the multivariate coefficient of variation

International audienc