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The Implementation of machine learning Methods in Six Sigma Projects - A Literature Review 1

8th international Scientific-Technical conference (MANUFACTURING)

作者： Kolbusz, Paula Antosz, Katarzyna Rzeszow Univ Technol Sch Engn & Tech Sci Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland Rzeszow Univ Technol Fac Mech Engn & Aeronaut Al Powstancow Warszawy 12 PL-35959 Rzeszow Poland

ISBN: (数字)9783031564673

ISBN: (纸本)9783031564697;9783031564673

One of the methods used today to improve processes in companies is Six Sigma. Within the application of this philosophy, machine learning methods are increasingly employed to analyse established procedures. In this article, the aim is to examine the present status of the use of supervised machine learning methods in the management of production process optimisation projects based on the Six Sigma approach. The analysis is presented based on a relevant literature review of applied/proposed machine learning methods in Six Sigma implementation. The work carried out included a literature review based on a search of significant databases on the issues presented. The research methodology was carried out in the following phases: 1) Identifying data sources and keywords combination;2) Determining the term;3) Searching the relevant websites;4) Evaluating the review and summarizing the results of the searches carried out in the databases Scopus, Science Direct, Web of Science between 2017 and 2022. Keywords and their possible combinations were defined for each machine learning method in combination with the term "Six Sigma." As a result of the research, MLs used in Six Sigma projects have been identified and linked to several areas of industrial substitution. The analyses produce identified targets suitable for regression and classification methods.

关键词： Six Sigma machine learning Classification Regression

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How machine learning-based competitiveness indexes analysis may help us become a wealthy nation with improved living standards: Case study Mexico

How machine learning-based competitiveness indexes analysis ...

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2024 IEEE international conference on engineering Veracruz, ICEV 2024

作者： Ortigoza, Gerardo M. Castillo, William A. Hermida, Guillermo Ponce, Roberto I. Universidad Veracruzana Instituto de Ingeniería Veracruz Boca del Río Mexico Universidad Veracruzana Facultad de Ingeniería Veracruz Boca del Río Mexico

ISBN: (纸本)9798350391619

In this work we analyze the competitiveness data of the IMCO (Mexican Institute for competitiveness) 2003-2022, the ten different subindexes provide us with an overview of the evolution and performance of the Mexican nation. By using machine learning techniques, we examine the subindexes (indicators) that most significantly affect the overall competitiveness score. Once these indicators have been identified, simulations are performed to obtain 6-years projections. Two scenarios are accounted: in the first, nothing is done and the future is simply predicted based on the status quo. In the latter, however, certain actions are applied to the previously determined indicators in order to enhance them. Lastly, suggestions are made in accordance with the estimates found from the scenario simulation. © 2024 IEEE.

关键词： Adversarial machine learning

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The multi-class Stackelberg prediction game with least squares loss

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OPTIMIZATION AND engineering 2025年第2期26卷 939-963页

作者： Han, Shanheng Lin, Yangjun Wang, Jiaxin Zhang, Lei-Hong Soochow Univ Sch Math Sci Suzhou 215006 Jiangsu Peoples R China

The Stackelberg prediction game (SPG) is an effective model that formulates the strategic interaction between the learner and data generator in a competition situation in which the learner controls the predictive model while the data generator reacts on the learner's move. Recently, SPG has received increasing interests, especially, in the binary class Stackelberg prediction game with least squares loss (SPG-LS) as it was shown in Wang et al. (in: international conference on machine learning, 2022) that an & varepsilon;\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon $$\end{document} optimal solution can be computed in O(N/& varepsilon;)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O(N/\sqrt{\epsilon })$$\end{document} flops where N is the number of non-zeros in the data matrix. Concerning that many practical problems involve multi-class situation, in this paper, we extend the current SPG-LS model as well as its computational approach to the multi-class case. In particular, by relying on a special nonlinear transformation, we show that the multi-class SPG-LS can be equivalently transformed to a special unbalanced Procrustes problem, and we propose an efficient numerical approach based on the unbalanced Procrustes problem to approximately tackle the multi-class SPG-LS. We particularly introduce two methods: the self-consistent-field (SCF) iteration and the Riemannian trust-region method (RTR), and conduct on numerical experiments to demonstrate the performance of the multi-class SPG-LS on synthetic and real data. The existence of the Stackelberg equilibrium of SPG-LS is also discussed.

关键词： Stackelberg prediction game Multi-class learning Unbalanced Procrustes problem Self-consistent-field Riemannian trust-region method

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Comparison of the Effectiveness of machine learning Techniques for the Number of Road Traffic Injuries 9

Comparison of the Effectiveness of Machine Learning Techniqu...

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Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Puttamat, Wirawan Phaphan, Wikanda Jitpattanakul, Anuchit Pianpailoon, Chainarong Mekruksavanich, Sakorn Chaiyaphum Rajabhat University Faculty of Education Department of Mathematics Chaiyaphum36000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Faculty of Education Sakon Nakhon47000 Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE. © 2024 IEEE.

关键词： Multiple linear regression

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Bedi, Pradeep Goyal, S.B. Rajawat, Anand Singh Bhaladhare, Pawan Aggarwal, Alok Prasad, Ajay Computer Science and Engineering Galgotias University India Faculty of Information Technology City University Malaysia School of Computer Science and Engineering Sandip University Nashik India School of Computer Science University of Petroleum and Energy Studies Dehradun India

From the perspective of the Industry 4.0 paradigm, the machine learning (ML) discipline has had a significant influence on the manufacturing sector. The industry 4.0 concept promotes intelligent sensors, gadgets, and equipment to create technology infrastructure sectors that collect information constantly. By analyzing the obtained data, machine learning approaches allow actionable insight to boost industrial productivity without dramatically altering the necessary resources. Furthermore, the capacity of machine learning applications to provide actionable analytics has facilitated the detection of complex manufacturing trends and paved the path for an integrated intelligent process in various activities in the supply chain, including smart and constant inspection, preventative maintenance, quality enhancement, process optimization, supply chain advancement, and workflow scheduling. This paper aims to present recent advances in the field of quality inspection in Industry 4.0 and develop a framework for quality inspection that can be fully utilized in the Industry 4.0 context using adaptive bilateral filtering and Feature Correlated Auto encoder (FCA) machine learning technique. The suggested approach makes full use of information from all sources along the manufacturing chain. Therefore, it complies with quality management standards within the context of Industry 4.0. The suggested model makes use of corrective measures based on data patterns discovered through predictive analysis. Result analysis was shown on some pre-trained deep learning models such as ResNet18, Vgg19, Alexnet, Squeezenet, auto encoder, and FCA and observed that the proposed FCA(Feature Correlated Auto encoder) achieved a better result. © 2022 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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An Empirical Investigation into machine learning Approaches for Breast Cancer Classification 2

An Empirical Investigation into Machine Learning Approaches ...

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2nd IEEE international conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Duggal, Anoushka Muhuri, Samya Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798350354461

Breast cancer was diagnosed in 2.3 million individuals worldwide in 2022, and was the cause of 670,000 deaths. It ranks as the second most common cancer overall and the most frequently diagnosed cancer in women. Since survival is highly dependent on early detection, the development of computational models for diagnosis is crucial. Among the various models available, we have chosen to focus on three supervised machine learning models in this study: Support Vector machine (SVM), K-Nearest Neighbor (KNN), and Feed-forward Neural Networks (FFNN). Their roles in breast cancer detection are investigated. We have pre-processed the data using normalization and feature selection to enhance these models' performances. They are trained on real-life documented patient data to predict breast cancer. It is determined that the FFNN is more accurate than both SVM and KNN. This paper provides a comprehensive analysis of the various machine learning models, highlighting their positive and negative aspects in diagnosing breast cancer. The results underscore the potential of advanced neural networks in medical diagnostics and pave the way for further research and development in this domain. © 2024 IEEE.

关键词： Adversarial machine learning

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Mobile Application Development for Disease Diagnosis based on Symptoms using machine learning Techniques

Mobile Application Development for Disease Diagnosis based o...

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2022 international conference on machine learning and data engineering, icmlde 2022

作者： Sridhar, Anirudh Mawia, Ahmed Amutha, A.L. Department of Computational Intelligence SRMIST Chennai603023 India

In recent times most of the people die due to unavailability of proper diagnosis and medical services. In such cases the patient doesn't have quick access to medical services or face difficulties in using them. This could be very helpful in those remote regions where proper medical services are not available in hand or the patient is in need of immediate medical attention. Therefore, we created a self-assessment application which uses the power of machine learning to predict around 40 diseases, based on the symptoms of the disease. In self-assessment it is important that the prediction from the model stays fairly accurate. The existing machine learning techniques suffer from various drawbacks, including decreased accuracy, which is of primary importance in such cases. Our proposed system diagnoses diseases from the symptoms using the following steps. Firstly, data preprocessing was done and then the data was passed through various machine learning classification models, like K Nearest Neighbor, Support Vector machine, Decision Tree, Random Forest Classifier, Naïve Bayes and Artificial Neural Network. After performing hyper parameter optimization, the probability vectors of all the models are added and then divided by the number of models. The number of diseases to be shown on application screen depends on the threshold values. The predicted disease(s) is received from the model and they are sent back to the application, where they are shown to the user. Our model proves to be the best when compared to the existing models. © 2022 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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Predictive data Analysis: Leveraging RNN and LSTM Techniques for Time Series dataset 2

Predictive Data Analysis: Leveraging RNN and LSTM Techniques...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Agarwal, Harsh Mahajan, Ginika Shrotriya, Anita Shekhawat, Deepika Manipal University Jaipur India

Recurrent neural networks (RNNs) and long short-term memory (LSTM) models have demonstrated tremendous effectiveness in modeling time series data due to the ability they possess to capture temporal dependencies. Stock market data is an example of time series data. Stock price data is very volatile and dynamic therefore one of the most turbulent areas to invest in might be the stock market. The choice to buy or sell stocks is heavily influenced by external circumstances and statistical analysis of previous stock performance. Stock price index forecasting has been a major area of research for many years and many machine learning and deep learning methods have been proposed to simplify this hard process, but little success has been discovered so far. In this paper, the application of RNNs and LSTMs on a stock price dataset to predict future values is explored. We start with Recurrent neural networks (RNNs) to predict the values but one of the major challenges with it is the "vanishing gradient" problem, which makes it difficult for the network to learn long-term dependencies in the data. To overcome this long short-term memory (LSTM) was used which eliminates the vanishing gradient problem. data were normalized and divided into time steps to determine the relationship between past values and future values to make accurate predictions. The outcomes show that the MLS LSTM method significantly outperforms existing algorithms, achieving a prediction accuracy of 98.1% on the training data set and a 91.97% accuracy on the testing data set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

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Research on financial time series prediction based on deep learning 24

Research on financial time series prediction based on deep l...

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international conference on Modeling, Natural Language Processing and machine learning (CMNM)

作者： Li, Ruijia Univ Waterloo Fac Math Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9798400709760

In contrast to conventional machine learning approaches, deep neural networks excel at extracting relevant features and adeptly identifying complex, nonlinear interactions within datasets. This attribute renders them particularly advantageous for the task of forecasting financial time series data. This article selects the five-dimensional indicators of the Shanghai Composite Industrial Index from April 17, 2020 to April 15, 2022, including closing price, opening price, highest price, lowest price, and trading volume, and conducts empirical mode decomposition on these five-dimensional indicators. This article uses the CNN-LSTM model to predict financial time series and obtain images of true and predicted values. The results show that the CNN-LSTM model proposed in this article has smaller prediction errors and improves prediction performance.

关键词： Financial time series Deep learning CNN-LSTM

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Predicting Malware Classification and Family using machine learning: A Cuckoo Environment Approach with Automated Feature Selection 2

Predicting Malware Classification and Family using Machine L...

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2nd international conference on machine learning and data engineering, icmlde 2023

作者： Niveditha, S. Prianka, R.R. Sathya, K. Shreyanth, S. Subramani, Nandhagopal Deivasigamani, Balakrishnan Karthikeyan, S. Department of Biotechnology Rajalakshmi Engineering College Thandalam Tamil Nadu Chennai602105 India Department of AIML New Horizon College of Engineering Karnataka Bengaluru560103 India School of Computer Science and Engineering VIT Tamilnadu Vellore632014 India MTech in Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani333031 India KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Information Technology KSR Institute for Engineering and Technology India

Malware's increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model's performance. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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