Application of Logistic Regression for Production Machinery Efficiency Evaluation

Borucka, Anna; Grzelak, Małgorzata

doi:10.3390/app9224770

Cited by 21 publications

(8 citation statements)

References 28 publications

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“…where X is a weighted sum of the input feature which is defined as (X = w 1 x 1 + w 2 x 2 + ... + w n x n ), and n is a number of input features. Now, the logit form of the logistic model can be obtained by the following formula [36]:…”

Section: Logistic Regressionmentioning

confidence: 99%

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Hossain¹,

Asadullah²,

Rahaman³

et al. 2021

Preprint

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In Southern Asia, Bangladesh is a well-known developing country. Because of COVID-19, we continuously face challenges. Not only can these issues occur beyond economic or health concerns, but they also generate dangerous social problems, such as family abuse. Since the inception of this epidemic, multiple social crimes are looming. Remaining home during the lockout period enhances divorce rates. This research presents a customized forecast of family violence during the COVID-19 outbreak by using machine learning methods. In this paper, we have applied Random Forest, Logistic Regression, and Naive Bayes machine learning classifiers to predict family violence and discovered the feature importance. The performance of the classifiers is evaluated based on accuracy, precision, recall, and F-score. We have employed an oversampling strategy named synthetic minority oversampling technique (SMOTE) to solve the imbalance problem of our data. Even, we have tried to compare three machine learning model performances before and after balancing of normalization data. Finally, ROC analyses and confusion matrices were developed and analyzed by using data augmentation. Our proposed system with the random forest classifier performed better with 77% accuracy in comparison with other two machine learning classifiers.

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Section: Logistic Regressionmentioning

confidence: 99%

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Hossain¹,

Asadullah²,

Rahaman³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Problems concerning classifications of output into classes are extensive in computational PM [24]. For instance, the classification of manufacturing variables [e.g., ambient temperature (Ta), sewage activity (Sw), machine health (Mh)] as leading to improvement of an observed industry is one common problem in predictive methodology.…”

Section: Logistic Regression Algorithmmentioning

confidence: 99%

Simulation for predictive maintenance using weighted training algorithms in machine learning

Jittawiriyanukoon¹,

Srisarkun²

2022

IJECE

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<span>In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artiﬁcial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classiﬁcation, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.</span>

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“…(Hoffman et al, 2007). The separate variables do not need to be regular multivariate in logistic regression models, but multivariate normality offers a more robust solution (Borucka & Grzelak, 2019). The error term of the logistic regression model does not need to be multivariate normally distributed (Gregor et al, 2018).…”

Section: Wherementioning

confidence: 99%

Effect of Ownership Diversity on Financial Distress: Evidence from Colombo Stock Exchange

Bogamuwa

Perera

2022

Int. J. Acct. Bus. Fin.

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Ownership diversity is defined as the distribution of ownership and control among various categories of shareholders. Many organizations' ownership structures have become increasingly diversified in terms of race, nationality, gender, and socioeconomic level in recent years. Globally, there's little consensus on how ownership diversity affects financial distress. Using the agency theory and entrenchment hypothesis, this study investigates how the diversity of ownership affects the financial distress of business firms. This research makes a contribution to the empirical literature by applying panel data analysis on 181 nonfinancial companies from 2012 to 2019 on the Colombo Stock Exchange of Sri Lanka. The study uses Herfindahl-Hirschman Index to measure ownership diversity. In contrast, Altman Z Score Analysis, Emerging Market Score, and Interest Coverage Ratio measure financial distress. The results of the logistic regression models demonstrate that ownership diversity significantly and positively affects financial distress. This signifies that the diversified ownership structure raises the agency cost as it incurs high monitoring costs to monitor diverse shareholders, leading to financial distress within business firms.

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Application of Logistic Regression for Production Machinery Efficiency Evaluation

Cited by 21 publications

References 28 publications

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Simulation for predictive maintenance using weighted training algorithms in machine learning

Effect of Ownership Diversity on Financial Distress: Evidence from Colombo Stock Exchange

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