scholarly journals Compliance with International Soft Law

2018 ◽  
Vol 9 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Michael D'Rosario ◽  
John Zeleznikow
Keyword(s):  
Neural Network ◽  
Legal System ◽  
Goodness Of Fit ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
System Structure ◽  
Soft Law ◽  
Network Modelling ◽  
Data Set

The present article considers the importance of legal system origin in compliance with ‘international soft law,' or normative provisions contained in non-binding texts. The study considers key economic and governance metrics on national acceptance an implementation of the first Basle accord. Employing a data set of 70 countries, the present study considers the role of market forces and bilateral and multi-lateral pressures on implementation of soft law. There is little known about the role of legal system structure-related variables as factors moderating the implementation of multi-lateral agreements and international soft law, such as the 1988 accord. The present study extends upon research within the extant literature by employing a novel estimation method, a neural network modelling technique, with multi-layer perceptron artificial neural network (MPANN). Consistent with earlier studies, the article identifies a significant and positive effect associated with democratic systems and the implementation of the Basle accord. However, extending upon traditional estimation techniques, the study identifies the significance of savings rates and government effectiveness in determining implementation. Notably, the method is able to achieve a superior goodness of fit and predictive accuracy in determining implementation.

Compliance with International Soft Law

2020 ◽  
pp. 49-64
Author(s):  
Michael D'Rosario ◽  
John Zeleznikow
Keyword(s):  
Neural Network ◽  
Legal System ◽  
Goodness Of Fit ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
System Structure ◽  
Soft Law ◽  
Network Modelling ◽  
Democratic Systems

The present article considers the importance of legal system origin in compliance with ‘international soft law,' or normative provisions contained in non-binding texts. The study considers key economic and governance metrics on national acceptance an implementation of the first Basle accord. Employing a data set of 70 countries, the present study considers the role of market forces and bilateral and multi-lateral pressures on implementation of soft law. There is little known about the role of legal system structure-related variables as factors moderating the implementation of multi-lateral agreements and international soft law, such as the 1988 accord. The present study extends upon research within the extant literature by employing a novel estimation method, a neural network modelling technique, with multi-layer perceptron artificial neural network (MPANN). Consistent with earlier studies, the article identifies a significant and positive effect associated with democratic systems and the implementation of the Basle accord. However, extending upon traditional estimation techniques, the study identifies the significance of savings rates and government effectiveness in determining implementation. Notably, the method is able to achieve a superior goodness of fit and predictive accuracy in determining implementation.

scholarly journals Predicting Credit Rating Migration Employing Neural Network Models

2020 ◽  
pp. 65-82
Author(s):  
Michael D'Rosario ◽  
Calvin Hsieh
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Credit Rating ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
Network Models ◽  
Legal Framework ◽  
Neural Network Models ◽  
Network Modelling ◽  
Extant Literature

Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

scholarly journals Predicting Credit Rating Migration Employing Neural Network Models

2018 ◽  
Vol 9 (4) ◽  
pp. 70-85
Author(s):  
Michael D'Rosario ◽  
Calvin Hsieh
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Credit Rating ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
Network Models ◽  
Legal Framework ◽  
Neural Network Models ◽  
Network Modelling ◽  
Extant Literature

Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

scholarly journals Institution, Financial Sector, and Economic Growth: Use The Institutions As An Instrument Variable

2016 ◽  
Vol 17 (1) ◽  
pp. 84
Author(s):  
Albertus Girik Allo
Keyword(s):  
Economic Growth ◽  
Estimation Method ◽  
Political Stability ◽  
Financial Sector ◽  
Data Set ◽  
Institutional Variables ◽  
The World Bank ◽  
Control Of Corruption

Institution has been investigated having indirect role on economic growth. This paper aims to evaluate whether the quality of institution matters for economic growth. By applying institution as instrumental variable at Foreign Direct Investment (FDI), quality of institution significantly influence economic growth. This study applies two set of data period, namely 1985-2013 and 2000-2013, available online in the World Bank (WB). The first data set, 1985-2013 is used to estimate the role of financial sector on economic growth, focuses on 67 countries. The second data set, 2000-2013 determine the role of institution on financial sector and economic growth by applying 2SLS estimation method. We define institutional variables as set of indicators: Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability provide declining impact of FDI to economic growth.

scholarly journals Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination With Process Signals in Resistance Spot Welding of Advanced High-Strength Steels

2021 ◽  
Author(s):  
Bassel El-Sari ◽  
Max Biegler ◽  
Michael Rethmeier
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Predictive Accuracy ◽  
Training Data ◽  
Multi Layer Perceptron ◽  
Data Set ◽  
Artificial Neural

Resistance spot welding is an established joining process in the production of safety-relevant components in the automotive industry. Therefore, a consecutive process monitoring is essential to meet the high-quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals to ensure the individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set and the prediction of untrained data is challenging. The aim of this paper is to investigate the extrapolation capability of the multi-layer perceptron model. That means, that the predictive performance of the model will be tested with data that clearly differs from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the trained datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of the process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

scholarly journals Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1874
Author(s):  
Bassel El-Sari ◽  
Max Biegler ◽  
Michael Rethmeier
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Predictive Accuracy ◽  
Training Data ◽  
Multi Layer Perceptron ◽  
Data Set ◽  
Artificial Neural

Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

scholarly journals Prediction of the Bicarbonate Amount in Drinking Water in the Region of Médéa Using Artificial Neural Network Modelling

2020 ◽  
Vol 69 (11-12) ◽  
pp. 595-602
Author(s):  
Hichem Tahraoui ◽  
Abd Elmouneïm Belhadj ◽  
Adhya Eddine Hamitouche
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Mean Square ◽  
Ann Model ◽  
Network Modelling ◽  
Data Set ◽  
Artificial Neural

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of <i>R</i> = 0.99276 with root mean square error RMSE = 11.52613 mg dm<sup>–3</sup>. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region.

scholarly journals The Effect of Weighting Data on the Goodness of Fit Indicators of the Six Sigma Structural Equation Modeling

2021 ◽  
Vol 2 (2) ◽  
pp. 36-49
Author(s):  
Mohammed Al-Ghmadi ◽  
Ezz Abdelfattah ◽  
Ahmed Ezz
Keyword(s):  
Structural Equation Modeling ◽  
Covariance Matrix ◽  
Six Sigma ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Equation Modeling ◽  
Rank Test ◽  
Data Set ◽  
Variance Covariance Matrix

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators.  A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea.   The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.

scholarly journals Estimation of Weibull parameters using artificial neural network

2021 ◽  
Author(s):  
Md. Sujauddin Mallick
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Test Data ◽  
Mathematical Formulation ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
Data Set

Weibull distribution is an important distribution in the field of reliability. In this distribution usually there are two parameters. The usual parameter estimation method is maximum likelihood estimation. Maximum likelihood estimation requires mathematical formulation and prior assumption. Non parametric method such as neural network does not require prior assumption and mathematical formulation. They need data to formulate the model. In this report feed forward neural network with back propagation is used to estimate the parameters of a two-parameter Weibull distribution based on four Scenarios. The Scenario consists of training and test data set. Training and test data set generated through simulated time to failure events using wblrnd function in MATLAB. The input to the network is time to failure, and the output is shape and scale parameters. The network is trained and tested using trainbr algorithm in MATLAB. The network performed better on Scenario 2 which has the larger number of training examples of shape and scale.

The Innocuous Impact of Pan-European General Principles of Good Administration on Hungarian Law and Legal Practice

2020 ◽  
pp. 449-477
Author(s):  
Petra Lea Láncos
Keyword(s):  
Human Rights ◽  
Legal System ◽  
Case Law ◽  
Soft Law ◽  
Full Potential ◽  
Council Of Europe ◽  
Legislation And Jurisprudence ◽  
European Court ◽  
The Impact

This chapter discusses the influence of the pan-European principles of good administration in the Hungarian legal system. It discloses that while the impact and role of these pan-European principles, in particular that of the case law of the European Court of Human Rights, are growing in Hungarian legislation and jurisprudence, clear traces of them are still difficult to discern. It also finds that, despite some influence stemming from the Council of Europe (CoE) in the codification concepts underlying recent procedural reforms, the full potential to that effect is far from being realized. In particular, reliance on soft law instruments of the CoE remains problematic, in part due to legal formalism inherited from the country’s socialist past.

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