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This paper examines the redistributive effects of financial liberalization, including domestic and external finance reforms, implemented in 64 emerging and low-income countries over the past four decades. To identify these effects, we employ a "doubly robust" estimation approach and generate impulse responses using the local projections method. Our findings reveal that financial reforms significantly reduce income inequality. These results are robust and hold across various specifications and alternative methods. We find that reducing income inequalities through financial reforms depends on several factors, including improved access to financial services, level of public expenditures, and institutional quality. Furthermore, we demonstrate that governments adopting a reform approach that considers sequencing and potential complementarity of measures can significantly reduce income inequality. Taking the business cycle into account, we observe that implementing financial reforms during periods of relatively slower economic growth would be more beneficial for developing countries. Financial reforms have an impact on reducing income inequality by increasing the income of individuals located at the bottom of the distribution while decreasing the income of individuals located at the top of the distribution. [ABSTRACT FROM AUTHOR]

We present an empirical analysis of the relationship between trading permits, informal entrepreneurship and unemployment. We exploit a unique regulatory framework of the informal sector in South Africa to estimate the effects of trading permits in the informal sector using individual panel data. To provide a causal effect of these regulations, we apply a difference-in-difference strategy. Trading permits decreased informal entrepreneurship and informal trading by respectively 11.5 per cent and 9.6 per cent for those who were working in the informal sector. We investigate potential indirect effects on formal entrepreneurship, unemployment, and informal wage employment. The adoption of trading permits in the informal sector significantly increases the probability of being unemployed by 4.5 per cent and significantly decreases the probability of being informally employed by 15.5 per cent for those who were in the informal sector. The results suggest that a policy increasing the costs of informality might reduce informality but at the cost of rising unemployment. [ABSTRACT FROM AUTHOR]

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default hyperparameters fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many hyperparameters need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate hyperparameters' relevance using 94 classification datasets from OpenML. The experimental results point out that different hyperparameter profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the Irace. Finally, we found out that tuning a specific small subset of hyperparameters is a good alternative for achieving optimal predictive performance. [ABSTRACT FROM AUTHOR]

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predicting sunspot numbers presents ongoing challenges in forecasting, including non-stationary patterns and unclear fluctuation dynamics. This study compares traditional methods and hybrid models, incorporating machine learning techniques, to predict monthly mean sunspot numbers (MMSNs) from January 1, 1900, to December 31, 2022. Among the traditional methods, ARIMA(5,0,4) demonstrated performance with an MSE of 580.949, RMSE of 24.103, MAE of 17.19, and MAPE of 0.511. However, the proposed hybrid model, which combines ARIMA(5,0,4) with additive regression (AR) using Regression by Discretization (RegbyDisc) based on J48, achieved markedly superior forecasting accuracy with an MSE of 114.653, RMSE of 10.708, MAE of 6.441, and MAPE of 0.438. We employed this hybrid model to forecast sunspot numbers from January 2023 to December 2025. [ABSTRACT FROM AUTHOR]

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps. [ABSTRACT FROM AUTHOR]

Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of feature selection has been analyzed based on the accuracy of two classifiers: J48 and Naïve Bayes. The Airline Customer Satisfaction datasets have been used for comparing with and without applying Information Gain. As a result, J48 achieved 0.33% and 0.29% improvements in accuracy after applying Information Gain for 10-fold and 20-fold cross-validation, respectively compared to Naïve Bayes. Most of the precision and F1-score for J48 with Information Gain have also improved for both evaluation methods compared to Naïve Bayes. In conclusion, J48 seems to be the classifier that is most sensitive to feature selection and has shown improvements compared to Naïve Bayes.

Abstract Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

IEC TC10 is the most widely used Dissolved Gas Analysis (DGA) measurement dataset nowadays. Many DGA-based studies have been carried out using conventional methods and methods based on Artificial Intelligence Techniques (AITs). DGA is a diagnostic test performed on power transformers to detect and diagnose potential faults. The test involves analyzing the gases that are dissolved in the transformer oil, which can provide important information about the condition of the transformer. DGA is a widely used technique for transformer monitoring and maintenance in the power industry. However, this dataset is not perfect. There are still many problems in this dataset, one of which is the problem of missing value data. This problem will be significant if not appropriately handled. More reliable data from DGA measurement results is an in-dispensable reference in diagnosing faults in power transformers. This study focuses on dealing with the problem of missing value data using the Tertius algorithm, then testing the results using the J48 and Random Forest algorithms. The results obtained are pretty significant. Of the total 56 missing data, 36 could be predicted perfectly. And received the results of measuring accuracy using the J48 method of 62.73% and the Random Forest method of 70.71%. This result shows that the approach we applied is relatively good for handling missing values in IEC TC10 datasets.

Purpose: Following the COVID-19 outbreak, there are concerns whether economies are becoming farther from equality and competency. While this matters to every economy, it is more crucial for developing ones who already suffer from income inequalities and lack of competency. The purpose of this paper is to address this issue. Design/methodology/approach: This study uses an administrative data from the Iran's Social Security Organization (ISSO) that provides insurance to workers entitled to the Labor Law of Iran. The data contain more than 7,000,000 workers. The authors assess heterogeneous impact of the first wave of the pandemic by firms' size and average payment. Findings: The authors' estimation results indicate that, following the initiation of the pandemic, the workers whose corresponding firms are smaller, overall, are more prone to the pandemic and are more likely to submit a request for unemployment benefits. However, the relation is neither homogeneous across sectors nor linear among micro-sized firms. Few sectors indicate a positive relationship between size and likelihood of request submission, including cultural activity, shoemaking and clothing sectors. Besides the size, the authors investigate whether pay grades could explain the probability of becoming unemployed after the pandemic. Results show that workers whose corresponding firms pay less are more likely to submit a request. This is robust within different sectors. Research limitations/implications: The ISSO dataset is not a panel, so the authors cannot employ methods of causal inferences. The authors' results should be seen as correlation; however, due to exogeneity and sharpness of the pandemic the result infers to some degree of causality. The data does not cover the informal sector, so the estimates are at lower boundary. Originality/value: Administrative data on unemployment benefits during COVID-19 show that the pandemic interferes with competition by forcing low-paid workers and small firms to exit the market. This is an alarm for the competition in every economy, specially developing ones. [ABSTRACT FROM AUTHOR]

We examine how the decentralization of public employment services (PES) affects the behavior and service provision of PES offices and the labor market outcomes of job seekers. We use difference-in-differences, utilizing a Finnish temporary reform during which PES were decentralized for specific groups of job seekers in treated municipalities and remained centralized for others. The reform presented the treated municipalities with the possibility of shifting costs to the central government. We find no evidence of better labor market outcomes and find evidence consistent with municipalities being able to shift 10% of their unemployment benefit costs to the central government. [ABSTRACT FROM AUTHOR]

Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s result indicates that the J48 classifier with LDA achieves the highest performance with 96.0001% accuracy. [ABSTRACT FROM AUTHOR]

Wilson's Disease (WD) is a rare, autosomal recessive disorder caused by excessive accumulation of Copper (Cu) in various human organs such as the liver, brain, and eyes. Accurate WD diagnosis is challenging because of: (1) subtle intensity variations in infected tissues, and (2) Biased training results in case of a small and imbalanced dataset. This study provides a novel WD classification model for a small MRI dataset (3072 scans). The proposed study explores multi-dimensional Gabor kernels in five scales and eight orientations to produce pixel-specific features and process them in the 4th-order tensor format. The tucker decomposition technique is applied to obtain approximate factors from the Gabor tensors set. Five-fold cross-validation results show that the proposed classification model achieves 99.91% classification accuracy which is better than four well-known feature extraction techniques: (1) 2D-Discrete Wavelet Transform, (2) Intensity histograms, (3) Histogram of oriented gradients, and (4) Grey level co-occurrence matrix. Also, our method improves the classification accuracy by an average of 33% and Area Under the Curve (AUC) by 25% over the above-mentioned feature extraction techniques. In the latter category, the performance of the proposed method is compared with three deep learning models: (1) Customized Convolution Neural Network (CCNN), (2) AlexNet, and (3) VGGNet. In addition, it enhances classification accuracy by 10%, 3.5%, and 3%, compared to CCNN, AlexNet, and VGGNet, respectively. Also, our proposed approach is computationally fast compared to discussed feature extraction techniques. [ABSTRACT FROM AUTHOR]

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features. [ABSTRACT FROM AUTHOR]

This study aims to classify students as successful and unsuccessful regarding mathematical literacy on Programme for International Student Assessment (PISA) 2015 database through data mining methods. The sample consists of all Turkish students who participated in PISA 2015. While data mining methods such as Support Vector Machine, Multi-Layer Perceptron, and J48 were used in data analysis, the data set was evaluated with 10-fold Crossvalidation. The evaluation criteria included F-measure, Precision, Recall, Matthews Correlation Coefficient, and Receiver Operating Characteristic (ROC). In the classification of successful and unsuccessful students, analyses were conducted with 13 statistically significant variables according to Chi-SquareAttributedEval, GainRatioAttributeEval, and InfoGainAttributeEval methods. The results showed that the most important variables for classifying successful and unsuccessful students were learning time per week in total, and father's education level. The highest ROC value was 0.720. When comparing the precision values, the lowest classification value for the Multilayer Perceptron method was 0.645. There was no single method that performed best for all criteria. Researchers should use at least two methods to obtain more accurate results. [ABSTRACT FROM AUTHOR]

Copyright of Sozialer Fortschritt is the property of Duncker & Humblot GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset containing a large number of features may degrade machine learning’s detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is complicated. This study proposes the Boruta algorithm as a suitable approach to achieve accuracy in identifying the relevant features. To evaluate the Boruta algorithm, multiple classifiers (J48, random forest, naïve bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the Boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers.

In the field of goods production, demand prediction is important. By doing sales predictions, companies can make calculations and forecasts for what raw materials are mostly ordered. J48 and Naïve Bayes algorithm are two popular machine learning technique. By using these two algorithms, this study aims to develop an accurate and more reliable predictive model that help the company to make data driven decision. This study focuses on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm. This research conducted 4 times testing on each algorithm. This study produces high accuracy values with the Naïve Bayes and J48 algorithms. Both algorithm results have a fairly high accuracy value of 94% for Naïve Bayes and 98% for J48. The findings of this study implicate that by using J48 and Naïve Bayes algorithm, company can make informed decisions lead to improved operational efficiency, cost-effective, and resource utilization. [ABSTRACT FROM AUTHOR]

Fluid - Level measurement is required in recognizing the state variable of a Level processing plant for monitoring the level deviations in most the industrial plants. This issue has mostly been addressed by conventional methods using level sensors and level transmitters. However, the cost associated with these mechanisms can be reduced. It has been identified the chances with the applications machine learning approach in this scenario. Such a solution first of its kind is proposed and tried in this article. Gabor feature selection is established with SMOTE (Synthetic Minority Oversampling Technique) to overcome the imbalanced data set to improve the accuracy performance using Weka software. This paper aims to construct a level predictor based on machine learning algorithms and to demonstrate the training and testing performance validated with more accuracy. At this juncture, we consider the fluid level images for analyzing and predicting the level measurement by building supervised models with selected classifiers like IBk, JRip, J48, and Random Forest machine learning algorithms. The java-implemented tool Weka is subjected to get the maximum accuracy of 76.4286% and weighted average ROC (Receiver Operator Characteristics) values of 0.903. Hence such smart measurements using Machine learning algorithms, part of Artificial intelligence provide us a vital role in measuring the level parameter in less time using image processing filters. [ABSTRACT FROM AUTHOR]

Copyright of Sozialer Fortschritt is the property of Duncker & Humblot GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The designation of enterprise zones is a place-based policy that seeks to revitalize economically blighted areas. The literature on place-based policies has found mixed results regarding their effects on local payroll employment. This paper examines the causal effects of five of New Jersey's Urban Enterprise Zones (UEZs) on local payroll employment: Bayonne, Gloucester City, New Brunswick, Roselle Borough, and The Wildwoods (Wildwood City, Wildwood Crest, North Wildwood, and West Wildwood). All were designated as UEZs by the state in the 2000s, and none have been previously evaluated in the academic literature. The program offers reduced local sales tax, tax credits for newly hired employees, subsidized unemployment insurance costs, worker training assistance, and tax-free purchases on capital equipment and facilities. A synthetic control approach is used with the industrial composition of local firms and poverty rate as the covariate group and no impact of UEZ status on local employment in the treatment periods of the five areas is found. These results suggest that enterprise zones may not be effective job creators for treated areas, particularly for those zones that were added long after the program's inception. [ABSTRACT FROM AUTHOR]