Machine Learning Models to Identify the Risk of Modern Slavery in Brazilian Cities

Santos, Marlu da Silva; Ladeira, Marcelo; Erven, Gustavo Cordeiro Galvão Van; Silva, Gladston Luiz da

doi:10.1109/icmla.2019.00132

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Debt risk of local government financing platforms refers to the risk of debt default caused by the failure of local government financing platforms at all levels to pay the due principal and interest in full as agreed. At present, the biggest problem faced by local financing platforms is the mismatch between expected cash outflows such as rigid principal and interest service and business expenses and poor cash inflows, which is the most fundamental cause of debt default of financing platforms [15].…”

Section: Risk Performance Of Government Debtmentioning

confidence: 99%

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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Government debt risk is an important factor affecting macroeconomic stability and public expectation. The key to its prevention and control lies in early warning and early prevention. This paper builds an effective government debt risk assessment system based on machine learning algorithm. According to forming the performance of local government debt risk and its internal and external influencing factors, this study applies the analytic hierarchy process, entropy method, and BP neural network method to construct the local government risk assessment index system, which includes the primary and secondary indexes including the explicit debt risk, the contingent implicit debt risk, and the financial and economic operation risk. Using this system, this study carries on the government debt risk comprehensive weight assignment, the fiscal revenue forecast, the default probability calculation, the safety scale forecast, and finally the government debt risk assessment of the validity analysis. The system can provide signal guidance and policy reference for finance to cope with risks in advance, arrange the priority order of debt repayment, optimize the structure of fiscal revenue and expenditure, etc.

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Section: Risk Performance Of Government Debtmentioning

confidence: 99%

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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“…As our training as well as testing data is imbalanced, only accuracy is not a good performance measure [34] that is why we select Area under Receiver Operating Characteristic curve (AUC) and Recall as our performance metric. In addition to these, we also report the results with other performance metrics which include Accuracy, Specificity, Precision, Geometric mean (G-mean) [49], F1 and F2 score [50]. AUC is calculated by the area under Receiver Operating curve which is defined in terms of true positive rate and false positive rate [51].…”

Section: Performance Measurementioning

confidence: 99%

A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax Using Class-Imbalanced Chest X-Rays Dataset

et al. 2022

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Pneumothorax, a life-threatening disease, needs to be diagnosed immediately and efficiently. The prognosis, in this case, is not only time-consuming but also prone to human errors. So, an automatic way of accurate diagnosis using chest X-rays is the utmost requirement. To date, most of the available medical image datasets have a class-imbalance (CI) issue. The main theme of this study is to solve this problem along with proposing an automated way of detecting pneumothorax. To find the optimal approach for CI problem, we first compare the existing approaches and find that under-bagging method (referred as data-levelensemble formed by creating subsets of majority class and then combining each subset with all samples of minority class) outperforms other existing approaches. After selection of best approach for CI problem, we propose a novel framework, named as VDV model, for pneumothorax detection from highly imbalance dataset. The proposed VDV model is a complex model-level ensemble of data-level-ensembles and uses three convolutional neural networks (CNN) including VGG16, VGG-19, and DenseNet-121 as fixed feature extractors. In each data-level-ensemble, features extracted from one of the pre-defined CNN architectures are fed to support vector machine (SVM) classifier, and output is calculated using the voting method. Once outputs from the three data-level-ensembles (corresponding to three different CNN architectures as feature extractor) are obtained, then, again, the voting method is used to calculate the final prediction. Our proposed framework is tested on the SIIM ACR Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For the first dataset, 85.17% Recall with 86.0% Area under the Receiver Operating Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall with 95.0% AUC is achieved with a random split of data while 85.45% recall with 77.06% AUC is obtained with a patient-wise split of data. The comparison of our results for both the datasets with related work proves the effectiveness of proposed VDV model for pneumothorax detection.

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“…The expressions for calculating accuracy, recall/ sensitivity, specificity and precision [129] are given below.…”

Section: Performance Metricsmentioning

confidence: 99%

“…The other performance measures can be defined with the help of a confusion matrix as shown in Table 3. The expressions for calculating accuracy, recall/sensitivity, specificity and precision [81] are given below.…”

Section: 𝑡𝑝𝑟 =mentioning

confidence: 99%

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Iqbal

Shaukat²,

Akram

et al. 2021

IEEE Access

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Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for the detection of thoracic pathologies. Remarkable research is being carried out to propose robust automatic diagnostic tools for the detection of pathologies from the chest radiographs. Artificial Intelligence techniques have been found to give promising results in automating the field of medicine. Lot of research has been done for automatic detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence techniques. Undoubtedly, several models are available for automatic diagnosis of pneumothorax, however a summarized review of the existing literature is still missing. This study summarizes the existing literature for pneumothorax detection from chest x-rays along with describing the available chest radiographs datasets. It will help the researchers to select the optimal and most effective model with respect to the real-time scenarios. The comparative analysis of the literature is provided in terms of goodness, usability and quality along with highlighting the research gaps for further investigation. From the literature, it is evident that pneumothorax is more common in men as compared to women. Additionally, the proposed models have achieved incredible results for pneumothorax detection on selected datasets, however, the effectiveness of proposed models in real-time cases cannot be claimed, as none of the models have been implemented clinically yet. This issue can be solved by external validation of the models. Furthermore, the class-imbalance problem in most of the medical image dataset has been solved by algorithm-level-techniques. Moreover, there is need to put more effort in combined detection and localization of pneumothorax as mostly research is limited to either classification or localization of pathology. So far, best results have been achieved by deep-learning based models with Area-underreceiver-operating-characteristic-curve (AUC) of 88.87% for classification, and Dice-similarity-coefficient (DSC) of 88.21% for localization of pneumothorax. Thus, the outstanding abilities of deep learning techniques can be deployed for developing robust models for pneumothorax detection.

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Machine Learning Models to Identify the Risk of Modern Slavery in Brazilian Cities

Cited by 5 publications

References 20 publications

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Risk Assessment of Government Debt Based on Machine Learning Algorithm

A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax Using Class-Imbalanced Chest X-Rays Dataset

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

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