scholarly journals ANALISIS KEBIJAKAN COMPLIANCE RISK MANAGEMENT BERBASIS MACHINE LEARNING PADA DIREKTORAT JENDERAL PAJAK

2021 ◽  
Vol 6 (3) ◽  
pp. 289-298
Author(s):  
Tia Diamendia ◽  
Milla S Setyowati
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Learning Algorithm ◽  
Big Data Analytics ◽  
Assessment System ◽  
Risk Level ◽  
Self Assessment ◽  
Optimum Result ◽  
Internal And External Factors ◽  
Technology Resources

Implementation of tax self-assessment system gives full trust to taxpayers to calculate, pay, and report their tax themselves. To get the optimum result, the implementation of this system is determined by the level of compliance of the taxpayers. It is affected by internal and external factors such as technology, resources, legislation where the tax authority operating, organization’s aim and strategy, and public general tax conformity. This study aim to analyze taxpayer conformity level with machine based Compliance Risk Management (CRM) policy. This study is using qualitative approach through interview with people who have roles in implementing tax policy in Indonesia. This study founds the importance of machine learning based CRM policy, in which the tax authority cannot apply the same treatment to all taxpayers, so it needs to decide which taxpayer needs to be investigated with rational justification based on risk level. Tax authority needs to focus on implementing big data analytics with machine learning algorithm as an important source of information in decision making process, and helps predict taxpayers with potential fraud, so it can be used to reduce task risk before it happens.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

scholarly journals Forming Performance Assessment System of Risk Management in Railway Transport Companies

2018 ◽  
Vol 7 (4.3) ◽  
pp. 404 ◽  
Author(s):  
Viktoriya Chobitok ◽  
Larysa Chumak ◽  
Tetiana Demianenko ◽  
Yulia Us
Keyword(s):  
Adverse Effect ◽  
Risk Management ◽  
Performance Assessment ◽  
Assessment System ◽  
Risk Level ◽  
Effective Management ◽  
Railway Transport ◽  
Industrial Enterprises ◽  
Performance Assessment System ◽  
Working Out

The paper discusses the problems of assessing risk management performance in railway transport companies. The authors developed an algorithm of forming the competitiveness and risk level assessment system for railway transport companies; they assessed the risk of competitiveness of Ukrainian railway industrial enterprises, suggested measures to mitigate the adverse effect of the risks, a mechanism of working out and making effective management decisions to neutralize risks for railway transport companies. 

scholarly journals A machine learning based approach for frame work of objective video quality assessment system

2021 ◽  
Vol 10 (1) ◽  
pp. 335-339
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Learning Algorithm ◽  
Video Quality ◽  
Assessment Method ◽  
Quality Metrics ◽  
Assessment System ◽  
Video Quality Assessment ◽  
Quality Assessment Method ◽  
Frame Work

Video quality assessment aims to predict viewer’s opinion through objective means. A single videoquality metricis not sufficient to predict and quantify the test video. Hence, more video quality metrics have to be used for the quantification of video quality. So, hybrid quality metrics are required for quantification of video quality. In this paper, we propose a quality assessment method using machine learning algorithm. The proposed method performs better than other classification techniques.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

2020 ◽  
pp. 142-153 ◽  
Author(s):  
Murugan Krishnamoorthy ◽  
Bazeer Ahamed B. ◽  
Sailakshmi Suresh ◽  
Solaiappan Alagappan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Learning Techniques ◽  
Sample Data ◽  
Increase Productivity ◽  
The Given

Construction of a neural network is the cardinal step to any machine learning algorithm. It requires profound knowledge for the developer in assigning the weights and biases to construct it. And the construction should be done for multiple epochs to obtain an optimal neural network. This makes it cumbersome for an inexperienced machine learning aspirant to develop it with ease. So, an automated neural network construction would be of great use and provide the developer with incredible speed to program and run the machine learning algorithm. This is a crucial assist from the developer's perspective. The developer can now focus only on the logical portion of the algorithm and hence increase productivity. The use of Enas algorithm aids in performing the automated transfer learning to construct the complete neural network from the given sample data. This algorithm proliferates on the incoming data. Hence, it is very important to inculcate it with the existing machine learning algorithms.

Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm

2021 ◽  
pp. 307-329
Author(s):  
Nagaraj V. Dharwadkar ◽  
Shivananda R. Poojara ◽  
Anil K. Kannur
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
Missing Values ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Risk Level ◽  
Machine Learning Algorithm ◽  
Risk Levels ◽  
Early Diagnosis Of Diabetes ◽  
Diabetes Patients

Diabetes is one of the four non-communicable diseases causing maximum deaths all over the world. The numbers of diabetes patients are increasing day by day. Machine learning techniques can help in early diagnosis of diabetes to overcome the influence of it. In this chapter, the authors proposed the system that imputes missing values present in diabetes dataset and parallel process diabetes data for the pattern discovery using Hadoop-MapReduce-based C4.5 machine learning algorithm. The system uses these patterns to classify the patient into diabetes and non-diabetes class and to predict risk levels associated with the patient. The two datasets, namely Pima Indian Diabetes Dataset (PIDD) and Local Diabetes Dataset (LDD), are used for the experimentation. The experimental results show that C4.5 classifier gives accuracy of 73.91% and 79.33% when applied on (PIDD) (LDD) respectively. The proposed system will provide an effective solution for early diagnosis of diabetes patients and their associated risk level so that the patients can take precaution and treatment at early stages of the disease.

scholarly journals The use of Big Data in Machine Learning Algorithm

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Model Building ◽  
Learning Algorithm ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Human Intervention ◽  
Tools And Techniques

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyse some of the different machine learning algorithms and methods which can be applied to big data analysis, as well as the opportunities provided by the application of big data analytics in various decision making domains.

scholarly journals DIABETES PREDICTION USING MACHINE LEARNING ALGORITHMS

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Rishab Bothra
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Diabetes Prediction ◽  
Hereditary Diabetes ◽  
Lack Of Exercise ◽  
Classification Prediction

Diabetes mellitus is a disease in which blood sugars level is abnormally high due to inability of the body to produce or respond normally to insulin. It is among the critical disease and lots of people are suffering from this disease. Due to age, lack of exercise, hereditary diabetes, bad diet, high blood pressure etc. can cause this disease. Healthcare Industries have large volume of databases so by Big Data Analytics we can extract meaningful insights such as hidden patterns, unknown correlations to discover knowledge from the data and predict the outcome accordingly. In this paper we have proposed a diabetes prediction model using Machine Learning algorithm for better classification prediction. We have tried different Machine Learning algorithms to find which gives the better accuracy of classification.

scholarly journals Risk Assessment of Government Debt Based on Machine Learning Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dan Chen
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Local Government ◽  
Learning Algorithm ◽  
Government Debt ◽  
Entropy Method ◽  
Assessment System ◽  
Machine Learning Algorithm ◽  
Fiscal Revenue ◽  
The Government

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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