Prediction of Financial Distress in Bangladesh’s Banking Sector Using Data Mining and Machine-Learning Technique

2020 ◽  
pp. 151-162
Author(s):  
Mohammed Mahmudur Rahman ◽  
Zinnia Sultana ◽  
Musrat Jahan ◽  
Ramis Fariha
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Financial Distress ◽  
Banking Sector ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Using Data

Prediction of the Tensile Strength and Electrical Resistivity of Concrete with Organic Polymer and their Influence on Carbonation Using Data Science and a Machine Learning Technique

2020 ◽  
Vol 862 ◽  
pp. 72-77
Author(s):  
José Alberto Guzmán Torres ◽  
Francisco Javier Domínguez Mota ◽  
Elia Mercedes Alonso-Guzmán ◽  
Wilfrido Martínez-Molina ◽  
José Gerardo Tinoco Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Electrical Resistivity ◽  
Data Science ◽  
Organic Polymer ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Improved Performance ◽  
Using Data ◽  
Non Destructive

The inclusion of additions to concrete blends helps to improve performance in certain conditions. The analysis of two concrete blends was performed, a blend with the addition of a natural organic polymer and a control blend to make predictive models and find a correlation. Tree tests were performed: Electrical resistivity (Er) test, Tensile strength (Ft) and Carbonation resistance. One of the most popular non-destructive tests on concrete is , due to the simplicity of measuring readings on concrete elements. It is a non-destructive test that determines the interconnectivity that exists in the concrete cementitious matrix by determining the quality of the concrete. The blend with the addition showed improved performance in all the tests. Data science techniques were used to generate artificial data, the Machine Learning technique (ML) is based on Tree regression (Tr) with satisfactory accuracy to assess the reliability.

scholarly journals A Comparative Study of Parameters Measuring in Data Mining Function Using SVM

2021 ◽  
Vol 10 (8) ◽  
pp. 15-22
Author(s):  
Meghna Utmal
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Machine Learning Technique ◽  
Vector Machines ◽  
Learning Technique ◽  
Evaluation Of Parameters ◽  
Statistical Approaches ◽  
Using Data ◽  
Improving Accuracy ◽  
Learning Issues

Due to the vast amount of data available on the internet nowadays, it is necessary to categorise the data, and fast, accurate, and resilient algorithms for data analysis are required. Support vector machines (SVMs) are a form of machine learning technique that is commonly used to solve a variety of statistical learning issues. It's been designed as a reliable categorization tool, and it's especially useful when there's a lot of data. Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, with the goal of steadily improving accuracy. Algorithms are trained to create classifications by using statistical approaches. These should ideally have an impact on important growth measures. In this study, we found that employing the Support Vector Machine technique provides the best accuracy and efficiency for our dataset. Our work is based on the evaluation of parameters like accuracy, recall and precision.

scholarly journals Forecasting of Campus Placement for Students Using Ensemble Voting Classifier

2020 ◽  
pp. 1-12
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Academic Career ◽  
Experimental Results ◽  
Supervised Machine Learning ◽  
Machine Learning Technique ◽  
Ensemble Approach ◽  
Forecasting Method ◽  
Learning Technique

Campus placement is a measure of students’ performance in a course. A forecasting method is proposed in this paper to predict possible campus placement of any institution. Data mining and knowledge discovery processes on academic career of students are applied. Supervised machine learning technique based classifiers are used for achieving this process. It uses an ensemble approach based voting classifier for choosing best classifier models to achieve better result over other classifiers. Experimental results have indicated 86.05% accuracy of ensemble based approach which is significantly better over other classifiers.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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