"Predicting and Analyzing Factors Affecting Financial Stress of Household using Machine Learning: Application of XGBoost"

2019 ◽  
Vol 30 (2) ◽  
pp. 21-43 ◽  
Author(s):  
Jihyung Han ◽  
Daekyun Ko ◽  
Hyuncha Choe
Keyword(s):  
Machine Learning ◽  
Financial Stress ◽  
Factors Affecting

scholarly journals Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 601
Author(s):  
Nelson K. Dumakor-Dupey ◽  
Sampurna Arya ◽  
Ankit Jha
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Academic Research ◽  
Empirical Models ◽  
Rock Breakage ◽  
Environmental Implications ◽  
Learning Methods ◽  
Factors Affecting ◽  
Impact Prediction ◽  
Machine Learning Methods

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

An Experimental Study of Spammer Detection on Chinese Microblogs

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1759-1777
Author(s):  
Jialing Liang ◽  
Peiquan Jin ◽  
Lin Mu ◽  
Jie Zhao
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
User Behavior ◽  
Real Data ◽  
User Profile ◽  
Data Set ◽  
Sina Weibo ◽  
Factors Affecting ◽  
The Government ◽  
Hot Event

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.

Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

scholarly journals Analysis of Factors Affecting Financial Well Being Millennial Generation in Batam

2020 ◽  
Vol 2 (1) ◽  
pp. 33
Author(s):  
Yandi Suprapto
Keyword(s):  
Financial Literacy ◽  
Millennial Generation ◽  
Negative Relationship ◽  
Financial Stress ◽  
Well Being ◽  
Financial Behavior ◽  
Financial Socialization ◽  
Factors Affecting ◽  
The People ◽  
Socialization Agents

The purpose of this study is to determine whether financial behavior, financial socialization agents, financial attitude,  financial stress, and financial literacy can influence financial well being in millennial generation in Batam City. Financial well being is described when a person is able to prosper in the field of financial finance. Welfare is reflected in the ability to meet and manage all needs and desires. While millennial is the most current generation so that it can be a hope and reflection of a country. This research method begins with the distribution of questionnaires to the people of Batam city aged 15-19 years. Data were collected as many as 300 respondents then processed with multiple regression research models using SPSS. Variable financial literacy, financial attitude and financial socialization agents provide a significant positive relationship to financial well being. Meanwhile financial stress has a significant negative relationship with financial well being. Then for financial behavior variables show no significant relationship to financial well being.

scholarly journals Factors Affecting The Use Of Information Communication Technology In Teaching And Learning In Saudi Arabia Universities

2021 ◽  
Vol 58 (1) ◽  
pp. 1012-1022
Author(s):  
Edrees A. Alkinani
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Educational System ◽  
Teaching And Learning ◽  
Resistance To Change ◽  
Digital Age ◽  
Structured Interview ◽  
Information Communication ◽  
Factors Affecting ◽  
Learning And Teaching

Technology and machine learning are becoming increasingly important in Saudi Arabia educational system. There is a growing demand for educational institutions to use machine learning to teach the skills and knowledge students need for the digital age towards Saudi Vision 2030. The integration and adoption ofdigital technologies into learning and teaching brings more opportunities for Saudi universities students and teachers to better embrace the globalized digital age. There is huge potential for the Saudi educational system to perceive the key role of digital technologies inenhancing the education process quality. The aim of this article is investigating the barriersthat affectteachers’ integration and adoptionof information communication technologies(ICT) in universityclassroom. The study adopted a qualitative research design to collect the data through the semi-structured interview. The sample of the study is four Saudi ICT-experts professors from four public universities in Saudi Arabia. The findings of the study showed that there are three types of barriers namely; teachers’ level barriers e.g. attitudes, knowledge, access, resistance to change. Technology level barriers e.g. compatibility, perceived of useful, institutional barriers. Institutional level e.g. leadership support, resources. The recommendation and suggestion for studies were suggested in light of the findings.

Application of Machine Learning in Predicting Crude Oil Production Volume

2021 ◽  
Author(s):  
Okechukwu Prince Innocent
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Predictor Variable ◽  
Oil Production ◽  
Water Volume ◽  
Production Volume ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Crude Oil Production ◽  
The Relationship

Abstract The production of oil is of great and immense significance as a source of energy worldwide. The major factors affecting the production volume of oil is classified into two groups namely the geological and the human factor. Each group comprises of factors affecting oilfield production volume. The challenge in this project is to find the variable for the crude oil production volume in an oilfield because there are numerous factors affecting the crude oil production volume in an oilfield. The objective of this paper is to provide a more accurate and efficient solution on how to predict the oil production volume. Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil production volume. After a several studies have been made, the affecting factors were provided from the oilfield which would be trained and tested in order to model the relationship between predictor variable and response variable which are the significant affecting factors and the oil production volume respectively. The predictor variables are the startup number of wells, the recovery percent of previous year, the injected water volume of previous year and the oil moisture content of previous year. The predictor variable is the oil production volume. Moreover, the model was found to possess greater utility in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion. This model can be of immense value in the oil and gas industry if implemented because of its ability to predict oilfield output more accurately. It is an invaluable and very efficient model for the oilfield manager and oil production manager.

Future Directions in Robotic Neurosurgery

2021 ◽  
Author(s):  
Christopher R Wagner ◽  
Timothy Phillips ◽  
Serge Roux ◽  
Joseph P Corrigan
Keyword(s):  
Machine Learning ◽  
Operating Room ◽  
Key Factors ◽  
Future Directions ◽  
Factors Affecting ◽  
Implementation Challenges ◽  
Image Guided ◽  
Robotic Neurosurgery ◽  
Specific Technology ◽  
Technology Trends

Abstract In this paper, we highlight promising technologies in each phase of a robotic neurosurgery operation, and identify key factors affecting how quickly these technologies will mature into products in the operating room. We focus on specific technology trends in image-guided cranial and spinal procedures, including advances in imaging, machine learning, robotics, and novel interfaces. For each technology, we discuss the required effort to overcome safety or implementation challenges, as well as identifying example regulatory approved products in related fields for comparison. The goal is to provide a roadmap for clinicians as to which robotic and automation technologies are in the developmental pipeline, and which ones are likely to impact their practice sooner, rather than later.

scholarly journals Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

2020 ◽  
Vol 32 (1) ◽  
pp. 39-53
Author(s):  
Dalia Shanshal ◽  
Ceni Babaoglu ◽  
Ayşe Başar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Injury Severity ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Spatio Temporal ◽  
Using Data

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

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