282,342 research outputs found
Machine Learning Methods in Individual Migration Behavior
Machine learning is described as “a field of computer science that gives a machine the ability to learn”. In fact, machine learning is considered as a sub branch of Artificial Intelligence(AI). In recent years the rise of big data and cloud computing gives AI expert and specifically machine learning expert to dive deeply in data and extract knowledge from it by using machine learning algorithms. In this paper we try to introduce the basic concepts of machine learning algorithms including supervised learning, unsupervised learning and reinforcement learning and its usage in different applications. We describe specifically how to use machine learning in migration process modeling and focus on an approach for migration description, that is based on one of machine learning methods, the decision tree algorithm. We apply this method for the description of the economic behavior of an individual in the question of continuing his work in Russia based on the panel data and the data from the sociological survey. The accuracy of our estimation using decision tree is 67 percent for this specific task. All in all, the main objective of this paper is to introduce the important aspects of machine learning and its usages in the state-of-the-art technologies
Machine learning methods for systemic risk analysis in financial sectors.
Financial systemic risk is an important issue in economics and financial systems. Trying
to detect and respond to systemic risk with growing amounts of data produced in financial markets
and systems, a lot of researchers have increasingly employed machine learning methods. Machine
learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial
network and improve the current regulation of the financial market and industry. In this paper, we
survey existing researches and methodologies on assessment and measurement of financial systemic
risk combined with machine learning technologies, including big data analysis, network analysis
and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research
topics. The main purpose of this paper is to introduce current researches on financial systemic risk
with machine learning methods and to propose directions for future work.This research has been partially supported by grants from the National Natural Science Foundation
of China (#U1811462, #71874023, #71771037, #71725001, and #71433001)
Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for research for the
past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The
modern advent of big data and the development of data-oriented fields like deep learning have provided advancements
in food category recognition. With increasing computational power and ever-larger food datasets,
the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied
to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We
survey the core components for constructing a machine learning system for food category recognition, including
datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a
particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer
learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments
in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial
Fund (RP202G0289)LIAS (P202ED10Data Science
Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK
Education Fund (OP202006)BBSRC (RM32G0178B8
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Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA
In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined
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