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Recent Advances in Numerical Methods, Machine Learning, and Computer Science
2021The chapter presents a brief description of chapters that contribute to the recent advances in numerical methods in continuum mechanics, computational physics. Also, this chapter deals with machine learning and computer science. The fourth part of the book presents novel computational methods in continuum mechanics.
Lakhmi C. Jain +4 more
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Computer vision and machine learning in science fiction
Science Robotics, 2019Science fiction has a cautionary view of computer vision and machine learning.
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The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 2012
This paper aims to provide a short review on the application of computational intelligence (CI) and machine learning (ML) in the bioenvironmental sciences. To clearly illustrate the current status, we limit our focus to some key approaches, namely fuzzy systems (FSs), artificial neural networks (ANNs) and genetic algorithms (GAs) as well as some ML ...
Bernard De Baets, Shinji Fukuda
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This paper aims to provide a short review on the application of computational intelligence (CI) and machine learning (ML) in the bioenvironmental sciences. To clearly illustrate the current status, we limit our focus to some key approaches, namely fuzzy systems (FSs), artificial neural networks (ANNs) and genetic algorithms (GAs) as well as some ML ...
Bernard De Baets, Shinji Fukuda
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Machine Learning in Building a Collection of Computer Science Course Syllabi
2012Syllabi are rich educational resources. However, finding Computer Science syllabi on a generic search engine does not work well. Towards our goal of building a syllabus collection we have trained various Decision Tree, Naive-Bayes, Support Vector Machine and Feed-Forward Neural Network classifiers to recognize Computer Science syllabi from other web ...
Lillian N. Cassel, Nakul Rathod
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2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020
This is an era of computers and technology. Nowadays Computer Science (C.S.) and other technology-related subjects are a hot cake for the students. Due to a good job market for these subjects, students are taking computer science and other related topics without thinking about their capability and without knowing the curriculum of these subjects.
Md. Aref Billah +2 more
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This is an era of computers and technology. Nowadays Computer Science (C.S.) and other technology-related subjects are a hot cake for the students. Due to a good job market for these subjects, students are taking computer science and other related topics without thinking about their capability and without knowing the curriculum of these subjects.
Md. Aref Billah +2 more
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Accelerating materials science with high-throughput computations and machine learning
Computational Materials Science, 2019Abstract With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design.
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Using Bayesian Networks and Machine Learning to Predict Computer Science Success
2018Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links inherent in Bayesian Networks allow for understanding of the contributing factors for ...
Zachary Nudelman +2 more
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Machine Learning for Data Science: Mathematical or Computational
2015Machine learning usually requires getting a training and testing set of samples. The training set is used to obtain the model, and then, the testing set is used to verify the model. In general, a machine learning method requires an iterated process for reaching a goal. Machine learning is one of the research areas in artificial intelligence.
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Journal of Science Education and Technology, 2020
This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test.
Richard Lamb, Brian Hand, Amanda Kavner
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This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test.
Richard Lamb, Brian Hand, Amanda Kavner
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Machine learning methods for computational social science
2021Richard D. De Veaux, Adam Eck
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