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problem gambling with high accuracy. A machine learning-based analysis method is well suited for building this model. The method includes a feature engineering technique, unlike the conventional statistical methods with limited feature engineering, such as

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Abstract  

The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received. The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.

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lifetime, which could be maximized. Machine Learning (ML) techniques like Q-learning and multi-armed bandit can play a significant role in scheduling and random access to manage the resources in the smart cities and smart home environment. It is because

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Abstract  

Patents represent the technological or inventive activity and output across different fields, regions, and time. The analysis of information from patents could be used to help focus efforts in research and the economy; however, the roles of the factors that can be extracted from patent records are still not entirely understood. To better understand the impact of these factors on patent value, machine learning techniques such as feature selection and classification are used to analyze patents in a sample industry, nanotechnology. Each nanotechnology patent was represented by a comprehensive set of numerical features that describe inventors, assignees, patent classification, and outgoing references. After careful design that included selection of the most relevant features, selection and optimization of the accuracy of classification models that aimed at finding most valuable (top-performing) patents, we used the generated models to analyze which factors allow to differentiate between the top-performing and the remaining nanotechnology patents. A few interesting findings surface as important such as the past performance of inventors and assignees, and the count of referenced patents.

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prediction of resource utilization is not only based upon current usage patterns but also past system behavior may be considered. In this review, it was found that machine learning (ML) models are suitable to predict resource utilization using historical data

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-term conditions. Hence some machine learning algorithms are used to predict CCI in mid and long-term operations namely K-nearest neighbour (NN) and perfect random tree ensembles (PERT) in USA [ 9 ] Neural Network, Time Series analysis and Regression methods It is

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compare the obtained indicators in terms of occurrence and contingencies. Overall, our observations reveal non-trivial differences for both indicators. Methodology for characterizing NPRs A supervised machine learning approach

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, Python. However, assigning numerical values to nominal attributes misleads the machine learning algorithms learning by making difference or order between values that are not originally existed in the attributes and this phenomenon is called subjectivity

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 Then, the KNN classifier is used to cross-validate the classification accuracy of the feature subsets. Classification by KNN classifier The KNN algorithm is amongst the simplest of all machine learning algorithms for

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Imaging
Authors: Bettina Katalin Budai, Veronica Frank, Sonaz Shariati, Bence Fejér, Ambrus Tóth, Vince Orbán, Viktor Bérczi, and Pál Novák Kaposi

the liver has been of great interest in the last decade ( Fig. 1 ). Fig. 1. The increasing number of articles found in PubMed® for keywords Artificial intelligence; Machine learning; Texture analysis; Radiomics published between 2000–2020 Texture

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