Citation prediction using diverse features

WebMay 1, 2024 · Conclusion. In this paper, we proposed a novel method for citation count prediction, which is based on artificial neural networks. We employed modern deep learning techniques (such as RNNs and sequence-to-sequence model) in order to learn a prediction method based on the sequence pattern of the citations from early years of … WebMay 24, 2024 · However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features. In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites.

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Web2 days ago · Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein … how many bollywood movies are there https://thepreserveshop.com

Multi-view feature transfer for click-through rate prediction

WebUniversity of California, Merced WebNov 17, 2015 · Citation Prediction Using Diverse Features. Abstract: Using a large database of nearly 8 million bibliographic entries spanning over 3 million unique authors, we build predictive models to classify a paper based on its citation count. Our approach … WebUsing a large database of nearly 8 million bibliographic entries spanning over 3 million unique authors, we build predictive models to classify a paper based on its citation … high pressure in eastern pacific weakens

Citation Count Prediction: Learning to Estimate Future …

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Citation prediction using diverse features

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Web12 rows · Feb 1, 2024 · Citation prediction of scholarly papers has been extensively investigated, and these studies are ... WebDec 26, 2024 · It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of …

Citation prediction using diverse features

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http://keg.cs.tsinghua.edu.cn/jietang/publications/CIKM11-Yan-Citation-Count-Prediction.pdf WebWe use these features in addition to features that are often used in citation prediction models. The best classifier that we build has a test set accuracy exceeding 87% and an …

WebSection 2 we first define a series of features which correlate with citation counts. We then formulate citation count prediction as a learning problem and introduce several … WebJan 18, 2024 · The idea implemented in this paper is the creation of a machine learning model, which utilizes past prices of Bitcoin, Google trends data and some custom features, which were created by text mining on tweets about Bitcoin. The aim of this study was to predict future Bitcoin prices. For this purpose, we compared a Deep Neural Network, and ...

WebAug 1, 2024 · We use a multilayer BP neural network to predict the citations of academic papers. First, we select 49,834 papers in the library, information and documentation field published from 2000 to 2013 and indexed in the Chinese Social Science Citation Index database (hereafter CSSCI) (Su, Deng, & Shen, 2014). Second, we extract six article … WebOct 19, 2024 · Here, we combine UniRep vectors of enzymes and diverse molecular fingerprints of their substrates to build a general, organism-, and reaction-independent model for the prediction of K M values, using machine and deep learning models. In the final model, we employ a 1,900-dimensional UniRep vector for the enzyme together with …

WebCitation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. ... Li-Hsuan Huang, Sebastian Rodriguez, Rick Dale, and Evan Heit. 2015. Citation prediction using diverse features. In Proceedings of the 2015 IEEE International ...

WebNew Citation Alert added! ... The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. ... “ A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs,” Journal ... how many bolts per flangeWebFeb 21, 2024 · The precise estimate of remaining useful life (RUL) is vital for the prognostic analysis and predictive maintenance that can significantly reduce failure rate and maintenance costs. The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction. … how many bolts in a poundWebJun 10, 2024 · By building a meta-path based prediction model on a topic discrim- inative search space, we here propose a two-phase cita- Tion probability learning approach, in order to predict citation ... how many bolts in a 4 flangeWebOct 28, 2024 · Citation: Chen X, Peng Y, Gao Y, Cai S (2024) A competition model for prediction of admission scores of colleges and universities in Chinese college entrance examination. PLoS ONE 17(10): e0274221. ... We use diverse features of colleges and universities to characterize their novel competition relationships and build competition … how many bolt holes in a 12 150# flangeWebMay 1, 2024 · Conclusion. In this paper, we proposed a novel method for citation count prediction, which is based on artificial neural networks. We employed modern deep … how many bolts in 24 flangeWebIn this paper we tackle the task of citation count prediction using existing and new features. We apply the technique to multiple domains, identifying differences both in the … high pressure in boiler when heating onWebOct 13, 2024 · To further improve prediction accuracy by increasing feature diversity, different features were selected in different degradation stages using the method described in Sec. 3.3. The hypothesis is that feature selection for ensembles is not necessarily the same as feature selection for a single base learner. high pressure in instant pot