Research Paper Influence Measurement and Applications: A Machine-Learning-Based Approach

2014 ◽  
Vol 1049-1050 ◽  
pp. 2073-2078
Author(s):  
San Shan Du ◽  
Yue Chun Wu
Keyword(s):  
Machine Learning ◽  
Graph Theory ◽  
Academic Research ◽  
Meaningful Work ◽  
Citation Network ◽  
Citation Networks ◽  
Research Publication ◽  
American Airline ◽  
Airline Network ◽  
Influence Measurement

Measuring the influence of academic research publication is an meaningful work in academe. In this paper, the co-author and the citation networks are built to calculate the influence of a researcher and a paper in the way of networks separately with the discussion of further applications. At the beginning, the co-author network is built to determine the influence of co-authors. Then, based on the citations among the papers in the database, we build up the citation network with the help of graph theory. Thirdly, the method is implemented with the application of American Airline network analysis. As the final, the analysis of strengths and weaknesses is conducted.

Prediction of Rising Venues in Citation Networks

2017 ◽  
Vol 21 (4) ◽  
pp. 650-658 ◽  
Author(s):  
Muhammad Azam Zia ◽  
◽  
Zhongbao Zhang ◽  
Guangda Li ◽  
Haseeb Ahmad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Continuous Improvement ◽  
Citation Count ◽  
Citation Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Citation Networks ◽  
Network Support ◽  
Core Issue ◽  
Over Time

Prediction of rising stars has become a core issue in data mining and social networks. Prediction of rising venues could unveil rapidly emerging research venues in citation network. The aim of this research is to predict the rising venues. First, we presented five effective prediction features along with their mathematical formulations for extracting rising venues. The underlying features are composed by incorporating the citation count, publications, cited to and cited by information at venue level. For prediction purpose, we employ four machine learning algorithms including Bayesian Network, Support Vector Machine, Multilayer Perceptron and Random Forest. Experimental results demonstrate that proposed features set are effective for rising venues prediction. Our empirical analysis spotlights the rising venues that demonstrate the continuous improvement over time and finally become the leading scientific venues.

scholarly journals Cocrystal Prediction Using Machine Learning Models and Descriptors

2021 ◽  
Vol 11 (3) ◽  
pp. 1323
Author(s):  
Medard Edmund Mswahili ◽  
Min-Jeong Lee ◽  
Gati Lother Martin ◽  
Junghyun Kim ◽  
Paul Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Academic Research ◽  
Pharmaceutical Research ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Pharmaceutical Ingredients ◽  
Learning Techniques ◽  
Comparable Performance ◽  
Selection Algorithms ◽  
Machine Learning Models

Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.

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.

scholarly journals Network-based machine learning and graph theory algorithms for precision oncology

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Wei Zhang ◽  
Jeremy Chien ◽  
Jeongsik Yong ◽  
Rui Kuang
Keyword(s):  
Machine Learning ◽  
Graph Theory ◽  
Precision Oncology

scholarly journals Citations Network Analysis of Vision and Sport

2020 ◽  
Vol 17 (20) ◽  
pp. 7574
Author(s):  
Henrique Nascimento ◽  
Clara Martinez-Perez ◽  
Cristina Alvarez-Peregrina ◽  
Miguel Ángel Sánchez-Tena
Keyword(s):  
Web Of Science ◽  
Citation Network ◽  
Comprehensive Analysis ◽  
Training Methods ◽  
Citation Networks ◽  
Publication Analysis ◽  
Research Areas ◽  
Visual Skills ◽  
Number Of Publications ◽  
Sports Vision

Background: Sports vision is a relatively new specialty, which has attracted particular interest in recent years from trainers and athletes, who are looking at ways of improving their visual skills to attain better performance on the field of play. The objective of this study was to use citation networks to analyze the relationships between the different publications and authors, as well as to identify the different areas of research and determine the most cited publication. Methods: The search for publications was carried out in the Web of Science database, using the terms “sport”, “vision”, and “eye” for the period between 1911 and August 2020. The publication analysis was performed using the Citation Network Explorer and CiteSpace software. Results: In total, 635 publications and 801 citations were found across the network, with 2019 being the year with the highest number of publications. The most cited publication was published in 2002 by Williams et al. By using the clustering functionality, four groups covering the different research areas in this field were found: ocular lesion, visual training methods and efficiency, visual fixation training, and concussions. Conclusions: The citation network offers an objective and comprehensive analysis of the main papers on sports vision.

Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification

2020 ◽  
pp. 016555152096277
Author(s):  
Rajmund Kleminski ◽  
Przemysiaw Kazienko ◽  
Tomasz Kajdanowicz
Keyword(s):  
Citation Network ◽  
Scientific Paper ◽  
Added Value ◽  
Bibliographic Coupling ◽  
Science Literature ◽  
Citation Networks ◽  
Research Topics ◽  
Topic Identification ◽  
Direct Citation ◽  
The Impact

In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships between the papers. The candidate research topics, in the form of keyphrases and n-grammes, are subsequently ranked and filtered by a graph-text ranking algorithm. This selection of the highest ranked potential topics is further evaluated by domain experts and through the Wikipedia knowledge base. The results obtained from these citation networks are complementary, returning valid but non-overlapping output phrases between some pairs of networks. In particular, bibliographic coupling appears to capture more unique information than either direct citation or co-citation. These findings point towards the possible added value in combining bibliographic coupling analysis with other structures. At the same time, combining direct citation and co-citation is put into question. We expect our findings to be utilised in method design for research topic identification.

Sign in / Sign up

Export Citation Format

Share Document