The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
Marta Gwinn - One of the best experts on this subject based on the ideXlab platform.
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Novel citation-Based Search Method for scientific literature: a validation study.
BMC Medical Research Methodology, 2020Co-Authors: A. Cecile J.w. Janssens, Marta Gwinn, J Elaine Brockman, Kimberley Powell, Michael GoodmanAbstract:BACKGROUND: We recently developed CoCites, a citation-Based Search Method that is designed to be more efficient than traditional keyword-Based Methods. The Method begins with identification of one or more highly relevant publications (query articles) and consists of two Searches: the co-citation Search, which ranks publications on their co-citation frequency with the query articles, and the citation Search, which ranks publications on frequency of all citations that cite or are cited by the query articles. MethodS: We aimed to reproduce the literature Searches of published systematic reviews and meta-analyses and assess whether CoCites retrieves all eligible articles while screening fewer titles. RESULTS: A total of 250 reviews were included. CoCites retrieved a median of 75% of the articles that were included in the original reviews. The percentage of retrieved articles was higher (88%) when the query articles were cited more frequently and when they had more overlap in their citations. Applying CoCites to only the highest-cited article yielded similar results. The co-citation and citation Searches combined were more efficient when the review authors had screened more than 500 titles, but not when they had screened less. CONCLUSIONS: CoCites is an efficient and accurate Method for finding relevant related articles. The Method uses the expert knowledge of authors to rank related articles, does not depend on keyword selection and requires no special expertise to build Search queries. The Method is transparent and reproducible.
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Novel citation-Based Search Method for scientific literature: a validation study
2019Co-Authors: Cecile Janssens, Marta Gwinn, J Elaine Brockman, Kimberley Powell, Michael GoodmanAbstract:Objective: We recently developed CoCites, a citation-Based Search Method that is designed to be more efficient than traditional keyword-Based Methods. The Method begins with identification of one or more highly relevant publications (query articles) and consists of two Searches: the co-citation Search, which ranks publications on their co-citation frequency with the query articles, and the citation Search, which ranks publications on frequency of all citations that cite or are cited by the query articles. Materials and Methods: We aimed to reproduce the literature Searches of published systematic reviews and meta-analyses (n=250) and assess whether CoCites retrieves all eligible articles while screening fewer titles. Results: CoCites retrieved a median of 75% of the articles that were included in the original reviews. The percentage of retrieved articles was higher (88%) when the query articles were cited more frequently and when they had more overlap in their citations. Applying CoCites to only the highest-cited article yielded similar results. The co-citation and citation Searches combined were more efficient when the review authors had screened more than 500 titles, but not when they had screened less. Discussion: CoCites uses the expert knowledge of authors to rank related articles. The Method does not depend on keyword selection and requires no special expertise to build Search queries. The Method is transparent and reproducible. Conclusion: CoCites is an efficient and accurate Method for finding relevant related articles.
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Erratum to: Novel citation-Based Search Method for scientific literature: application to meta-analyses
BMC Medical Research Methodology, 2015Co-Authors: A. Cecile J.w. Janssens, Marta GwinnAbstract:Erratum Unfortunately, the original version of this article [1] contained an error. Under ‘Study 2’, the text read: “The first Search was the same as in the Study 1, except that we screened all articles that were co-cited in more than 1 % of the citing articles.” However, this sentence should have also included articles that were “co-cited more than once”, to read: “The first Search was the same as in the Study 1, except that we screened all articles that were co-cited more than once and co-cited in more than 1 % of the citing articles.” We apologise for this error.
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novel citation Based Search Method for scientific literature application to meta analyses
BMC Medical Research Methodology, 2015Co-Authors: Cecile A J W Janssens, Marta GwinnAbstract:Background Finding eligible studies for meta-analysis and systematic reviews relies on keyword-Based Searching as the gold standard, despite its inefficiency. Searching Based on direct citations is not sufficiently comprehensive. We propose a novel strategy that ranks articles on their degree of co-citation with one or more “known” articles before reviewing their eligibility.
Arash Rezazadeh - One of the best experts on this subject based on the ideXlab platform.
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Artificial neural network training using a new efficient optimization algorithm
Applied Soft Computing Journal, 2013Co-Authors: Alireza Askarzadeh, Arash RezazadehAbstract:Because Search space in artificial neural networks (ANNs) is high dimensional and multimodal which is usually polluted by noises and missing data, the process of weight training is a complex continuous optimization problem. This paper deals with the application of a recently invented metaheuristic optimization algorithm, bird mating optimizer (BMO), for training feed-forward ANNs. BMO is a population-Based Search Method which tries to imitate the mating ways of bird species for designing optimum Searching techniques. In order to study the usefulness of the proposed algorithm, BMO is applied to weight training of ANNs for solving three real-world classification problems, namely, Iris flower, Wisconsin breast cancer, and Pima Indian diabetes. The performance of BMO is compared with those of the other classifiers. Simulation results indicate the superior capability of BMO to tackle the problem of ANN weight training. BMO is also applied to model fuel cell system which has been addressed as an open and demanding problem in electrical engineering. The promising results verify the potential of BMO algorithm. © 2012 Elsevier B.V. All rights reserved.
Hadi Moradi - One of the best experts on this subject based on the ideXlab platform.
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a new graph signature calculation Method Based on power centrality for modular robots
Distributed Autonomous Robotic Systems, 2013Co-Authors: Keyvan Golestan, Masoud Asadpour, Hadi MoradiAbstract:Graph signature is a fast isomorphism test that is used in self reconfiguration planning of modular robots. In case of dealing with homomorphic modules, the required time to calculate the signature grows exponentially with the number of symmetry lines. We tackle this problem by introducing an isomorphism- invariant signature calculation Method, which is Based on the power centrality of nodes. We also introduce a new sample-Based Search Method. Simulation results show the new Method finds better solutions in a significantly shorter time.
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DARS - A New Graph Signature Calculation Method Based on Power Centrality for Modular Robots
Springer Tracts in Advanced Robotics, 2013Co-Authors: Keyvan Golestan, Masoud Asadpour, Hadi MoradiAbstract:Graph signature is a fast isomorphism test that is used in self reconfiguration planning of modular robots. In case of dealing with homomorphic modules, the required time to calculate the signature grows exponentially with the number of symmetry lines. We tackle this problem by introducing an isomorphism- invariant signature calculation Method, which is Based on the power centrality of nodes. We also introduce a new sample-Based Search Method. Simulation results show the new Method finds better solutions in a significantly shorter time.
Cecile A J W Janssens - One of the best experts on this subject based on the ideXlab platform.
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novel citation Based Search Method for scientific literature application to meta analyses
BMC Medical Research Methodology, 2015Co-Authors: Cecile A J W Janssens, Marta GwinnAbstract:Background Finding eligible studies for meta-analysis and systematic reviews relies on keyword-Based Searching as the gold standard, despite its inefficiency. Searching Based on direct citations is not sufficiently comprehensive. We propose a novel strategy that ranks articles on their degree of co-citation with one or more “known” articles before reviewing their eligibility.
Alireza Askarzadeh - One of the best experts on this subject based on the ideXlab platform.
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Artificial neural network training using a new efficient optimization algorithm
Applied Soft Computing Journal, 2013Co-Authors: Alireza Askarzadeh, Arash RezazadehAbstract:Because Search space in artificial neural networks (ANNs) is high dimensional and multimodal which is usually polluted by noises and missing data, the process of weight training is a complex continuous optimization problem. This paper deals with the application of a recently invented metaheuristic optimization algorithm, bird mating optimizer (BMO), for training feed-forward ANNs. BMO is a population-Based Search Method which tries to imitate the mating ways of bird species for designing optimum Searching techniques. In order to study the usefulness of the proposed algorithm, BMO is applied to weight training of ANNs for solving three real-world classification problems, namely, Iris flower, Wisconsin breast cancer, and Pima Indian diabetes. The performance of BMO is compared with those of the other classifiers. Simulation results indicate the superior capability of BMO to tackle the problem of ANN weight training. BMO is also applied to model fuel cell system which has been addressed as an open and demanding problem in electrical engineering. The promising results verify the potential of BMO algorithm. © 2012 Elsevier B.V. All rights reserved.
