FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2896768022> ?p ?o ?g. }

• W2896768022 endingPage "353" @default.
• W2896768022 startingPage "324" @default.
• W2896768022 abstract "Rock bursts constitute serious hazards in underground mining and excavating. Up to now, numerous researches in the form of empirical, experimental, analytical, intelligent and numerical methods with their own specific scope, characteristics, strengths and weaknesses, have been conducted for rock burst prediction. The weaknesses and limitations of the mentioned prediction methods, especially the intelligent studies, indicate the need for continuing the researches in this field. In this research, a rock burst database, consisting of 188 datasets, was considered. Each dataset corresponds to a series of predictor variables and one of defined classes for the dependent variable “rock burst intensity”. To design classification models, describing important characteristics of datasets and predicting future trends, a data preprocessing procedure was conducted. The procedure consisted of a statistical analysis strategy, a metaheuristic technique for feature (variable) subset selection and some feature extraction techniques. The statistical analysis led to conclude that by considering the available datasets, some predictor variables have statistically insignificant contributions for rock burst prediction. By contrast, the other predictor variables have considerable ordinal contributions. These statistical inferences were completely in accordance with the results of the feature subset selection technique. Besides, the application of this technique revealed specific combinations of significant predictor variables having the highest priorities for modelling the dependent variable. The application of feature extraction techniques to construct derived components from initial datasets did not lead to representative results. Therefore, a high rank combination of significant predictor variables can be adopted to design and develop new classification models based on the considered datasets." @default.
• W2896768022 created "2018-10-26" @default.
• W2896768022 creator A5013503314 @default.
• W2896768022 creator A5014299826 @default.
• W2896768022 creator A5074980044 @default.
• W2896768022 date "2019-01-01" @default.
• W2896768022 modified "2023-10-16" @default.
• W2896768022 title "Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: Literature review and data preprocessing procedure" @default.
• W2896768022 cites W1713401900 @default.
• W2896768022 cites W1963492742 @default.
• W2896768022 cites W1967405664 @default.
• W2896768022 cites W1968774956 @default.
• W2896768022 cites W1971891346 @default.
• W2896768022 cites W1974844300 @default.
• W2896768022 cites W1979018856 @default.
• W2896768022 cites W1979567159 @default.
• W2896768022 cites W1983231119 @default.
• W2896768022 cites W1986127492 @default.
• W2896768022 cites W1986476321 @default.
• W2896768022 cites W1989855602 @default.
• W2896768022 cites W1996860155 @default.
• W2896768022 cites W1999565652 @default.
• W2896768022 cites W2004197509 @default.
• W2896768022 cites W2005461675 @default.
• W2896768022 cites W2005572880 @default.
• W2896768022 cites W2005587694 @default.
• W2896768022 cites W2008809232 @default.
• W2896768022 cites W2009506086 @default.
• W2896768022 cites W2012594398 @default.
• W2896768022 cites W2018165924 @default.
• W2896768022 cites W2020124992 @default.
• W2896768022 cites W2022858863 @default.
• W2896768022 cites W2027710413 @default.
• W2896768022 cites W2030579506 @default.
• W2896768022 cites W2030746406 @default.
• W2896768022 cites W2035032965 @default.
• W2896768022 cites W2040231612 @default.
• W2896768022 cites W2043534441 @default.
• W2896768022 cites W2044565070 @default.
• W2896768022 cites W2044962124 @default.
• W2896768022 cites W2046687317 @default.
• W2896768022 cites W2047072771 @default.
• W2896768022 cites W2048628678 @default.
• W2896768022 cites W2051379557 @default.
• W2896768022 cites W2052970885 @default.
• W2896768022 cites W2057750216 @default.
• W2896768022 cites W2063336256 @default.
• W2896768022 cites W2064817630 @default.
• W2896768022 cites W2066813588 @default.
• W2896768022 cites W2069704530 @default.
• W2896768022 cites W2072299458 @default.
• W2896768022 cites W2073587988 @default.
• W2896768022 cites W2073729911 @default.
• W2896768022 cites W2073731078 @default.
• W2896768022 cites W2074232696 @default.
• W2896768022 cites W2078043553 @default.
• W2896768022 cites W2085029947 @default.
• W2896768022 cites W2085560463 @default.
• W2896768022 cites W2085571671 @default.
• W2896768022 cites W2088668619 @default.
• W2896768022 cites W2096476627 @default.
• W2896768022 cites W2097900674 @default.
• W2896768022 cites W2126105956 @default.
• W2896768022 cites W2139195964 @default.
• W2896768022 cites W2155908097 @default.
• W2896768022 cites W2159934719 @default.
• W2896768022 cites W2202489580 @default.
• W2896768022 cites W2219557035 @default.
• W2896768022 cites W2325928976 @default.
• W2896768022 cites W2335246468 @default.
• W2896768022 cites W2477869812 @default.
• W2896768022 cites W2501792429 @default.
• W2896768022 cites W2520345362 @default.
• W2896768022 cites W2620996464 @default.
• W2896768022 cites W4248376243 @default.
• W2896768022 cites W62275428 @default.
• W2896768022 doi "https://doi.org/10.1016/j.tust.2018.09.022" @default.
• W2896768022 hasPublicationYear "2019" @default.
• W2896768022 type Work @default.
• W2896768022 sameAs 2896768022 @default.
• W2896768022 citedByCount "51" @default.
• W2896768022 countsByYear W28967680222019 @default.
• W2896768022 countsByYear W28967680222020 @default.
• W2896768022 countsByYear W28967680222021 @default.
• W2896768022 countsByYear W28967680222022 @default.
• W2896768022 countsByYear W28967680222023 @default.
• W2896768022 crossrefType "journal-article" @default.
• W2896768022 hasAuthorship W2896768022A5013503314 @default.
• W2896768022 hasAuthorship W2896768022A5014299826 @default.
• W2896768022 hasAuthorship W2896768022A5074980044 @default.
• W2896768022 hasConcept C10551718 @default.
• W2896768022 hasConcept C119857082 @default.
• W2896768022 hasConcept C124101348 @default.
• W2896768022 hasConcept C134306372 @default.
• W2896768022 hasConcept C138885662 @default.
• W2896768022 hasConcept C148483581 @default.
• W2896768022 hasConcept C153180895 @default.
• W2896768022 hasConcept C154945302 @default.

• next