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Influence of the concrete strength and the type of supports on the stress-strain state of a hyperbolic paraboloid shell footbridge structure
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In: Structural Mechanics of Engineering Constructions and Buildings, Vol 17, Iss 4, Pp 379-390 (2021) (2021)
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Towards High-End Scalability on Bio-Inspired Computational Models
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In: Computer Science: Faculty Publications and Other Works (2020)
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Phonetic Algorithm Performance
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In: Senior Honors Projects (2020)
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Abstract:
AARON SCHNEIDEREIT (Computer Science BS) Phonetic Algorithm Performance Sponsors: Noah Daniels (Computer Science and Statistics) Phonetic Algorithms are used for classifying words based on their pronunciation. These algorithms are used in many text-to-speech technologies and spell-checkers to ensure that a word can be correctly recognized despite minor spelling/pronunciation errors. The process of encoding a word to its phonetic surname is known as Phonetic Matching. Since 1918, there have only been a handful of phonetic algorithms that have been created. The main three algorithms that other phonetic algorithms are built from are Soundex, New York State Identification and Intelligence System (NYSIIS), and Metaphone. This research explores the performance of these phonetic algorithms and analyzes their performance of the phonetic matching under specified test cases. These test cases include homophones, swapping of specified vowels, and swapping of characters. The purpose of these tests is to understand which algorithm performs the best when the words are slightly different from what they are expected to be. These tests simulate the mispronunciation of words, common spelling errors, and characters that may have removed due to background noise. The results indicate that there is no algorithm that performs perfectly under these test cases; however, some algorithms perform better than others in specific circumstances. Soundex performed perfectly under the swapped vowel tests, while Metaphone and NYSIIS had similar results that under performed compared to Soundex. However, Metaphone performed very well when comparing the phonetic matching of homophones, and marginally outperformed NYSIIS and Soundex. These results have displayed the strengths and weakness of these algorithms and provide some insight on how certain types of words should be approached when searching for their phonetic matching.
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Keyword:
Algorithms; Computational Engineering; Performance; Phonetic
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URL: https://digitalcommons.uri.edu/context/srhonorsprog/article/1879/type/native/viewcontent
https://digitalcommons.uri.edu/srhonorsprog/882
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Phonetic Algorithm Performance
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In: Senior Honors Projects (2020)
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Performance-Based Seismic Design for buildings
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In: Structural Mechanics of Engineering Constructions and Buildings, Vol 16, Iss 2, Pp 161-166 (2020) (2020)
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Adaptation and Implementation of the ISO42010 Standard to Software Design and Modeling Tools
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In: Model-Driven Engineering and Software Development. MODELSWARD 2018, Communications in Computer and Information Science ; https://hal-cea.archives-ouvertes.fr/cea-02572737 ; Model-Driven Engineering and Software Development. MODELSWARD 2018, Communications in Computer and Information Science, pp.236-258, 2019, ⟨10.1007/978-3-030-11030-7_11⟩ (2019)
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An LSTM-Based Neural Network Architecture for Model Transformations
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In: 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS) ; https://hal-cea.archives-ouvertes.fr/cea-02572669 ; 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), Sep 2019, Munich, Germany. pp.294-299, ⟨10.1109/MODELS.2019.00013⟩ (2019)
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Preface to MDE Intelligence 2019: 1st Workshop on Artificial Intelligence and Model-Driven Engineering
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In: 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) ; https://hal-cea.archives-ouvertes.fr/cea-02572659 ; 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Sep 2019, Munich, Germany. pp.168-169, ⟨10.1109/MODELS-C.2019.00028⟩ (2019)
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The Future of Model Transformation Languages: An Open Community Discussion.
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In: ISSN: 1660-1769 ; The Journal of Object Technology ; https://hal-cea.archives-ouvertes.fr/cea-02572743 ; The Journal of Object Technology, Chair of Software Engineering, 2019, 18 (3), pp.7:1. ⟨10.5381/jot.2019.18.3.a7⟩ (2019)
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A Model Driven Tool for Requirements and Hardware Engineering
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In: 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) ; https://hal-cea.archives-ouvertes.fr/cea-02572673 ; 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Sep 2019, Munich, Germany. pp.769-773, ⟨10.1109/MODELS-C.2019.00120⟩ (2019)
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On the Need for Intellectual Property Protection in Model-Driven Co-Engineering Processes
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In: International Conference on Evaluation and Modeling Methods for Systems Analysis and Development ; https://hal-cea.archives-ouvertes.fr/cea-02572729 ; International Conference on Evaluation and Modeling Methods for Systems Analysis and Development, Jun 2019, Rome, Italy. pp.169-177, ⟨10.1007/978-3-030-20618-5_12⟩ ; https://www.emmsad.org/2019-program (2019)
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Belief Uncertainty in Software Models
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In: 2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering (MiSE) ; https://hal-cea.archives-ouvertes.fr/cea-02572731 ; 2019 IEEE/ACM 11th International Workshop on Modelling in Software Engineering (MiSE), May 2019, Montreal, Canada. pp.19-26, ⟨10.1109/MiSE.2019.00011⟩ (2019)
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On modularity and performance of External Domain-Specific Language implementations ; Modularité et performance des implémentations de langages dédiés externes
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In: https://hal.inria.fr/tel-02418676 ; Software Engineering [cs.SE]. Université de rennes 1, 2019. English (2019)
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Transforming big social data into forecasts - methods and technologies ; Transformer les big social data en prévisions - méthodes et technologies : Application à l'analyse de sentiments
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In: https://tel.archives-ouvertes.fr/tel-02060594 ; Ingénierie, finance et science [cs.CE]. Université d'Angers; Université Ibn Tofail. Faculté des sciences de Kénitra, 2018. Français. ⟨NNT : 2018ANGE0011⟩ (2018)
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Cognifying Model-Driven Software Engineering
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In: Software Technologies: Applications and Foundations ; https://hal-cea.archives-ouvertes.fr/cea-02572650 ; Software Technologies: Applications and Foundations, pp.154-160, 2018, ⟨10.1007/978-3-319-74730-9_13⟩ (2018)
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Get Rid of Your Student's Fear and Intimidation of learning a Programming Language
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In: Publications (2017)
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Exploring Tensions between Appropriability and Openness to Collaboration in Innovation
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Stefan, Ioana. - : KTH, Industriell ekonomi och organisation (Inst.), 2017. : Högskolan i Gävle, 2017. : Stockholm, 2017
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