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Investigating Multi-Label Classification for Bio-Inspired Design by Using Text Mining and Natural Language Processing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Investigating Multi-Label Classification for Bio-Inspired Design by Using Text Mining and Natural Language Processing./
作者:	Sun, Siyuan.
面頁冊數:	1 online resource (96 pages)
附註:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
標題:	Language. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30347320click for full text (PQDT)
ISBN:	9798377677765

Investigating Multi-Label Classification for Bio-Inspired Design by Using Text Mining and Natural Language Processing.
Sun, Siyuan.

Investigating Multi-Label Classification for Bio-Inspired Design by Using Text Mining and Natural Language Processing. - 1 online resource (96 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.E.)--McGill University (Canada), 2022.

Includes bibliographical references

Nowadays, bio-inspiration has enhanced the creation of sustainable and innovative solutions to modern engineering problems. Nature is a great source for multi-functional and optimized designs which could inspire mechanical engineers with innovative new ideas. However, it is very difficult to extract desired design knowledge from databases that are primarily text-based and focus on describing nature and biological systems. The main objective of this research is to build a multi- label classification system to classify bio-inspired designs to support design ideation. The method proposed in this study is to fuse text-based techniques such as natural language processing and text mining with machine learning to learn and predict the functionalities of bio-inspired design. Various design multi-functionalities were summarized based on the available resources from the AskNature database, then the main information extracted from the database and papers were labelled with corresponding multi-functionalities. Due to the high complexity of multi-label classification, multi-label classifiers were built based on various combinations of basic classifiers and trained to classify selected examples. One case study was conducted to verify the impact of the proposed system. The results showed that the proposed system is feasible and would be a solution for classifying the bio-inspired design and functional basis knowledge extraction method.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798377677765Subjects--Topical Terms:

643551
Language.
Index Terms--Genre/Form:

542853
Electronic books.

Investigating Multi-Label Classification for Bio-Inspired Design by Using Text Mining and Natural Language Processing.
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504 $a Includes bibliographical references
520 $a Nowadays, bio-inspiration has enhanced the creation of sustainable and innovative solutions to modern engineering problems. Nature is a great source for multi-functional and optimized designs which could inspire mechanical engineers with innovative new ideas. However, it is very difficult to extract desired design knowledge from databases that are primarily text-based and focus on describing nature and biological systems. The main objective of this research is to build a multi- label classification system to classify bio-inspired designs to support design ideation. The method proposed in this study is to fuse text-based techniques such as natural language processing and text mining with machine learning to learn and predict the functionalities of bio-inspired design. Various design multi-functionalities were summarized based on the available resources from the AskNature database, then the main information extracted from the database and papers were labelled with corresponding multi-functionalities. Due to the high complexity of multi-label classification, multi-label classifiers were built based on various combinations of basic classifiers and trained to classify selected examples. One case study was conducted to verify the impact of the proposed system. The results showed that the proposed system is feasible and would be a solution for classifying the bio-inspired design and functional basis knowledge extraction method.
520 $a De nos jours, la bio-inspiration a ameliore la creation de solutions durables et innovantes aux problemes d'ingenierie modernes. La nature est une excellente source de conceptions multifonctionnelles et optimisees qui pourraient inspirer les ingenieurs en mecanique avec de nouvelles idees innovantes. Cependant, il est tres difficile d'extraire les connaissances de conception souhaitees a partir de bases de donnees qui sont principalement basees sur du texte et se concentrent sur la description de la nature et des systemes biologiques. L'objectif principal de cette recherche est de construire un systeme de classification multi-etiquettes pour classer les conceptions bio-inspirees afin de soutenir l'ideation de conception. La methode proposee dans cette etude consiste a fusionner des techniques basees sur le texte telles que le traitement du langage naturel et l'exploration de texte avec l'apprentissage automatique pour apprendre et predire les fonctionnalites de la conception bio-inspiree. Diverses multifonctionnalites de conception ont ete resumees sur la base des ressources disponibles dans la base de donnees AskNature, puis les principales informations extraites de la base de donnees et des articles ont ete etiquetees avec les multifonctionnalites correspondantes. En raison de la grande complexite de la classification multietiquettes, des classificateurs multi-etiquettes ont ete construits sur la base de diverses combinaisons de classificateurs de base et formes pour classer des exemples selectionnes. Une etude de cas a ete menee pour verifier l'impact du systeme propose. Les resultats ont montre que le systeme propose est faisable et serait une solution pour classer la conception bio-inspiree et la methode d'extraction des connaissances de base fonctionnelle.
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