Relevant bibliographies by topics / Classification des motifs

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Classification des motifs'

Author: Grafiati
Published: 2 August 2022
Last updated: 31 July 2025

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Journal articles on the topic "Classification des motifs"

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Nalle, Derwi Rainord, Luh Gede Astuti, I. Gede Santi Astawa, Luh Arida Ayu Rahning Putri, AAIN Eka Karyawati, and I. Wayan Supriana. "Implementasi Metode Convolutional Neural Network Untuk Pengenalan Pola Motif Kain Tenun Rote Ndao Berbasis Android." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 11, no. 1 (2022): 157. http://dx.doi.org/10.24843/jlk.2022.v11.i01.p17.

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Rote ndao Ikat Weaving has interesting characteristics in each fabric motif because it has different motifs which indicate the ethnic differences contained in each of the resulting motifs. Rote Ndao weaving has a variety of motifs that are still unknown to many people, so in this study a classification of motifs of rote ndao woven fabrics was carried out using the Convolutional Neural Network method. Weaving motif classification uses 3 motifs with a total of 1050 data including 70 data for Ai Bunak, Dula Kakaik and Lafa Langgak motifs each. Data for 3 fabric motifs is divided into 80% training
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Meranggi, Dewa Gede Trika, Novanto Yudistira, and Yuita Arum Sari. "Batik Classification Using Convolutional Neural Network with Data Improvements." JOIV : International Journal on Informatics Visualization 6, no. 1 (2022): 6. http://dx.doi.org/10.30630/joiv.6.1.716.

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Batik is one of the Indonesian cultures that UNESCO has recognized. Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have a 'core motif' printed repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. However, with so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assi
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Prayoga, Arya, Maimunah, Pristi Sukmasetya, Muhammad Resa Arif Yudianto, and Rofi Abul Hasani. "Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta." Journal of Applied Computer Science and Technology 4, no. 2 (2023): 82–89. http://dx.doi.org/10.52158/jacost.v4i2.486.

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Batik is an Indonesian culture that has been recognized as a world heritage by UNESCO. Indonesian batik has a variety of different motifs in each region. One area that is famous for its batik motifs is Yogyakarta. Yogyakarta has a variety of batik motifs such as ceplok, kawung, and parang which can be differentiated based on the pattern. Yogyakarta batik motifs need to be preserved so they do not experience extinction, one way is by introducing Yogyakarta batik motifs. The recognition of Yogyakarta batik motifs can utilize technology to classify images of Yogyakarta batik motifs based on patte
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Perdana, Am Akbar Mabrur, Muhammad Fajar B, and Abdul Muis Mappalotteng. "Enhancing Batik Classification Leveraging CNN Models and Transfer Learning." JOIV : International Journal on Informatics Visualization 9, no. 3 (2025): 1033. https://doi.org/10.62527/joiv.9.3.2535.

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Batik is a traditional art originating from Indonesia and recognized by UNESCO. Batik motifs vary depending on the region of origin. The diverse batik motifs reflect the rich cultural heritage and unique traditions owned by each region in Indonesia. From Sabang to Merauke, each motif features a different story and values, depicting the beauty and diversity of nature and the lives of diverse local people. However, in the context of the modern era that continues to develop, batik motifs also experience renewal and creativity that always adapts to the times. As a result, the diversity of batik mo
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Sinaga, Daurat, Cahaya Jatmoko, Suprayogi Suprayogi, and Novi Hedriyanto. "Multi-Layer Convolutional Neural Networks for Batik Image Classification." Scientific Journal of Informatics 11, no. 2 (2024): 477–84. https://doi.org/10.15294/sji.v11i2.3309.

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Purpose: The purpose of this study is to enhance the classification of batik motifs through the implementation of a novel approach utilizing Multi-Layer Convolutional Neural Networks (CNN). Batik, a traditional Indonesian textile art form, boasts intricate motifs reflecting rich cultural heritage. However, the diverse designs often pose challenges in accurate classification. Leveraging advancements in deep learning, this research proposes a methodological framework employing Multi-Layer CNN to improve classification accuracy. Methods: The methodology integrates Multi-Layer CNN architecture wit
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Sulastri, Endang, Ana Yuniasti Retno Wulandari, Dwi Bagus Rendy Astid Putera, and Vita Dwi Darmawati. "EXPLORATION OF SCIENCE CONCEPTS IN KLAMPAR PAMEKASAN BATIK MOTIFS AS A SCIENCE LEARNING RESOURCE." Jurnal Pendidikan Matematika dan IPA 16, no. 2 (2025): 206–21. https://doi.org/10.26418/jpmipa.v16i2.87928.

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Science learning fosters logical and critical thinking by integrating real word contexts, such as the Klampar Pamekasan batik motif, to enhance students understanding of biological classification. Therefore, the objective of this research is to examine the concept of science in the Pamekasan Klampar batik motif as a source for learning science. This study employs an exploratory descriptive method, which involves field observations, interviews with batik artisans and educators, and documentation analysis to comprehensively examine the classification of living organisms depicted in Klampar Pamek
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Elvitaria, Luluk, Ezak Fadzrin Ahmad, Noor Azah Samsudin, Shamsul Kamal Ahmad Khalid, Salamun, and Zul Indra. "An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification." JOIV : International Journal on Informatics Visualization 9, no. 1 (2025): 115. https://doi.org/10.62527/joiv.9.1.2591.

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Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architectu
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Ariessaputra, Suthami, Viviana Herlita Vidiasari, Sudi Mariyanto Al Sasongko, Budi Darmawan, and Sabar Nababan. "Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024): 38. http://dx.doi.org/10.62527/joiv.8.1.1386.

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Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and
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Sriani, Sriani, Muhammad Siddik Hasibuan, and Rizkika Ananda. "Classification of Batu Bara Songket Using Gray-Level Co-Occurrence Matrix and Support Vector Machine." Jurnal Riset Informatika 5, no. 1 (2022): 481–90. http://dx.doi.org/10.34288/jri.v5i1.469.

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Songket is a traditional woven cloth from the Malay and Minangkabau tribes. Songket can also be classified from the brocade woven family and woven with gold or silver thread. Songket cloth's beauty is the Indonesian people's wealth and preservation. Batu Bara Regency is one of Indonesia's regions with several Songket motifs characteristics. Public knowledge of Batu Bara Songket motifs is still minimal, and the differences between one motif and another are still unknown. This research provides information about the variety of Songket fabrics by classifying six types of Batu Bara Songket motifs,
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Sutresno, Stephen Aprius. "The Classification of Batik Besurek Fabric Motifs in Indonesia Utilizing YOLOv8 for Enhanced Cultural Preservation." Journal of Computer System and Informatics (JoSYC) 6, no. 1 (2024): 86–95. https://doi.org/10.47065/josyc.v6i1.6123.

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Batik Besurek is an Indonesian cultural heritage that presents a variety of motifs reflecting the richness of creativity and symbolic meanings. A significant challenge in this field is accurately and efficiently identifying and classifying batik Besurek motifs, known for their intricate designs and cultural significance. In efforts towards cultural preservation and development, a combination of modern technology and local wisdom is required. One technology that can be utilized is object detection technology using You Only Look Once (YOLO), specifically the latest version, YOLOv8, for the class
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Dissertations / Theses on the topic "Classification des motifs"

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Piipari, Matias. "Inference and classification of eukaryotic cis-regulatory motifs." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609801.

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Gay, Dominique. "Calcul de motifs sous contraintes pour la classification supervisée." Phd thesis, Nouvelle Calédonie, 2009. http://portail-documentaire.univ-nc.nc/files/public/bu/theses_unc/TheseDominiqueGay2009.pdf.

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Ces dernières années, l'extraction de motifs locaux (itemsets fréquents et règles d'association) a suscité beaucoup d'entrain pour la classification supervisée. Cette thèse traite du calcul et de l'usage de motifs sous contraintes pour la classification supervisée. Nous nous attaquons à deux problèmes difficiles en classification supervisée à base de motifs et proposons deux contributions méthodologiques : D'un côté, lorsque les attributs sont bruités, les performances des classifieurs peuvent être désastreuses. Les méthodes existantes consistent à corriger les valeurs d'attributs ou supprimer
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Gay, Dominique. "Calcul de motifs sous contraintes pour la classification supervisée." Phd thesis, Université de Nouvelle Calédonie, 2009. http://tel.archives-ouvertes.fr/tel-00516706.

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Ces dernières années, l'extraction de motifs locaux (itemsets fréquents et règles d'association) a suscité beaucoup d'entrain pour la classification supervisée. Cette thèse traite du calcul et de l'usage de motifs sous contraintes pour la classification supervisée. Nous nous attaquons à deux problèmes difficiles en classification supervisée à base de motifs et proposons deux contributions méthodologiques : D'un côté, lorsque les attributs sont bruités, les performances des classifieurs peuvent être désastreuses. Les méthodes existantes consistent à corriger les valeurs d'attributs ou supprimer
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Claudon, Nicolas. "Classification automatique des diatomées : une approche par les motifs des structures internes /." Thèse, Trois-Rivières : Université du Québec à Trois-Rivières, 2007. http://www.uqtr.ca/biblio/notice/resume/30024826R.pdf.

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Claudon, Nicolas. "Classification automatique des diatomées : une approche par les motifs des structures internes." Thèse, Université du Québec à Trois-Rivières, 2007. http://depot-e.uqtr.ca/1244/1/030024826.pdf.

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Pensa, Ruggero Gaetano. "Un Cadre générique pour la co-classification sous contraintes : application à l'analyse du transcriptome." Lyon, INSA, 2006. http://theses.insa-lyon.fr/publication/2006ISAL0078/these.pdf.

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La recherche de groupements intéressants dans les données booléennes (ensembles d'objets décrits par un ensemble de propriétés) a motivé la conception de méthodes d'extractions de motifs globaux (partitions) et de motifs locaux (ensembles fréquents, règles d'association et concepts formels). Cette thèse concerne la co-classification c'est-à-dire le calcul de bi-partitions (couplage de partitions sur les deux dimensions). Les algorithmes de co-classification disponibles ne permettent aux analystes d'exploiter leur connaissance du domaine qu'à travers un nombre réduit de paramètres. D'autre part
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Pensa, Ruggero Gaetano Boulicaut Jean-François Robardet Céline. "Un Cadre générique pour la co-classification sous contraintes application à l'analyse du transcriptome /." Villeurbanne : Doc'INSA, 2007. http://docinsa.insa-lyon.fr/these/pont.php?id=pensa.

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Gosselin, Stéphane. "Recherche de motifs fréquents dans une base de cartes combinatoires." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00838571.

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Une carte combinatoire est un modèle topologique qui permet de représenter les subdivisions de l'espace en cellules et les relations d'adjacences et d'incidences entre ces cellules en n dimensions. Cette structure de données est de plus en plus utilisée en traitement d'images, mais elle manque encore d'outils pour les analyser. Notre but est de définir de nouveaux outils pour les cartes combinatoires nD. Nous nous intéressons plus particulièrement à l'extraction de sous-cartes fréquentes dans une base de cartes. Nous proposons deux signatures qui sont également des formes canoniques de cartes
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Grunert, Steffen. "Strukturelles und funktionelles Verständnis von Membranproteinen im Kontext sequenzmotivbasierter Methoden." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-229383.

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Die vorliegende Arbeit wurde im Rahmen einer kooperativen Promotion zwischen der TU Dresden und der Hochschule Mittweida angefertigt. In dieser werden neuartige, computerorientierte Ansätze für die Analyse von Membranproteinen vorgestellt. Membranproteine sind von essentieller Bedeutung für eine Vielzahl biologischer Prozesse innerhalb eines Organismus und stellen wichtige Zielmoleküle für eine breite Palette von Pharmazeutika dar. Ihre Sequenzen liefern wertvolle und teilweise noch nicht entschlüsselte Informationen über die dreidimensionale Struktur und funktionale Eigenschaften. Innerhalb d
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Salah, Saber. "Parallel itemset mining in massively distributed environments." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT297/document.

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Le volume des données ne cesse de croître. À tel point qu'on parle aujourd'hui de "Big Data". La principale raison se trouve dans les progrès des outils informatique qui ont offert une grande flexibilité pour produire, mais aussi pour stocker des quantités toujours plus grandes. Les méthodes d'analyse de données ont toujours été confrontées à des quantités qui mettent en difficulté les capacités de traitement, ou qui les dépassent. Pour franchir les verrous technologiques associés à ces questions d'analyse, la communauté peut se tourner vers les techniques de calcul distribué. En particulier,
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Books on the topic "Classification des motifs"

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Jane, Garry, and El-Shamy Hasan M. 1938-, eds. Archetypes and motifs in folklore and literature: A handbook. M.E. Sharpe, 2005.

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Weitlaner-Johnson, Irmgard. Mexican Indian folk designs: 252 motifs from textiles. Dover Publications, 1993.

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Judith, Weingarten, and Bombardieri Luca, eds. Cretan hieroglyphic seals: A new classification of symbols and ornamental/ filling motifs. F. Serra, 2009.

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Haboucha, Reginetta. Types and motifs of the Judeo-Spanish folktales. Garland, 1992.

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Wilbert, Johannes. In their own words: Introduction, concordance of new motifs, and bibliography. Harvard University, Center for the Study of World Religions, 1992.

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Hayao, Kawai. The Japanese psyche: Major motifs in fairy tales of Japan. Spring, 1996.

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Ashliman, D. L. A guide to folktales in the English language: Based on the Aarne-Thompson classification system. Greenwood Press, 1987.

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Delarue, Paul. Le conte populaire français: Catalogue raisonné des versions de France et des pays de langue française d'outre-mer. Editions du C.T.H.S., 2000.

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Delarue, Paul. Le conte populaire français: Édition en un seul volume reprenant les quatre tomes publiés entre 1976 et 1985. Maisonneuve et Larose, 2002.

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Delarue, Paul. Le Conte populaire français. Maisonneuve et Larose, 1997.

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Book chapters on the topic "Classification des motifs"

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Gendron, Patrick, Daniel Gautheret, and Francois Major. "Structural Ribonucleic Acid Motifs Identification and Classification." In High Performance Computing Systems and Applications. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5611-4_31.

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Maletzke, André Gustavo, Huei Diana Lee, Gustavo Enrique, et al. "Time Series Classification with Motifs and Characteristics." In Soft Computing for Business Intelligence. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53737-0_8.

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Furfaro, Angelo, Maria Carmela Groccia, and Simona E. Rombo. "Image Classification Based on 2D Feature Motifs." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40769-7_30.

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Tohid, Suhaimi, Rafeah Legino, Ruzaika Omar Basaree, Ponirin Amin, and Rahman Amin. "Classification Design Motifs of Traditional Malay Wood Carving." In Proceedings of the International Symposium on Research of Arts, Design and Humanities (ISRADH 2014). Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-530-3_6.

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Orengo, C., J. Thornton, L. Holm, and C. Sander. "Protein folds and motifs: representation, comparison and classification." In International Tables for Crystallography. International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000714.

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Blekas, Konstantinos, Dimitrios I. Fotiadis, and Aristidis Likas. "Protein Sequence Classification Using Probabilistic Motifs and Neural Networks." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_84.

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Schuchhardt, Johannes, Gisbert Schneider, Joachim Reichelt, Dietmar Schomburg, and Paul Wrede. "Classification of Local Protein Structural Motifs by Kohonen Networks." In Bioinformatics: From Nucleic Acids and Proteins to Cell Metabolism. Wiley-VCH Verlag GmbH, 2007. http://dx.doi.org/10.1002/9783527615193.ch7.

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Mikolajczack, Jérôme, Gérard Ramstein, and Yannick Jacques. "SVM-Based Classification of Distant Proteins Using Hierarchical Motifs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_4.

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Kangkachit, Thanapat, and Kitsana Waiyamai. "Comprehensible Enzyme Function Classification Using Reactive Motifs with Negative Patterns." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_44.

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Sarrazin-Gendron, Roman, Jérôme Waldispühl, and Vladimir Reinharz. "Classification and Identification of Non-canonical Base Pairs and Structural Motifs." In Methods in Molecular Biology. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3519-3_7.

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Conference papers on the topic "Classification des motifs"

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Utami, Marissa, Ermatita Ermatita, and Abdiansyah Abdiansyah. "Transfer Learning Implementation for Batik Besurek Motifs Classification: A Comparative Study." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10957440.

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Wu, Xubin. "Motifs-Based Multi-Scale Graph Convolution Learning Framework for Attention Deficit/Hyperactivity Disorder Classification." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00040.

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Lian, Wilson, Fabian Monrose, and John McHugh. "Traffic classification using visual motifs." In the Seventh International Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1850795.1850804.

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Nguyen, Luong Phat, Julien Mille, Dominique H. Li, Donatello Conte, and Nicolas Ragot. "Efficient Dynamic Texture Classification with Probabilistic Motifs." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956557.

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Eser, Ercument M., Burak R. Arslan, and Ugur O. Sezerman. "Classification of cohesin family using class specific motifs." In 2013 8th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2013. http://dx.doi.org/10.1109/hibit.2013.6661687.

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Mullane, Sean, Ruoyan Chen, Sri Vaishnavi Vemulapalli, et al. "Machine Learning for Classification of Protein Helix Capping Motifs." In 2019 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2019. http://dx.doi.org/10.1109/sieds.2019.8735646.

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Arango-Argoty, G. A., J. A. Jaramillo-Garzon, S. Rothlisberger, and C. G. Castellanos-Dominguez. "Classification of unaligned sequences based on prototype motifs representation." In 2011 6th Colombian Computing Congress (CCC). IEEE, 2011. http://dx.doi.org/10.1109/colomcc.2011.5936297.

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Sheng, Huitao, Kishan Mehrotra, Chilukuri Mohan, and Ramesh Raina. "Classification of gene expression levels using activator and repressor motifs." In 2008 IEEE International Conference on Bioinformatics and Biomeidcine Workshops, BIBMW. IEEE, 2008. http://dx.doi.org/10.1109/bibmw.2008.4686239.

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Aras, Suhardi, Arief Setyanto, and Rismayani. "Classification of Papuan Batik Motifs Using Deep Learning and Data Augmentation." In 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2022. http://dx.doi.org/10.1109/icoris56080.2022.10031320.

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Maletzke, Andre G., Huei D. Lee, Gustavo E. A. P. A. Batista, et al. "Time Series Classification using Motifs and Characteristics Extraction: A Case Study on ECG Databases." In Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Atlantis Press, 2013. http://dx.doi.org/10.2991/.2013.40.

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Reports on the topic "Classification des motifs"

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Lades, M. Motion description for data compression and classification. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/8300.

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Fetterer, F., and D. Gineris. Evaluating ERS-1 Ice Motion and Classification Products. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada267614.

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Ravani, Bahram. Classification and Simulation of Single and Multi-Degree-of-Freedom Motions. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada214925.

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Li, Shuo, Yingzi (Eliza) Du, and Yi Jiang. Site Verification of Weigh-in-Motion Traffic and TIRTL Classification Data. Purdue University, 2011. http://dx.doi.org/10.5703/1288284314247.

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Schwartz, Daniel F., Robert R. Bennett, Kenneth J. Graham, Thomas L. Boggs, and Alice I. Atwood. Current Efforts to Develop Alternate TB700-2" Test Protocols for the Hazard Classification of Large Rocket Motors". Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada407047.

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Costantini, Orsola, and Carlo D’Ippoliti. Mapping fragility – Functions of wealth and social classes in U.S. household finance. Institute for New Economic Thinking Working Paper Series, 2024. http://dx.doi.org/10.36687/inetwp215.

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Which households are more exposed to financial risk and to what extent is their debt systemically relevant? To provide an answer, we advance a new classification of the population, adapted from Fessler and Schürz (2017), based on the type of wealth families own and their sources of income. Then, we investigate data from eleven waves of the Survey of Consumer Finances (SCF), a triennial survey run by the U.S. Federal Reserve, to explore the association of different debt configurations and motives to get into debt with our class distinctions. Our new approach allows us to assess competing hypot
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7

Welch, David, and Gregory Deierlein. Technical Background Report for Structural Analysis and Performance Assessment (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2020. http://dx.doi.org/10.55461/yyqh3072.

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Abstract:
This report outlines the development of earthquake damage functions and comparative loss metrics for single-family wood-frame buildings with and without seismic retrofit of vulnerable cripple wall and stem wall conditions. The underlying goal of the study is to quantify the benefits of the seismic retrofit in terms of reduced earthquake damage and repair or reconstruction costs. The earthquake damage and economic losses are evaluated based on the FEMA P-58 methodology, which incorporates detailed building information and analyses to characterize the seismic hazard, structural response, earthqu
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