Journal articles: 'Extraction de motifs fréquents'

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Leleu, Marion, Nicolas Méger, and Christophe Rigotti. "Extraction de motifs séquentiels fréquents sous contraintes dans des données contenant des répétitions consécutives." Ingénierie des systèmes d'information 9, no. 3-4 (August 24, 2004): 133–59. http://dx.doi.org/10.3166/isi.9.3-4.133-159.

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Hamrouni, Tarek, Sadok Ben Yahia, and Engelbert Mephu Nguifo. "Construction efficace du treillis des motifs fermés fréquents et extraction simultanée des bases génériques de règles." Mathématiques et sciences humaines, no. 195 (December 15, 2011): 5–54. http://dx.doi.org/10.4000/msh.12081.

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Casali, Alain, Rosine Cicchetti, Lofti Lahal, and Stéphane Lopes. "Couvertures parfaites des motifs fréquents." Ingénierie des systèmes d'information 10, no. 2 (April 24, 2005): 117–38. http://dx.doi.org/10.3166/isi.10.2.117-138.

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BALLIVET DERÉGLOIX, S., O. MAURIN, S. HABAS, and P. LAFOURCADE. "La demande de soins dans un service médical à N’Djamena, Tchad." Médecine et Armées Vol. 41 No. 4, Volume 41, Numéro 4 (October 1, 2013): 317–22. http://dx.doi.org/10.17184/eac.6690.

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Introduction : les éléments français du Tchad, basés sur la base Kosseï à N’Djamena, installés au Tchad depuis plus d’une trentaine d’années dans le cadre de l’opération « Épervier », disposent d’un service médical qui offre à la population locale des soins gratuits. L’étude des motifs de consultation dans cette structure nous paraissait intéressante, d’une part pour aider à la description des champs de la pratique de médecine « générale » en milieu urbain, et d’autre part pour identifier la liste des 20 motifs de consultation les plus fréquents. Ce travail consistait à collecter les motifs de consultation de la structure de soins à travers une enquête descriptive menée au cours de 30 journées aléatoirement choisies entre mars et juin 2011. Résultats : au cours de cette investigation, 614 motifs de consultations ont été notifiés et décrits selon la classification internationale des soins primaires. Les consultants étaient majoritairement des femmes (55 %), avec un âge médian de 21 ans. Les maladies digestives et respiratoires étaient respectivement les deux premiers motifs de consultation. Les 20 motifs les plus fréquents représentaient 79,4 % de l’ensemble des motifs de consultation. Les « diarrhées », « toux » et les « fièvres » représentaient les trois causes les plus fréquentes avec des fréquences respectives de 17 %, 10 % et 8 %. Discussion : la demande de soins dans cette structure de médecine générale reflétait l’ampleur des infections digestives et respiratoires aiguës. Conclusion : la liste des 20 principaux motifs de consultation peut être le point de départ pour la création d’une matrice d’association symptômes-diagnostics utilisable sur le terrain par le praticien, mais aussi dans les universités locales pour la constitution d’un curriculum de formation médicale répondant aux besoins de la population.

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Yaya, Traoré, Talibouya Diop Cheik, Kamara-Sangare Fatou, Malo Sadouanouan, Lo Moussa, and Ouaro Stanislas. "Motifs fréquents pour améliorer la catégorisation dans un wiki sémantique." Ingénierie des systèmes d'information 21, no. 5-6 (December 28, 2016): 83–106. http://dx.doi.org/10.3166/isi.21.5-6.83-106.

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Ales, Zacharie, Alexandra Pauchet, Arnaud Klippel, Laurent Vercouter, and Chritian Gout. "Extraction de motifs dialogiques bidimensionnels." Revue d'intelligence artificielle 29, no. 6 (December 28, 2015): 655–83. http://dx.doi.org/10.3166/ria.29.655-683.

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Rioult, François. "Découverte de motifs fréquents dans les bases de données. Un cadre formel pour les méthodes." Ingénierie des systèmes d'information 9, no. 3-4 (August 24, 2004): 211–40. http://dx.doi.org/10.3166/isi.9.3-4.211-240.

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Clive, Barbara A., Kenneth I. Shulman, and Maria L. Zorzitto. "Geriatric Liaison: An Approach to Medical Management on a Psychogeriatric Unit in a General Hospital." Canadian Journal on Aging / La Revue canadienne du vieillissement 10, no. 1 (1991): 56–63. http://dx.doi.org/10.1017/s071498080000725x.

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RÉSUMÉCette étude descriptive et rétrospective s'est déroulée dans une unité psychogériatrique d'un hôpital universitaire sur une période de deux ans. Les caractéristiques démographiques, la médication et les motifs de la fin du séjour de 98 patients ont été observés. Les gériatres de l'unité ont vu 39.8 pour cent des patients. Les hommes ont un besoin plus élevé que les femmes de soins d'un gériatre. La durée de séjour est plus élevé pour ceux qui ont une consultation gériatrique. Les diagnostics les plus fréquents sont de l'ordre des troubles affectifs. La consultation est justifiée par des affections neurologiques et cardiaques. L'encadrement offert par l'unité gériatrique est excellent pour la formation gériatrique et psychiatrique.

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BERKHOUT, C. "Luminothérapie dans les troubles du sommeil. Une revue systématique et méta-analyse." EXERCER 35, no. 203 (May 1, 2024): 203–5. http://dx.doi.org/10.56746/exercer.2024.203.203.

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Publication originale de Chambe J, Reynaud E, Maruani J, Fraih E, Geoffroy PA, Bourgin P (Université de Strasbourg) L’insomnie touche 30 à 50 % de la population adulte et la prévalence de l’insomnie chronique est estimée entre 5 et 7 %, selon les études, les pays et les classifications. Les troubles du sommeil font partie des dix motifs de consultation les plus fréquents en médecine générale et les médecins comme les patients recherchent des traitements alternatifs aux hypnotiques. Ces derniers représentent une solution à court terme. Néanmoins, ils sont régulièrement utilisés comme traitement à long terme devant la pression des patients insomniaques et la difficulté de mise en place des règles d’hygiène du sommeil et des thérapies cognitivo-comportementales (TCC). Des alternatives thérapeutiques sont donc toujours recherchées, et la luminothérapie (LT) pourrait en faire partie.

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Masseglia, Florent, Maguelonne Teisseire, and Pascal Poncelet. "Extraction de motifs séquentiels. Problèmes et méthodes." Ingénierie des systèmes d'information 9, no. 3-4 (August 24, 2004): 183–210. http://dx.doi.org/10.3166/isi.9.3-4.183-210.

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Apostolico, A., M. Comin, and L. Parida. "Conservative extraction of over-represented extensible motifs." Bioinformatics 21, Suppl 1 (June 1, 2005): i9—i18. http://dx.doi.org/10.1093/bioinformatics/bti1051.

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Ludot, Maude, Sarah Breton, Nour Ibrahim, Mayssa’ El Husseini, and Marie Rose Moro. "Les expressions somatiques à l’adolescence, au service du processus de séparation-individuation ?" Enfances & Psy N° 97, no. 3 (October 30, 2023): 97–108. http://dx.doi.org/10.3917/ep.097.0097.

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Les expressions somatiques constituent des motifs de consultation fréquents en médecine de l’adolescent. Par trois illustrations cliniques d’adolescentes douloureuses, nous souhaitons montrer la tension exercée sur le processus de séparation-individuation par le corps adolescent, et la manière dont l’adolescent et sa famille se saisissent du tiers dans le soin, pour élaborer progressivement sur ces manifestations somatiques et relancer le processus de séparation-individuation. L’association, dans ces vignettes cliniques, des expressions somatiques avec un trouble du comportement alimentaire donne, dans notre lecture, un poids supplémentaire à cette question de l’entrave du processus de séparation-individuation, bien décrite dans l’anorexie mentale ; et, en nous décentrant davantage encore, elle nous permet d’interroger les liens intimes et complexes qu’entretiennent le soma et la psyché, ainsi que les rapports, à un niveau plus méta encore, qu’entretiennent la médecine et la psychiatrie.

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Sourabié, Oumar, Désiré Nanema, Patrice Goumbri, B. Sawadogo, M. Téguéra, Kapouné Karfo, Jean-Gabriel Ouango, and Arouna Ouedraogo. "Caractéristiques des patients sortis sans avis médical au service de psychiatrie du Centre Hospitalier Universitaire Yalgodo Ouédraogo (CHU-YO), Ouagadougou, Burkina Faso." Psy Cause N° 79, no. 4 (October 3, 2021): 61–67. http://dx.doi.org/10.3917/psca.079.0062.

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Les sorties sans avis médicale (SSAM) ont fait l’objet de peu de travaux en Afrique. L’objectif de cette étude était d’étudier les SSAM au service de psychiatrie du CHU-YO. Il s’est agi d’une étude transversale rétrospective de 10 ans. A partir du registre d’hospitalisation nous avons inclus les patients. Les données collectées ont été saisies traitées avec le logiciel SPSS 20. La prévalence moyenne des SSAM était de 12.02 %. L’âge moyen des patients était de 31.32 ans. La durée moyenne de leur hospitalisation était de 9.54 jours. Ces patients avaient des ATCD psychiatriques (70.70 %). L’agitation (69.10 %) et l’agressivité (60.90 %) étaient les motifs d’hospitalisation les plus fréquents. Près de la moitié des patients (47.30 %) consommaient des SPA. Les troubles schizophréniques étaient le diagnostic le plus fréquent (51.20 %). Près de la moitié des patients (48.80 %) quittaient l’hôpital pendant la première semaine.

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SHRESTHA, NRIPENDRA L., YOUHEI KAWAGUCHI, and TAKENAO OHKAWA. "SUMOMO: A PROTEIN SURFACE MOTIF MINING MODULE." International Journal of Computational Intelligence and Applications 04, no. 04 (December 2004): 431–49. http://dx.doi.org/10.1142/s1469026804001392.

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Protein surface motifs, which can be defined as commonly appearing patterns of shape and physical properties in protein molecular surfaces, can be considered "possible active sites". We have developed a system for mining surface motifs: SUMOMO which consists of two phases: surface motif extraction and surface motif filtering. In the extraction phase, a given set of protein molecular surface data is divided into small surfaces called unit surfaces. After extracting several common unit surfaces as candidate motifs, they are repetitively merged into surface motifs. However, a large amount of surface motifs is extracted in this phase, making it difficult to distinguish whether the extracted motifs are significant to be considered active sites. Since active sites from proteins with a particular function have similar shape and physical properties, proteins can be classified based on similarity among local surfaces. Thus, in the filtering phase, local surfaces extracted from proteins of the same group are considered significant motifs, and the rest are filtered out. The proposed method was applied to discover surface motifs from 15 proteins belonging to four function groups. Motifs corresponding to all 4 known functional sites were recognised.

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Baetens, Jan. "Motifs of extraction: Photographic images on book covers." History of Photography 29, no. 1 (March 2005): 81–89. http://dx.doi.org/10.1080/03087298.2005.10441356.

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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 (November 18, 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 patterns using the Convolutional Neural Network (CNN). The Yogyakarta batik motif images used for classification totaled 600 images consisting of 3 different motifs such as ceplok, kawung, and parang. Image classification using CNN depends on the architectural model used. The CNN architecture consists of two stages, namely Convolutional for feature extraction and Neural Network for classification. The CNN architectural models made for the introduction of Yogyakarta batik motifs totaled 7 models which were distinguished at the feature extraction stage. The highest accuracy results in the classification of Yogyakarta batik motif images using CNN were obtained in the 6th model. The 6th model has an accuracy of 87.83%, an average precision of 88.46% and an average recall of 87.66%. The accuracy, precision, and recall values obtained by the 6th model are above 80%, which means that the 6th model can classify Yogyakarta batik motifs quite well.

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Barfety-Servignat, V. "Du mot commun et transnosographique à la pathologie grave : que repérer derrière la plainte fatigue ?" Douleur et Analgésie 35, no. 1 (March 2022): 19–25. http://dx.doi.org/10.3166/dea-2022-0209.

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Définir la fatigue est difficile alors qu’elle est l’un des symptômes les plus fréquents de consultation chez l’adulte. En clinique de la douleur, elle est même devenue un des motifs premiers de consultation. De la fatigue « normale » à la fatigue « pathologique », du symptôme de nombreuses entités médicales et/ou psychopathologiques au syndrome de fatigue chronique, comment peut-on penser la fatigue en clinique de la douleur ? Parler de la fatigue, c’est également parler de la plainte « fatigue », notamment puisque, avec les années, la fatigue s’est installée comme marqueur commun au somatique, au psychique et au social. Ainsi, la fatigue nous parle de résistance et de désir face à un environnement difficilement compréhensible où tout s’accélère, dans un moment où nous peinons à trouver des représentations pour penser et vivre le monde d’aujourd’hui. Tout comme en clinique de la douleur, il convient sans doute de cesser de vouloir départager la vraie fatigue de la fausse fatigue, ce qui revient au corps propre de ce qui revient au corps imaginaire, pour appréhender les réalités du sujet et permettre une prise en charge clinique efficiente.

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Thomas-Chollier, Morgane, Matthieu Defrance, Olivier Sand, Carl Herrmann, Denis Thieffry, and Jacques Van Helden. "RSAT peak-motifs: fast extraction of transcription factor binding motifs from full-size ChIP-seq datasets." EMBnet.journal 17, B (February 28, 2012): 20. http://dx.doi.org/10.14806/ej.17.b.266.

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Seran, Anderias Bai, Aviv Yuniar Rahman, and Istiadi Istiadi. "Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 5 (October 29, 2021): 958–66. http://dx.doi.org/10.29207/resti.v5i5.3484.

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Indonesia is a country with cultural diversity. One of the famous cultural heritages in Indonesia is Woven Fabrics. East Nusa Tenggara Province, especially South Central Timor, is an area that also produces weaving. There are 3 types of woven fabric motifs, namely the Buna, Lotis, and Futus motifs which were inherited from their ancestors. Woven cloth is unique because it is made through a ritual process and is used for traditional ceremonies, weddings, funerals, and so on. However, along with the development of technology, ordinary people increasingly forget the motifs of woven fabrics and have difficulty distinguishing the motifs. The function of this research is to improve the performance of previous studies in the process of finding the similarity of weaving image motifs using discrete wavelet transforms and GLCM. The results are known, calculations using a confusion matrix on discrete wavelet transformation feature extraction and GLCM, comparisons on discrete wavelet transformations produce an accuracy rate of 70% Minkowski matrix, 60% Manhattan matrix, 60% Canberra matrix, 20% Euclidean matrix. Comparison of feature extraction calculations on GLCM produces an average quality of the Minkowski matrix of 90% and the lowest level of accuracy on the Euclidean, Manhattan, and Canberra matrices of 80%.

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Kurumatani, Natsumi, Hiroyuki Monji, and Takenao Ohkawa. "Binding Site Extraction by Similar Subgraphs Mining from Protein Molecular Surfaces and Its Application to Protein Classification." International Journal on Artificial Intelligence Tools 23, no. 03 (May 28, 2014): 1460007. http://dx.doi.org/10.1142/s0218213014600070.

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Most proteins express their functions by binding with other proteins or molecular compounds (ligands). Since the characteristics of the local portion involved in binding (binding site) often determine the function of the protein, clarifying the location of the binding site of the protein helps analyze the function of proteins. Binding sites that bind to similar ligands often have common surface structures (surface motifs). Extracting the surface motifs among several proteins with similar functions improves binding site prediction. We propose a method that predicts binding sites by extracting the surface motifs that are frequently observed in only a specific set of proteins that bind to the same ligand (group). Since most binding sites have concave structures (pockets), the pockets are compared and common structures are searched for to extract the surface motifs by applying similar graph mining to the pocket data, which are represented as graphs. Common binding sites across several groups can be predicted in such a way to integrate more than one group. We also proposed a method of protein classification, in which the surface motifs extracted using the above method are evaluated on the assumption that a protein belongs to each one of the groups.

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Handayani, Sri, Ahmad Zuhdi, and Ratna Shofiati. "Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of Batik Bekasi Motifs Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of B." Intelmatics 2, no. 2 (July 31, 2022): 67–72. http://dx.doi.org/10.25105/itm.v2i2.14423.

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Batik is the identity and wealth of the Indonesian people in the field of culture, whose existence has been recognized by the world. However, many of the Indonesian people themselves are still unable to distinguish and recognize the motifs found on batik cloth. This is due to the large variety of motifs and patterns that are owned in different batik from each region in Indonesia. Therefore, we need a merchine leraning model that can be used to identify and classify the motifs contained in batik cloth. In this research, the method that will be used is the gray level co-occurrence matrix method which is used as a feature extraction method by utilizing five calculated parameters, namely, energy, contrast, correlation, homogeneity and entopy, as well as using the support vector machine method which is used as a motif classification method. . The batik motifs used are Bekasi batik motifs in the form of tamarind motifs, machete motifs, ondel motifs and mandar bird motifs. In this study, information on batik and the batik motifs used were obtained from a trusted source, namely the Bekasi Batik Community (KOMBAS).

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Gangemi, Aldo, Diego Reforgiato Recupero, Misael Mongiovì, Andrea Giovanni Nuzzolese, and Valentina Presutti. "Identifying motifs for evaluating open knowledge extraction on the Web." Knowledge-Based Systems 108 (September 2016): 33–41. http://dx.doi.org/10.1016/j.knosys.2016.05.023.

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Lemieux, S. "Automated extraction and classification of RNA tertiary structure cyclic motifs." Nucleic Acids Research 34, no. 8 (April 28, 2006): 2340–46. http://dx.doi.org/10.1093/nar/gkl120.

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Vidovic, Marina M. C., Marius Kloft, Klaus-Robert Müller, and Nico Görnitz. "ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines." PLOS ONE 12, no. 3 (March 27, 2017): e0174392. http://dx.doi.org/10.1371/journal.pone.0174392.

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Martínez-Abad, Antonio, Amparo Jiménez-Quero, Jakob Wohlert, and Francisco Vilaplana. "Influence of the molecular motifs of mannan and xylan populations on their recalcitrance and organization in spruce softwoods." Green Chemistry 22, no. 12 (2020): 3956–70. http://dx.doi.org/10.1039/d0gc01207f.

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Lamasigi, Zulfrianto Yusrin, and Andi Bode. "Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor." ILKOM Jurnal Ilmiah 13, no. 3 (December 8, 2021): 322–33. http://dx.doi.org/10.33096/ilkom.v13i3.1025.322-333.

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Batik is one type of fabric that is unique because it has a special motif, in Indonesia itself batik is unique because it has certain motifs that are made based on the culture from which batik was made. This study aims to examine the effect of the texture feature extraction method on the identification of batik motifs from five major islands in Indonesia. The method used in this study is the Gray Level Co-occurrence Matrix as the texture feature extraction of batik motifs to obtain good batik motif identification accuracy results and to determine the value of the proximity of the training data and image testing of batik motifs, the K-Nearest Neighbor classification method will be used based on texture feature extraction value obtained. In this experiment, 5 experiments will be carried out based on angles 0 degrees, 45 degrees, 90 degrees, 135 degrees, and 180 degrees using the values of k is1, 3, 5, and 7. The confusion matrix will be used to calculate the accuracy level of the K-Nearest Neighbor classification. From the results of experiments carried out using training data as many as 607 images and testing as many as 344 images in five classes used with angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, and values of k are 1, 3, 5, and 7, getting the highest accuracy results is at an angle of 135 degrees and 180 degrees with a value of k is 1 of 89.24% and the lowest is at an angle of 90 degrees with a value of k is 3 of 67.44%. This shows that the Gray level co-occurrence matrix method is good for extracting the texture features of batik motifs from five major islands in Indonesia, it is evidenced by the results of the average accuracy of the classification obtained.

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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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Alkaff, Muhammad, Husnul Khatimi, Nur Lathifah, and Yuslena Sari. "Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)." MATEC Web of Conferences 280 (2019): 05023. http://dx.doi.org/10.1051/matecconf/201928005023.

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Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

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Nurhalimah, Nurhalimah, I. Gede Pasek Suta Wijaya, and Fitri Bimantoro. "Klasifikasi Kain Songket Lombok Berdasarkan Fitur GLCM dan Moment Invariant Dengan Teknik Pengklasifikasian Linear Discriminant Analysis (LDA)." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 2, no. 2 (September 29, 2020): 173–83. http://dx.doi.org/10.29303/jtika.v2i2.98.

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Songket is one of Indonesia's cultural heritage that is still present today. One of the most famous songket woven fabrics is the Lombok songket. Lombok songket has diverse, unique, and beautiful motifs. However public knowledge of Lombok songket motifs is still minimal and the difference between one motif with another is still unknown. The lack of digitalized data collection is one reason for this. Therefore, we need a system that can classify the Lombok songket automatically. In this study, a system was developed based on texture features and shape features using Linear Discriminant Analysis (LDA). The GLCM method is used in the texture feature extraction process and the Invariant Moment method is used in the feature extraction process. The total data used in this study is 1000 images from 10 Lombok songket motifs which are divided into training data and test data. The highest accuracy is obtained on the Invariant Moment and GLCM feature with an image resolution of 300x300 pixels using the most effective feature that is equal to 96.67%.

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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 (December 14, 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, namely the Bunga Tanjung motif, Pucuk Betikam motif, Pucuk Cempaka motif, Pucuk pandan motif, Tampuk Manggis motif and Tolab Berantai motif based on the extraction of the Gray Level texture feature. The Co-Occurrence Matrix includes four parameters: Contrast, Correlation, Energy, and Homogeneity, as well as a classification method with a Support Vector Machine. The feature extraction values process as input for classification using a Support Vector Machine. The highest accuracy achieved in this study was 57%, using 60 training data and 30 test data.

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Salifou, Saliou, Daméga Wenkourama, Mawuko Gbétogbé, Sonia Kanékatoua, Charfoundine Affo, Bikiénime Biyante-Afeto, Mayéna Kpinsaga, and Kolou Simliwa Dassa. "Deux ans de pratiques psychiatriques à la Clinique Médico-Psychiatrique de Lomé : bilan." Psy Cause N° 80, no. 1 (January 3, 2022): 41–48. http://dx.doi.org/10.3917/psca.080.0041.

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Introduction : Au Togo, depuis quelques décennies, la fréquentation des structures de prise en charge psychiatrique est sans cesse croissante et la Clinique Médico-Psychiatrique de Lomé (une clinique privée) a vu le jour il y a deux ans. Le but de cette étude était de faire un bilan panoramique des pratiques psychiatriques de la Clinique Médico-Psychiatrique de Lomé. Matériel et méthode. Il s’est agi d’une étude rétrospective et descriptive portant sur les patients reçus du 3 février 2020 au 31 janvier 2022 à la Clinique Médico-Psychiatrique de Lomé. Résultats. Au cours de la période d’étude, 515 patients ont été suivis à la CMP-L. Le sexe féminin a représenté 52,43 % des patients. L’âge moyen des patients était de 36,59 ans ± 15,27 avec des extrêmes de 05 ans et 84 ans. Les fonctionnaires/ opérateurs économiques ont représenté 44,66 % des patients. Les motifs de consultation les plus fréquents étaient l’insomnie (34,17 %), l’agitation (21,17 %) et le délire (20,39 %). Les troubles de l’humeur ont prédominé chez nos patients (le trouble bipolaire à 28,35 % et le trouble dépressif à 20,97 %). Conclusion. Première et seule clinique psychiatrique privée au Togo, la CMP-L se devait de fournir des données scientifiques exploitables après deux ans d’exercice.

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Kaya, Mehmet. "Automated extraction of extended structured motifs using multi-objective genetic algorithm." Expert Systems with Applications 37, no. 3 (March 15, 2010): 2421–26. http://dx.doi.org/10.1016/j.eswa.2009.06.101.

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Hizukuri, Yoshiyuki, Yoshihiro Yamanishi, Osamu Nakamura, Fumio Yagi, Susumu Goto, and Minoru Kanehisa. "Extraction of leukemia specific glycan motifs in humans by computational glycomics." Carbohydrate Research 340, no. 14 (October 2005): 2270–78. http://dx.doi.org/10.1016/j.carres.2005.07.012.

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Zijlstra, Douwe S., Coen A. Analbers, Joren de Korte, Erwin Wilbers, and Peter J. Deuss. "Efficient Mild Organosolv Lignin Extraction in a Flow-Through Setup Yielding Lignin with High β-O-4 Content." Polymers 11, no. 12 (November 20, 2019): 1913. http://dx.doi.org/10.3390/polym11121913.

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Current lignin fractionation methods use harsh conditions that alter the native lignin structure, resulting in a recalcitrant material which is undesired for downstream processing. Milder fractionation processes allow for the isolation of lignins that are high in β-aryl ether (β-O-4) content, however, at reduced extraction efficiency. The development of improved lignin extraction methods using mild conditions is therefore desired. For this reason, a flow-through setup for mild ethanosolv extraction (120 °C) was developed. The influence of acid concentration, ethanol/water ratio, and the use of other linear alcohol co-solvents on the delignification efficiency and the β-O-4 content were evaluated. With walnut shells as model feedstock, extraction efficiencies of over 55% were achieved, yielding lignin with a good structural quality in terms of β-O-4 linking motifs (typically over 60 per 100 aromatic units). For example, lignin containing 66 β-O-4 linking motifs was obtained with an 80:20 n-propanol/water ratio, 0.18 M H2SO4 with overall a good extraction efficiency of 57% after 5 h. The majority of the lignin was extracted in the first 2 hours and this lignin showed the best structural quality. Compared to batch extractions, both higher lignin extraction efficiency and higher β-O-4 content were obtained using the flow setup.

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MICOT, M., A. VALOIS, F. DELON, G. TEXIER, and J. J. MORAND. "Evaluation des pratiques professionnelles concernant l’exemption de rasage dans les armées françaises." Médecine et Armées Vol.49 No.4, Volume 49, Numéro 4 (December 20, 2023): 113–26. http://dx.doi.org/10.17184/eac.8049.

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Les pathologies pilaires liées au rasage sont des motifs fréquents de consultation en médecine générale. Dans les armées, celles-ci posent la problématique de l’exemption de rasage. L’objectif de notre étude est d’évaluer les pratiques professionnelles concernant ces pathologies afin de proposer des algorithmes de prise en charge. Une évaluation des pratiques professionnelles a été menée à partir de la diffusion d’un questionnaire à l’ensemble des médecins des forces françaises. Ce dernier comprenait une partie sociodémographique et quatre cas cliniques (irritation, folliculite, rosacée et pili incarnati). 181 médecins des forces ont répondu au questionnaire. Plus de la moitié (52,5%) a rapporté la réalisation d’au moins une exemption de rasage par mois. Le nombre d’exemptions réalisées était statistiquement associé au fait de travailler dans l’armée de terre sans que cela n’est modifié les réponses aux cas cliniques. Parmi les variables sociodémographiques, le grade était le seul facteur associé statistiquement au score global des cas cliniques et le sexe féminin avait un meilleur score concernant l’exemption de rasage. Cette étude nous a permis d’objectiver les problématiques auxquelles sont confrontés les médecins des forces en pratique courante et d’apporter des aides et algorithmes de prise en charge à adapter selon les situations. Une prévention primaire de ces pathologies serait intéressante, notamment durant les formations militaires initiales en agissant directement chez les incorporés ou, indirectement en sensibilisant les équipes encadrantes.

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Amilpur, Santhosh, and Raju Bhukya. "EDeepSSP: Explainable deep neural networks for exact splice sites prediction." Journal of Bioinformatics and Computational Biology 18, no. 04 (July 22, 2020): 2050024. http://dx.doi.org/10.1142/s0219720020500249.

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Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.

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Hakenberg, Jörg, Conrad Plake, Loic Royer, Hendrik Strobelt, Ulf Leser, and Michael Schroeder. "Gene mention normalization and interaction extraction with context models and sentence motifs." Genome Biology 9, Suppl 2 (2008): S14. http://dx.doi.org/10.1186/gb-2008-9-s2-s14.

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APOSTOLICO, ALBERTO, and CLAUDIA TAGLIACOLLO. "OPTIMAL EXTRACTION OF IRREDUNDANT MOTIF BASES." International Journal of Foundations of Computer Science 21, no. 06 (December 2010): 1035–47. http://dx.doi.org/10.1142/s0129054110007714.

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The problem of extracting a basis of irredundant motifs from a sequence is considered. In previous work such bases were built incrementally for all suffixes of the input string s in O(n3), where n is the length of s. Faster, non-incremental algorithms have been based on the landmark approach to string searching due to Fischer and Paterson, and exhibit respective time bounds of O(n2 log n log |Σ|) and O(|Σ|n2 log 2 n log log n), with Σ denoting the alphabet. The algorithm by Fischer and Paterson makes crucial use of the FFT, which is impractical with long sequences. The present paper describes an off-line algorithm for binary strings that takes O(n2) time. The algorithm does not need to resort to the FFT and yet its performance is optimal for finite Σ.

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Susanti, Indah Dwi. "Yogyakarta Batik Image Classification Based on Convolutional Neural Network." Advance Sustainable Science, Engineering and Technology 6, no. 1 (January 13, 2024): 02401020. http://dx.doi.org/10.26877/asset.v6i1.18002.

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This paper studies the efficiency of identifying motifs and patterns in Yogyakarta batik using the Convolutional Neural Network (CNN) algorithm. This research uses the AlexNet architecture on CNN to increase the accuracy of batik image classification. Apart from that, it also involves the use of Canny edge detection techniques and feature extraction using the Gray Level Co-occurrence Matrix (GLCM) to improve the feature extraction process in batik images. There are 6 folders representing 6 types of motifs containing -+20 to 25 data that have been prepared for the training session. Next, the data is processed with 20% of the data used for training and 80% for testing. The accuracy of this research using the SGDM optimizer reached 100%. The evaluation results provide insight into the extent to which edge emphasis can improve the model's ability to recognize and classify batik patterns. It also presents classification test results and evaluation metrics such as precision, recall, and F1 score.

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Khalida, Rakhmi. "GENERATE ASIAN GAMES 2018 MASCOT FOR BATIK MOTIF WITH NEURAL STYLE TRANSFER." Moestopo International Review on Social, Humanities, and Sciences 2, no. 1 (April 28, 2022): 1–6. http://dx.doi.org/10.32509/mirshus.v2i1.25.

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In the past, batik artists to create a batik motif required special skills and a long time because motif is the main element in batik, and the uniqueness of batik is from the motif itself. Recently computers were able to produce many ordinary images turned into works of art even producing a new motif variant product. The method used is neural transfer style based on CNN architecture. It has performed feature extraction on batik motifs and has produced variations of batik motifs from collaborations of existing batik motifs such as the kawung batik motif and Asian Games 2018 mascot image. Neural transfer style empowers people around the world to experiment with their own creativity seeing the importance of style transfer in the commercial arts world and how art exists in the real world Index Terms—CNN, Batik, Motif, Mascot, Silhouette.

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Drout, Riki J., Ashlee J. Howarth, Ken-ichi Otake, Timur Islamoglu, and Omar K. Farha. "Efficient extraction of inorganic selenium from water by a Zr metal–organic framework: investigation of volumetric uptake capacity and binding motifs." CrystEngComm 20, no. 40 (2018): 6140–45. http://dx.doi.org/10.1039/c8ce00992a.

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Li, Xiang, Linna Ma, Xinyue Mei, Yixiang Liu, and Huichuan Huang. "ggmotif: An R Package for the extraction and visualization of motifs from MEME software." PLOS ONE 17, no. 11 (November 3, 2022): e0276979. http://dx.doi.org/10.1371/journal.pone.0276979.

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MEME (Multiple Em for Motif Elicitation) is the most commonly used tool to identify motifs within deoxyribonucleic acid (DNA) or protein sequences. However, the results generated by the MEMEare saved using file formats .xml and .txt, which are difficult to read, visualize, or integrate with other widely used phylogenetic tree packages, such as ggtree. To overcome this problem, we developed the ggmotif R package, which provides two easy-to-use functions that can facilitate the extraction and visualization of motifs from the results files generated by the MEME. ggmotif can extract the information of the location of motif(s) on the corresponding sequence(s) from the .xml format file and visualize it. Additionally, the data extracted by ggmotif can be easily integrated with the phylogenetic data. On the other hand, ggmotif can obtain the sequence of each motif from the .txt format file and draw the sequence logo with the function ggseqlogo from the ggseqlogo R package. The ggmotif R package is freely available (including examples and vignettes) from GitHub at https://github.com/lixiang117423/ggmotif or from CRAN at https://CRAN.R-project.org/package=ggmotif.

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Al Sasongko, Sudi Mariyanto, Erni Dwi Jayanti, and Suthami Ariessaputra. "Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning." JOIV : International Journal on Informatics Visualization 6, no. 4 (December 31, 2022): 835. http://dx.doi.org/10.30630/joiv.6.4.1532.

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Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.

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Ebrahimi Majdar, Reza, Ali Ghasemian, Hossein Resalati, Ahmadreza Saraeian, Claudia Crestini, and Heiko Lange. "Facile Isolation of LCC-Fraction from Organosolv Lignin by Simple Soxhlet Extraction." Polymers 11, no. 2 (January 31, 2019): 225. http://dx.doi.org/10.3390/polym11020225.

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A new fractionation protocol for wheat straw organosolv lignin was developed on the basis of the dominating H-bonding orientations of its components. Acetone as H-bond accepting aprotic polar solvent and methanol as H-bond donating and accepting protic polar solvent were used in sequence. Obtained fractions were structurally and thermally analysed. The protocol allowed for the generation of purified lignin fractions and the isolation of a novel, yet unobserved lignin carbohydrate complex (LCC) fraction. This LCC fraction was found to contain exclusively phenyl glycosides and γ-esters as LCC motifs.

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KUANG, RUI, EUGENE IE, KE WANG, KAI WANG, MAHIRA SIDDIQI, YOAV FREUND, and CHRISTINA LESLIE. "PROFILE-BASED STRING KERNELS FOR REMOTE HOMOLOGY DETECTION AND MOTIF EXTRACTION." Journal of Bioinformatics and Computational Biology 03, no. 03 (June 2005): 527–50. http://dx.doi.org/10.1142/s021972000500120x.

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We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We further examine how to incorporate predicted secondary structure information into the profile kernel to obtain a small but significant performance improvement. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs" — short regions of the original profile that contribute almost all the weight of the SVM classification score — and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels" give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results also outperform cluster kernels while providing much better scalability to large datasets. Supplementary website:.

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Fathul Am and Enny Itje Sela. "Klasifikasi Batik Pekalongan Berdasarkan Citra dengan Metode GLCM dan JST Backpropagation." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 5, no. 1 (January 10, 2024): 614–21. http://dx.doi.org/10.35870/jimik.v5i1.532.

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Batik is an Indonesian cultural heritage that is internationally recognized by UNESCO. However, knowledge about the types of batik, especially traditional Pekalongan batik, is increasingly forgotten due to globalization. This research aims to create a Pekalongan traditional batik image classification system through Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Artificial Neural Network (ANN) classification method. This system aims to make it easier for people to identify Pekalongan batik motifs without requiring special skills. The results showed that the GLCM and JST methods can be used to classify Pekalongan batik can predict correctly. The use of JST Backpropagation architecture with 3 hidden layers resulted in train data accuracy of 46.6% and test data accuracy of 55.5%. This system is expected to help preserve the cultural heritage of batik and increase public understanding of Pekalongan batik motifs.

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Reddy, Ayaluri Mallikarjuna, Vakulabharanam Venkata Krishna, Lingamgunta Sumalatha, and Avuku Obulesh. "Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 965–76. http://dx.doi.org/10.2174/2213275912666190417151247.

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Background: Age estimation using face images has become increasingly significant in the recent years, due to diversity of potentially useful applications. Age group feature extraction, the local features, has received a great deal of attention. Objective: This paper derived a new age estimation operator called “Gradient Dual-Complete Motif Matrix (GD-CMM)” on the 3 x 3 neighborhood of gradient image. The GD-CMM divides the 3 x 3 neighborhood in to dual grids of size 2 x 2 each and on each 2 x 2 grid complete motif matrices are derived. Methods: The local features are extracted by using Motif Co-occurrence Matrix (MCM) and it is derived on 2 x 2 grid and the main disadvantage of this Motifs or Peano Scan Motifs (PSM) is they are static i.e. the initial position on a 2 x2 grid is fixed in deriving motifs, resulting with six different motifs. The advantage 3 x 3 neighborhood approaches over 2x 2 grids is the 3x3 grid identify the spatial relations among the pixels more precisely. The gradient images represent facial features more efficiently and human beings are more sensitive to gradient changes than original grey level intensities. Results: The proposed method is compared with other existing methods on FGNET, Google and scanned facial image databases. The experimental outcomes exhibited the superiority of proposed method than existing methods. Conclusion: On the GD-CMM, this paper derived co-occurrence features and machine learning classifiers are used for age group classification.

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Andono, Pulung Nurtantio, and Eko Hari Rachmawanto. "Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 13, 2021): 1–9. http://dx.doi.org/10.29207/resti.v5i1.2615.

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Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.

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El-Falah, Tarek, Thierry Lecroq, and Mourad Elloumi. "Extraction of Infrequent Simple Motifs from a Finite Set of Sequences using a Lattice Structure." Recent Patents on DNA & Gene Sequences 7, no. 2 (July 1, 2013): 123–27. http://dx.doi.org/10.2174/1872215611307020006.

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Parlea, Lorena G., Blake A. Sweeney, Maryam Hosseini-Asanjan, Craig L. Zirbel, and Neocles B. Leontis. "The RNA 3D Motif Atlas: Computational methods for extraction, organization and evaluation of RNA motifs." Methods 103 (July 2016): 99–119. http://dx.doi.org/10.1016/j.ymeth.2016.04.025.

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Journal articles on the topic 'Extraction de motifs fréquents'

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