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18 results on '"Tseng, T.-L."'

1. Rough-set-based approach to manufacturing process document retrieval.

Author

Huang, C.-C., Tseng, T.-L., Chuang, H.-F., and Liang, H.-F.

Subjects
MANUFACTURING processes, INFORMATION retrieval, VECTOR spaces, DATA mining, QUANTITATIVE research, ROUGH sets
Abstract

The large amount of digital information that is increasingly available in the manufacturing process makes information retrieval (IR) a critical issue in this knowledge-based manufacturing era. Many IR models including the vector space model (VSM), Boolean model, fuzzy set model and probability model have been proposed in the literature. However, the performance based on these models is not satisfactory to the expectations of the end users. The reasons contributing to the end users’ dissatisfaction are the imprecise query formulation and poor document representations. Most of the models of document representation are based on conventional statistical techniques. However, during manufacturing process document retrieval, the various qualitative data and attributes in the document database could not be easily analysed by using the current statistical approaches. This paper proposes a rough-set-based approach to enrich document representation. The document classification rules are generated and the premise terms are provided by the rough-set approach. Therefore, the retrieval performance of the VSM is enhanced through support from the rough-set-based approach. A case study that includes the comparison of manufacturing process document retrieval performance through the standard VSM and the rough-set-based approach is illustrated by empirical data. This paper forms the basis for solving many other similar document-retrieval problems that occur in manufacturing industry. [ABSTRACT FROM AUTHOR]

Published
2006
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2. Applying a hybrid data-mining approach to prediction problems: a case of preferred suppliers prediction.

Author

Tseng, T.-L., Huang, C.-C., Jiang, F., and Ho, J. C.

Subjects
DATA mining, DECISION making, VIDEO games, SUPPLY chains, KNOWLEDGE management, DATABASE searching
Abstract

Data mining has emerged as powerful tools to automate the decision-making processes. The deficiency of the traditional approach of data mining for prediction is that the low-accuracy decision rules that cannot be used and the quality rules, with high accuracy, do not guarantee that they will be used since the premise (condition) part of the rule should match with the input domain of the testing data sets. Furthermore, negative information/data, which are defined as information/data, misclassify the outcomes from the testing data set and are normally discarded. It is possible to reuse the information/data for training purposes and they contain a positive impact on prediction accuracy. This paper presents a data-mining-based hybrid approach that consists of a novel rough-set algorithm for feature selection and an enhanced multi-class support vector machines (SVMs) method for accurate prediction. The approach can simultaneously derive decision rules, identify the most significant features and generate a well-tuned prediction model with high accuracy. A case study of supplier selection of a video game system is validated by historical data and the results show the practical viability of the hybrid approach for predicting preferred suppliers. [ABSTRACT FROM AUTHOR]

Published
2006
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7. Primary and secondary glomerulonephritis II

Author

Valdivia Vega, R. P., primary, Perez Carlos, J., additional, LI, X., additional, Xu, X., additional, Zhang, W., additional, Ren, H., additional, Chen, N., additional, Yorioka, N., additional, Doi, T., additional, Hirashio, S., additional, Arita, M., additional, Hirabayashi, A., additional, Tilkiyan, E., additional, Chonova, E., additional, Ronchev, Y., additional, Kumchev, E., additional, Giamalis, P., additional, Spartalis, M., additional, Stangou, M., additional, Tsouchnikas, I., additional, Moysiades, D., additional, Dimopoulou, D., additional, Garyfalos, A., additional, Efstratiadis, G., additional, Memmos, D., additional, Schonermarck, U., additional, Eichhorn, P., additional, Sitter, T., additional, Wendler, T., additional, Vielhauer, V., additional, Lederer, S., additional, Fechner, K., additional, Fischereder, M., additional, Bantis, C., additional, Heering, P., additional, Kouri, N.-M., additional, Schwandt, C., additional, Kuhr, N., additional, Ivens, K., additional, Rump, L.-C., additional, Matta, V., additional, Melis, P., additional, Conti, M., additional, Cao, R., additional, Binda, V., additional, Altieri, P., additional, Asunis, A. M., additional, Catani, W., additional, Floris, M., additional, Angioi, A., additional, Congia, M., additional, Cucca, F., additional, Minerba, L., additional, Peri, M., additional, Pani, A., additional, Beck, L. H., additional, Fervenza, F. C., additional, Bomback, A. S., additional, Ayalon, R., additional, Irazabal, M. V., additional, Eirin, A., additional, Cattran, D. C., additional, Appel, G. B., additional, Salant, D. J., additional, Santoro, D., additional, Postorino, A., additional, Costantino, G., additional, Bellinghieri, G., additional, Savica, V., additional, Weiner, M., additional, Goh, S. M., additional, Mohammad, A., additional, Eriksson, P., additional, Westman, K., additional, Selga, D., additional, Salama, A., additional, Segelmark, M., additional, Chocova, Z., additional, Hruskova, Z., additional, Mareckova, H., additional, Svobodova, B., additional, Jancova, E., additional, Bednarova, V., additional, Rysava, R., additional, Tesar, V., additional, Hanzal, V., additional, Zamboch, K., additional, Grussmannova, M., additional, Svojanovsky, J., additional, Klaboch, J., additional, Kubisova, M., additional, Sevcik, J., additional, Olsanska, R., additional, Sobotkova, M., additional, Becvar, R., additional, Nemec, P., additional, Kodeda, M., additional, Jilek, D., additional, Hussain, M., additional, Dhaygude, A., additional, Cartery, C., additional, Huart, A., additional, Plaisier, E., additional, Bongard, V., additional, Montastruc, F., additional, Ronco, P., additional, Pourrat, J., additional, Chauveau, D., additional, Prasad, N., additional, Gurjar, D., additional, Bhadauria, D., additional, Sharma, R. K., additional, Gupta, A., additional, Kaul, A., additional, Jain, M., additional, Venning, M., additional, Brown, N., additional, Bruce, I., additional, Noor, S., additional, Bekker, P., additional, Potarca, A., additional, Dairaghi, D., additional, Miao, S., additional, Powers, J. P., additional, Jaen, J. C., additional, Schall, T. J., additional, Kalavrizioti, D., additional, Gerolymos, M., additional, Komninakis, D., additional, Rodi, M., additional, Mouzaki, A., additional, Kalliakmani, P., additional, Goumenos, D., additional, Choi, B. S., additional, Park, C. W., additional, Kim, Y.-S., additional, Yang, C. W., additional, Sun, I. O., additional, Qin, W., additional, Xie, L., additional, Tan, C., additional, Mian, W., additional, Fu, P., additional, Kaminskyy, V., additional, Hao, X., additional, Wang, W., additional, Cengiz, C., additional, Nur, C., additional, Nurdan, Y., additional, Selman, G., additional, Pinar, T., additional, Mehmet, T., additional, Lale, S., additional, Caliskan, S., additional, Shinzawa, M., additional, Yamamoto, R., additional, Nagasawa, Y., additional, Oseto, S., additional, Mori, D., additional, Niihata, K., additional, Fukunaga, M., additional, Yamauchi, A., additional, Tsubakihara, Y., additional, Rakugi, H., additional, Isaka, Y., additional, Chen, J.-S., additional, Lin, Y.-F., additional, Lin, W.-Y., additional, Shu, K.-H., additional, Chen, H.-H., additional, Wu, C.-J., additional, Yang, C.-S., additional, Tseng, T.-L., additional, Zaza, G., additional, Bernich, P., additional, Lupo, A., additional, Panizo, N., additional, Rivera, F., additional, Lopez Gomez, J. M., additional, Regn, S. R. o. G., additional, Ceresini, G., additional, Vaglio, A., additional, Urban, M. L., additional, Corradi, D., additional, Usberti, E., additional, Palmisano, A., additional, Buzio, C., additional, Zineb, H., additional, Ramdani, B., additional, Marques, L. P. J., additional, Rioja, L. D. S., additional, Rocco, R., additional, Nery, A. C. F., additional, Novaes, B. C., additional, Bridoux, F., additional, Sicard, A., additional, Labatut, D., additional, Touchard, G., additional, Sarkozy, C., additional, Vanhille, P., additional, Callard, P., additional, Essig, M., additional, Provot, F., additional, Nony, A., additional, Karras, A., additional, Agustin, C. P., additional, M Belen, H. R., additional, Carmen, C. P., additional, Eliana, O., additional, Elisa, P., additional, Luis, P., additional, Alberto, M.-C., additional, Javier, N., additional, Isabel, F., additional, Atzeni, A., additional, Fois, A., additional, Piras, D., additional, Maxia, S., additional, Sau, G., additional, Pili, G., additional, Porcu, M., additional, Derudas, D., additional, Angelucci, E., additional, Ledda, A., additional, La Nasa, G., additional, Ossareh, S., additional, Asgari, M., additional, Savaj, S., additional, Ataipour, Y., additional, Abdi, E., additional, Malakoutian, T., additional, Rajaa, R., additional, Berkchi, F. Z., additional, Haffane, L., additional, Squalli, Z., additional, Rouass, L., additional, Al Hamany, Z., additional, Ezzaitouni, F., additional, Benamar, L., additional, Bayahya, R., additional, Ouzeddoun, N., additional, Gao-Yuan, H., additional, Yao, X., additional, Xin, C., additional, Zhen, C., additional, Yong-Chun, G., additional, Qing-Wen, W., additional, Hui-Ping, C., additional, Da-XI, J., additional, De-Hua, G., additional, Wei-Xin, H., additional, Zhi-Hong, L., additional, Fatima Zahra, B., additional, Laila, H., additional, Zoubair, S., additional, Naima, O., additional, Smykal-Jankowiak, K., additional, Niemir, Z., additional, Polcyn-Adamczak, M., additional, Szramka-Pawlak, B., additional, Zaba, R., additional, Zhang, C., additional, MA, Y., additional, Shen, P., additional, Ouyang, Y., additional, Pan, X., additional, Wang, Z., additional, Feng, X., additional, and Ni, L., additional

Published
2012
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14. Rough set theory for data mining for fault diagnosis on distribution feeder.

Author

Peng, J.-T., Chien, C. F., and Tseng, T. L. B.

Subjects
ELECTRIC power failures, DATABASES, SET theory, DATA mining
Abstract

Distribution feeder faults cause power outages, therefore it is crucial to diagnose and thus locate the fault quickly to reduce the duration of the outage. In practice, feeder patrols usually identify the fault locations by referencing the regional distribution of the calls reporting trouble, the abnormal observations of the feeders that have been reported in the calls, and the observed conditions in the surrounding environments. Feeder patrols in Taiwan have recorded each fault on a table that includes time, date, month, year, address, equipment at fault, causes or accidents, and etc. The database has accumulated a large base of information for many years. This study aims to use rough set theory as a data-mining tool to derive useful patterns and rules for distribution feeder faulty equipment diagnosis and fault location. In particular, the historical data of distribution feeder faults of Taiwan Power Company was used for validation and the results show the practical viability of the proposed approach. [ABSTRACT FROM AUTHOR]

Published
2004
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18. Synthetic and cytotoxic studies of alpha-methylene-gamma-butyrolactone bearing pyrimidines.

Author

Huang BR, Lee KH, Tseng TL, Wang HM, Chen CF, and Tzeng CC

Subjects
4-Butyrolactone chemical synthesis, 4-Butyrolactone pharmacology, Antineoplastic Agents pharmacology, Humans, Pyrimidines pharmacology, Tumor Cells, Cultured drug effects, 4-Butyrolactone analogs & derivatives, Antineoplastic Agents chemical synthesis, Pyrimidines chemical synthesis
Abstract

A total of ten pyrimidine alpha-methylene-gamma-butyrolactones were synthesized as potential antitumor agents on the basis of their possible action as Michael acceptors for DNA/RNA and cellular enzymes. The synthesis of these heterocycles involved a convenient Reformatsky-type reaction of pyrimidinyl ketones with ethyl alpha-(bromomethyl)acrylate. The preliminary in vitro cytotoxic assay indicated that these synthetic compounds were essentially active against the growth of KB, Hep-2, HeLa and Colo-205 cells. Among them, 5'-biphenyl-5'-(uracil-1-ylmethyl)-2'-oxo-3'-methylenetetrahydr ofuran (5f) demonstrated to be the most potent antileukemic agent.

Published
1993

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