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Your search keyword '"Lin, Mu"' showing total 32 results
Start Over Author "Lin, Mu" Publication Type Books
32 results on '"Lin, Mu"'

3. Emerging Research in Medical Sciences Vol. 4

Author

Sandouk, Fayez, additional, Jerf, Feras Al, additional, Al-Halabi, M. H. D. Bassel, additional, AlDallal, Salma, additional, Ufoaroh, Chinyelu Uchenna, additional, Ele, Prince Udegbunam, additional, Anyabolu, Arthur Ebelenna, additional, Enemuo, Emeka Hyacinth, additional, Emegoakor, Chiemelu Dickson, additional, Okoli, Chinedu Christian, additional, Umeh, Eric Okechukwu, additional, Anyabolu, Ernest Ndukaife, additional, Audu, L. I., additional, Otuneye, A. T., additional, Mairami, A. B., additional, Mshelia, L. J., additional, Nwatah, V. E., additional, Lin, Mu-Lien, additional, Chiu, Hung-Wei, additional, Shih, Zao-Ming, additional, Lee, Po-Ying, additional, Li, Pei-Zhi, additional, Guo, Chin-Hong, additional, Luo, Yuan-Jie, additional, Lin, Shen-Chieh, additional, Lin, Kwan-Yu, additional, Hsu, Yu-Ming, additional, Pang, Angela, additional, Pang, Weiwu, additional, Mohamed, Fathy, additional, Mohamed, M. Khedre, additional, Mohamed, Nagaty, additional, Naser, Zaghloul, additional, Vega-Jiménez, Oscar, additional, Herrera-Santoyo, Walter, additional, Díaz-Becerril, Luis Antonio, additional, Ortega, Mayra del Carmen Jiménez, additional, Padilla-Raygoza, Nicolás, additional, Ayama, Umanah, additional, Abdul-Azeez, Omogbai, additional, Bamidele, Osagbemiro Babatope, additional, Zuzana, Hujová, additional, Jabeen, Farhat, additional, Gupta, Rajeev, additional, Nagpal, Archana, additional, Katna, Vishal, additional, Mahajan, Vishwas, additional, Bali, Pragya, additional, Smit, Jan H. T., additional, Todd, Matthew R. H., additional, Kastanis, G., additional, Lazanaki, E., additional, Velivasakis, G., additional, Spyrantis, M., additional, Pantouvaki, A., additional, Breen, H., additional, Chikadibia, Amadi, Fyneface, additional, Roseline, Emeji,, additional, Eedee, Konne, Felix, additional, Baribefii, Jacob, Ransom, additional, Yongu, Williams T., additional, Elachi, Itodo C., additional, Mue, Daniel D., additional, Kortor, Joseph N., additional, Orkuma, Joseph, additional, Ochoga, M., additional, Chukwuka, I. O., additional, Adio, A. O., additional, and Chinawa, N. E., additional

Published
2020
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30. Removing Smoothing from Naive Bayes Text Classifier.

Author

Fan, Wenfei, Wu, Zhaohui, Yang, Jun, Zhu, Wang-bin, Lin, Ya-ping, Lin, Mu, and Chen, Zhi-ping

Abstract

Smoothing is applied in Bayes classifier when the maximum likelihood (ML) estimate can't solve the problem in the absence of some features in training data. However, smoothing doesn't have firm theoretic base to rely on as ML estimate does. In this paper, we propose two novel strategies to remove smoothing from the classifier without sacrificing classification accuracy: NB_TF and NB_TS. NB_TF adjusts the classifier by adding the test document before classification and it is suitable for online categorization. NB_TS improves the performance by adding the whole test set to the classifier in the training stage and it is more efficient for batch categorization. The experiments and analysis show that NB_TS outperforms Laplace additive smoothing and Simple Good-Turing (SGT) smoothing, and NB_TF performs better than Laplace additive smoothing. [ABSTRACT FROM AUTHOR]

Published
2005
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31. Creative Design by Chance Based Interactive Evolutionary Computation.

Author

Wang, Lipo, Chen, Ke, Ong, Yew, Hong, Chao-Fu, Yang, Hsiao-Fang, and Lin, Mu-Hua

Abstract

Kotler and Trias De Bes (2003) at Lateral Marketing defined the creativity: each cluster had its own concepts, when a new need was generated and the designer could not find a solution from his own clusters, therefore he had a gap to overcome. This gap was as the original of creativity. If he wants to solve this problem, chose a new important concept for beginning was the only way he could do. This phenomenon was called the laterally transmitting. Then according to the designer's subjective to choose the concept and connected other cluster to generate or enter a new cluster. This kind designing process could generate a creative product. But it also brought a new problem, there had many concepts in conceptual space, how to decide an effectiveness concept and extents it to create a good product. Here we combined Watt's (2003) Small World model and Ohsawa and McBurney (2003) Chance Discovery Model to decide the creative probability and decreased the searching path length. Finally, we integrated the choosing mechanism and recombination mechanism into our chance based IEC (CBIEC) model. And we applied on the cell phone design. After the experiment we analyzed the interactive data found the choosing mechanism could bring the effective creativity and the recombination mechanism could quickly search as we expected the short-cut effect. Beside these results we also directly investigated the subjective of designer found our CBIEC model also better than the IGA (interactive genetic algorithms, Caldwell and Johnston, 1991). Keywords: lateral marketing, chance discovery, small-world, interactive evolutionary computation (IEC), value-focused thinking (VFT). [ABSTRACT FROM AUTHOR]

Published
2005
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32. Curve-Based Greedy Routing Algorithm for Sensor Networks.

Author

Lu, Xicheng, Zhao, Wei, Zhang, Jin, Lin, Ya-ping, Lin, Mu, Li, Ping, and Zhou, Si-wang

Abstract

Routing packets along a specified curve is a new approach to forwarding packets in large-scale dense sensor networks. Forwarding packets along trajectories can be very effective in implementing many networking functions when standard bootstrapping or configuration services are not available, as will be the case in sensor networks where nodes are thrown or dropped to form a one-time use network. In this paper, investigating Trajectory-Based Forwarding (TBF), we propose a novel curve-based greedy routing algorithm (CBGR) for sensor networks. In CBGR, by using the location information of a source node and the sink, the source constructs a B-spline curve as forwarding trajectory and encodes the curve into the packets. Upon receiving each packet, the intermediate nodes decode it and construct a simple dynamic forwarding table (DFT) by different greedy forwarding strategies. Then, the packets are forwarded along the selected curve based on DFT. Several greedy forwarding strategies are discussed. CBGR is a distributed routing strategy and easy to implementation. By selecting multiple forwarding curves, CBGR can balance the energy consumption of the nodes effectively. The analysis and simulation also show that CBGR has better performance. [ABSTRACT FROM AUTHOR]

Published
2005
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