International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 229-243
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Changwon Baek, Jiho Kang, and SangSoo Choi
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 244-258
https://doi.org/10.5391/IJFIS.2023.23.3.244*Keywords: KoBERT, Word2vec, Public opinion analysis, Sentiment classification
JunTak Lee, Tae-Won Kang, Yong-Sik Choi, and Jin-Woo Jung
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 259-269
https://doi.org/10.5391/IJFIS.2023.23.3.259*Keywords: Generalized Voronoi diagram, Path finding, DP algorithm, A-star algorithm
Faiz Muhammad Khan, Naila Bibi , Saleem Abdullah, and Azmat Ullah
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 270-293
https://doi.org/10.5391/IJFIS.2023.23.3.270*Keywords: Complex fuzzy sets, Rough sets, Averaging and geometric operators, EDAS method, MCGDM
Thiti Gaketem , Pannawit Khamrot
, Pongpun Julatha
, Rukchart Prasertpong
, and Aiyared Iampan
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 294-302
https://doi.org/10.5391/IJFIS.2023.23.3.294*Keywords: UP-algebra, Shift UP-filter, Fuzzy shift UP-filter, Bipolar fuzzy shift UP-filter, Neutrosophic shift UP-filter
Yeon Seok Eom , Sang Min Yun
, and Seok Jong Lee
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 303-310
https://doi.org/10.5391/IJFIS.2023.23.3.303*Keywords: Generalized fuzzy (r, s)-continuous, Generalized fuzzy (r, s)-irresolute.
Mitali Routaray, Prakash Kumar Sahu, and Sunima Naik
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 311-317
https://doi.org/10.5391/IJFIS.2023.23.3.311*Keywords: Fuzzy &image,*-structure space, Fuzzy &image,*-structure Hausdorff space, Fuzzy &image,*-structure compact spaces, Fuzzy &image,*-structure compact-open topology
Zahra Roohanizadeh, Ezzatallah Baloui Jamkhaneh , and Einolah Deiri
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 318-335
https://doi.org/10.5391/IJFIS.2023.23.3.318*Keywords: (&alpha,1, ,&alpha,2)-cut set, Generalized intuitionistic fuzzy distribution, Generalized intuitionistic fuzzy number, Generalized intuitionistic fuzzy reliability, Moore and Bilikam.
Sultan H. Almotiri , Mohd Nadeem
, Mohammed A. Al Ghamdi
, and Raees Ahmad Khan
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 336-352
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Chayan Bhatt and Sunita Singhal
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 353-364
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Alhanouf Alburaikan, Hamiden Abd El-Wahed Khalifa, and Muhammad Saeed
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(3): 365-374
https://doi.org/10.5391/IJFIS.2023.23.3.365*Keywords: Optimization problems, Mathematical model, Cooperative continuous static games, Multi-objective decision making, Piecewise quadratic fuzzy number, Fuzzy logic, Close interval approximation, Weighting approach, &alpha,-Pareto optimal solution, Sensitivity analysis
Sangyun Lee and Sungjun Hong
International Journal of Fuzzy Logic and Intelligent Systems 2022;22: 339-349 https://doi.org/10.5391/IJFIS.2022.22.4.339Hamzeh Zureigat, Abd Ulazeez Alkouri, Areen Al-khateeb, Eman Abuteen, and Sana Abu-Ghurra
International Journal of Fuzzy Logic and Intelligent Systems 2023;23: 11-19 https://doi.org/10.5391/IJFIS.2023.23.1.11Hamzeh Zureigat, Abd Ulazeez Alkouri, Areen Al-khateeb, Eman Abuteen, and Sana Abu-Ghurra
International Journal of Fuzzy Logic and Intelligent Systems 2023;23: 11-19Sangyun Lee and Sungjun Hong
International Journal of Fuzzy Logic and Intelligent Systems 2022;22: 339-349Architecture of a conventional SCNN. The network is trained by contrastive loss in the training stage, whereas a distance function is used to compute the similarity metric in the testing stage.
|@|~(^,^)~|@|The proposed ESCNN architecture, which consists of three parts: (a) Siamese, (b) extension, and (c) decision parts. The feature dimensions are denoted as
Visualization of the features learned by the ESCNN: (a) positive and (b) negative samples.
|@|~(^,^)~|@|Training strategy of the proposed network. The network is optimized by a combination of two loss functions: 1) contrastive loss for the Siamese part and 2) cross-entropy loss for all parts, including the extension and decision parts.
|@|~(^,^)~|@|Examples from the iLIDS–VID dataset.
|@|~(^,^)~|@|Some example results: (a) positive and (b) negative samples.
|@|~(^,^)~|@|ROC curves for the methods under consideration.