Wang-Su Jeon, and Sang-Yong Rhee
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(1): 26-34 https://doi.org/10.5391/IJFIS.2017.17.1.26*Keywords : Leaf, Classification, Visual system, CNN, GoogleNet
Do Khac Tiep, Kinam Lee, Dae-Yeong Im, Bongwoo Kwak, and Young-Jae Ryoo
International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3): 220-228 https://doi.org/10.5391/IJFIS.2018.18.3.220*Keywords : Differential drive, Mobile robot, Path tracking, Fuzzy-PID controller, MATLAB/Simulink
Hye-Woo Lee, Noo-ri Kim, and Jee-Hyong Lee
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(1): 1-9 https://doi.org/10.5391/IJFIS.2017.17.1.1*Keywords : Neural network, Pre-training, Semi-supervised learning, Self-training, Dropout
Hansoo Lee, Jonggeun Kim, and Sungshin Kim
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(4): 229-234 https://doi.org/10.5391/IJFIS.2017.17.4.229*Keywords : Skewed class distribution, SMOTE, Gaussian random variable, Classification
Yagya Raj Pandeya, and Joonwhoan Lee
Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 154-160 https://doi.org/10.5391/IJFIS.2018.18.2.154*Keywords : Labeled dataset, Transfer learning, Ensemble method, Data augmentation
Wang-Su Jeon, and Sang-Yong Rhee
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(3): 170-176 https://doi.org/10.5391/IJFIS.2017.17.3.170*Keywords : Fingerprint recognition, Fingerpirnt classification, Batch normalization, Ensemble, CNN
Mohammed Rabah, Ali Rohan, Yun-Jong Han, and Sung-Ho Kim
International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3): 204-213 https://doi.org/10.5391/IJFIS.2018.18.3.204*Keywords : Fuzzy logic controller, Fuzzy-PID, Quadcopter, MATLAB/Simulink, Trajectory-tracking
Van-Suong Nguyen, Van-Cuong Do;, and Nam-Kyun Im
Int. J. Fuzzy Log. Intell. Syst. 2018; 18(1): 41-49 https://doi.org/10.5391/IJFIS.2018.18.1.41*Keywords : Artificial neural network (ANN), Automatic ship berthing, Distance measurement system, Real ports, Numerical simulations
Akmaljon Palvanov, and Young Im Cho
Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 126-134 https://doi.org/10.5391/IJFIS.2018.18.2.126*Keywords : Capsule networks, Dynamic routing, Residual learning, CNN, Logistic regression
Przemyslaw Grzegorzewski
Int. J. Fuzzy Log. Intell. Syst. 2017; 17(3): 137-144 https://doi.org/10.5391/IJFIS.2017.17.3.137*Keywords : Fuzzy relation, Noninteractive fuzzy relation, Separable fuzzy relation, Connected/disconnected variables
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.