| Nome |
# |
|
Interpretability of Fuzzy Systems: Current Research Trends and Prospects, file dd9e0c6c-4e7f-1e9c-e053-3a05fe0a45ef
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309
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Py4JFML: A Python wrapper for using the IEEE Std 1855-2016 through JFML, file dd9e0c6c-1ad5-1e9c-e053-3a05fe0a45ef
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190
|
|
FISDeT: Fuzzy Inference System Development Tool, file dd9e0c63-e28c-1e9c-e053-3a05fe0a45ef
|
113
|
|
A framework for intelligent Twitter data analysis with non-negative matrix factorization, file dd9e0c6c-6b12-1e9c-e053-3a05fe0a45ef
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93
|
|
Interpretable fuzzy partitioning of classified data with variable granularity, file dd9e0c6c-70f6-1e9c-e053-3a05fe0a45ef
|
67
|
|
The FISDeT software: Application to beer style classification, file dd9e0c6c-5d23-1e9c-e053-3a05fe0a45ef
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61
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|
Py4JFML: A Python wrapper for using the IEEE Std 1855-2016 through JFML, file dd9e0c66-89d2-1e9c-e053-3a05fe0a45ef
|
19
|
|
Interpretable fuzzy partitioning of classified data with variable granularity, file dd9e0c65-7a94-1e9c-e053-3a05fe0a45ef
|
15
|
|
The FISDeT software: Application to beer style classification, file dd9e0c64-ba81-1e9c-e053-3a05fe0a45ef
|
14
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|
Fine-tuning the fuzziness of strong fuzzy partitions through PSO, file dd9e0c68-c3f4-1e9c-e053-3a05fe0a45ef
|
14
|
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Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images, file dd9e0c6a-a7e9-1e9c-e053-3a05fe0a45ef
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12
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Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks, file dd9e0c66-6585-1e9c-e053-3a05fe0a45ef
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11
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Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks, file dd9e0c68-cc35-1e9c-e053-3a05fe0a45ef
|
9
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|
null, file dd9e0c68-53f7-1e9c-e053-3a05fe0a45ef
|
8
|
|
A framework for intelligent Twitter data analysis with non-negative matrix factorization, file dd9e0c6a-5c84-1e9c-e053-3a05fe0a45ef
|
8
|
|
Drone safe-landing with real-time route optimization, file dd9e0c6b-866b-1e9c-e053-3a05fe0a45ef
|
8
|
|
null, file dd9e0c65-a229-1e9c-e053-3a05fe0a45ef
|
7
|
|
null, file dd9e0c67-7b52-1e9c-e053-3a05fe0a45ef
|
7
|
|
null, file dd9e0c64-7b9d-1e9c-e053-3a05fe0a45ef
|
6
|
|
Preliminary Evaluation of TinyYOLO on a New Dataset for Search-and-Rescue with Drones, file dd9e0c6a-6141-1e9c-e053-3a05fe0a45ef
|
6
|
|
null, file dd9e0c64-7b9f-1e9c-e053-3a05fe0a45ef
|
5
|
|
null, file dd9e0c65-41c9-1e9c-e053-3a05fe0a45ef
|
5
|
|
Interpretability of Fuzzy Systems: Current Research Trends and Prospects, file dd9e0c69-404a-1e9c-e053-3a05fe0a45ef
|
5
|
|
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results, file dd9e0c6a-5b14-1e9c-e053-3a05fe0a45ef
|
5
|
|
null, file dd9e0c64-0f60-1e9c-e053-3a05fe0a45ef
|
4
|
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null, file dd9e0c63-4f90-1e9c-e053-3a05fe0a45ef
|
3
|
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null, file dd9e0c64-9e32-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c64-c736-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c65-74f6-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c65-79cd-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c66-c1cd-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c66-fa10-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c68-a610-1e9c-e053-3a05fe0a45ef
|
3
|
|
Exploiting Particle Swarm Optimization to Attune Strong Fuzzy Partitions Based on Cuts, file dd9e0c69-0ca6-1e9c-e053-3a05fe0a45ef
|
3
|
|
2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), file dd9e0c69-556e-1e9c-e053-3a05fe0a45ef
|
3
|
|
Descriptive Stability of Fuzzy Rule-Based Systems, file dd9e0c6b-8e19-1e9c-e053-3a05fe0a45ef
|
3
|
|
Effect of fuzziness in fuzzy rule-based classifiers defined by strong fuzzy partitions and winner-takes-all inference, file 4f8fd516-0ac6-4549-82e0-2d6bb4dce9d1
|
2
|
|
null, file dd9e0c62-45fb-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c62-72b8-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c62-88a1-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c64-352c-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c66-d299-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c67-46de-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c69-0e18-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c69-48a3-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c69-5cc0-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c69-6d29-1e9c-e053-3a05fe0a45ef
|
2
|
|
Segmentation of remotely sensed images with a neuro-fuzzy inference system, file dd9e0c6b-b321-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c62-4a3e-1e9c-e053-3a05fe0a45ef
|
1
|
|
Designing Strong Fuzzy Partitions from data with DC*, file dd9e0c62-6103-1e9c-e053-3a05fe0a45ef
|
1
|
|
Logica, file dd9e0c62-624d-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-7c4f-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-8977-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c63-49b7-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c64-67de-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c67-0f68-1e9c-e053-3a05fe0a45ef
|
1
|
|
Explainable AI beer style classifier, file dd9e0c69-4044-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c69-7563-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c6a-ca89-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c6b-230c-1e9c-e053-3a05fe0a45ef
|
1
|
| Totale |
1.070 |