| Nome |
# |
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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
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Data stream classification by dynamic incremental semi-supervised fuzzy clustering, file dd9e0c6c-4d48-1e9c-e053-3a05fe0a45ef
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169
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A framework for intelligent Twitter data analysis with non-negative matrix factorization, file dd9e0c6c-6b12-1e9c-e053-3a05fe0a45ef
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93
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Interpretable fuzzy partitioning of classified data with variable granularity, file dd9e0c6c-70f6-1e9c-e053-3a05fe0a45ef
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67
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A fuzzy method for RNA-Seq differential expression analysis in presence of multireads, file dd9e0c63-e827-1e9c-e053-3a05fe0a45ef
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42
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GrCount: Counting method for uncertain data, file dd9e0c66-89cc-1e9c-e053-3a05fe0a45ef
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41
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A Granular Computing Method for OWL Ontologies, file dd9e0c6a-f529-1e9c-e053-3a05fe0a45ef
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41
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Granular counting of uncertain data, file dd9e0c6c-62ca-1e9c-e053-3a05fe0a45ef
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26
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Data stream classification by dynamic incremental semi-supervised fuzzy clustering, file dd9e0c66-8b80-1e9c-e053-3a05fe0a45ef
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25
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Py4JFML: A Python wrapper for using the IEEE Std 1855-2016 through JFML, file dd9e0c66-89d2-1e9c-e053-3a05fe0a45ef
|
19
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Interpretable fuzzy partitioning of classified data with variable granularity, file dd9e0c65-7a94-1e9c-e053-3a05fe0a45ef
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15
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Identification and evaluation of cognitive deficits in schizophrenia using "Machine learning", file dd9e0c66-c1ca-1e9c-e053-3a05fe0a45ef
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14
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Fine-tuning the fuzziness of strong fuzzy partitions through PSO, file dd9e0c68-c3f4-1e9c-e053-3a05fe0a45ef
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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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A Granular Computing Method for OWL Ontologies, file dd9e0c65-0257-1e9c-e053-3a05fe0a45ef
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10
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Granular counting of uncertain data, file dd9e0c67-95c5-1e9c-e053-3a05fe0a45ef
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9
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Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks, file dd9e0c68-cc35-1e9c-e053-3a05fe0a45ef
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9
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null, file dd9e0c68-53f7-1e9c-e053-3a05fe0a45ef
|
8
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A framework for intelligent Twitter data analysis with non-negative matrix factorization, file dd9e0c6a-5c84-1e9c-e053-3a05fe0a45ef
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8
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Drone safe-landing with real-time route optimization, file dd9e0c6b-866b-1e9c-e053-3a05fe0a45ef
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8
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null, file dd9e0c67-7b52-1e9c-e053-3a05fe0a45ef
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7
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null, file dd9e0c64-7b9d-1e9c-e053-3a05fe0a45ef
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6
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Incremental adaptive semi-supervised fuzzy clustering for data stream classification, file dd9e0c65-72b9-1e9c-e053-3a05fe0a45ef
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6
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Credit card fraud detection by dynamic incremental semi-supervised fuzzy clustering, file dd9e0c68-a452-1e9c-e053-3a05fe0a45ef
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6
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Preliminary Evaluation of TinyYOLO on a New Dataset for Search-and-Rescue with Drones, file dd9e0c6a-6141-1e9c-e053-3a05fe0a45ef
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6
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null, file dd9e0c64-7b9f-1e9c-e053-3a05fe0a45ef
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5
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null, file dd9e0c65-41c9-1e9c-e053-3a05fe0a45ef
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5
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Interpretability of Fuzzy Systems: Current Research Trends and Prospects, file dd9e0c69-404a-1e9c-e053-3a05fe0a45ef
|
5
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VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results, file dd9e0c6a-5b14-1e9c-e053-3a05fe0a45ef
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5
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null, file dd9e0c62-be3c-1e9c-e053-3a05fe0a45ef
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4
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null, file dd9e0c64-0f60-1e9c-e053-3a05fe0a45ef
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4
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null, file dd9e0c66-19ad-1e9c-e053-3a05fe0a45ef
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4
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Human Detection in Drone Images Using YOLO for Search-and-Rescue Operations, file 37f1deba-dc87-4ed8-8dd3-cd1264ba456d
|
3
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|
null, file dd9e0c62-7924-1e9c-e053-3a05fe0a45ef
|
3
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null, file dd9e0c63-24cf-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c63-4f90-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c63-e43d-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c64-9e32-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c64-c736-1e9c-e053-3a05fe0a45ef
|
3
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|
null, file dd9e0c65-74f6-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c65-79cd-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c65-94d9-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c66-0ee1-1e9c-e053-3a05fe0a45ef
|
3
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|
null, file dd9e0c66-c1cd-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c68-a610-1e9c-e053-3a05fe0a45ef
|
3
|
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Exploiting Particle Swarm Optimization to Attune Strong Fuzzy Partitions Based on Cuts, file dd9e0c69-0ca6-1e9c-e053-3a05fe0a45ef
|
3
|
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2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), file dd9e0c69-556e-1e9c-e053-3a05fe0a45ef
|
3
|
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Descriptive Stability of Fuzzy Rule-Based Systems, file dd9e0c6b-8e19-1e9c-e053-3a05fe0a45ef
|
3
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Tecniche di Computer Vision per applicazioni di IA sostenibile mediante droni, file dd9e0c6b-a66a-1e9c-e053-3a05fe0a45ef
|
3
|
|
Incremental adaptive semi-supervised fuzzy clustering for data stream classification, file dd9e0c6b-d0b5-1e9c-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
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DCf : A Double Clustering framework for fuzzy information granulation, file dd9e0c62-7849-1e9c-e053-3a05fe0a45ef
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2
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null, file dd9e0c64-352c-1e9c-e053-3a05fe0a45ef
|
2
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|
null, file dd9e0c65-98ca-1e9c-e053-3a05fe0a45ef
|
2
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|
null, file dd9e0c66-fee6-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c66-feec-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c67-0261-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c67-46de-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
|
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Application of machine learning to predict obstructive sleep apnea syndrome severity, file dd9e0c69-e72c-1e9c-e053-3a05fe0a45ef
|
2
|
|
Possibilistic Granular Count: Derivation and Extension to Granular Sum, file dd9e0c6b-9c68-1e9c-e053-3a05fe0a45ef
|
2
|
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Computer Vision Meets Drones: Our Research Experience, file 1c090834-bc01-4ffa-ba6c-b767bfe8266e
|
1
|
|
Semi-Supervised Fuzzy C-Means for Regression, file 2fe8e7a7-0139-4592-a0b4-61a5ea0bcf64
|
1
|
|
Density-based clustering with fully-convolutional networks for crowd flow detection from drones, file 9ef5390e-da49-4d5c-bc79-b2b9fc4937e6
|
1
|
|
null, file dd9e0c62-4a3e-1e9c-e053-3a05fe0a45ef
|
1
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Designing Strong Fuzzy Partitions from data with DC*, file dd9e0c62-6103-1e9c-e053-3a05fe0a45ef
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1
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Interpretability constraints for fuzzy information granulation, file dd9e0c62-7677-1e9c-e053-3a05fe0a45ef
|
1
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null, file dd9e0c62-7c4f-1e9c-e053-3a05fe0a45ef
|
1
|
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null, file dd9e0c62-c480-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c64-67de-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c64-a4ce-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c64-a571-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c65-0e94-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c66-fa25-1e9c-e053-3a05fe0a45ef
|
1
|
|
An incremental algorithm for granular counting with possibility theory, file dd9e0c68-c3e9-1e9c-e053-3a05fe0a45ef
|
1
|
|
Possibilistic Bounds for Granular Counting, file dd9e0c68-df3b-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-11b3-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c6b-230c-1e9c-e053-3a05fe0a45ef
|
1
|
|
Crowd Flow Detection from Drones with Fully Convolutional Networks and Clustering, file f5e29bd4-f2c6-4808-9f75-93ccddae5a8a
|
1
|
| Totale |
1.304 |