| Nome |
# |
|
Energy Signature Analysis: Knowledge at Your Fingertips, file e384c42d-ba62-d4b2-e053-9f05fe0a1d67
|
562
|
|
Energy saving models for wireless sensor networks, file e384c42e-171b-d4b2-e053-9f05fe0a1d67
|
545
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-13a8-d4b2-e053-9f05fe0a1d67
|
470
|
|
Frequent Itemsets Mining for Big Data: A Comparative Analysis, file e384c42f-9940-d4b2-e053-9f05fe0a1d67
|
395
|
|
BAC: A bagged associative classifier for big data frameworks, file e384c42f-1653-d4b2-e053-9f05fe0a1d67
|
345
|
|
Forecasting: theory and practice, file e384c432-e7a2-d4b2-e053-9f05fe0a1d67
|
309
|
|
Network Digest analysis by means of association rules, file e384c42f-41e5-d4b2-e053-9f05fe0a1d67
|
276
|
|
PaMPa-HD: A Parallel MapReduce-Based Frequent Pattern Miner for High-Dimensional Data, file e384c42e-d384-d4b2-e053-9f05fe0a1d67
|
190
|
|
DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-5eb3-d4b2-e053-9f05fe0a1d67
|
160
|
|
SaFe-NeC: A scalable and flexible system for network data characterization, file e384c42f-45ab-d4b2-e053-9f05fe0a1d67
|
153
|
|
Automating concept-drift detection by self-evaluating predictive model degradation, file e384c430-f738-d4b2-e053-9f05fe0a1d67
|
104
|
|
Double-Step deep learning framework to improve wildfire severity classification, file e384c432-df3b-d4b2-e053-9f05fe0a1d67
|
84
|
|
Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images, file e384c432-c00a-d4b2-e053-9f05fe0a1d67
|
79
|
|
Evaluating espresso coffee quality by means of time-series feature engineering, file e384c432-4b00-d4b2-e053-9f05fe0a1d67
|
71
|
|
Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining, file e384c431-5af1-d4b2-e053-9f05fe0a1d67
|
61
|
|
From Hotel Reviews to City Similarities: A Unified Latent-Space Model, file e384c431-4486-d4b2-e053-9f05fe0a1d67
|
58
|
|
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction, file e384c434-1d09-d4b2-e053-9f05fe0a1d67
|
54
|
|
Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine, file e384c430-c403-d4b2-e053-9f05fe0a1d67
|
49
|
|
REDTag: A Predictive Maintenance Framework for Parcel Delivery Services, file e384c431-b91a-d4b2-e053-9f05fe0a1d67
|
45
|
|
Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis, file e384c431-235c-d4b2-e053-9f05fe0a1d67
|
38
|
|
Exploiting scalable machine-learning distributed frameworks to forecast power consumption of buildings, file e384c431-9d46-d4b2-e053-9f05fe0a1d67
|
23
|
|
Effective video hyperlinking by means of enriched feature sets and monomodal query combinations, file e384c431-78f1-d4b2-e053-9f05fe0a1d67
|
20
|
|
Exploiting scalable machine-learning distributed frameworks to forecast power consumption of buildings, file e384c431-abb8-d4b2-e053-9f05fe0a1d67
|
20
|
|
A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data, file e384c433-128d-d4b2-e053-9f05fe0a1d67
|
20
|
|
METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models, file e384c430-2a55-d4b2-e053-9f05fe0a1d67
|
19
|
|
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery, file 8cd55256-8bf6-4199-8a56-2b82f0bf8569
|
16
|
|
Theory-Guided Deep Learning Algorithms: An Experimental Evaluation, file 183cec93-efb4-4ee6-a609-ddb034067a93
|
11
|
|
Exploring waste-collection fleet data: challenges in a real-world use case from multiple data providers, file e384c434-7e4a-d4b2-e053-9f05fe0a1d67
|
11
|
|
A hybrid cloud-to-edge predictive maintenance platform, file e384c433-e15a-d4b2-e053-9f05fe0a1d67
|
8
|
|
SaFe-NeC: A scalable and flexible system for network data characterization, file e384c42f-1b84-d4b2-e053-9f05fe0a1d67
|
7
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-2c56-d4b2-e053-9f05fe0a1d67
|
7
|
|
Frequent Itemsets Mining for Big Data: A Comparative Analysis, file e384c431-7923-d4b2-e053-9f05fe0a1d67
|
7
|
|
DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-3460-d4b2-e053-9f05fe0a1d67
|
7
|
|
Training physics-informed neural networks: One learning to rule them all?, file 8d629659-3415-43f6-aa75-57279a9f7318
|
6
|
|
Explaining deep convolutional models by measuring the influence of interpretable features in image classification, file cc9ea5b1-ee6c-408e-a9fb-d519d701da53
|
6
|
|
Real-time Individuation of Global Unsafe Anomalies and Alarm Activation, file e384c42f-3aaf-d4b2-e053-9f05fe0a1d67
|
6
|
|
A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data, file e384c42f-d55b-d4b2-e053-9f05fe0a1d67
|
6
|
|
SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-0dbd-d4b2-e053-9f05fe0a1d67
|
5
|
|
Trusting deep learning natural-language models via local and global explanations, file e384c434-9a7b-d4b2-e053-9f05fe0a1d67
|
5
|
|
Trusting deep learning natural-language models via local and global explanations, file 377303a3-27ff-4cb9-82be-2c54f1ebe2f3
|
4
|
|
Energy saving models for wireless sensor networks, file e384c42e-a47a-d4b2-e053-9f05fe0a1d67
|
4
|
|
Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis, file e384c431-5482-d4b2-e053-9f05fe0a1d67
|
4
|
|
AI models for automated segmentation of engineered polycystic kidney tubules, file d92deba4-d0e1-4785-ade0-e8777c1af501
|
3
|
|
Enhancing manufacturing intelligence through an unsupervised data-driven methodology for cyclic industrial processes, file e384c433-ab2d-d4b2-e053-9f05fe0a1d67
|
3
|
|
Combining fault-tolerant persistence and low-latency streaming access to binary data for AI models, file 8c305529-8cd0-4655-92ae-e6e3b0d8e170
|
2
|
|
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery, file b967c1fa-a1b5-4aef-b12a-963b309979dd
|
2
|
|
Enhancing energy awareness through the analysis of thermal energy consumption, file e384c42e-3991-d4b2-e053-9f05fe0a1d67
|
2
|
|
PaMPa-HD: A Parallel MapReduce-Based Frequent Pattern Miner for High-Dimensional Data, file e384c42e-d79e-d4b2-e053-9f05fe0a1d67
|
2
|
|
Effective video hyperlinking by means of enriched feature sets and monomodal query combinations, file e384c431-9609-d4b2-e053-9f05fe0a1d67
|
2
|
|
Enhancing manufacturing intelligence through an unsupervised data-driven methodology for cyclic industrial processes, file e384c433-d314-d4b2-e053-9f05fe0a1d67
|
2
|
|
Cyst segmentation on kidney tubules by means of U-Net deep-learning models, file e384c434-2b58-d4b2-e053-9f05fe0a1d67
|
2
|
|
A Model-based Curriculum Learning Strategy for Training SegFormer, file 51e2ba50-5e3c-4d63-b2b9-666856503051
|
1
|
|
DriftLens: A Concept Drift Detection Tool, file 79d3eb39-d03d-45ec-b661-ffcbd4005e4f
|
1
|
|
Real-time analysis of physiological data to support medical applications, file e384c42d-ff6d-d4b2-e053-9f05fe0a1d67
|
1
|
|
Characterizing network traffic by means of the NetMine framework, file e384c42e-02ba-d4b2-e053-9f05fe0a1d67
|
1
|
|
Towards a real-time unsupervised estimation of predictive model degradation, file e384c430-e859-d4b2-e053-9f05fe0a1d67
|
1
|
| Totale |
4.295 |
Archivio Istituzionale della Ricerca