| Nome |
# |
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Analysis of Twitter Data Using a Multiple-level Clustering Strategy, file e384c42e-2bcc-d4b2-e053-9f05fe0a1d67
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2.713
|
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Macroscopic view of malware in home networks, file e384c42e-67c1-d4b2-e053-9f05fe0a1d67
|
965
|
|
Generalized association rule mining with constraints, file e384c42e-110c-d4b2-e053-9f05fe0a1d67
|
762
|
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Network Connectivity Graph for Malicious Traffic Dissection, file e384c42e-67c3-d4b2-e053-9f05fe0a1d67
|
684
|
|
Multi-document summarization based on the Yago ontology, file e384c42e-2d97-d4b2-e053-9f05fe0a1d67
|
666
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|
GraphSum: discovering correlations among multiple terms for graph-based summarization, file e384c42e-2e1b-d4b2-e053-9f05fe0a1d67
|
660
|
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PaWI: Parallel Weighted Itemset Mining by means of MapReduce, file e384c42e-d2e4-d4b2-e053-9f05fe0a1d67
|
581
|
|
LAS: a software platform to support oncological data management, file e384c42e-1b83-d4b2-e053-9f05fe0a1d67
|
580
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|
Energy Signature Analysis: Knowledge at Your Fingertips, file e384c42d-ba62-d4b2-e053-9f05fe0a1d67
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564
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Energy saving models for wireless sensor networks, file e384c42e-171b-d4b2-e053-9f05fe0a1d67
|
547
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Expressive generalized itemsets, file e384c42e-3120-d4b2-e053-9f05fe0a1d67
|
546
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NetCluster: A clustering-based framework to analyze internet passive measurements data, file e384c42e-296a-d4b2-e053-9f05fe0a1d67
|
520
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EnBay: A Novel Pattern-Based Bayesian Classifier, file e384c42e-2423-d4b2-e053-9f05fe0a1d67
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501
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CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules., file e384c42e-10cf-d4b2-e053-9f05fe0a1d67
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487
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A spectral analysis of crimes in San Francisco, file e384c42f-25d5-d4b2-e053-9f05fe0a1d67
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479
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SeLINA: a Self-Learning Insightful Network Analyzer, file e384c42f-13a8-d4b2-e053-9f05fe0a1d67
|
472
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|
Digging deep into weighted patient data through multiple-level patterns, file e384c42d-b1f8-d4b2-e053-9f05fe0a1d67
|
468
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Predicting Your Next Stop-over from Location-based Social Network Data with Recurrent Neural Networks, file e384c42f-94f8-d4b2-e053-9f05fe0a1d67
|
468
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NetCluster: a Clustering-Based Framework for Internet Tomography, file e384c42e-02bc-d4b2-e053-9f05fe0a1d67
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466
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SQL versus NoSQL Databases for Geospatial Applications, file e384c430-1a16-d4b2-e053-9f05fe0a1d67
|
452
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NEMICO: Mining network data through cloud-based data mining techniques, file e384c42e-38c8-d4b2-e053-9f05fe0a1d67
|
425
|
|
SeLeCT: Self-Learning Classifier
for Internet Traffic, file e384c42e-27c7-d4b2-e053-9f05fe0a1d67
|
422
|
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Combining news sentiment and technical analysis to predict stock trend reversal, file e384c431-44f3-d4b2-e053-9f05fe0a1d67
|
420
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Early prediction of the highest workload in incremental cardiopulmonary tests, file e384c42e-2de4-d4b2-e053-9f05fe0a1d67
|
412
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Frequent Itemsets Mining for Big Data: A Comparative Analysis, file e384c42f-9940-d4b2-e053-9f05fe0a1d67
|
398
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Data Mining Techniques for Effective Flow-based Analysis of Multi-Gigabit Network Traffic, file e384c42d-f9f6-d4b2-e053-9f05fe0a1d67
|
391
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I-prune: Item selection for associative classification, file e384c42e-2110-d4b2-e053-9f05fe0a1d67
|
391
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Analyzing Air Pollution on the Urban Environment, file e384c42f-1ab4-d4b2-e053-9f05fe0a1d67
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385
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MeTA: Characterization of medical treatments at different abstraction levels, file e384c42d-b258-d4b2-e053-9f05fe0a1d67
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359
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Measuring gene similarity by means of the classification distance, file e384c42e-0b5b-d4b2-e053-9f05fe0a1d67
|
358
|
|
Test-driven summarization: combining formative assessment with teaching document summarization, file e384c42f-a361-d4b2-e053-9f05fe0a1d67
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333
|
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Hierarchical Learning for Fine Grained Internet Traffic Classification, file e384c42e-2609-d4b2-e053-9f05fe0a1d67
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301
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Experimental validation of a massive educational service in a blended learning environment, file e384c42f-9f6a-d4b2-e053-9f05fe0a1d67
|
294
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Network Digest analysis by means of association rules, file e384c42f-41e5-d4b2-e053-9f05fe0a1d67
|
277
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Self-Learning Classifier for Internet traffic, file e384c42e-2965-d4b2-e053-9f05fe0a1d67
|
269
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Detecting Anomalies in Image Classification by Means of Semantic Relationships, file e384c430-e6f2-d4b2-e053-9f05fe0a1d67
|
264
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|
Educational video services in universities: a systematic effectiveness analysis, file e384c42f-dfd9-d4b2-e053-9f05fe0a1d67
|
250
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Supporting stock trading in multiple foreign markets: a multilingual news summarization approach, file e384c42f-3fc3-d4b2-e053-9f05fe0a1d67
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245
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Scaling associative classification for very large datasets, file e384c42f-e73f-d4b2-e053-9f05fe0a1d67
|
208
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YouLighter: A Cognitive Approach to Unveil YouTube CDN and Changes, file e384c42e-d5fe-d4b2-e053-9f05fe0a1d67
|
199
|
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PaMPa-HD: A Parallel MapReduce-Based Frequent Pattern Miner for High-Dimensional Data, file e384c42e-d384-d4b2-e053-9f05fe0a1d67
|
190
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|
YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes, file e384c42e-5cf9-d4b2-e053-9f05fe0a1d67
|
168
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Network Highlighter, file e384c42f-8f6b-d4b2-e053-9f05fe0a1d67
|
165
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Identifying collaborations among researchers: a pattern-based approach, file e384c42f-96cd-d4b2-e053-9f05fe0a1d67
|
161
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DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-5eb3-d4b2-e053-9f05fe0a1d67
|
161
|
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Data mining for better healthcare: A path towards automated data analysis?, file e384c42f-3998-d4b2-e053-9f05fe0a1d67
|
157
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SaFe-NeC: A scalable and flexible system for network data characterization, file e384c42f-45ab-d4b2-e053-9f05fe0a1d67
|
154
|
|
Highlighter: automatic highlighting of electronic learning documents, file e384c42f-abcb-d4b2-e053-9f05fe0a1d67
|
143
|
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Learning from summaries: supporting e-learning activities by means of document summarization, file e384c42f-32e7-d4b2-e053-9f05fe0a1d67
|
119
|
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Exploring energy performance certificates through visualization, file e384c431-06f0-d4b2-e053-9f05fe0a1d67
|
106
|
|
Automating concept-drift detection by self-evaluating predictive model degradation, file e384c430-f738-d4b2-e053-9f05fe0a1d67
|
105
|
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Heterogeneous industrial vehicle usage predictions: A real case, file e384c430-d5f7-d4b2-e053-9f05fe0a1d67
|
99
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Unsupervised methodology to unveil content delivery network structures, file e384c42f-9105-d4b2-e053-9f05fe0a1d67
|
93
|
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Double-Step deep learning framework to improve wildfire severity classification, file e384c432-df3b-d4b2-e053-9f05fe0a1d67
|
87
|
|
Predicting cardiopulmonary response to incremental exercise test, file e384c42e-9687-d4b2-e053-9f05fe0a1d67
|
86
|
|
Quantitative cryptocurrency trading: exploring the use of machine learning techniques, file e384c433-d6b2-d4b2-e053-9f05fe0a1d67
|
83
|
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Cross-Lingual Propagation of Sentiment Information Based on Bilingual Vector Space Alignment, file e384c432-2e88-d4b2-e053-9f05fe0a1d67
|
79
|
|
Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images, file e384c432-c00a-d4b2-e053-9f05fe0a1d67
|
78
|
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Evaluating espresso coffee quality by means of time-series feature engineering, file e384c432-4b00-d4b2-e053-9f05fe0a1d67
|
77
|
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Enhancing Interpretability of Black Box Models by means of Local Rules, file e384c431-5af2-d4b2-e053-9f05fe0a1d67
|
73
|
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Knowledge Graph Embeddings with node2vec for Item Recommendation, file e384c430-2a91-d4b2-e053-9f05fe0a1d67
|
72
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|
Summarize Dates First: A Paradigm Shift in Timeline Summarization, file e384c433-c88e-d4b2-e053-9f05fe0a1d67
|
72
|
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Predicting student academic performance by means of associative classification, file e384c433-3ab8-d4b2-e053-9f05fe0a1d67
|
69
|
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MAGMA network behavior classifier for malware traffic, file e384c42f-322e-d4b2-e053-9f05fe0a1d67
|
67
|
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Fast Self-Organizing Maps Training, file e384c431-9bde-d4b2-e053-9f05fe0a1d67
|
62
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Training ensembles of faceted classification models for quantitative stock trading, file e384c433-a7c9-d4b2-e053-9f05fe0a1d67
|
61
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Leveraging the explainability of associative classifiers to support quantitative stock trading, file e384c432-51be-d4b2-e053-9f05fe0a1d67
|
56
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Bring Your Own Data to X-PLAIN, file e384c432-66e8-d4b2-e053-9f05fe0a1d67
|
54
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Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction, file e384c434-1d09-d4b2-e053-9f05fe0a1d67
|
54
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|
YouLighter, file e384c42f-88d4-d4b2-e053-9f05fe0a1d67
|
53
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Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine, file e384c430-c403-d4b2-e053-9f05fe0a1d67
|
51
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An empirical comparison of knowledge graph embeddings for item recommendation, file e384c430-2a92-d4b2-e053-9f05fe0a1d67
|
49
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Dissecting a Data-driven Prognostic Pipeline: A Powertrain use case, file e384c433-5ac2-d4b2-e053-9f05fe0a1d67
|
49
|
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A data-driven energy platform: from energy performance certificates to human-readable knowledge through dynamic high-resolution geospatial maps, file e384c432-dc2b-d4b2-e053-9f05fe0a1d67
|
46
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Characterizing situations of dock overload in bicycle sharing stations, file e384c430-bc23-d4b2-e053-9f05fe0a1d67
|
45
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Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data, file e384c433-9067-d4b2-e053-9f05fe0a1d67
|
44
|
|
Recommending Personalized Summaries of Teaching Materials, file e384c431-72b4-d4b2-e053-9f05fe0a1d67
|
43
|
|
Discovering profitable stocks for intraday trading, file e384c433-18e7-d4b2-e053-9f05fe0a1d67
|
42
|
|
Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis, file e384c431-235c-d4b2-e053-9f05fe0a1d67
|
38
|
|
Machine learning supported next-maintenance prediction for industrial vehicles, file e384c432-790c-d4b2-e053-9f05fe0a1d67
|
38
|
|
Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence, file e384c433-516f-d4b2-e053-9f05fe0a1d67
|
35
|
|
entity2rec: Property-specific knowledge graph embeddings for item recommendation, file e384c431-41c5-d4b2-e053-9f05fe0a1d67
|
25
|
|
Effective video hyperlinking by means of enriched feature sets and monomodal query combinations, file e384c431-78f1-d4b2-e053-9f05fe0a1d67
|
24
|
|
Exploiting pivot words to classify and summarize discourse facets of scientific papers, file e384c432-78ea-d4b2-e053-9f05fe0a1d67
|
24
|
|
Leveraging full-text article exploration for citation analysis, file e384c433-c8fa-d4b2-e053-9f05fe0a1d67
|
22
|
|
A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data, file e384c433-128d-d4b2-e053-9f05fe0a1d67
|
21
|
|
How Divergent Is Your Data?, file e384c433-ba54-d4b2-e053-9f05fe0a1d67
|
21
|
|
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery, file 8cd55256-8bf6-4199-8a56-2b82f0bf8569
|
19
|
|
Cross-lingual timeline summarization, file e384c434-28a3-d4b2-e053-9f05fe0a1d67
|
16
|
|
Time-of-Flight Cameras in Space: Pose Estimation with Deep Learning Methodologies, file a89219f6-2990-4146-8f7f-e584e6053ec2
|
14
|
|
Exploring Subgroup Performance In End-to-End Speech Models, file 58230c2a-c650-4083-a8d1-c92320fd43cb
|
12
|
|
How Much Attention Should we Pay to Mosquitoes?, file a927875e-3d1e-4c89-8639-f0999c498a11
|
12
|
|
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists, file e384c434-e5ba-d4b2-e053-9f05fe0a1d67
|
12
|
|
Discovering cross-topic collaborations among researchers by exploiting weighted association rules, file e384c432-f85b-d4b2-e053-9f05fe0a1d67
|
11
|
|
Transformer-based Non-Verbal Emotion Recognition: Exploring Model Portability across Speakers’ Genders, file 18f9639e-c5cf-4416-b3b3-5c638dadfc9d
|
10
|
|
Bring Your Own Data to X-PLAIN, file e384c432-6d95-d4b2-e053-9f05fe0a1d67
|
10
|
|
Identifying Biased Subgroups in Ranking and Classification, file f025bf35-b3db-411a-b8bb-28ae04b01123
|
9
|
|
RECLAIM: Reverse Engineering Classification Metrics, file 8a669737-cc37-4eda-a763-b767534b8aa8
|
8
|
|
Semantic Image Collection Summarization with Frequent Subgraph Mining, file 814b87a1-1af0-455f-8f03-0d516b07259c
|
7
|
|
SaFe-NeC: A scalable and flexible system for network data characterization, file e384c42f-1b84-d4b2-e053-9f05fe0a1d67
|
7
|
| Totale |
23.950 |
Archivio Istituzionale della Ricerca