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Robert Moskovitch
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2020 – today
- 2024
- [j44]Nevo Itzhak
, Szymon Jaroszewicz
Event prediction by estimating continuously the completion of a single temporal pattern's instances. J. Biomed. Informatics 156: 104665 (2024) - [c35]Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla:
Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint). AAAI 2024: 22707 - [c34]Robert Moskovitch, Perry Sinai, Denis Rize, Liam Holohan, Paulo Saldanha:
The Velodrome - ProCyclists Data Analytics. ISACE 2024: 167-172 - [c33]Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch:
Early Multiple Temporal Patterns Based Event Prediction in Heterogeneous Multivariate Temporal Data. SDM 2024: 199-207 - [e3]Joseph Finkelstein
Artificial Intelligence in Medicine - 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9-12, 2024, Proceedings, Part I. Lecture Notes in Computer Science 14844, Springer 2024, ISBN 978-3-031-66537-0 [contents] - [e2]Joseph Finkelstein
Artificial Intelligence in Medicine - 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9-12, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14845, Springer 2024, ISBN 978-3-031-66534-9 [contents] - 2023
- [j43]Nevo Itzhak
Prediction of acute hypertensive episodes in critically ill patients. Artif. Intell. Medicine 139: 102525 (2023) - [j42]Alexander Shknevsky, Yuval Shahar
The Semantic Adjacency Criterion in Time Intervals Mining. Big Data Cogn. Comput. 7(4): 173 (2023) - [j41]Omer David Harel, Robert Moskovitch:
TIRPClo: efficient and complete mining of time intervals-related patterns. Data Min. Knowl. Discov. 37(5): 1806-1857 (2023) - [j40]Nofar Sarafian Ben Ari
Predictive temporal patterns discovery. Expert Syst. Appl. 226: 119974 (2023) - [j39]Omer David Harel
INSTINCT: Inception-based Symbolic Time Intervals series classification. Inf. Sci. 642: 119147 (2023) - [j38]Martin Michalowski
Introduction to the Special Track on Artificial Intelligence and COVID-19. J. Artif. Intell. Res. 76: 523-525 (2023) - [j37]Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch:
Continuous prediction of a time intervals-related pattern's completion. Knowl. Inf. Syst. 65(11): 4797-4846 (2023) - [c32]Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch:
Continuously Predicting the Completion of a Time Intervals Related Pattern. PAKDD (1) 2023: 239-251 - 2022
- [j36]Pavel Novitski, Cheli Melzer Cohen, Avraham Karasik, Varda Shalev, Gabriel Hodik, Robert Moskovitch:
All-cause mortality prediction in T2D patients with iTirps. Artif. Intell. Medicine 130: 102325 (2022) - [j35]Guy Shitrit
Visualization of frequent temporal patterns in single or two populations. J. Biomed. Informatics 134: 104169 (2022) - [j34]Pavel Novitski, Cheli Melzer Cohen, Avraham Karasik, Gabriel Hodik, Robert Moskovitch:
Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs. J. Biomed. Informatics 134: 104198 (2022) - [j33]Robert Moskovitch
Multivariate temporal data analysis - a review. WIREs Data Mining Knowl. Discov. 12(1) (2022) - [c31]Nevo Itzhak, Shahar Tal, Hadas Cohen, Osher Daniel, Roze Kopylov, Robert Moskovitch:
Classification of Univariate Time Series via Temporal Abstraction and Deep Learning. IEEE Big Data 2022: 1260-1265 - 2021
- [j32]Amit Shlomo, Meir Kalech, Robert Moskovitch:
Temporal pattern-based malicious activity detection in SCADA systems. Comput. Secur. 102: 102153 (2021) - [j31]Roni Mateless, Oren Tsur
Pkg2Vec: Hierarchical package embedding for code authorship attribution. Future Gener. Comput. Syst. 116: 49-60 (2021) - [j30]Maya Schvetz, Lior Fuchs, Victor Novack
Outcomes prediction in longitudinal data: Study designs evaluation, use case in ICU acquired sepsis. J. Biomed. Informatics 117: 103734 (2021) - [j29]Roni Mateless
THAAD: Efficient matching queries under temporal abstraction for anomaly detection. Perform. Evaluation 149-150: 102219 (2021) - [j28]Roni Mateless
IPvest: Clustering the IP Traffic of Network Entities Hidden Behind a Single IP Address Using Machine Learning. IEEE Trans. Netw. Serv. Manag. 18(3): 3647-3661 (2021) - [c30]Omer David Harel, Robert Moskovitch:
Complete Closed Time Intervals-Related Patterns Mining. AAAI 2021: 4098-4105 - [i2]Alexander Shknevsky, Yuval Shahar, Robert Moskovitch:
The Semantic Adjacency Criterion in Time Intervals Mining. CoRR abs/2101.03842 (2021) - 2020
- [j27]Roni Mateless, Daniel Rejabek, Oded Margalit, Robert Moskovitch:
Decompiled APK based malicious code classification. Future Gener. Comput. Syst. 110: 135-147 (2020) - [c29]Pavel Novitski, Cheli Melzer Cohen, Avraham Karasik, Varda Shalev, Gabriel Hodik, Robert Moskovitch:
All-Cause Mortality Prediction in T2D Patients. AIME 2020: 3-13 - [c28]Nevo Itzhak
Acute Hypertensive Episodes Prediction. AIME 2020: 392-402 - [c27]Ofir Dvir, Paul Wolfson, Laurence B. Lovat
Falls Prediction in Care Homes Using Mobile App Data Collection. AIME 2020: 403-413 - [e1]Martin Michalowski
Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25-28, 2020, Proceedings. Lecture Notes in Computer Science 12299, Springer 2020, ISBN 978-3-030-59136-6 [contents]
2010 – 2019
- 2019
- [j26]Robert Moskovitch
Temporal biomedical data analytics. J. Biomed. Informatics 90 (2019) - [c26]Meir Kalech, Amit Shlomo, Robert Moskovitch:
Temporal Pattern-Based Malicious Activity Detection in SCADA Systems (Brief Announcement). CSCML 2019: 316-319 - [i1]Roni Mateless, Michael Segal, Robert Moskovitch:
THAAD: Efficient Matching Queries under Temporal Abstraction for Anomaly Detection. CoRR abs/1911.00336 (2019) - 2017
- [j25]Nir Nissim, Yuval Shahar
Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods. Artif. Intell. Medicine 81: 12-32 (2017) - [j24]Robert Moskovitch, Fei Wang, Jian Pei
JASIST special issue on biomedical information retrieval. J. Assoc. Inf. Sci. Technol. 68(11): 2525-2528 (2017) - [j23]Robert Moskovitch
Procedure prediction from symbolic Electronic Health Records via time intervals analytics. J. Biomed. Informatics 75: 70-82 (2017) - [j22]Alexander Shknevsky, Yuval Shahar
Consistent discovery of frequent interval-based temporal patterns in chronic patients' data. J. Biomed. Informatics 75: 83-95 (2017) - [j21]Robert Moskovitch, Hyunmi Choi, George Hripcsak, Nicholas P. Tatonetti:
Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection. IEEE ACM Trans. Comput. Biol. Bioinform. 14(3): 555-563 (2017) - 2016
- [j20]Nir Nissim, Mary Regina Boland
Improving condition severity classification with an efficient active learning based framework. J. Biomed. Informatics 61: 44-54 (2016) - [j19]Robert Moskovitch, Fei Wang, Yuval Shahar, George Hripcsak:
Temporal data analytics. J. Biomed. Informatics 62: 276-277 (2016) - [j18]Nir Nissim, Robert Moskovitch, Oren Bar-Ad, Lior Rokach, Yuval Elovici:
ALDROID: efficient update of Android anti-virus software using designated active learning methods. Knowl. Inf. Syst. 49(3): 795-833 (2016) - [j17]Nir Nissim, Aviad Cohen, Robert Moskovitch, Asaf Shabtai, Matan Edri, Oren Bar-Ad, Yuval Elovici:
Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework. Secur. Informatics 5(1): 1 (2016) - 2015
- [j16]Robert Moskovitch, Yuval Shahar
Classification-driven temporal discretization of multivariate time series. Data Min. Knowl. Discov. 29(4): 871-913 (2015) - [j15]Robert Moskovitch, Yuval Shahar
Fast time intervals mining using the transitivity of temporal relations. Knowl. Inf. Syst. 42(1): 21-48 (2015) - [j14]Robert Moskovitch, Yuval Shahar
Classification of multivariate time series via temporal abstraction and time intervals mining. Knowl. Inf. Syst. 45(1): 35-74 (2015) - [c25]Nir Nissim, Mary Regina Boland
An Active Learning Framework for Efficient Condition Severity Classification. AIME 2015: 13-24 - [c24]Robert Moskovitch, Colin G. Walsh, Fei Wang, George Hripcsak, Nicholas P. Tatonetti
Outcomes Prediction via Time Intervals Related Patterns. ICDM 2015: 919-924 - 2014
- [j13]Nir Nissim, Robert Moskovitch, Lior Rokach, Yuval Elovici:
Novel active learning methods for enhanced PC malware detection in windows OS. Expert Syst. Appl. 41(13): 5843-5857 (2014) - [c23]Nir Nissim, Aviad Cohen, Robert Moskovitch, Asaf Shabtai, Mattan Edry, Oren Bar-Ad, Yuval Elovici:
ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files. JISIC 2014: 91-98 - 2012
- [j12]Clint Feher, Yuval Elovici, Robert Moskovitch, Lior Rokach, Alon Schclar:
User identity verification via mouse dynamics. Inf. Sci. 201: 19-36 (2012) - [j11]Nir Nissim, Robert Moskovitch, Lior Rokach, Yuval Elovici:
Detecting unknown computer worm activity via support vector machines and active learning. Pattern Anal. Appl. 15(4): 459-475 (2012) - [j10]Asaf Shabtai, Robert Moskovitch, Clint Feher, Shlomi Dolev
Detecting unknown malicious code by applying classification techniques on OpCode patterns. Secur. Informatics 1(1): 1 (2012) - 2011
- [j9]Asaf Shabtai, Dennis Potashnik, Yuval Fledel, Robert Moskovitch, Yuval Elovici:
Monitoring, analysis, and filtering system for purifying network traffic of known and unknown malicious content. Secur. Commun. Networks 4(8): 947-965 (2011) - 2010
- [c22]Tomer Shimshon, Robert Moskovitch, Lior Rokach, Yuval Elovici:
Continuous Verification Using Keystroke Dynamics. CIS 2010: 411-415
2000 – 2009
- 2009
- [j8]Asaf Shabtai, Robert Moskovitch, Yuval Elovici, Chanan Glezer
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Inf. Secur. Tech. Rep. 14(1): 16-29 (2009) - [j7]Robert Moskovitch, Yuval Shahar
Vaidurya: A multiple-ontology, concept-based, context-sensitive clinical-guideline search engine. J. Biomed. Informatics 42(1): 11-21 (2009) - [j6]Dima Stopel, Robert Moskovitch, Zvi Boger, Yuval Shahar
Using artificial neural networks to detect unknown computer worms. Neural Comput. Appl. 18(7): 663-674 (2009) - [j5]Robert Moskovitch, Dima Stopel, Clint Feher, Nir Nissim, Nathalie Japkowicz
Unknown malcode detection and the imbalance problem. J. Comput. Virol. 5(4): 295-308 (2009) - [c21]Robert Moskovitch, Yuval Shahar:
Medical Temporal-Knowledge Discovery via Temporal Abstraction. AMIA 2009 - [c20]Robert Moskovitch, Clint Feher, Arik Messerman, Niklas Kirschnick, Tarik Mustafic, Seyit Ahmet Çamtepe
Identity theft, computers and behavioral biometrics. ISI 2009: 155-160 - [c19]Robert Moskovitch, Clint Feher, Yuval Elovici:
A Chronological Evaluation of Unknown Malcode Detection. PAISI 2009: 112-117 - 2008
- [j4]Robert Moskovitch, Yuval Elovici, Lior Rokach:
Detection of unknown computer worms based on behavioral classification of the host. Comput. Stat. Data Anal. 52(9): 4544-4566 (2008) - [c18]Robert Moskovitch, Clint Feher, Nir Tzachar, Eugene Berger, Marina Gitelman, Shlomi Dolev, Yuval Elovici:
Unknown Malcode Detection Using OPCODE Representation. EuroISI 2008: 204-215 - [c17]Robert Moskovitch, Nir Nissim, Roman Englert, Yuval Elovici:
Active learning to improve the detection of unknown computer worms activity. FUSION 2008: 1-8 - [c16]Robert Moskovitch, Dima Stopel, Clint Feher, Nir Nissim, Yuval Elovici:
Unknown malcode detection via text categorization and the imbalance problem. ISI 2008: 156-161 - [c15]Robert Moskovitch, Clint Feher, Yuval Elovici:
Unknown malcode detection - A chronological evaluation. ISI 2008: 267-268 - [c14]Robert Moskovitch, Nir Nissim, Yuval Elovici:
Malicious Code Detection Using Active Learning. PinKDD 2008: 74-91 - 2007
- [j3]Robert Moskovitch, Susana B. Martins, Eytan Behiri, Aviram Weiss, Yuval Shahar
Application of Information Technology: A Comparative Evaluation of Full-text, Concept-based, and Context-sensitive Search. J. Am. Medical Informatics Assoc. 14(2): 164-174 (2007) - [c13]Robert Moskovitch, Ido Gus, Shay Pluderman, Dima Stopel, Clint Feher, Chanan Glezer
Detection of Unknown Computer Worms Activity Based on Computer Behavior using Data Mining. CIDM 2007: 202-209 - [c12]Robert Moskovitch, Ido Gus, Shay Pluderman, Dima Stopel, Chanan Glezer
Detection of Unknown Computer Worms Activity Based on Computer Behavior using Data Mining. CISDA 2007: 169-177 - [c11]Robert Moskovitch, Shay Pluderman, Ido Gus, Dima Stopel, Clint Feher, Yisrael Parmet, Yuval Shahar, Yuval Elovici:
Host Based Intrusion Detection using Machine Learning. ISI 2007: 107-114 - [c10]Robert Moskovitch, Nir Nissim, Yuval Elovici:
Malicious Code Detection and Acquisition Using Active Learning. ISI 2007: 371 - [c9]Yuval Elovici, Asaf Shabtai, Robert Moskovitch, Gil Tahan, Chanan Glezer:
Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic. KI 2007: 44-50 - [c8]Robert Moskovitch, Nir Nissim, Dima Stopel, Clint Feher, Roman Englert, Yuval Elovici:
Improving the Detection of Unknown Computer Worms Activity Using Active Learning. KI 2007: 489-493 - [c7]Robert Moskovitch, Roee Sa'adon, Eytan Behiri, Susana B. Martins, Aviram Weiss, Yuval Shahar:
Experiments with Hierarchical Concept-Based Search. MedInfo 2007: 422-426 - 2006
- [j2]Robert Moskovitch, Shiva Cohen-Kashi, Uzi Dror, Iftah Levy, Amit Maimon, Yuval Shahar
Multiple hierarchical classification of free-text clinical guidelines. Artif. Intell. Medicine 37(3): 177-190 (2006) - [c6]Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici:
Application of Artificial Neural Networks Techniques to Computer Worm Detection. IJCNN 2006: 2362-2369 - 2005
- [c5]Diego Sona
Helping Physicians to Organize Guidelines Within Conceptual Hierarchies. AIME 2005: 141-145 - 2004
- [j1]Yuval Shahar
A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools. J. Biomed. Informatics 37(5): 325-344 (2004) - [c4]Robert Moskovitch, Yuval Shahar
A Multiple-Ontology Customizable Search Interface for Retrieval of Clinical Guidelines. CGP 2004: 127-131 - [c3]Yuval Shahar
The Digital Electronic Guideline Library (DeGeL): A Hybrid Framework for Representation and Use of Clinical Guidelines. CGP 2004: 147-151 - [c2]Robert Moskovitch, Alon Hessing, Yuval Shahar
Vaidurya - A Concept-Based, Context-Sensitive Search Engine For Clinical Guidelines. MedInfo 2004: 140-144 - 2003
- [c1]Yuval Shahar
DEGEL: A Hybrid, Multiple-Ontology Framework for Specification and Retrieval of Clinical Guidelines. AIME 2003: 122-131
Coauthor Index
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last updated on 2024-10-10 22:17 CEST by the dblp team
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