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Mohamed Medhat Gaber
Person information
- affiliation: Birmingham City University, UK
- affiliation: Galala University, Egypt
- affiliation (former): Robert Gordon University
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2020 – today
- 2024
- [j82]Suhaib Chughtai, Zakaria Senousy
, Ahmed Mahany
DeepCon: Unleashing the Power of Divide and Conquer Deep Learning for Colorectal Cancer Classification. IEEE Open J. Comput. Soc. 5: 380-388 (2024) - [i14]Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard, Nicoleta Bocaneala, Katja Breitenfelder
CODE-ACCORD: A Corpus of Building Regulatory Data for Rule Generation towards Automatic Compliance Checking. CoRR abs/2403.02231 (2024) - 2023
- [j81]Besher Alhalabi
FedNets: Federated Learning on Edge Devices Using Ensembles of Pruned Deep Neural Networks. IEEE Access 11: 30726-30738 (2023) - [j80]Lorraine Chambers
AdaDeepStream: streaming adaptation to concept evolution in deep neural networks. Appl. Intell. 53(22): 27323-27343 (2023) - [j79]Zakaria Senousy, Mohamed Medhat Gaber, Mohammed M. Abdelsamea
AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification. Appl. Soft Comput. 146: 110666 (2023) - [j78]Patryk Buczek, Usama Zidan, Mohamed Medhat Gaber, Mohammed M. Abdelsamea:
Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs. Discov. Artif. Intell. 3(1) (2023) - [j77]Usama Zidan
SwinCup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer. Expert Syst. Appl. 216: 119452 (2023) - [j76]Hansi Hettiarachchi
WhatsUp: An event resolution approach for co-occurring events in social media. Inf. Sci. 625: 553-577 (2023) - [j75]Hani Ragab Hassen
D2TS: a dual diversity tree selection approach to pruning of random forests. Int. J. Mach. Learn. Cybern. 14(2): 467-481 (2023) - [j74]Hansi Hettiarachchi
TTL: transformer-based two-phase transfer learning for cross-lingual news event detection. Int. J. Mach. Learn. Cybern. 14(8): 2739-2760 (2023) - [j73]Usama Zidan
From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 16: 9474-9486 (2023) - [c79]John Hayes, Adel Aneiba, Mohamed Medhat Gaber, Md Shantanu Islam
FBA-SDN: A Federated Byzantine Approach for Blockchain-Based Collaborative Intrusion Detection in Edge SDN. ICC Workshops 2023: 427-433 - [c78]John Hayes, Adel Aneiba
SymbIoT: Towards An Extensible Blockchain Integration Testbed for IIoT. IIoT-NETs@SIGCOMM 2023: 8-14 - [c77]Edlira Vakaj, Maxime Lefrançois, Amna Dridi, Thomas Beach, Mohamed Medhat Gaber, Gonçal Costa Jutglar, He Tan:
Semantisation of rules for automated compliance checking. LDAC 2023: 245-246 - 2022
- [j72]Luke White
Agent-Based Simulations Using Genetic Algorithm Calibration: A Children's Services Application. IEEE Access 10: 88386-88397 (2022) - [j71]Amna Dridi
Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords - Machine Learning as a Case Study. Big Data Cogn. Comput. 6(1): 21 (2022) - [j70]Hansi Hettiarachchi
TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments. Data 7(7): 90 (2022) - [j69]Khadijah Muzzammil Hanga
PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes. Entropy 24(7): 910 (2022) - [j68]Hansi Hettiarachchi
Embed2Detect: temporally clustered embedded words for event detection in social media. Mach. Learn. 111(1): 49-87 (2022) - [j67]Lorraine Chambers, Mohamed Medhat Gaber:
DeepStreamOS: Fast open-Set classification for convolutional neural networks. Pattern Recognit. Lett. 154: 75-82 (2022) - [j66]Asmaa Abbas
XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification. Sensors 22(24): 9875 (2022) - [j65]Zakaria Senousy
MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification. IEEE Trans. Biomed. Eng. 69(2): 818-829 (2022) - [j64]Mohammed M. Abdelsamea
A survey on artificial intelligence in histopathology image analysis. WIREs Data Mining Knowl. Discov. 12(6) (2022) - 2021
- [j63]Asmaa Abbas, Mohammed M. Abdelsamea
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell. 51(2): 854-864 (2021) - [j62]Besher Alhalabi
MicroNets: A multi-phase pruning pipeline to deep ensemble learning in IoT devices. Comput. Electr. Eng. 96(Part): 107581 (2021) - [j61]Zakaria Senousy
3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images. Entropy 23(5): 620 (2021) - [j60]Frederic T. Stahl
A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams. Inf. 12(1): 24 (2021) - [j59]Asmaa Abbas
4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection. IEEE Trans. Neural Networks Learn. Syst. 32(7): 2798-2808 (2021) - [j58]Amna Dridi
Scholarly data mining: A systematic review of its applications. WIREs Data Mining Knowl. Discov. 11(2) (2021) - [c76]Hansi Hettiarachchi
Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media: Extended Abstract. DSAA 2021: 1-2 - [c75]Nicole P. Mugova, Mohammed M. Abdelsamea
On The Effect Of Decomposition Granularity On DeTraC For COVID-19 Detection Using Chest X-Ray Images. ECMS 2021: 29-34 - [c74]Saif Alzubi, Frederic T. Stahl
Towards Intrusion Detection Of Previously Unknown Network Attacks. ECMS 2021: 35-41 - [i13]Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber, Moloud Abdar, U. Rajendra Acharya, Abbas Khosravi, Saeid Nahavandi:
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification. CoRR abs/2108.10709 (2021) - 2020
- [j57]Asmaa Abbas, Mohammed M. Abdelsamea
DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks. IEEE Access 8: 74901-74913 (2020) - [j56]Khadijah M. Hanga
A Graph-Based Approach to Interpreting Recurrent Neural Networks in Process Mining. IEEE Access 8: 172923-172938 (2020) - [j55]Julian Hatwell, Mohamed Medhat Gaber
CHIRPS: Explaining random forest classification. Artif. Intell. Rev. 53(8): 5747-5788 (2020) - [j54]Khaled Fawagreh
eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning. Big Data Cogn. Comput. 4(4): 37 (2020) - [j53]Khaled Fawagreh, Mohamed Medhat Gaber
Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach. Computing 102(5): 1187-1198 (2020) - [j52]Hossein Ghomeshi
A non-canonical hybrid metaheuristic approach to adaptive data stream classification. Future Gener. Comput. Syst. 102: 127-139 (2020) - [j51]Julian Hatwell
Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences. BMC Medical Informatics Decis. Mak. 20(1): 250 (2020) - [j50]Zahraa S. Abdallah
Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series. Mach. Learn. 109(11): 2029-2061 (2020) - [i12]Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber:
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. CoRR abs/2003.13815 (2020) - [i11]Lorraine Chambers, Mohamed Medhat Gaber, Zahraa S. Abdallah
DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks. CoRR abs/2004.04116 (2020) - [i10]Besher Alhalabi, Mohamed Medhat Gaber, Shadi Basurra:
Prune2Edge: A Multi-Phase Pruning Pipelines to Deep Ensemble Learning in IIoT. CoRR abs/2004.04710 (2020) - [i9]Zahraa S. Abdallah
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification. CoRR abs/2004.06668 (2020) - [i8]Hansi Hettiarachchi, Mariam Adedoyin-Olowe, Jagdev Bhogal, Mohamed Medhat Gaber:
Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media. CoRR abs/2006.05908 (2020) - [i7]Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber:
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection. CoRR abs/2007.11450 (2020)
2010 – 2019
- 2019
- [j49]Fatima Abdallah
A Non-Intrusive Heuristic for Energy Messaging Intervention Modeled Using a Novel Agent-Based Approach. IEEE Access 7: 1627-1646 (2019) - [j48]Hossein Ghomeshi
RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification. IEEE Access 7: 173944-173954 (2019) - [j47]Amna Dridi
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends. IEEE Access 7: 176414-176428 (2019) - [j46]Hossein Ghomeshi
EACD: evolutionary adaptation to concept drifts in data streams. Data Min. Knowl. Discov. 33(3): 663-694 (2019) - [j45]Alfredo Cuzzocrea, Mohamed Medhat Gaber, Edoardo Fadda
An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis. J. Ambient Intell. Humaniz. Comput. 10(9): 3383-3398 (2019) - [j44]Mohamed Medhat Gaber
Internet of Things and data mining: From applications to techniques and systems. WIREs Data Mining Knowl. Discov. 9(3) (2019) - [c73]Mona Nabil Demaidi
TONE: A Method for Terminological Ontology Evaluation. ArabWIC 2019: 14:1-14:10 - [c72]Amna Dridi
DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS. IJCNN 2019: 1-8 - [c71]Besher Alhalabi
EnSyth: A Pruning Approach to Synthesis of Deep Learning Ensembles. SMC 2019: 3466-3473 - [i6]Besher Alhalabi, Mohamed Medhat Gaber, Shadi Basurra:
EnSyth: A Pruning Approach to Synthesis of Deep Learning Ensembles. CoRR abs/1907.09286 (2019) - [i5]Safwan Shatnawi
A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education. CoRR abs/1910.13561 (2019) - 2018
- [j43]Mona Nabil Demaidi
OntoPeFeGe: Ontology-Based Personalized Feedback Generator. IEEE Access 6: 31644-31664 (2018) - [j42]Mahmut Yazici
Edge Machine Learning: Enabling Smart Internet of Things Applications. Big Data Cogn. Comput. 2(3): 26 (2018) - [j41]Zahraa Said Abdallah
Activity Recognition with Evolving Data Streams: A Review. ACM Comput. Surv. 51(4): 71:1-71:36 (2018) - [j40]Ahmed Hussein
Deep imitation learning for 3D navigation tasks. Neural Comput. Appl. 29(7): 389-404 (2018) - [c70]Amna Dridi
k-NN Embedding Stability for word2vec Hyper-Parametrisation in Scientific Text. DS 2018: 328-343 - [c69]Fatima Abdallah, Shadi Basurra, Mohamed Medhat Gaber
Cascading Probability Distributions in Agent-Based Models: An Application to Behavioural Energy Wastage. ICAISC (2) 2018: 489-503 - [c68]Fatima Abdallah, Shadi Basurra, Mohamed Medhat Gaber
An Agent-Based Collective Model to Simulate Peer Pressure Effect on Energy Consumption. ICCCI (1) 2018: 283-296 - [c67]Diana Haidar, Mohamed Medhat Gaber
Adaptive One-Class Ensemble-based Anomaly Detection: An Application to Insider Threats. IJCNN 2018: 1-9 - [i4]Diana Haidar, Mohamed Medhat Gaber, Yevgeniya Kovalchuk:
AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection. CoRR abs/1812.00257 (2018) - 2017
- [j39]Ahmed Hussein
Imitation Learning: A Survey of Learning Methods. ACM Comput. Surv. 50(2): 21:1-21:35 (2017) - [j38]Thien Le, Frederic T. Stahl
On expressiveness and uncertainty awareness in rule-based classification for data streams. Neurocomputing 265: 127-141 (2017) - [j37]Eyad Elyan
A genetic algorithm approach to optimising random forests applied to class engineered data. Inf. Sci. 384: 220-234 (2017) - [j36]Muhammad Habib Ur Rehman
RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments. J. Sens. Actuator Networks 6(3): 17 (2017) - [j35]Mona Nabil Demaidi
Evaluating the quality of the ontology-based auto-generated questions. Smart Learn. Environ. 4(1): 7 (2017) - [j34]Mohammed M. Abdelsamea
A SOM-based Chan-Vese model for unsupervised image segmentation. Soft Comput. 21(8): 2047-2067 (2017) - [c66]Fatima Abdallah, Shadi Basurra, Mohamed Medhat Gaber
A Hybrid Agent-Based and Probabilistic Model for Fine-Grained Behavioural Energy Waste Simulation. ICTAI 2017: 991-995 - [c65]Ahmed Hussein, Eyad Elyan, Mohamed Medhat Gaber
Deep reward shaping from demonstrations. IJCNN 2017: 510-517 - 2016
- [j33]Mariam Adedoyin-Olowe
A rule dynamics approach to event detection in Twitter with its application to sports and politics. Expert Syst. Appl. 55: 351-360 (2016) - [j32]Zahraa Said Abdallah
AnyNovel: detection of novel concepts in evolving data streams. Evol. Syst. 7(2): 73-93 (2016) - [j31]Eyad Elyan
A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Neural Comput. Appl. 27(8): 2279-2288 (2016) - [c64]Ahmed Hussein, Mohamed Medhat Gaber
Deep Active Learning for Autonomous Navigation. EANN 2016: 3-17 - [c63]Khaled Fawagreh, Mohamed Medhat Gaber
An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests. EANN 2016: 267-282 - [c62]Thien Le, Frederic T. Stahl, Chris Wrench, Mohamed Medhat Gaber
A Statistical Learning Method to Fast Generalised Rule Induction Directly from Raw Measurements. ICMLA 2016: 935-938 - [c61]Alfredo Cuzzocrea, Mohamed Medhat Gaber
Clustering-Based Spatio-Temporal Analysis of Big Atmospheric Data. ICC 2016 2016: 74:1-74:8 - 2015
- [j30]Mohammed M. Abdelsamea, Giorgio Gnecco, Mohamed Medhat Gaber, Eyad Elyan:
On the Relationship between Variational Level Set-Based and SOM-Based Active Contours. Comput. Intell. Neurosci. 2015: 109029:1-109029:19 (2015) - [j29]Mohammed M. Abdelsamea
An efficient Self-Organizing Active Contour model for image segmentation. Neurocomputing 149: 820-835 (2015) - [j28]Zahraa Said Abdallah
Adaptive mobile activity recognition system with evolving data streams. Neurocomputing 150: 304-317 (2015) - [j27]Frederic T. Stahl, David May, Hugo Mills, Max Bramer, Mohamed Medhat Gaber
A Scalable Expressive Ensemble Learning Using Random Prism: A MapReduce Approach. Trans. Large Scale Data Knowl. Centered Syst. 20: 90-107 (2015) - [c60]Alfredo Cuzzocrea, Mohamed Medhat Gaber
Spatio-temporal analysis of Greenhouse Gas data via clustering techniques. CSCWD 2015: 478-483 - [c59]Alfredo Cuzzocrea
Distributed Classification of Data Streams: An Adaptive Technique. DaWaK 2015: 296-309 - [c58]Khaled Fawagreh
A Replicator Dynamics Approach to Collective Feature Engineering in Random Forests. SGAI Conf. 2015: 25-41 - [c57]Khaled Fawagreh
CLUB-DRF: A Clustering Approach to Extreme Pruning of Random Forests. SGAI Conf. 2015: 59-73 - [e8]Mohamed Medhat Gaber
Advances in Social Media Analysis. Studies in Computational Intelligence 602, Springer 2015, ISBN 978-3-319-18457-9 [contents] - [i3]Khaled Fawagreh, Mohamed Medhat Gaber
On Extreme Pruning of Random Forest Ensembles for Real-time Predictive Applications. CoRR abs/1503.04996 (2015) - [i2]Khaled Fawagreh, Mohamed Medhat Gaber
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests. CoRR abs/1503.05187 (2015) - 2014
- [j26]Mariam Adedoyin-Olowe
A Survey of Data Mining Techniques for Social Media Analysis. J. Data Min. Digit. Humanit. 2014 (2014) - [j25]Dang-Hoan Tran, Mohamed Medhat Gaber, Kai-Uwe Sattler:
Change detection in streaming data in the era of big data: models and issues. SIGKDD Explor. 16(1): 30-38 (2014) - [j24]João Bártolo Gomes, Mohamed Medhat Gaber
Mining Recurring Concepts in a Dynamic Feature Space. IEEE Trans. Neural Networks Learn. Syst. 25(1): 95-110 (2014) - [j23]Mohamed Medhat Gaber
Data stream mining in ubiquitous environments: state-of-the-art and current directions. WIREs Data Mining Knowl. Discov. 4(2): 116-138 (2014) - [c56]Joarder Mohammad Mustafa Kamal, M. Manzur Murshed
Progressive Data Stream Mining and Transaction Classification for Workload-Aware Incremental Database Repartitioning. BDC 2014: 8-15 - [c55]Mariam Adedoyin-Olowe
Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events. ICMLA 2014: 207-212 - [c54]Safwan Shatnawi
Automatic Content Related Feedback for MOOCs Based on Course Domain Ontology. IDEAL 2014: 27-35 - [c53]Khaled Fawagreh
Diversified Random Forests Using Random Subspaces. IDEAL 2014: 85-92 - [c52]Alfredo Cuzzocrea
Adaptive data stream mining for wireless sensor networks. IDEAS 2014: 284-287 - [c51]Thien Le, Frederic T. Stahl, João Bártolo Gomes, Mohamed Medhat Gaber, Giuseppe Di Fatta:
Computationally Efficient Rule-Based Classification for Continuous Streaming Data. SGAI Conf. 2014: 21-34 - [c50]Joarder Mohammad Mustafa Kamal, M. Manzur Murshed
Predicting Hot-Spots in Distributed Cloud Databases Using Association Rule Mining. UCC 2014: 800-805 - [c49]Mohammed M. Abdelsamea
A Concurrent SOM-Based Chan-Vese Model for Image Segmentation. WSOM 2014: 199-208 - [c48]Mohammed M. Abdelsamea
A Survey of SOM-Based Active Contour Models for Image Segmentation. WSOM 2014: 293-302 - [e7]Sherif Sakr, Mohamed Medhat Gaber
Large Scale and Big Data - Processing and Management. Auerbach Publications 2014, ISBN 978-1-4665-8150-0 [contents] - 2013
- [j22]Mohamed Medhat Gaber
An entropy-based approach to enhancing Random Forests. Intell. Decis. Technol. 7(4): 319-327 (2013) - [j21]João Bártolo Gomes, Mohamed Medhat Gaber
Collaborative Data Stream Mining in Ubiquitous Environments using Dynamic Classifier Selection. Int. J. Inf. Technol. Decis. Mak. 12(6): 1287-1308 (2013) - [j20]Mohamed Medhat Gaber
Interactive self-adaptive clutter-aware visualisation for mobile data mining. J. Comput. Syst. Sci. 79(3): 369-382 (2013) - [j19]Pari Delir Haghighi
Open Mobile Miner: A Toolkit for Building Situation-Aware Data Mining Applications. J. Organ. Comput. Electron. Commer. 23(3): 224-248 (2013) - [j18]Frederic T. Stahl
An overview of interactive visual data mining techniques for knowledge discovery. WIREs Data Mining Knowl. Discov. 3(4): 239-256 (2013) - [c47]Mariam Adedoyin-Olowe
TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter. ICAISC (2) 2013: 135-145 - [c46]Kieran Jay Edwards, Mohamed Medhat Gaber
Identifying Uncertain Galaxy Morphologies Using Unsupervised Learning. ICAISC (2) 2013: 146-157 - [c45]João Bártolo Gomes, Mariam Adedoyin-Olowe
Rule Type Identification Using TRCM for Trend Analysis in Twitter. SGAI Conf. 2013: 273-278 - [c44]Alfredo Cuzzocrea
An Information-Theoretic Approach for Setting the Optimal Number of Decision Trees in Random Forests. SMC 2013: 1013-1019 - [e6]Mohamed Medhat Gaber, Nirmalie Wiratunga, Ayse Göker, Mihaela Cocea:
Proceedings of the BCS SGAI Workshop on Social Media Analysis 2013 co-located with 33rd Annual International Conference of the British Computer Society's Specialist Group on Artificial Intelligence (BCS SGAI 2013), Cambridge, UK, December 10, 2013. CEUR Workshop Proceedings 1110, CEUR-WS.org 2013 [contents] - [i1]Mariam Adedoyin-Olowe, Mohamed Medhat Gaber
A Survey of Data Mining Techniques for Social Media Analysis. CoRR abs/1312.4617 (2013) - 2012
- [j17]Indre Zliobaite
Next challenges for adaptive learning systems. SIGKDD Explor. 14(1): 48-55 (2012) - [j16]Frederic T. Stahl, Mohamed Medhat Gaber
Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining. Trans. Large Scale Data Knowl. Centered Syst. 5: 183-205 (2012) - [j15]Mohamed Medhat Gaber
Advances in data stream mining. WIREs Data Mining Knowl. Discov. 2(1): 79-85 (2012) - [c43]Zahraa Said Abdallah
CBARS: Cluster Based Classification for Activity Recognition Systems. AMLTA 2012: 82-91 - [c42]João Bártolo Gomes, Shonali Krishnaswamy, Mohamed Medhat Gaber
Mobile Activity Recognition Using Ubiquitous Data Stream Mining. DaWaK 2012: 130-141 - [c41]Mohamed Bahy Bader-El-Den, Mohamed Medhat Gaber
GARF: Towards Self-optimised Random Forests. ICONIP (2) 2012: 506-515 - [c40]Zahraa Said Abdallah
StreamAR: Incremental and Active Learning with Evolving Sensory Data for Activity Recognition. ICTAI 2012: 1163-1170 - [c39]Ranjani Nagarajan, Alfredo Cuzzocrea
Deploying Mobile Software Agents for Distributed Data Mining on Wireless Sensor Networks: A Comparative Analysis. ICTAI 2012: 1179-1185 - [c38]Alfredo Cuzzocrea, Mohamed Medhat Gaber
Data Science and Distributed Intelligence: Recent Developments and Future Insights. IDC 2012: 139-147 - [c37]Mohamed Medhat Gaber
Optimisation of Ensemble Classifiers using Genetic Algorithm. KES 2012: 39-48 - [c36]Daniel Vaccaro-Senna, Mohamed Medhat Gaber
Incorporating Farthest Neighbours in Instance Space Classification. KES 2012: 345-355 - [c35]Lorraine Chambers, Erik Tromp, Mykola Pechenizkiy
Mobile Sentiment Analysis. KES 2012: 470-479 - [c34]João Bártolo Gomes, Shonali Krishnaswamy, Mohamed Medhat Gaber
MARS: A Personalised Mobile Activity Recognition System. MDM 2012: 316-319 - [c33]Shonali Krishnaswamy, João Gama
Mobile Data Stream Mining: From Algorithms to Applications. MDM 2012: 360-363 - [c32]Frederic T. Stahl, Mohamed Medhat Gaber
eRules: A Modular Adaptive Classification Rule Learning Algorithm for Data Streams. SGAI Conf. 2012: 65-78 - [c31]Marwan Hassani
Density-Based Projected Clustering of Data Streams. SUM 2012: 311-324 - [p7]Mohamed Medhat Gaber
Introduction. Journeys to Data Mining 2012: 1-11 - [e5]Mohamed Medhat Gaber
Journeys to Data Mining. Springer 2012, ISBN 978-3-642-28046-7 [contents] - [e4]Mohamed Medhat Gaber, Mihaela Cocea, Stephan Weibelzahl, Ernestina Menasalvas, Cyril Labbé:
Proceedings of the 1st International Workshop on Sentiment Discovery from Affective Data, SDAD@ECML/PKDD 2012, Bristol, UK, September 28, 2012. CEUR Workshop Proceedings 917, CEUR-WS.org 2012 [contents] - 2011
- [j14]Suan Khai Chong, Mohamed Medhat Gaber
Energy conservation in wireless sensor networks: a rule-based approach. Knowl. Inf. Syst. 28(3): 579-614 (2011) - [j13]Suan Khai Chong, Mohamed Medhat Gaber
Energy-Aware Data Processing Techniques for Wireless Sensor Networks: A Review. Trans. Large Scale Data Knowl. Centered Syst. 3: 117-137 (2011) - [c30]Zahraa Said Abdallah
KB-CB-N classification: Towards unsupervised approach for supervised learning. CIDM 2011: 283-290 - [c29]Shonali Krishnaswamy, João Gama
Advances in data stream mining for mobile and ubiquitous environments. CIKM 2011: 2607-2608 - [c28]João Bártolo Gomes, Mohamed Medhat Gaber
Context-Aware Collaborative Data Stream Mining in Ubiquitous Devices. IDA 2011: 22-33 - [c27]Frederic T. Stahl
Distributed hoeffding trees for pocket data mining. HPCS 2011: 686-692 - [c26]Frederic T. Stahl, Mohamed Medhat Gaber
Distributed Classification for Pocket Data Mining. ISMIS 2011: 336-345 - [c25]Hossein Tayebi, Shonali Krishnaswamy, Agustinus Borgy Waluyo, Abhijat Sinha, Mohamed Medhat Gaber
RA-SAX: Resource-Aware Symbolic Aggregate Approximation for Mobile ECG Analysis. Mobile Data Management (1) 2011: 289-290 - [c24]Abhijat Sinha, Hossein Tayebi, Shonali Krishnaswamy, Agustinus Borgy Waluyo, Mohamed Medhat Gaber
Resource-aware ECG analysis on mobile devices. SAC 2011: 1012-1013 - [p6]Luke Albert Steller, Shonali Krishnaswamy, Mohamed Medhat Gaber
Enabling Scalable Semantic Reasoning for Mobile Services. Semantic Services, Interoperability and Web Applications 2011: 178-204 - 2010
- [j12]Varun Chandola, Olufemi A. Omitaomu, Auroop R. Ganguly, Ranga Raju Vatsavai, Nitesh V. Chawla, João Gama
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 12(2): 50-53 (2010) - [c23]Raymes Khoury, Tim Dawborn, Bulat Gafurov, Glen Pink, Edmund Tse, Quincy Tse, Khaled Almiani, Mohamed Medhat Gaber
Corona: Energy-Efficient Multi-query Processing in Wireless Sensor Networks. DASFAA (2) 2010: 416-419 - [c22]Brett Gillick, Hasnain AlTaiar, Shonali Krishnaswamy, Jonathan Liono, Nicholas Nicoloudis, Abhijat Sinha, Arkady B. Zaslavsky, Mohamed Medhat Gaber
Clutter-Adaptive Visualization for Mobile Data Mining. ICDM Workshops 2010: 1381-1384 - [c21]Mohamed Medhat Gaber
Adaptive Clutter-Aware Visualization for Mobile Data Stream Mining. ICTAI (2) 2010: 304-311 - [c20]Frederic T. Stahl
Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments. ICTAI (2) 2010: 323-330 - [c19]Mohamed Medhat Gaber
Distributed data stream classification for wireless sensor networks. SAC 2010: 1629-1630 - [p5]Mohamed Medhat Gaber
Introduction. Scientific Data Mining and Knowledge Discovery 2010: 1-4 - [p4]Mohamed Medhat Gaber
Data Stream Mining. Data Mining and Knowledge Discovery Handbook 2010: 759-787 - [p3]Pari Delir Haghighi
Situation-Aware Adaptive Processing (SAAP) of Data Streams. Pervasive Computing, Innovations in Intelligent Multimedia and Applications 2010: 313-338 - [e3]Mohamed Medhat Gaber
Scientific Data Mining and Knowledge Discovery - Principles and Foundations. Springer 2010, ISBN 978-3-642-02787-1 [contents] - [e2]Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama
Knowledge Discovery from Sensor Data, Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers. Lecture Notes in Computer Science 5840, Springer 2010, ISBN 978-3-642-12518-8 [contents]
2000 – 2009
- 2009
- [j11]João Gama
Knowledge discovery from data streams. Intell. Data Anal. 13(3): 403-404 (2009) - [j10]Pari Delir Haghighi
Context-aware adaptive data stream mining. Intell. Data Anal. 13(3): 423-434 (2009) - [j9]Luke Steller, Shonali Krishnaswamy, Mohamed Medhat Gaber:
Enabling Scalable Semantic Reasoning for Mobile Services. Int. J. Semantic Web Inf. Syst. 5(2): 91-116 (2009) - [j8]Mohamed Medhat Gaber
An analytical study of central and in-network data processing for wireless sensor networks. Inf. Process. Lett. 110(2): 62-70 (2009) - [j7]Olufemi A. Omitaomu, Ranga Raju Vatsavai, Auroop R. Ganguly, Nitesh V. Chawla, João Gama
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 11(2): 84-87 (2009) - [c18]Pari Delir Haghighi
Mobile Data Mining for Intelligent Healthcare Support. HICSS 2009: 1-10 - [c17]Luke Albert Steller, Shonali Krishnaswamy, Mohamed Medhat Gaber
Cost efficient, adaptive reasoning strategies for pervasive service discovery. ICPS 2009: 11-20 - [c16]Luke Albert Steller, Shonali Krishnaswamy, Mohamed Medhat Gaber
A Weighted Approach to Partial Matching for Mobile Reasoning. ISWC 2009: 618-633 - [p2]Mohamed Medhat Gaber
Data Stream Mining Using Granularity-Based Approach. Foundations of Computational Intelligence (6) 2009: 47-66 - [e1]Olufemi A. Omitaomu, Auroop R. Ganguly, João Gama, Ranga Raju Vatsavai, Nitesh V. Chawla, Mohamed Medhat Gaber:
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, Paris, France, June 28, 2009. ACM 2009, ISBN 978-1-60558-668-7 [contents] - 2008
- [j6]Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 10(2): 68-73 (2008) - [c15]Uwe Röhm, Mohamed Medhat Gaber, Quincy Tse:
Enabling Resource-Awareness for In-Network Data Processing in Wireless Sensor Networks. ADC 2008: 95-102 - [c14]Pari Delir Haghighi, Shonali Krishnaswamy, Arkady B. Zaslavsky, Mohamed Medhat Gaber:
Reasoning about Context in Uncertain Pervasive Computing Environments. EuroSSC 2008: 112-125 - [c13]Jie Yin, Mohamed Medhat Gaber
Clustering Distributed Time Series in Sensor Networks. ICDM 2008: 678-687 - [c12]Suan Khai Chong, Mohamed Medhat Gaber
ARTS: Adaptive Rule Triggers on Sensors for Energy Conservation in Applications using Coarse-Granularity Data. ICESS 2008: 314-321 - [c11]Pari Delir Haghighi
Situation-Aware Adaptive Visualization for Sensory Data Stream Mining. KDD Workshop on Knowledge Discovery from Sensor Data 2008: 43-58 - [c10]Suan Khai Chong, Shonali Krishnaswamy, Seng Wai Loke, Mohamed Medhat Gaber
Using association rules for energy conservation in wireless sensor networks. SAC 2008: 971-975 - 2007
- [j5]Osnat Horovitz, Shonali Krishnaswamy, Mohamed Medhat Gaber:
A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety. Intell. Data Anal. 11(1): 89-108 (2007) - [c9]Nhan Duc Phung, Mohamed Medhat Gaber
Resource-aware Online Data Mining in Wireless Sensor Networks. CIDM 2007: 139-146 - [c8]Bernhard Scholz, Mohamed Medhat Gaber, Tim Dawborn
Efficient Time Triggered Query Processing in Wireless Sensor Networks. ICESS 2007: 391-402 - [c7]Uwe Röhm
On the Integration of Data Stream Clustering into a Query Processor for Wireless Sensor Networks. MDM 2007: 331-335 - [p1]Mohamed Medhat Gaber
A Survey of Classification Methods in Data Streams. Data Streams - Models and Algorithms 2007: 39-59 - 2006
- [j4]Mohamed Medhat Gaber
Detection and Classification of Changes in Evolving Data Streams. Int. J. Inf. Technol. Decis. Mak. 5(4): 659-670 (2006) - [j3]Mohamed Medhat Gaber
A Holistic Approach for Resource-aware Adaptive Data Stream Mining. New Gener. Comput. 25(1): 95-115 (2006) - [c6]Brett Gillick, Shonali Krishnaswamy, Mohamed Medhat Gaber, Arkady B. Zaslavsky:
Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining. IWUC 2006: 29-38 - [c5]Mohamed Medhat Gaber, Philip S. Yu:
A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering. SAC 2006: 649-656 - 2005
- [j2]Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady B. Zaslavsky:
Resource-aware Mining of Data Streams. J. Univers. Comput. Sci. 11(8): 1440-1453 (2005) - [j1]Mohamed Medhat Gaber
Mining data streams: a review. SIGMOD Rec. 34(2): 18-26 (2005) - [c4]Osnat Horovitz, Mohamed Medhat Gaber, Shonali Krishnaswamy:
Making Sense of Ubiquitous Data Streams - A Fuzzy Logic Approach. KES (2) 2005: 922-928 - 2004
- [c3]Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady B. Zaslavsky:
Cost-Efficient Mining Techniques for Data Streams. ACSW 2004: 109-114 - [c2]Mohamed Medhat Gaber, Arkady B. Zaslavsky, Shonali Krishnaswamy:
Towards an Adaptive Approach for Mining Data Streams in Resource Constrained Environments. DaWaK 2004: 189-198 - [c1]Mohamed Medhat Gaber, Shonali Krishnaswamy, Arkady B. Zaslavsky:
A Wireless Data Stream Mining Model. Wireless Information Systems 2004: 152-160
Coauthor Index
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