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All Outputs (13)

Multiple fake classes GAN for data augmentation in face image dataset. (2019)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E. and JAYNE, C. 2019. Multiple fake classes GAN for data augmentation in face image dataset. In Proceedings of the 2019 International joint conference on neural networks (IJCNN 2019), 14-19 July 2019, Budapest, Hungary. Piscataway: IEEE [online], article ID 8851953. Available from: https://doi.org/10.1109/IJCNN.2019.8851953

Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. In such a situation, learning algorithms are often biased toward the majority class instances. Therefore, some modification to the learning algorithm o... Read More about Multiple fake classes GAN for data augmentation in face image dataset..

Overlap-based undersampling for improving imbalanced data classification. (2018)
Conference Proceeding
VUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling me... Read More about Overlap-based undersampling for improving imbalanced data classification..

Toward video tampering exposure: inferring compression parameters from pixels. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Toward video tampering exposure: inferring compression parameters from pixels. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 44-57, Available from: https://doi.org/10.1007/978-3-319-98204-5_4

Video tampering detection remains an open problem in the field of digital media forensics. Some existing methods focus on recompression detection because any changes made to the pixels of a video will require recompression of the complete stream. Rec... Read More about Toward video tampering exposure: inferring compression parameters from pixels..

Deep imitation learning with memory for robocup soccer simulation. (2018)
Conference Proceeding
HUSSEIN, A., ELYAN, E. and JAYNE, C. 2018. Deep imitation learning with memory for robocup soccer simulation. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 31-43. Available from: https://doi.org/10.1007/978-3-319-98204-5_3

Imitation learning is a field that is rapidly gaining attention due to its relevance to many autonomous agent applications. Providing demonstrations of effective behaviour to teach the agent is useful in real world challenges such as sparse rewards a... Read More about Deep imitation learning with memory for robocup soccer simulation..

Symbols classification in engineering drawings. (2018)
Conference Proceeding
ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from: https://doi.org/10.1109/IJCNN.2018.8489087

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we p... Read More about Symbols classification in engineering drawings..

Spatial effects of video compression on classification in convolutional neural networks. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Spatial effects of video compression on classification in convolutional neural networks. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489370. Available from: https://doi.org/10.1109/IJCNN.2018.8489370

A collection of Computer Vision application reuse pre-learned features to analyse video frame-by-frame. Those features are classically learned by Convolutional Neural Networks (CNN) trained on high quality images. However, available video content is... Read More about Spatial effects of video compression on classification in convolutional neural networks..

Few-shot classifier GAN. (2018)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., SAVOYE, Y. and JAYNE, C. 2018. Few-shot classifier GAN. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489387. Available from: https://doi.org/10.1109/IJCNN.2018.8489387

Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications. In this paper, we present Few-shot Classifier Generative Adversarial Network as an approach for fe... Read More about Few-shot classifier GAN..

Deep reward shaping from demonstrations. (2017)
Conference Proceeding
HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2017. Deep reward shaping from demonstrations. In Proceedings of the 2017 International joint conference on neural networks (IJCNN 2017), 14-19 May 2017, Anchorage, USA. Piscataway: IEEE [online], article number 7965896, pages 510-517. Available from: https://doi.org/10.1109/IJCNN.2017.7965896

Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety of problems. The combination of deep learning and reinforcement learning allows for a generic learning process that does not consider specific knowledge of... Read More about Deep reward shaping from demonstrations..

Selective dropout for deep neural networks. (2016)
Conference Proceeding
BARROW, E., EASTWOOD, M. and JAYNE, C. 2016. Selective dropout for deep neural networks. In Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M. and Liu, D. (eds.) Neural information processing: Proceedings of the 23rd International conference on neural information processing (ICONIP 2016), 16-21 October 2016, Kyoto, Japan. Lecture notes in computer science, 9949. Cham: Springer [online], pages 519-528. Available from: https://doi.org/10.1007/978-3-319-46675-0_57

Dropout has been proven to be an effective method for reducing overfitting in deep artificial neural networks. We present 3 new alternative methods for performing dropout on a deep neural network which improves the effectiveness of the dropout method... Read More about Selective dropout for deep neural networks..

Designing a context-aware cyber physical system for smart conditional monitoring of platform equipment. (2016)
Conference Proceeding
MAJDANI, F., PETROVSKI, A. and DOOLAN, D. 2016. Designing a context-aware cyber physical system for smart conditional monitoring of platform equipment. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 198-210. Available from: https://doi.org/10.1007/978-3-319-44188-7_15

An adaptive multi-tiered framework, which can be utilised for designing a context-aware cyber physical system is proposed and applied within the context of assuring offshore asset integrity. Adaptability is achieved through the combined use of machin... Read More about Designing a context-aware cyber physical system for smart conditional monitoring of platform equipment..

Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. (2016)
Conference Proceeding
PETROVSKI, A., RATTADILOK, P. and PETROVSKII, S. 2016. Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12

An adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to the presence of anomalies in... Read More about Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance..

Deep active learning for autonomous navigation. (2016)
Conference Proceeding
HUSSEIN, A., GABER, M.M. and ELYAN, E. 2016. Deep active learning for autonomous navigation. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 3-17. Available from: https://doi.org/10.1007/978-3-319-44188-7_1

Imitation learning refers to an agent's ability to mimic a desired behavior by learning from observations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate for learning and efficien... Read More about Deep active learning for autonomous navigation..

An outlier ranking tree selection approach to extreme pruning of random forests. (2016)
Conference Proceeding
FAWAGREH, K., GABER, M.M. and ELYAN, E. 2016. An outlier ranking tree selection approach to extreme pruning of random forests. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 267-282. Available from: https://doi.org/10.1007/978-3-319-44188-7_20

Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still ro... Read More about An outlier ranking tree selection approach to extreme pruning of random forests..