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1,196 results on '"Ryan, Conor A."'

1. Do Weibo platform experts perform better at predicting stock market?

Author

Ma, Ziyuan, Ryan, Conor, Buckley, Jim, and Chochlov, Muslim

Subjects
Quantitative Finance - Statistical Finance, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Sentiment analysis can be used for stock market prediction. However, existing research has not studied the impact of a user's financial background on sentiment-based forecasting of the stock market using artificial neural networks. In this work, a novel combination of neural networks is used for the assessment of sentiment-based stock market prediction, based on the financial background of the population that generated the sentiment. The state-of-the-art language processing model Bidirectional Encoder Representations from Transformers (BERT) is used to classify the sentiment and a Long-Short Term Memory (LSTM) model is used for time-series based stock market prediction. For evaluation, the Weibo social networking platform is used as a sentiment data collection source. Weibo users (and their comments respectively) are divided into Authorized Financial Advisor (AFA) and Unauthorized Financial Advisor (UFA) groups according to their background information, as collected by Weibo. The Hong Kong Hang Seng index is used to extract historical stock market change data. The results indicate that stock market prediction learned from the AFA group users is 39.67% more precise than that learned from the UFA group users and shows the highest accuracy (87%) when compared to existing approaches.

Published
2024
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2. Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation

Author

Hasan, Yumnah, de Lima, Allan, Amerehi, Fatemeh, de Bulnes, Darian Reyes Fernandez, Healy, Patrick, and Ryan, Conor

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Medical imaging diagnosis increasingly relies on Machine Learning (ML) models. This is a task that is often hampered by severely imbalanced datasets, where positive cases can be quite rare. Their use is further compromised by their limited interpretability, which is becoming increasingly important. While post-hoc interpretability techniques such as SHAP and LIME have been used with some success on so-called black box models, the use of inherently understandable models makes such endeavors more fruitful. This paper addresses these issues by demonstrating how a relatively new synthetic data generation technique, STEM, can be used to produce data to train models produced by Grammatical Evolution (GE) that are inherently understandable. STEM is a recently introduced combination of the Synthetic Minority Oversampling Technique (SMOTE), Edited Nearest Neighbour (ENN), and Mixup; it has previously been successfully used to tackle both between class and within class imbalance issues. We test our technique on the Digital Database for Screening Mammography (DDSM) and the Wisconsin Breast Cancer (WBC) datasets and compare Area Under the Curve (AUC) results with an ensemble of the top three performing classifiers from a set of eight standard ML classifiers with varying degrees of interpretability. We demonstrate that the GE-derived models present the best AUC while still maintaining interpretable solutions.

Published
2024

3. A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution

Author

Gupt, Krishn Kumar, Kshirsagar, Meghana, Dias, Douglas Mota, Sullivan, Joseph P., and Ryan, Conor

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Computational cost in metaheuristics such as Evolutionary Algorithms (EAs) is often a major concern, particularly with their ability to scale. In data-based training, traditional EAs typically use a significant portion, if not all, of the dataset for model training and fitness evaluation in each generation. This makes EAs suffer from high computational costs incurred during the fitness evaluation of the population, particularly when working with large datasets. To mitigate this issue, we propose a Machine Learning (ML)-driven Distance-based Selection (DBS) algorithm that reduces the fitness evaluation time by optimizing test cases. We test our algorithm by applying it to 24 benchmark problems from Symbolic Regression (SR) and digital circuit domains and then using Grammatical Evolution (GE) to train models using the reduced dataset. We use GE to test DBS on SR and produce a system flexible enough to test it on digital circuit problems further. The quality of the solutions is tested and compared against the conventional training method to measure the coverage of training data selected using DBS, i.e., how well the subset matches the statistical properties of the entire dataset. Moreover, the effect of optimized training data on run time and the effective size of the evolved solutions is analyzed. Experimental and statistical evaluations of the results show our method empowered GE to yield superior or comparable solutions to the baseline (using the full datasets) with smaller sizes and demonstrates computational efficiency in terms of speed.

Published
2023

4. STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup

Author

Hasan, Yumnah, Amerehi, Fatemeh, Healy, Patrick, and Ryan, Conor

Subjects
Computer Science - Machine Learning
Abstract

Imbalanced datasets in medical imaging are characterized by skewed class proportions and scarcity of abnormal cases. When trained using such data, models tend to assign higher probabilities to normal cases, leading to biased performance. Common oversampling techniques such as SMOTE rely on local information and can introduce marginalization issues. This paper investigates the potential of using Mixup augmentation that combines two training examples along with their corresponding labels to generate new data points as a generic vicinal distribution. To this end, we propose STEM, which combines SMOTE-ENN and Mixup at the instance level. This integration enables us to effectively leverage the entire distribution of minority classes, thereby mitigating both between-class and within-class imbalances. We focus on the breast cancer problem, where imbalanced datasets are prevalent. The results demonstrate the effectiveness of STEM, which achieves AUC values of 0.96 and 0.99 in the Digital Database for Screening Mammography and Wisconsin Breast Cancer (Diagnostics) datasets, respectively. Moreover, this method shows promising potential when applied with an ensemble of machine learning (ML) classifiers., Comment: 7 pages, 4 figures, International Conference on Intelligent Computer Communication and Processing

Published
2023

5. Feature Encapsulation by Stages in the Regression Domain Using Grammatical Evolution

Author

Reyes Fernández de Bulnes, Darian, de Lima, Allan, Galván, Edgar, Ryan, Conor, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Affenzeller, Michael, editor, Winkler, Stephan M., editor, Kononova, Anna V., editor, Trautmann, Heike, editor, Tušar, Tea, editor, Machado, Penousal, editor, and Bäck, Thomas, editor

Published
2024
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6. Fuzzy Pattern Trees for Classification Problems Using Genetic Programming

Author

de Lima, Allan, Carvalho, Samuel, Dias, Douglas Mota, Amaral, Jorge, Sullivan, Joseph P., Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Giacobini, Mario, editor, Xue, Bing, editor, and Manzoni, Luca, editor

Published
2024
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7. In-situ wavelength calibration without a calibration target: use of Fraunhofer lines after landing on planetary surface

Author

Mori, Shoki, Boettger, Ute, Buder, Maximilian, Cho, Yuichiro, Dietz, Enrico, Hagelschuer, Till, Hübers, Heinz-Wilhelm, Kameda, Shingo, Kopp, Emanuel, Prieto-Ballesteros, Olga, Rull, Fernando, Ryan, Conor, Schroeder, Susanne, Sugita, Seiji, Tabata, Haruhisa, Usui, Tomohiro, and Yumoto, Koki

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Accurate wavelength calibration is critical for qualitative and quantitative spectroscopic measurements. Many spectrometers for planetary exploration are equipped with onboard calibration sources. However, such calibration sources are not always available because planetary lander missions often have strong limitations in size and mass. In this study, we propose and validate a wavelength calibration method using solar Fraunhofer lines observed in reflective spectra. As a result, for a visible Raman spectrometer, the accuracy is better than 0.6 cm-1 in 0-4000 cm-1 range, and the magnesium abundance of olivine is estimated more accurately than 2%.

Published
2022

10. Ethical Standards for Research on Marine Mammals

Author

Papastavrou, Vassili and Ryan, Conor

Abstract

Conducting marine mammal research can raise several important ethical issues. For example, the continuation of whaling for commercial purposes despite the international moratorium provides opportunities for scientists to obtain data and tissue samples. In 2021 we analysed 35 peer-reviewed papers reporting research based on collaborations with Icelandic whalers. Results highlighted little consideration or understanding of the legal and ethical issues associated with the deliberate killing of whales amongst those researchers, funding bodies, universities and journals involved. Ethical statements were rarely provided. Those that were written were incomplete. Whilst research using whaling data may seem acceptable to some, it often becomes hard to justify when subject to scrutiny by the media and the public. Thus, there is a particular danger of reputational harm for early career researchers who may become unwittingly involved in such activities. Here we also consider the broader variety of ethical issues raised by non-lethal research (both historical and recent) on marine mammals including tagging and biopsy. We discuss instances where study animals were harmed or even killed and where the public mistook tags for harpoons. Without clear guidelines, reviewers and journal editors are put in an impossible position when considering whether to reject papers on ethical grounds. We propose that for such studies, universities, funders, journals, and permit issuers must require ethical assessments and that journals more effectively implement their existing policies on publishing ethical statements. The professional marine mammal societies need to work together to produce modern ethical guidance. Such guidance should require transparency in the provenance of data and samples while including advice on law, welfare issues, involvement of local scientists, and offshoring. Furthermore, it should require appraisal of and justification for the absolute necessity of invasive procedures. As is already the case in biomedical disciplines, ethical statements should be required in marine mammal science.

Published
2023
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11. Geographical movements, site fidelity and connectivity of killer whales within and outside herring grounds in Icelandic coastal waters

Author

Marchon, Tatiana M. J., Rasmussen, Marianne H., Basran, Charla J., Whittaker, Megan, Bertulli, Chiara G., Harlow, Cathy, Lott, Rob, Boisseau, Oliver, Gendron, Frédéric, Guo, Luisa, Hudson, Tess, Jónsson, Hörður, Kershaw, Alexa, Kinni, Joonas, Lionnet, Laetitia A. M. G., Louis, Marie, Messina, Matt, Michel, Hanna, Neubarth, Barbara K., Ovide, Belén G., Podt, Annemieke E., Rempel, Jonathan N., Ryan, Conor, Savage, Eddy, Scott, Judith, Smit, Ronald, Verdaat, Hans, Vignisson, Solvi R., and Samarra, Filipa I. P.

Published
2024
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14. Fuzzy Pattern Trees with Pre-classification

Author

Murphy, Aidan, Ventresque, Anthony, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Collet, Pierre, editor, Gardashova, Latafat, editor, El Zant, Samer, editor, and Abdulkarimova, Ulviya, editor

Published
2023
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15. Performance Upgrade of Sequence Detector Evolution Using Grammatical Evolution and Lexicase Parent Selection Method

Author

Majeed, Bilal, Carvalho, Samuel, Dias, Douglas Mota, Youssef, Ayman, Murphy, Aidan, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Collet, Pierre, editor, Gardashova, Latafat, editor, El Zant, Samer, editor, and Abdulkarimova, Ulviya, editor

Published
2023
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16. Grammar-Guided Evolution of the U-Net

Author

Mahdinejad, Mahsa, Murphy, Aidan, Tetteh, Michael, de Lima, Allan, Healy, Patrick, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Correia, João, editor, Smith, Stephen, editor, and Qaddoura, Raneem, editor

Published
2023
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22. Biogeography in the deep: Hierarchical population genomic structure of two beaked whale species

Author

Onoufriou, Aubrie B., Gaggiotti, Oscar E., Aguilar de Soto, Natacha, McCarthy, Morgan L., Morin, Phillip A., Rosso, Massimiliano, Dalebout, Merel, Davison, Nicholas, Baird, Robin W., Baker, C. Scott, Berrow, Simon, Brownlow, Andrew, Burns, Daniel, Caurant, Florence, Claridge, Diane, Constantine, Rochelle, Demaret, Fabien, Dreyer, Sascha, Ðuras, Martina, Durban, John W., Frantzis, Alexandros, Freitas, Luis, Genty, Gabrielle, Galov, Ana, Hansen, Sabine S., Kitchener, Andrew C., Martin, Vidal, Mignucci-Giannoni, Antonio A., Montano, Valeria, Moulins, Aurelie, Olavarría, Carlos, Poole, M. Michael, Reyes Suárez, Cristel, Rogan, Emer, Ryan, Conor, Schiavi, Agustina, Tepsich, Paola, Urban R., Jorge, West, Kristi, Olsen, Morten Tange, and Carroll, Emma L.

Published
2022
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29. Insights into the Advancements of Artificial Intelligence and Machine Learning, the Present State of Art, and Future Prospects: Seven Decades of Digital Revolution

Author

Bindra, Prabhleen, Kshirsagar, Meghana, Ryan, Conor, Vaidya, Gauri, Gupt, Krishn Kumar, Kshirsagar, Vivek, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Satapathy, Suresh Chandra, editor, Bhateja, Vikrant, editor, Favorskaya, Margarita N., editor, and Adilakshmi, T., editor

Published
2021
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30. Running to Get Recognised

Author

Carty, Gerard, Raja, Muhammad Adil, Ryan, Conor, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Thampi, Sabu M., editor, Krishnan, Sri, editor, Hegde, Rajesh M., editor, Ciuonzo, Domenico, editor, Hanne, Thomas, editor, and Kannan R., Jagadeesh, editor

Published
2021
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32. Towards Incorporating Human Knowledge in Fuzzy Pattern Tree Evolution

Author

Murphy, Aidan, Murphy, Gráinne, Amaral, Jorge, MotaDias, Douglas, Naredo, Enrique, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hu, Ting, editor, Lourenço, Nuno, editor, and Medvet, Eric, editor

Published
2021
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33. Evolution of Complex Combinational Logic Circuits Using Grammatical Evolution with SystemVerilog

Author

Tetteh, Michael Kwaku, Mota Dias, Douglas, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hu, Ting, editor, Lourenço, Nuno, editor, and Medvet, Eric, editor

Published
2021
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35. GELAB and Hybrid Optimization Using Grammatical Evolution

Author

Raja, Muhammad Adil, Murphy, Aidan, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Analide, Cesar, editor, Novais, Paulo, editor, Camacho, David, editor, and Yin, Hujun, editor

Published
2020
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36. Trading Cryptocurrency with Deep Deterministic Policy Gradients

Author

Tummon, Evan, Raja, Muhammad Adil, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Analide, Cesar, editor, Novais, Paulo, editor, Camacho, David, editor, and Yin, Hujun, editor

Published
2020
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37. Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing

Author

Anjum, Muhammad Sheraz, Ryan, Conor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hu, Ting, editor, Lourenço, Nuno, editor, Medvet, Eric, editor, and Divina, Federico, editor

Published
2020
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45. Technology Selection for Inline Topography Measurement with Rover-Borne Laser Spectrometers.

Author

Ryan, Conor, Haist, Tobias, Laskin, Gennadii, Schröder, Susanne, and Reichelt, Stephan

Subjects
*LASER measurement, *SPECTROMETERS, *LASER spectroscopy, *TOPOGRAPHY, *THREE-dimensional imaging, *DIGITAL holographic microscopy
Abstract

This work studies enhancing the capabilities of compact laser spectroscopes integrated into space-exploration rovers by adding 3D topography measurement techniques. Laser spectroscopy enables the in situ analysis of sample composition, aiding in the understanding of the geological history of extraterrestrial bodies. To complement spectroscopic data, the inclusion of 3D imaging is proposed to provide unprecedented contextual information. The morphological information aids material characterization and hence the constraining of rock and mineral histories. Assigning height information to lateral pixels creates topographies, which offer a more complete spatial dataset than contextual 2D imaging. To aid the integration of 3D measurement into future proposals for rover-based laser spectrometers, the relevant scientific, rover, and sample constraints are outlined. The candidate 3D technologies are discussed, and estimates of performance, weight, and power consumptions guide the down-selection process in three application examples. Technology choice is discussed from different perspectives. Inline microscopic fringe-projection profilometry, incoherent digital holography, and multiwavelength digital holography are found to be promising candidates for further development. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Computer Vision for 3D Perception : A Review

Author

O’ Mahony, Niall, Campbell, Sean, Krpalkova, Lenka, Riordan, Daniel, Walsh, Joseph, Murphy, Aidan, Ryan, Conor, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Kapoor, Supriya, editor, and Bhatia, Rahul, editor

Published
2019
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48. Ariadne: Evolving Test Data Using Grammatical Evolution

Author

Anjum, Muhammad Sheraz, Ryan, Conor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Sekanina, Lukas, editor, Hu, Ting, editor, Lourenço, Nuno, editor, Richter, Hendrik, editor, and García-Sánchez, Pablo, editor

Published
2019
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