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166 results on '"Ooi, Melanie"'

1. Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients

Author

Huang, Victoria, Sohail, Shaleeza, Mayo, Michael, Botran, Tania Lorido, Rodrigues, Mark, Anderson, Chris, and Ooi, Melanie

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unreliable devices (e.g., dropping out, misconfiguration, poor data quality) in real-world applications is not fully investigated. We carefully chose two representative, real-world classification problems with a limited numbers of clients to better analyze FL fault tolerance. Contrary to the intuition, simple FL algorithms can perform surprisingly well in the presence of unreliable clients.

Published
2023
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5. Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications

Author

Ooi, Melanie Po-Leen, primary, Sohail, Shaleeza, additional, Huang, Victoria Guiying, additional, Hudson, Nathaniel, additional, Baughman, Matt, additional, Rana, Omer, additional, Hinze, Annika, additional, Chard, Kyle, additional, Chard, Ryan, additional, Foster, Ian, additional, Spyridopoulos, Theodoros, additional, and Nagra, Harshaan, additional

Published
2023
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10. Rural AI: Serverless-Powered Federated Learning for Remote Applications

Author

Patros, Panos, primary, Ooi, Melanie, additional, Huang, Victoria, additional, Mayo, Michael, additional, Anderson, Chris, additional, Burroughs, Stephen, additional, Baughman, Matt, additional, Almurshed, Osama, additional, Rana, Omer, additional, Chard, Ryan, additional, Chard, Kyle, additional, and Foster, Ian, additional

Published
2023
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11. Sparse reproducible machine learning for near infrared hyperspectral imaging: Estimating the tetrahydrocannabinolic acid concentration in Cannabis sativa L.

Author

Abeysekera, Sanush K., primary, Robinson, Amanda, additional, Ooi, Melanie Po-Leen, additional, Kuang, Ye Chow, additional, Manley-Harris, Merilyn, additional, Holmes, Wayne, additional, Hirst, Evan, additional, Nowak, Jessika, additional, Caddie, Manu, additional, Steinhorn, Gregor, additional, and Demidenko, Serge, additional

Published
2023
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14. Adaptive Edge-Cloud Environments for Rural AI

Author

Almurshed, Osama, primary, Patros, Panos, additional, Huang, Victoria, additional, Mayo, Michael, additional, Ooi, Melanie, additional, Chard, Ryan, additional, Chard, Kyle, additional, Rana, Omer, additional, Nagra, Harshaan, additional, Baughman, Matt, additional, and Foster, Ian, additional

Published
2022
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15. Contributors

Author

Adetiloye, Taiwo, primary, Adhikari, Sondipon, additional, Aljarah, Ibrahim, additional, An, Senjian, additional, Aslan, Serdar, additional, Awasthi, Anjali, additional, Bakshi, Ashish, additional, Bennamoun, Mohammed, additional, Beyhan, Selami, additional, Bhatia, Vimal, additional, Bhattacharya, Gautam, additional, Bosaghzadeh, Alirezah, additional, Boussaid, Farid, additional, Bui, Dieu Tien, additional, Bui, Kien-Trinh Thi, additional, Bui, Quang-Thanh, additional, Chakraborty, Anusheema, additional, Chatterjee, Tanmoy, additional, Chowdhury, Rajib, additional, Crosky, Alan, additional, Das, Sarat Kumar, additional, Dash, Pradipta K., additional, Dash, Rajashree, additional, Dellen, Babette, additional, Demidenko, Serge, additional, Demir, Vahdettin, additional, Diker, Murat, additional, Dilmen, Erdem, additional, Doan, Chinh Van, additional, Dornaika, Fadi, additional, Fakhari, Nikoo, additional, Faris, Hossam, additional, Fisher, Robert B., additional, Gandomi, Amir H., additional, Gholami, Raoof, additional, Ghosh, Kuntal, additional, Hoang, Nhat-Duc, additional, Hovey, Renae, additional, Husain, Farzad, additional, Ilia, Ioanna, additional, Jiang, Peng, additional, Joshi, Pawan K., additional, Kavzoglu, Taskin, additional, Kendrick, Gary, additional, Kisi, Ozgur, additional, Kuang, Ye Chow, additional, Madadi, Sajad, additional, Maghrebi, Mojtaba, additional, Mahmood, Ammar, additional, Mandloi, Manish, additional, Mellal, Mohamed Arezki, additional, Merabet, Youssef El, additional, Metya, Subhadeep, additional, Mirjalili, Seyedali, additional, Mohammadi-Ivatloo, Behnam, additional, Mohanty, Ranajeet, additional, Moujahid, Abdelmalik, additional, Mukherjee, Aparajita, additional, Mukherjee, V., additional, Mukhopadhyay, Tanmoy, additional, Mukund Nilakantan, J., additional, Nazari-Heris, Morteza, additional, Nielsen, Peter, additional, Ntalampiras, Stavros, additional, Ooi, Melanie Po-Leen, additional, Panigrahi, Ashalata, additional, Patra, Manas R., additional, Ponnambalam, S.G., additional, Prasad, Dharmbir, additional, Ruichek, Yassine, additional, Sachdeva, Kamna, additional, Sahab, Mohamed G., additional, Salmane, Houssam, additional, Saydam, Serkan, additional, Shiri, Jalal, additional, Sohel, Ferdous, additional, Sok, Hong Kuan, additional, Suman, Shakti, additional, Toropov, Vassili V., additional, Torras, Carme, additional, Tsangaratos, Paraskevas, additional, Williams, Edward J., additional, Yilmaz, Selim, additional, and Zamani-Gargari, Milad, additional

Published
2017
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19. Roadmap on signal processing for next generation measurement systems

Author

Iakovidis, Dimitris K, primary, Ooi, Melanie, additional, Kuang, Ye Chow, additional, Demidenko, Serge, additional, Shestakov, Alexandr, additional, Sinitsin, Vladimir, additional, Henry, Manus, additional, Sciacchitano, Andrea, additional, Discetti, Stefano, additional, Donati, Silvano, additional, Norgia, Michele, additional, Menychtas, Andreas, additional, Maglogiannis, Ilias, additional, Wriessnegger, Selina C, additional, Chacon, Luis Alberto Barradas, additional, Dimas, George, additional, Filos, Dimitris, additional, Aletras, Anthony H, additional, Töger, Johannes, additional, Dong, Feng, additional, Ren, Shangjie, additional, Uhl, Andreas, additional, Paziewski, Jacek, additional, Geng, Jianghui, additional, Fioranelli, Francesco, additional, Narayanan, Ram M, additional, Fernandez, Carlos, additional, Stiller, Christoph, additional, Malamousi, Konstantina, additional, Kamnis, Spyros, additional, Delibasis, Konstantinos, additional, Wang, Dong, additional, Zhang, Jianjing, additional, and Gao, Robert X, additional

Published
2021
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44. Complex feature alternating decision tree

Author

Kuang, Ye Chow and Ooi, Melanie Po-Leen

Subjects
Complex systems -- Analysis, Decision tree -- Analysis, Computers and office automation industries
Abstract

Byline: Ye Chow Kuang, Melanie Po-Leen Ooi Complex number features are ubiquitous in many engineering and scientific applications. Many traditional classification algorithms including alternating decision tree (ADTree) are very powerful but not capable of handling complex domain data. ADTree is classifier that is intrinsically support boosting, hence inherent all desirable statistical properties of boosting methodology. This work introduces base learners that enable application of ADTree algorithm to complex domain data. The presented results show that the proposed base learners enhance performance of ADTrees on complex domain features.

Published
2010

45. Design of embedded differential equation solver

Author

Kuang, Ye Chow, Ooi, Melanie Po-Leen, Loh, Wei Hong, and Demidenko, Serge

Subjects
Differential equations -- Analysis, Real-time control -- Usage, Real-time systems -- Usage, Sensors -- Usage, Real-time system, Computers and office automation industries
Abstract

Byline: Ye Chow Kuang, Melanie Po-Leen Ooi, Wei Hong Loh, Serge Demidenko The capability to solve ordinary differential equations (ODE) in hardware will increase the operation capacity of sensing systems in areas such as self-diagnostics, model-based measurement and self-calibration. The computational complexity of solving ODE must be reduced in order to implement a real-time embedded ODE solver. The research proposes a novel design that proves the possibility of solving ODE in real-time embedded systems with reasonably high degree of precision and efficiency. The application of three approximation methods namely, multi-layer perceptron, radial basis network and Lipschitz continuous interpolation is researched and compared.

Published
2009

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