ORCID Profile
0000-0002-9485-9216
Current Organisations
Trinity College Dublin
,
Pusan National University
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Publisher: Elsevier BV
Date: 02-2021
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: arXiv
Date: 2022
Publisher: arXiv
Date: 2022
Publisher: Hindawi Limited
Date: 18-11-2021
DOI: 10.1155/2021/7156420
Abstract: Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the in idual cluster accuracy by integrating the density-based clustering and genetic hyperparameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR-10 dataset with only 10 training rounds.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: arXiv
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Association for Computing Machinery (ACM)
Date: 03-02-2023
DOI: 10.1145/3501296
Abstract: Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Elsevier BV
Date: 10-2022
Publisher: Elsevier BV
Date: 04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-01-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Elsevier BV
Date: 2023
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Cambridge University Press (CUP)
Date: 11-2022
DOI: 10.7120/09627286.31.4.005
Abstract: Play and welfare have long been linked within animal research literature, with play considered as both a potential indicator and promoter of welfare. An indicator due to observations that play is exhibited most frequently in times when an animal's fitness is not under threat and when immediate needs such as food, water and adequate space are met. And a promoter, because of observations that animals who play more also have better welfare outcomes. However, limited research has been undertaken to investigate this link, especially in companion animals. The domestic cat (Felis catus) is one of the most popular companion animals in the world, yet little is known about the impact of play behaviour on cat welfare. We review the current literature on play and welfare in cats. This includes examining the role of cat play in mitigating negative welfare outcomes, such as reducing problem behaviours, one of the leading reasons for guardian dissatisfaction and cat relinquishment to shelters. Play is also discussed as a potential tool to provide environmental enrichment and to improve cat-human relationships. Future areas for research are suggested. We find that further research is needed that uses a multi-faceted approach to assess how quantity, type and quality of play impact subsequent cat behaviour and welfare. Future research could also assess cat play needs and preferences as well as investigate the role of play in mitigating threats to cat welfare such as reducing problem behaviour and improving human-cat relationships. If play is an indicator and promoter of welfare, studies into the impact of play may offer an accessible approach for monitoring and improving domestic cat welfare.
Publisher: Elsevier BV
Date: 06-2022
Publisher: Hindawi Limited
Date: 27-06-2022
DOI: 10.1155/2022/2218594
Abstract: In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems.
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 07-2022
Publisher: Elsevier BV
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2021
Publisher: arXiv
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
No related grants have been discovered for Viet Quoc Pham.