Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method

Dong, Le; Nguyen, Vu; Le, Dinh Tuan; Tiep, Mai Viet; Hien, Vu Thanh; Huy, Phu Phuoc; Hieu, Phan Trung

doi:10.1007/978-981-16-8062-5_28

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The findings demonstrate that AQFL achieves roughly the same quality and fairness in diverse environments as the model trained in homogeneous conditions. • Deep learning model architecture: DNN is proven to outperform other models in previous research, but its architecture in both traditional ML methods and FL was often pre-fixed [44,76]. Such a pre-defined setting carries many subjective factors that lead to the consequence that the models may fall into local optimal states.…”

Section: Challenges Of Federated Learningmentioning

confidence: 99%

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

Khoa

Dao

et al. 2022

Algorithms

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The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.

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Section: Challenges Of Federated Learningmentioning

confidence: 99%

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

Khoa

Dao

et al. 2022

Algorithms

Self Cite

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“…Consequently, developing industrial health prognostic models to guide condition-based maintenance has attracted significant scientific and industry interest, with a growing emphasis on data-driven models that can leverage the real-time data generated by edge devices [3]- [6]. Amongst data-driven methods, deep learning models have gained notable prominence due to their ability build accurate models from raw input data without needing extensive domain knowledge [7] [8]. For instance, Chen et al [9] developed an attention-enhanced long short-term memory (LSTM) model trained on sensor data to estimate the remaining useful life (RUL) of turbofan engines.…”

Section: Introductionmentioning

confidence: 99%

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Arunan

Yan

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacypreserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5% and 39.3% for state-of-health estimation and remaining useful life estimation, respectively.

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Combination of Hamming Distance and Entropy Measure of Picture Fuzzy Sets: Case Study of COVID-19 Medicine Selection

Nguyen

et al. 2023

Smart Innovation, Systems and Technologies

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Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method

Cited by 4 publications

References 13 publications

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Combination of Hamming Distance and Entropy Measure of Picture Fuzzy Sets: Case Study of COVID-19 Medicine Selection

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