Deep Learning based Energy-Efficient Task Scheduling in Cloud Computing DOI
K. Anuradha,

T. Sampath Kumar

2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), Journal Year: 2024, Volume and Issue: unknown, P. 1761 - 1766

Published: Feb. 28, 2024

A growing number of manufacturing businesses are becoming more concerned with energy efficiency because rising costs and environmental consciousness. The enormous shift an organization's resource requirements from on-site technology to cloud-based systems is resulting in a significant rise the expenses that cloud reviews face when it comes building, maintaining, providing hardware for servers, storage, networks, processing. Because operate on such massive scale, even little decrease performance can result increases or usage expenses. Numerous studies have put forth techniques designed optimize throughput consumption while accounting varied settings. This review's scope reviewing several survey offloading methods based GGCN discussing advantages drawbacks each. An exhaustive scientific examination latest related unloading. Subsequently, examined. Lastly, few recommendations made further research.

Language: Английский

Cross-dataset COVID-19 transfer learning with data augmentation DOI
Bagus Tris Atmaja,

Zanjabila,

Suyanto Suyanto

et al.

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

Language: Английский

Citations

0
Performance Improvement of Covid-19 Cough Detection Based on Deep Learning with Segmentation Methods DOI Open Access

Suyanto Suyanto

Journal of Applied Data Sciences, Journal Year: 2024, Volume and Issue: 5(2), P. 520 - 531

Published: May 31, 2024

COVID-19 is an emergency problem that being widely discussed in the world, one of which deep learning-based detection method has been developed based on images patient's chest or cough. In this research, we propose a way to improve performance cough by using segmentation produce several audio files containing signal from file sound signals. addition, enabled two automatic methods, namely Hysteresis Comparator power spectrum and RMS threshold energy value. The results obtained show for sounds can model's detecting coughs 4% 8%. process also remove noise between signals provide standard input model form signal. information related characteristics evaluation hysteresis comparator better with unweighted accuracy (UA) value 83.19% compared UA 79.06%.

Language: Английский

Citations

1
Deep Learning based Energy-Efficient Task Scheduling in Cloud Computing DOI
K. Anuradha,

T. Sampath Kumar

2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), Journal Year: 2024, Volume and Issue: unknown, P. 1761 - 1766

Published: Feb. 28, 2024

A growing number of manufacturing businesses are becoming more concerned with energy efficiency because rising costs and environmental consciousness. The enormous shift an organization's resource requirements from on-site technology to cloud-based systems is resulting in a significant rise the expenses that cloud reviews face when it comes building, maintaining, providing hardware for servers, storage, networks, processing. Because operate on such massive scale, even little decrease performance can result increases or usage expenses. Numerous studies have put forth techniques designed optimize throughput consumption while accounting varied settings. This review's scope reviewing several survey offloading methods based GGCN discussing advantages drawbacks each. An exhaustive scientific examination latest related unloading. Subsequently, examined. Lastly, few recommendations made further research.

Language: Английский

Citations

0