Energy-efficient communication-aware VM placement in cloud datacenter using hybrid ACO–GWO

Keshri, Rashmi; Vidyarthi, Deo Prakash

doi:10.1007/s10586-024-04623-z

Cluster Comput

2024

DOI: 10.1007/s10586-024-04623-z

|View full text |Cite

Energy-efficient communication-aware VM placement in cloud datacenter using hybrid ACO–GWO

Rashmi Keshri,

Deo Prakash Vidyarthi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Sustainability

View full text Add to dashboard Cite

Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various agricultural and climatic variables are included in the analysis, encompassing crop type, year, season, and the specific climatic conditions of the Indian state during the crop’s growing season. Features such as crop and season were one-hot encoded. The primary objective was to predict yield using a deep neural network (DNN), with hyperparameters optimized through genetic algorithms (GAs) to maximize the R2 score. The best-performing model, achieved by fine-tuning its hyperparameters, achieved an R2 of 0.92, meaning it explains 92% of the variation in crop yields, indicating high predictive accuracy. The optimized DNN models were further analyzed using explainable AI (XAI) techniques, specifically local interpretable model-agnostic explanations (LIME), to elucidate feature importance and enhance model interpretability. The analysis underscored the significant role of features such as crops, leading to the incorporation of an additional dataset to classify the most optimal crops based on more detailed soil and climate data. This classification task was also executed using a GA-optimized DNN, aiming to maximize accuracy. The results demonstrate the effectiveness of this approach in predicting crop yields and classifying optimal crops.

show abstract

Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Energy-efficient communication-aware VM placement in cloud datacenter using hybrid ACO–GWO

Cited by 1 publication

References 38 publications

Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools

Contact Info

Product

Resources

About