Machine Learning and Deep Learning Algorithms for Green Computing

View Sample PDF

Author(s): Rajashri Roy Choudhury (Brainware University, India), Piyal Roy (Brainware University, India), Shivnath Ghosh (Brainware University, India)and Ayan Ghosh (Brainware University, India)
Copyright: 2024
Pages: 23
Source title: Computational Intelligence for Green Cloud Computing and Digital Waste Management
Source Author(s)/Editor(s): K. Dinesh Kumar (Amrita Vishwa Vidyapeetham, India), Vijayakumar Varadarajan (The University of New South Wales, Australia), Nidal Nasser (College of Engineering, Alfaisal University, Saudi Arabia)and Ravi Kumar Poluru (Institute of Aeronautical Engineering, India)
DOI: 10.4018/979-8-3693-1552-1.ch001

Keywords: Cloud Computing / Computer Science & IT / Engineering Science Reference / High Performance Computing

Purchase

View Machine Learning and Deep Learning Algorithms for Green Computing on the publisher's website for pricing and purchasing information.

Abstract

Green computing is an innovative approach to making computer systems environmentally friendly, energy-efficient, and low in carbon emissions. It uses advanced techniques from machine learning and deep learning to optimize real-time resource allocation, reducing energy consumption. This approach enhances workload patterns and uses methods like convolutional and recurrent neural networks to enhance architectural efficiency. The integration of ML and DL techniques allows for accurate temperature forecasting and alternative cooling strategies. Despite challenges, the synergistic fusion of ML and DL algorithmic software with green computing holds great promise for reducing energy consumption and enhancing environmental sustainability.

The IRMA Community

Research IRM

Machine Learning and Deep Learning Algorithms for Green Computing

Purchase

Abstract

Related Content

IRMA Sponsors