Daily Archives: August 29, 2024

Trends of Renewable Energy Stocks in the era of Viksit & Aatmanirbhar Bharat
Authors:-Research Scholar Ms. Versha Gupta, Assistant Professor Dr. Neetu Jindal

Abstract-In this turbulent phase of climate issues and geopolitical tension, a move towards long-term clean and secure energy alternative is becoming one of the major societal concerns of the 21st century. Government of India’s movement of Viksit & Aatmanirbhar Bharat to make the country independent and self-sufficient is on boom. The Sustainable Development Goal 7 (SDG7) calls “India for an affordable, sustainable, reliable and modern energy for everyone” by year 2030, Self-sufficiency in energy production, an energy independent nation by 2047 & Net-zero carbon emissions targets by 2070 staggering the renewable energy companies’ growth. Indian Government’s established a set of programs, incentives, investments, schemes & policies, 100 % FDI permit, etc. to accelerate the expansion of new green. This transition is giving a new wave to the Green and Renewable energy stocks and make the green energy segment multibagger stocks. Where, India is meeting its 43% energy (about 181 GW) from renewable energy sources in 2024, the targets call India to generate 450-500 GW renewable energy by year 2030. India is running the vast opportunity of renewable energy expansion in the world and thus its stocks too. The objective of this study is to present the trends of Green and Renewable Energy stocks, when the whole world is running for green & sustainability. The present paper highlights the Governments role in promoting the green & renewable energy and its relative impact on best performing Indian Energy companies.

DOI: 10.61137/ijsret.vol.10.issue4.204

Published by: Kajal Tripathi

Effective Techniques for Generative AI Precision
Authors:-Sachin Vighe

Abstract-Generative AI systems have demonstrated remarkable capabilities in various domains, such as natural language processing and image and audio generation, yet achieving high precision and accuracy in these systems remains challenging. This paper comprehensively reviews effective techniques for enhancing generative AI precision, focusing on three key areas: data preparation, model architecture optimization, and fine-tuning strategies. We explore advanced data curation, synthetic data generation, and data augmentation methods that improve model accuracy. For model architecture optimization, we examine recent advancements in attention mechanisms, hierarchical structures, and multi-modal integration that promise increased precision. Fine-tuning strategies analyzed include few-shot learning, continual learning, and domain-specific adaptation. Additionally, I will discuss novel framework for evaluating and benchmarking generative AI precision, offering researchers and practitioners a standardized approach for assessing improvements. Case studies and empirical evidence demonstrate these techniques’ efficacy across various generative AI applications. My findings underscore the importance of a holistic approach to precision enhancement, combining multiple strategies for optimal results, contributing to efforts to make generative AI systems more reliable, accurate, and trustworthy.

DOI: 10.61137/ijsret.vol.10.issue4.203

Published by: Kajal Tripathi