PREVIOUS ISSUES » Volume 8 Issue 1 January-March 2020

Volume 8 Issue 1                         January – March 2020

 

A Review on Congestion Management Using Genetic Algorithm

Country- INDIA

Sandeep Patil

Abstract: Congestion management is a major challenge in front of system operators particularly in deregulated system. Now a day’s power system is undergoing the process of deregulation everywhere to fulfill the increased demand, by inducing competition in electricity market and for the better utilization of distributed generating units. Various conventional and latest optimization techniques like nonlinear programming (NLP), dynamic programming (DP), quadratic programming (QP), Lagrange relaxation method, genetic algorithm (GA), ant colony optimization (ACO), bee colony optimization (BCO) etc.

Cite this Article

Sandeep Patil. A Review on Congestion Management Using Genetic Algorithm. International Research Journal of Engineering & Applied Sciences, IRJEAS, 8(1), pp. 01-04, 2020.

 

Exploring the Landscape: A Systematic Review of Quantum Machine Learning and Its Diverse Applications [pp 5-9]

Country- INDIA

https://doi.org/10.55083/irjeas.2020.v08i01003

Dr. Sajeeda Parveen Shaik

Abstract: Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum Machine Learning.