P
P. Pandiyan
Researcher at Coimbatore Institute of Engineering and Technology
Publications - 28
Citations - 175
P. Pandiyan is an academic researcher from Coimbatore Institute of Engineering and Technology. The author has contributed to research in topics: Computer science & Photovoltaic system. The author has an hindex of 3, co-authored 21 publications receiving 36 citations. Previous affiliations of P. Pandiyan include Sri Ramakrishna Institute of Technology & Sri Krishna College of Engineering & Technology.
Papers
More filters
Journal ArticleDOI
Analysis on performance and emission characteristics of corn oil methyl ester blended with diesel and cerium oxide nanoparticle
TL;DR: In this article, the analysis of cerium oxide nanoparticles at different concentration in corn oil methyl ester diesel blend with an optimum concentration of biodiesel of 10% with diesel fuel was conducted.
Journal ArticleDOI
The influence of different parameters in tribological characteristics of pineapple/sisal/TiO2 filler incorporation:
KR Sumesh,G Saikrishnan,P. Pandiyan,L. Prabhu,S. Gokulkumar,AK Priya,Petr Špatenka,Syam Krishna +7 more
TL;DR: In this article, a hybrid composites using natural fibers and titanium oxide nano filler were fabricated using compression molding technique, which used Sisal fibers and Pineapple (P) and Sisal (S) fibers were used.
Journal ArticleDOI
A comprehensive review of the prospects for rural electrification using stand-alone and hybrid energy technologies
P. Pandiyan,R. Sitharthan,S. Saravanan,Natarajan Prabaharan,M. Ramji Tiwari,T. Chinnadurai,T. Yuvaraj,K.R. Devabalaji +7 more
TL;DR: In this paper , a comprehensive review delivers enhanced hybrid electrification in rural areas using renewable energy sources like hydro, wind, biogas, and biomass, and also highlights sustainable and reliable hybrid renewable power generation system operation.
Journal ArticleDOI
Determination and Classification of Blood Types using Image Processing Techniques
TL;DR: An automated method based on processing of images acquired during the slide test to determine the blood group without human error is developed.
Journal ArticleDOI
Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion
TL;DR: In this paper, a review of recent work performed in the field of tomato leaf disease identification using image processing, machine learning, and deep learning approaches is presented, and suggestions are provided to figure out the appropriate techniques in order to obtain the better prediction accuracy.