{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:57:39Z","timestamp":1776311859761,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T00:00:00Z","timestamp":1664064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In recent years, machine learning algorithms have been applied in many real-time applications. Crises in the energy sector are the primary challenges experienced today among all countries across the globe, regardless of their economic status. There is a huge demand to acquire and produce environmentally friendly renewable energy and to distribute and utilize it efficiently because of its huge production cost. PEMFC are known for their energy efficiency and comparatively low cost, and can be an alternative energy source. The efficiency of these PEMFC can still be enhanced with the help of advanced technologies like machine learning and artificial intelligence, as they provide an optimal solution to explore the hidden knowledge from the generated data. The proposed model attempts to compare several design techniques with varied humidity levels. To enhance the performance of PEMFC, the various humidification processes were considered during the experimental study. The humidification reduces the heat during energy generation and increases the performance of PEM fuel cell. The humidity levels such as 100%, 50%, and 10% were considered to be tested with the machine learning models. The SVMR, LR, and KNN algorithms were tested and observed with the RMSE value as the evaluation parameters. The observed results show that SVMR has an RMSE rate of 0.0046, the LR method has an RMSE rate of 0.0034, and KNN has an RMSE rate of 0.004. The analysis shows that the LR model provides better accuracy than other models. The LR model enhances the PEMFC performance.<\/jats:p>","DOI":"10.3390\/a15100346","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T21:14:28Z","timestamp":1664140468000},"page":"346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["An Optimized Data Analysis on a Real-Time Application of PEM Fuel Cell Design by Using Machine Learning Algorithms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3526-1350","authenticated-orcid":false,"given":"Arun","family":"Saco","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Sri Venkateswara College of Engineering and Technology, Chitoor 517127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1188-5353","authenticated-orcid":false,"given":"P. Shanmuga","family":"Sundari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sri Venkateswara College of Engineering and Technology, Chittoor 517127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9489-6394","authenticated-orcid":false,"given":"Karthikeyan","family":"J","sequence":"additional","affiliation":[{"name":"Professor of Humanities and Sciences, Sri Venkateswara College of Engineering and Technology, Chittoor 517127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0737-2021","authenticated-orcid":false,"given":"Anand","family":"Paul","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Kyungpook National University, Daegu 37224, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.1016\/j.ijhydene.2015.04.122","article-title":"Numerical simulation of water droplet dynamics in a right angle gas channel of a polymer electrolyte membrane fuel cell","volume":"40","author":"Jo","year":"2015","journal-title":"Int. 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