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Joshuva Arockia Dhanraj

Researcher at Hindustan University

Publications -  32
Citations -  244

Joshuva Arockia Dhanraj is an academic researcher from Hindustan University. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 3, co-authored 16 publications receiving 30 citations. Previous affiliations of Joshuva Arockia Dhanraj include Kumaraguru College of Technology & Prince of Songkla University.

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Investigating the performance of Hadoop and Spark platforms on machine learning algorithms

TL;DR: The K-nearest neighbor (KNN) algorithm is implemented on datasets with different sizes within both Hadoop and Spark frameworks, and the results show that the runtime of the KNN algorithm implemented on Spark is 4 to 4.5 times faster than Hadoops.
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Blockchain Technology Application Challenges in Renewable Energy Supply Chain Management

TL;DR: Among the identified challenges, “high investment cost” is found to be the most important challenge to the application of blockchain in sustainable energy supply chains.
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An Effective Evaluation on Fault Detection in Solar Panels

TL;DR: In this paper, the authors focus on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels and (iii) the role of machine learning techniques in the fault diagnosis of PV panel, (iv) various sensors used for different fault detections in PV panel and (v) the benefits of fault identification.
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Performance optimization of a new flash-binary geothermal cycle for power/hydrogen production with zeotropic fluid

TL;DR: In this article, the performance of a system consisting of an organic Rankine cycle (ORC) for generating power and an electrolyzer for producing hydrogen with a zeotropic mixture as working fluid to recover waste heat in a geothermal flash-binary cycle is investigated from energy and exergy point of view.
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Machine Learning for Prediction of Energy in Wheat Production

TL;DR: In this article, a novel prediction method based on consumed energy in the production period is proposed, which is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR).