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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Shanmugam, Nithyadevi
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Deep learning and machine learning classification technique for integrated forecasting Prem Monickaraj, Vigilson; Rani Devakadacham, Sterlin; Shanmugam, Nithyadevi; Nandhakumar, Nithya; Alagarsamy, Manjunathan; Suriyan, Kannadhasan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1519-1525

Abstract

Smart fisheries are increasingly using artificial intelligence (AI) technologies to increase their sustainability. The potential fishing zone (PFZ) forecasts several fish aggregation zones throughout the duration of the prediction in any sea. The autoregressive integrated moving average (ARIMA) and random forest model are used in the current study to provide a technique for locating viable fishing zones in deep marine seas. A significant amount of data was gathered for the database's creation, including monitoring information for Indian fishing fleets from 2017 to 2019. Using expert label datasets for validation, it was discovered that the model's detection accuracy was 98%. Our method uses salinity and dissolved oxygen, two crucial markers of water quality, to identify suitable fishing zones for the first time. In the current research, a system was created to identify and map the quantity of fishing activity. The tests use a number of parameter measurements to evaluate the contrast-enhanced computed tomography (CECT) approach to machine learning (ML) and deep learning (DL) methodologies. The findings showed that the CECT had a 94% accuracy rate compared to a convolutional neural network's 92% accuracy rate for the 80% training data and 20% testing data.
Performance improvement of fuel cell and photovoltaic system Alagarsamy, Manjunathan; Chaudhary, Neera; Shanmugam, Nithyadevi; Suriyan, Kannadhasan; Loganathan, Arulmurugan; Manikandan, Raja
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.4250

Abstract

This article considers and studies a hybrid energy storage system as a potential replacement for a utility grid. It also examines its organisational structure. The hybrid energy storage technology is used to ensure a constant supply of convenient grid electricity that is sufficient to handle changing power spikes. Batteries are used to stabilise the surges with measurable variation, whereas a massive capacitor is utilised to stabilise the surges with fast variation. In isolated areas where connecting to the main utility grid is impractical, standalone renewable generation may provide the advantage of a reduced operational cost as well as a reduction in protection fees. In order to encourage non-conventional power production in the overall renewable energy system, advancements in solid oxide fuel-cell technology and solar photovoltaic (PV) technology have also been made. Grid-coupled solar PV energy producing systems are being extensively used worldwide, and solar PV modules are increasingly being used in residential applications linked to the electrical grid.
Co-Authors Alagarsamy, Manjunathan Chaudhary, Neera Loganathan, Arulmurugan Manikandan, Raja Nandhakumar, Nithya Prem Monickaraj, Vigilson Rani Devakadacham, Sterlin Suriyan, Kannadhasan