Documents published in Scipedia

• Prediction of EV charging behavior using BOA-based deep residual attention network

  J. Appadurai, D. G, R. Yugha, R. Sarkar, A. Thirumalraj, B. kavin, D. Seng

  Rev. int. métodos numér. cálc. diseño ing. (2024). Vol. 40, (2), 16

  
  

  Abstract

  In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

  Abstract
  In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, [...]

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Articles (Documents not available in Scipedia)

• LW-CNN-based extraction with optimized encoder-decoder model for detection of diabetic retinopathy

  Manjunatha B B. Gunapriya, T. Rajesh, Arunadevi Thirumalraj

  Journal of Autonomous Intelligence

• Detection of Data imbalance in MANET network based on ADSY-AEAMBi-LSTM with DBO Feature selection

  Kavitha Nagarajan Venkatasubramanian Srinivasan, Vijilius Helena Raj, Arunadevi Thirumalraj

  Journal of Autonomous Intelligence

• Future food production prediction using AROA based hybrid deep learning model in agri-sector

  Swathi Baswaraju, V Uma Maheswari, krishna Keerthi Chennam, Arunadevi Thirumalraj, MVV Prasad Kantipudi, Rajanikanth Aluvalu

  521-536

Books, book chapters and edited books (Documents not available in Scipedia)

• Detection of Ephemeral Sand River Flow Using Hybrid Sandpiper Optimization-Based CNN Model

  B. Manjunatha, Arunadevi Thirumalraj, V. S. Anusuya

  IGI global

• An Improved Hunter-Prey Optimizer-Based DenseNet Model for Classification of Hyper-Spectral Images

  Arunadevi Thirumalraj, V. Asha, Balasubramanian Prabhu Kavin

  IGI global