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The number of global energy issues is rising daily. Everyone is attempting to conserve energy while also focusing on producing ever-more of it. Numerous methods exist for producing electricity, which is subsequently coordinated for use on a major grid. Technical or non-technical losses are caused by weather. The calculation of technical losses can be achieved with ease, as we have covered in the mathematical modeling part. On the other hand, if technical losses are known, nontechnical losses can be assessed. Electrical theft results in non-technical losses. One can save his financial resources in order to lessen or manage theft. Because of its superior security, optimal efficiency, and outstanding resistance to numerous theft schemes found in electromechanical meters, smart meters may be the ideal choice for reducing electricity theft. Thus, the focus of this work has primarily been on theft-related issues. In order to detect electricity cyber attacks, this project assessed the performance of many deep learning techniques, including CNN, recurrent neural networks with gated recurrent units (RNN-GRU), and deep feed forward neural networks (DNN).

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An Assessment on Convolutional Neural Networks for Recognizing and Detecting Intracranial Hemorrhage from CT Images

  • 会议论文

  • 发表日期：

    2025年

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  • Jonnadula Narasimharao,
  • G. Parvathidevi,
  • M. Nagaraju Naik,
  • Abdul Subhani Shaik,
  • B. Revathi,
  • Radhe Shyam Panda

摘要

The number of global energy issues is rising daily. Everyone is attempting to conserve energy while also focusing on producing ever-more of it. Numerous methods exist for producing electricity, which is subsequently coordinated for use on a major grid. Technical or non-technical losses are caused by weather. The calculation of technical losses can be achieved with ease, as we have covered in the mathematical modeling part. On the other hand, if technical losses are known, nontechnical losses can be assessed. Electrical theft results in non-technical losses. One can save his financial resources in order to lessen or manage theft. Because of its superior security, optimal efficiency, and outstanding resistance to numerous theft schemes found in electromechanical meters, smart meters may be the ideal choice for reducing electricity theft. Thus, the focus of this work has primarily been on theft-related issues. In order to detect electricity cyber attacks, this project assessed the performance of many deep learning techniques, including CNN, recurrent neural networks with gated recurrent units (RNN-GRU), and deep feed forward neural networks (DNN).