Neural fuzzy system design in forest fire detection

Rao, Gudikandhula Narasimha; Rao, Peddada Jagadeeswara; Duvvuru, Rajesh; Beulah, Kondapalli; Lydia, E. Laxmi; Rathnala, Prasanthi; Balakrishna, Bangaru; Motru, Vijaya Raju

doi:10.1007/s00542-023-05496-9

Microsyst Technol

2023

DOI: 10.1007/s00542-023-05496-9

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Neural fuzzy system design in forest fire detection

Gudikandhula Narasimha Rao

Peddada Jagadeeswara Rao

Rajesh Duvvuru

et al.

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2023

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Gas leakage detection using convolution neural networks

Kondapalli,

Penmetsa,

P. Krishna

2023

JCOM

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In many different industrial, residential, and commercial situations, gas leakage poses a serious hazard. Its discovery is crucial since it might have severe effects like explosions and fires. For the protection of persons and property, as well as to avert catastrophic tragedies, accurate and prompt gas leak detection is essential. Convolutional Neural Networks (CNNs), in particular, have demonstrated encouraging results in the detection of gas leaks in recent years. Here, a CNNbased method is provided for detecting gas leaks from image data. The suggested method employs a Softmax classifier for gas classification after extracting features from the image dataset using a combination of convolution, pooling, and fully connected layers. The usefulness of the suggested approach in accurately detecting gas leakage is shown by the experimental findings and the proposed approach is tested on a real-world gas leakage dataset. It can be added to gas detection systems to improve their functionality, lowering the likelihood of gas-related mishaps. The findings support current work to create accurate and effective machine learning-based gas leak detection systems.

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Gas leakage detection using convolution neural networks

Kondapalli,

Penmetsa,

P. Krishna

2023

JCOM

View full text Add to dashboard Cite

show abstract

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Neural fuzzy system design in forest fire detection

Cited by 1 publication

References 35 publications

Gas leakage detection using convolution neural networks

Gas leakage detection using convolution neural networks

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