Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and economy.The current benchmark method for the detection of COVID-19 is the reverse tr...The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and economy.The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)testing.Nevertheless,this testing method is accurate enough for the diagnosis of COVID-19.However,it is time-consuming,expensive,expert-dependent,and violates social distancing.In this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural networks.In multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently.We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.展开更多
Purpose-The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage,lack of effective features,and small sample sizes,resulting in low fault identifica...Purpose-The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage,lack of effective features,and small sample sizes,resulting in low fault identification accuracy and slow efficiency.The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approach-First,to address the impact of background noise on the accuracy of anomaly signals,the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD)method is used to eliminate strong noise in pipeline signals.Secondly,to address the strong data dependency and loss of local features in the Swin Transformer network,a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed.This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities.Thirdly,to address the sparsity and imbalance of anomaly samples,the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.Findings-In the pipeline anomaly audio and environmental datasets such as ESC-50,the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods.Additionally,the model achieved 98.7%accuracy on the preprocessed anomaly audio dataset and 99.0%on the ESC-50 dataset.Originality/value-This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model,addressing noise interference and low accuracy issues in pipeline anomaly detection,and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.展开更多
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
文摘The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and economy.The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)testing.Nevertheless,this testing method is accurate enough for the diagnosis of COVID-19.However,it is time-consuming,expensive,expert-dependent,and violates social distancing.In this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural networks.In multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently.We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.
文摘Purpose-The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage,lack of effective features,and small sample sizes,resulting in low fault identification accuracy and slow efficiency.The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approach-First,to address the impact of background noise on the accuracy of anomaly signals,the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD)method is used to eliminate strong noise in pipeline signals.Secondly,to address the strong data dependency and loss of local features in the Swin Transformer network,a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed.This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities.Thirdly,to address the sparsity and imbalance of anomaly samples,the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.Findings-In the pipeline anomaly audio and environmental datasets such as ESC-50,the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods.Additionally,the model achieved 98.7%accuracy on the preprocessed anomaly audio dataset and 99.0%on the ESC-50 dataset.Originality/value-This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model,addressing noise interference and low accuracy issues in pipeline anomaly detection,and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.
