Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Nor, Ahmad Kamal Mohd; Pedapati, Srinivasa Rao; Muhammad, Masdi; Leiva, Víctor

doi:10.3390/math10040554

Cited by 36 publications

(17 citation statements)

References 48 publications

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“…There are still many challenges to be overcome before DL can be used for safety-critical applications [ 23 ]. DL networks are not fully explained in terms of how and why they work, which is a significant obstacle.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Ahmed

Lin

2023

J Supercomput

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This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Ahmed

Lin

2023

J Supercomput

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“…As further research directions, we aim to apply the same model using different feature extraction methods according to the sequence and the structure of the proteins to obtain more detailed biological information about the virus behavior and its infection cycle. Other classification methods will also be explored in future studies such as principal components analysis and its new derivations, including supervised and unsupervised approaches, as well as functional data analysis, partial least squares structures, and other recent methodologies [ [36] , [37] , [38] , [39] , [40] , [41] , [46] , [47] , [48] , [49] ].…”

Section: Discussion and Conclusion Limitations And Future Researchmentioning

confidence: 99%

Classifying COVID-19 based on amino acids encoding with machine learning algorithms

Alkady

El-Bahnasy

Leiva

et al. 2022

Chemometrics and Intelligent Laboratory Systems

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“…In a similar way, Mehran et al [16] proposed a socialinteracting method whereby optical flow was used to recognize regular and aberrant actions and estimate cooperative forces. In addition, Nor et al [17] presented a paradigm for interpretable anomaly detection that aids prognostic and health management PHM. Their methodology relies on a Bayesian learning algorithm with predetermined prior and probabilities [18].…”

Section: Conventional Feature-based Approachesmentioning

confidence: 99%

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

Amin¹,

Kim²,

Sami³

et al. 2023

Computer Systems Science and Engineering

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In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input of the ADSV, a shots boundary detection technique is used to segment prominent frames. Next, The Lightweight Convolution Neural Network (LWCNN) model receives the segmented frames to extract spatial and temporal information from the intermediate layer. Following that, spatial and temporal features are learned using Long Short-Term Memory (LSTM) cells and Attention Network from a series of frames for each anomalous activity in a sample. To detect motion and action, the LWCNN received chronologically sorted frames. Finally, the anomaly activity in the video is identified using the proposed trained ADSV model. Extensive experiments are conducted on complex and challenging benchmark datasets. In addition, the experimental results have been compared to state-ofthe-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.

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Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

Cited by 36 publications

References 48 publications

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Classifying COVID-19 based on amino acids encoding with machine learning algorithms

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

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