Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

Bandy, Adrian D.; Spyridis, Yannis; Villarini, Barbara; Argyriou, Vasileios

doi:10.3390/s23020926

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…The input layer contains a set of network flow subsequences and a set of labels. The CNN layer has two parts: double one-dimensional convolutional components (1D-CNN) [21] and one max pooling component. The first convolutional component extracts the features from the input flow subsequence.…”

Section: Nfpbul Prediction Modelmentioning

confidence: 99%

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Lei,

Zhao,

Chen

2024

Sensors

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Classifying the flow subsequences of sensor networks is an effective way for fault detection in the Industrial Internet of Things (IIoT). Traditional fault detection algorithms identify exceptions by a single abnormal dataset and do not pay attention to the factors such as electromagnetic interference, network delay, sensor sample delay, and so on. This paper focuses on fault detection by continuous abnormal points. We proposed a fault detection algorithm within the module of sequence state generated by unsupervised learning (SSGBUL) and the module of integrated encoding sequence classification (IESC). Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the different network cards in the IIoT gateway, and then combined the multiple code sequences into one integrated sequence. Next, we classified the integrated sequence by comparing the integrated sequence with the encoding fault type. The results obtained from the three IIoT datasets of a sewage treatment plant show that the accuracy of the SSGBUL–IESC algorithm exceeds 90% with subsequence length 10, which is significantly higher than the accuracies of the dynamic time warping (DTW) algorithm and the time series forest (TSF) algorithm. The proposed algorithm reaches the classification requirements for fault detection for the IIoT.

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Section: Nfpbul Prediction Modelmentioning

confidence: 99%

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Lei,

Zhao,

Chen

2024

Sensors

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show abstract

“…The input layer contains a set of network flow subsequences and a set of labels. The CNN layer has two parts: double one-dimensional convolutional components (1D-CNN) [15] and one max pooling component. The first convolutional component extracts the features from the input flow subsequence.…”

Section: Ssgbul-iknn Algorithmmentioning

confidence: 99%

Research on Fault Detection by Flow Sequence Algorithm with Deep Learning Model for Industrial Internet of Things

Lei,

Zhao,

Chen

2024

Preprint

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Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

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“…This allows CNN to extract useful features from soybean NIR spectra for the year classification task. The deep architecture of CNN enables hierarchical learning (Bandy Adrian et al, 2023). By stacking multiple convolutional and pooling layers, CNN can gradually abstract and encode higher-level features (Jason et al, 2022).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

CNN Classification of Soybeans With Storage Time Based on Near Infrared Spectroscopy

He,

Kang,

Yin

et al. 2023

Eng. Agríc.

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In soybeans from different years, the infrared spectroscopy waveforms exhibit similarities, yet variations in aging time lead to significant differences in their fatty acid content. To rapidly discern the year of soybean production, this study employed ten feature extraction algorithms in conjunction with Convolutional Neural Networks (CNN) to establish models for classifying soybeans from different years (2019, 2020, and 2021). The research findings reveal the following: (1) The CNN models, after feature extraction, all demonstrated improved accuracy. Notably, the optimal models for both powdered and granulated states were the Kpca-CNN models, achieving an accuracy of 100%. The corresponding loss function values were 0.0002 and 0.0007, with processing times of 0.19 seconds and 0.20 seconds, respectively. (2) The modeling results of all models suggest that the classification accuracy of soybean powder spectral data is higher compared to soybean particle spectral data. (3) Validation of the optimal Kpca-CNN model confirmed its consistent accuracy of 100% when introducing new data or reducing the size of the training dataset. In conclusion, the fusion of near-infrared spectroscopy analysis and CNN technology is considered an effective method for classifying soybeans from different years. This method provides a practical solution for the rapid and precise determination of seed production years.

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Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

Cited by 14 publications

References 22 publications

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Research on Fault Detection by Flow Sequence Algorithm with Deep Learning Model for Industrial Internet of Things

CNN Classification of Soybeans With Storage Time Based on Near Infrared Spectroscopy

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