A transfer-learning approach for corrosion prediction in pipeline infrastructures

AbstractPipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due to potential leakages) and the human beings (due to accidents that may cause explosions in presence of gas leakages). For this reason, predictive mechanisms are needed to detect and address the corrosion phenomenon. Recently, we have seen a first attempt at leveraging Machine Learning (ML) techniques in this field thanks to their high ability in modeling highly complex phenomena. In order to rely on these techniques, we need a set of data, representing factors influencing the corrosion in a given pipeline, together with their related supervised information, measuring the corrosion level along the considered infrastructure profile. Unfortunately, it is not always possible to access supervised information for a given pipeline since measuring the corrosion is a costly and time-consuming operation. In this paper, we will address the problem of devising a ML-based predictive model for internal corrosion under the assumption that supervised information is unavailable for the pipeline of interest, while it is available for some other pipelines that can be leveraged through Transfer Learning (TL) to build the predictive model itself. We will cover all the methodological steps from data set creation to the usage of TL. The whole methodology will be experimentally validated on a set of real-world pipelines.

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A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI

Technology in Cancer Research & Treatment ◽

10.1177/1533033819858363 ◽

2019 ◽

Vol 18 ◽

pp. 153303381985836 ◽

Cited By ~ 8

Author(s):

Quan Chen ◽

Shiliang Hu ◽

Peiran Long ◽

Fang Lu ◽

Yujie Shi ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Transfer Learning ◽

Area Under The Curve ◽

Learning Approach ◽

Resonance Imaging ◽

Validation Data ◽

Data Set ◽

Noncancerous Tissue ◽

Multiparametric Magnetic Resonance Imaging

Purpose: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning–based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. Methods: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. Results: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. Conclusion: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images.

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Recognition of Epileptic Seizures in EEG Records: A Transfer Learning Approach

Frontiers in Health Informatics ◽

10.30699/fhi.v10i1.268 ◽

2021 ◽

Vol 10 (1) ◽

pp. 61

Author(s):

Elena Caires Silveira ◽

Caio Fellipe Santos Corrêa

Keyword(s):

Transfer Learning ◽

Brain Activity ◽

Epileptic Seizures ◽

Brain Dysfunction ◽

Learning Approach ◽

Transient Phenomenon ◽

Data Set ◽

Data Points ◽

The Brain ◽

Neuronal Electrical Activity

Introduction: Seizure is a transient phenomenon with genesis in excessive abnormal or synchronous neuronal electrical activity in the brain, while epilepsy is defined as a brain dysfunction characterized by persistent predisposition to generate seizures. The identification of epileptogenic electroencephalographic patterns can be performed using machine learning.the present study aimed to develop a transfer learning based classifier able to detect epileptic seizures in images generated from electroencephalographic data graphic representation.Material and Methods: We used the Epileptic Seizure Recognition Data Set,which consists of 500 brain activity records for 23.6 seconds comprising 23 chunks of 178 data points, and transformed the resulting 11500 instances into images by graphically plotting its data points. Those images were then splitted in training and test set and used to build and assess, respectvely, a transfer learning-based deep neural network, which classified the images according the presence or absence of epileptic seizures.Results: The model achieved 100% accuracy, sensitivity and specificity, with a AUC-score of 1.0, demonstrating the great potential of transfer learning for the analysis of graphically represented electroencephalographic data.Conclusion: It is opportune to raise new studies involving transfer learning for the analysis of signal data, with the aim of improving, disseminating and validating its use for daily clinical practice.

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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/3/3 ◽

2020 ◽

Vol 17 (3) ◽

pp. 299-305 ◽

Cited By ~ 1

Author(s):

Riaz Ahmad ◽

Saeeda Naz ◽

Muhammad Afzal ◽

Sheikh Rashid ◽

Marcus Liwicki ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Data Augmentation ◽

Short Term Memory ◽

Recognition System ◽

Learning Approach ◽

Arabic Text ◽

Data Set ◽

Processing Step ◽

Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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Classification of Neurodegenerative Diseases Based on VGG 19 Deep Transfer Learning Architecture: A Deep Learning Approach

Bioscience Biotechnology Research Communications ◽

10.21786/bbrc/13.4/51 ◽

2020 ◽

Vol 13 (4) ◽

pp. 1972-1980

Author(s):

Kirti Raj Bhatele

Keyword(s):

Deep Learning ◽

Neurodegenerative Diseases ◽

Transfer Learning ◽

Learning Approach

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Trajectory Design for UAV-Assisted Emergency Communications: A Transfer Learning Approach

GLOBECOM 2020 - 2020 IEEE Global Communications Conference ◽

10.1109/globecom42002.2020.9348212 ◽

2020 ◽

Author(s):

Xinruo Zhang ◽

Gan Zheng ◽

Sangarapillai Lambotharan

Keyword(s):

Transfer Learning ◽

Learning Approach ◽

Trajectory Design ◽

Emergency Communications

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A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images

Neuroscience ◽

10.1016/j.neuroscience.2021.01.002 ◽

2021 ◽

Author(s):

Atif Mehmood ◽

Shuyuan yang ◽

Zhixi feng ◽

Min wang ◽

AL Smadi Ahmad ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Diagnosis ◽

Transfer Learning ◽

Learning Approach

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Classification of jujube defects in small data sets based on transfer learning

Neural Computing and Applications ◽

10.1007/s00521-021-05715-2 ◽

2021 ◽

Author(s):

Jianping Ju ◽

Hong Zheng ◽

Xiaohang Xu ◽

Zhongyuan Guo ◽

Zhaohui Zheng ◽

...

Keyword(s):

Transfer Learning ◽

Loss Function ◽

Training Model ◽

Parameter Distribution ◽

Test Accuracy ◽

Small Data ◽

Data Sets ◽

Data Set ◽

Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

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