scholarly journals Data Driven Leakage Detection and Classification of a Boiler Tube

2019 ◽  
Vol 9 (12) ◽  
pp. 2450 ◽  
Author(s):  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Wavelet Packet ◽  
Thermal Power ◽  
Leak Detection ◽  
Classification Model ◽  
Support Vector ◽  
Boiler Tube ◽  
Proposed Model ◽  
Ae Signals

Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.

scholarly journals Material Thickness Classification Using Scattering Parameters, Dielectric Constants, and Machine Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10682
Author(s):  
Pham-The Hien ◽  
Ic-Pyo Hong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Neural Network ◽  
Dielectric Constants ◽  
Classification Model ◽  
Support Vector ◽  
Scattering Parameters ◽  
Systematic Monitoring ◽  
Material Thickness

Wall-thinning in building structures due to corrosion and surface erosion occurs due to the severe operating conditions and the changing of the surrounding environment, or it can result from poor workmanship and a lack of systematic monitoring during construction. Hence, the continuous monitoring of structures plays an important role in decreasing unexpected accidents. In this paper, a novel method based on the deep neural network and support vector machine approaches is investigated to build up a thickness classification model by incorporating different input features, including the dielectric constants of the material under test, which are extracted from the scattering parameters proceeded by the National Institute of Standards and Technology iterative method. The attained classification results from both machine learning algorithms are then compared and show that both of the models have a good prediction ability. While the deep neural network is the better solution with a large amount of data, the support vector machine is the more appropriate solution when employing small dataset. It can be stated that the proposed method is able to support systematic monitoring as it can help to improve the accuracy of the prediction of material thickness.

scholarly journals BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

2021 ◽  
Author(s):  
Vinay Dubey ◽  
Rahul Katarya
Keyword(s):  
Neural Network ◽  
Natural Disasters ◽  
Urban Areas ◽  
Deep Neural Network ◽  
Developed Countries ◽  
Support Vector ◽  
Human Society ◽  
Batch Normalization ◽  
Proposed Model ◽  
The Impact

Abstract Disaster is a very serious dissipation that arises for a short time period, but the impact of that disaster on human society is very dangerous and very long-lasting. Disasters are categorized into two types like natural disasters and manmade disasters. Among all disasters, of all the natural disasters, flood is the commonplace natural disaster. Flood disaster that causes huge loss of human life, diversity as well as economic loss, which is very dangerous for the developing countries and developed countries also. Nowadays during the monsoon season flood is dangerous for all the geographical areas located nearby water bodies. Much research has been done for flood detection. Machine Learning and many other recent technologies are playing a vital role in predicting the occurrence of floods. For prediction purposes, a huge amount of data is requiring collected from sensors deployed in various locations. In this paper, we used the Batch normalization with Deep Neural Network (BNDNN) technique for the classification of data in three classes as Low, Moderate, and High. The result obtained from our proposed model is compared with some other models like Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN). In this our proposed BNDNN provides 89% accuracy which is higher among all existing models. Models are compared based on some parameters like Accuracy, Precision, Recall, F –Score. The compression among all the models used in this paper shows that our proposed model provides better results.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

2021 ◽  
pp. 1-14
Author(s):  
Sachin Sharma ◽  
Vineet Kumar ◽  
K.P.S. Rana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Filter Design ◽  
Support Vector ◽  
Control Loop ◽  
External Interference ◽  
Iir Filter ◽  
Prony Method ◽  
Oscillation Detection ◽  
Detection And Quantification

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

scholarly journals Sales Growth Rate Forecasting Using Improved PSO and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xibin Wang ◽  
Junhao Wen ◽  
Shafiq Alam ◽  
Xiang Gao ◽  
Zhuo Jiang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Decisive Role ◽  
Convergence Speed ◽  
Classification Model ◽  
Support Vector ◽  
Local Optimum ◽  
Sales Growth ◽  
Local Optima ◽  
Proposed Model ◽  
Forecasting Performance

Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

scholarly journals Enhancing the performance of cancer text classification model based on cancer hallmarks

2021 ◽  
Vol 10 (2) ◽  
pp. 316
Author(s):  
Noha Ali ◽  
Ahmed H. AbuEl-Atta ◽  
Hala H. Zayed
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
State Of The Art ◽  
Classification Model ◽  
Biomedical Text ◽  
Cancer Hallmarks ◽  
Embedding Technique ◽  
Proposed Model ◽  
Biomedical Text Classification

<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.</span></span>

scholarly journals Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

10.2196/23230 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e23230
Author(s):  
Pei-Fu Chen ◽  
Ssu-Ming Wang ◽  
Wei-Chih Liao ◽  
Lu-Cheng Kuo ◽  
Kuan-Chih Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Network ◽  
University Hospital ◽  
Classification Model ◽  
Icd 10 ◽  
And Training

Background The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning– and natural language processing–related approaches have been studied to assist disease coders. Objective This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort. Methods We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F1-score and the coding time by coders before and after using our model. Results In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F1-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. With this service, coders can code with the F1-score significantly increased from a median of 0.832 to 0.922 (P<.05), but not in a reduced interval. Conclusions The proposed model significantly improved the F1-score but did not decrease the time consumed in coding by disease coders.

Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

2021 ◽  
Vol 16 ◽  
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Melting Curve ◽  
Classification Model ◽  
Proposed Model ◽  
Unbalanced Dataset ◽  
Manual Selection ◽  
Low Efficiency ◽  
Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

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