scholarly journals A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 494
Author(s):  
Erin McGowan ◽  
Vidita Gawade ◽  
Weihong (Grace) Guo
Keyword(s):  
Deep Learning ◽  
Pore Formation ◽  
Metal Deposition ◽  
Loss Functions ◽  
Learning Approach ◽  
Laser Metal Deposition ◽  
Prediction Ability ◽  
Time Prediction ◽  
Physics Knowledge ◽  
Deep Learning Model

Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy).

ECG Image Classification Using Deep Learning Approach

2021 ◽  
pp. 343-357
Author(s):  
Pratik Kanani ◽  
Mamta Chandraprakash Padole
Keyword(s):  
Deep Learning ◽  
Sampling Frequency ◽  
Heart Beat ◽  
Learning Approach ◽  
Recording Device ◽  
Noisy Signal ◽  
Arrhythmia Detection ◽  
Ecg Signals ◽  
Ecg Arrhythmia ◽  
Deep Learning Model

Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2021 ◽  
pp. 467-489
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Deep Learning Model

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 21-34
Author(s):  
Mostefai Abdelkader
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Learning Approach ◽  
Probabilistic Representation ◽  
Effective Manner ◽  
Textual Data ◽  
Positive Class ◽  
Negative Class ◽  
Deep Learning Model

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.

scholarly journals MAKEDONKA: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language

2020 ◽  
Vol 10 (19) ◽  
pp. 6882
Author(s):  
Kostadin Mishev ◽  
Aleksandra Karovska Ristovska ◽  
Dimitar Trajanov ◽  
Tome Eftimov ◽  
Monika Simjanoska
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Speech Synthesis ◽  
Feature Engineering ◽  
Learning Approach ◽  
Acoustic Model ◽  
Text To Speech ◽  
Text To Speech Synthesis ◽  
Smooth Transitions ◽  
Deep Learning Model

This paper presents MAKEDONKA, the first open-source Macedonian language synthesizer that is based on the Deep Learning approach. The paper provides an overview of the numerous attempts to achieve a human-like reproducible speech, which has unfortunately shown to be unsuccessful due to the work invisibility and lack of integration examples with real software tools. The recent advances in Machine Learning, the Deep Learning-based methodologies, provide novel methods for feature engineering that allow for smooth transitions in the synthesized speech, making it sound natural and human-like. This paper presents a methodology for end-to-end speech synthesis that is based on a fully-convolutional sequence-to-sequence acoustic model with a position-augmented attention mechanism—Deep Voice 3. Our model directly synthesizes Macedonian speech from characters. We created a dataset that contains approximately 20 h of speech from a native Macedonian female speaker, and we use it to train the text-to-speech (TTS) model. The achieved MOS score of 3.93 makes our model appropriate for application in any kind of software that needs text-to-speech service in the Macedonian language. Our TTS platform is publicly available for use and ready for integration.

scholarly journals Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1316
Author(s):  
Luisa F. Sánchez-Peralta ◽  
Artzai Picón ◽  
Juan Antonio Antequera-Barroso ◽  
Juan Francisco Ortega-Morán ◽  
Francisco M. Sánchez-Margallo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Survival Rates ◽  
Principal Component ◽  
Loss Functions ◽  
Cancer Death ◽  
Linear Combinations ◽  
The Individual ◽  
Deep Learning Model ◽  
Death Causes

Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.

scholarly journals Newspaper Article Classification using Machine Learning Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 872-877
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Training Time ◽  
Time Prediction ◽  
Learning Techniques ◽  
Prediction Time ◽  
Newspaper Articles

Newspaper articles offer us insights on several news. They can be one of many categories like sports, politics, Science and Technology etc. Text classification is a need of the day as large uncategorized data is the problem everywhere. Through this study, We intend to compare several algorithms along with data preprocessing approaches to classify the newspaper articles into their respective categories. Convolutional Neural Networks(CNN) is a deep learning approach which is currently a strong competitor to other classification algorithms like SVM, Naive Bayes and KNN. We hence intend to implement Convolutional Neural Networks - a deep learning approach to classify our newspaper articles, develop an understanding of all the algorithms implemented and compare their results. We also attempt to compare the training time, prediction time and accuracies of all the algorithms.

Comparative Study of Neural Network-Based Approaches for QRS Segmentation

2020 ◽  
Vol 11 (4) ◽  
pp. 80-103
Author(s):  
George Kolokolnikov ◽  
Anna Borde ◽  
Victor Skuratov ◽  
Roman Gaponov ◽  
Anastasiya Rumyantseva
Keyword(s):  
Deep Learning ◽  
Signal Analysis ◽  
State Of The Art ◽  
Learning Approach ◽  
Review Section ◽  
Advantages And Disadvantages ◽  
Holter Monitor ◽  
Life Threatening ◽  
Deep Learning Model ◽  
Segmentation Problem

The paper is devoted to the development of QRS segmentation system based on deep learning approach. The considered segmentation problem plays an important role in the automatic analysis of heart rhythms, which makes it possible to identify life-threatening pathologies. The main goal of the research is to choose the best segmentation pipeline in terms of accuracy and time-efficiency. Process of ECG-signal analysis is described, and the problem of QRS segmentation is discussed. State-of-the-art algorithms are analyzed in literature review section and the most prominent are chosen for further research. In the course of the research, four hypotheses about appropriate deep learning model are checked: LSTM-based model, 2-input 1-dimensional CNN model, “signal-to-picture” approach based on 2-dimensional CNN, and the simplest 1-dimensional CNN model. All the architectures are tested, and their advantages and disadvantages are discussed. The proposed ECG segmentation pipeline is developed for Holter monitor software.

A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Parth K. Shah ◽  
Jennifer C. Ginestra ◽  
Lyle H. Ungar ◽  
Paul Junker ◽  
Jeff I. Rohrbach ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Prospective Evaluation ◽  
Time Prediction ◽  
Deep Learning Model

scholarly journals Kudo’s Classification for Colon Polyps Assessment Using a Deep Learning Approach

2020 ◽  
Vol 10 (2) ◽  
pp. 501 ◽  
Author(s):  
Sebastian Patino-Barrientos ◽  
Daniel Sierra-Sosa ◽  
Begonya Garcia-Zapirain ◽  
Cristian Castillo-Olea ◽  
Adel Elmaghraby
Keyword(s):  
Deep Learning ◽  
Fine Tuning ◽  
Learning Approach ◽  
Colon Polyps ◽  
Cancer Death ◽  
Timely Manner ◽  
The World ◽  
Feature Extractor ◽  
The University ◽  
Deep Learning Model

Colorectal cancer (CRC) is the second leading cause of cancer death in the world. This disease could begin as a non-cancerous polyp in the colon, when not treated in a timely manner, these polyps could induce cancer, and in turn, death. We propose a deep learning model for classifying colon polyps based on the Kudo’s classification schema, using basic colonoscopy equipment. We train a deep convolutional model with a private dataset from the University of Deusto with and without using a VGG model as a feature extractor, and compared the results. We obtained 83% of accuracy and 83% of F1-score after fine tuning our model with the VGG filter. These results show that deep learning algorithms are useful to develop computer-aided tools for early CRC detection, and suggest combining it with a polyp segmentation model for its use by specialists.

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