Data analytics for cybersecurity enhancement of transformer protection

2021 ◽  
Vol 1 (1) ◽  
pp. 12-19
Author(s):  
Martiya Zare Jahromi ◽  
Amir Abiri Jahromi ◽  
Deepa Kundur ◽  
Scott Sanner ◽  
Marthe Kassouf
Keyword(s):  
Short Term Memory ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Remote Access ◽  
Short Term ◽  
Transformer Protection ◽  
Detection Systems ◽  
Long Short Term Memory ◽  
Effective Models ◽  
Fully Connected

Electric power substations are experiencing an accelerated pace of digital transformation including the deployment of LAN-based IEC 61850 communication protocols that facilitate accessibility to substation data while also increasing remote access points and exposure to complex cyberattacks. In this environment, machine learning algorithms will play a vital role in cyberattack detection and mitigation and natural questions arise as to the most effective models in the context of smart grid substations. This paper compares the performance of three autoencoder-based anomaly detection systems including linear, fully connected, and convolutional autoencoders, as well as long short-term memory (LSTM) neural network for cybersecurity enhancement of transformer protection. The simulation results indicated that the LSTM model outperforms the other models for detecting cyberattacks targeting asymmetrical fault data. The linear autoencoder, fully connected autoencoder and 1D CNN further outperform the LSTM model for detecting cyberattacks targeting the symmetrical fault data.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A semiautomatic annotation approach for sentiment analysis

2021 ◽  
pp. 016555152110065
Author(s):  
Rahma Alahmary ◽  
Hmood Al-Dossari
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Annotation Process ◽  
Learning Classifiers ◽  
Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

scholarly journals Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1804
Author(s):  
Wentai Lei ◽  
Jiabin Luo ◽  
Feifei Hou ◽  
Long Xu ◽  
Ruiqing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Vital Role ◽  
Short Term ◽  
Term Memory ◽  
Region Detection ◽  
Field Environment ◽  
Long Short Term Memory ◽  
Lstm Network

Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

scholarly journals Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor

2020 ◽  
Vol 9 (4) ◽  
pp. 2057-2063 ◽  
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Short Term ◽  
Stock Market Prediction ◽  
Term Memory ◽  
Stock Market Performance ◽  
Long Short Term Memory ◽  
Lending Rates

Stock market prediction problem is considered to be NP-hard problem because of highly volatile nature of stock market. In this paper, effort has been made to design efficient stock forecasting model using log Bilinear and long short term memory (LBL-LSTM) considering external fluctuating factor such as varying Bank's lending rates. The external factor bank's lending rates affects stock market performance ,as it plays vital role for the purchase of stocks in case of financial crisis faced by various business enterprises. Proposed LBL-LSTM based model shows performance improvement over existing machine learning algorithms used for stock market prediction.

scholarly journals SEO Ministration : Content Related Tag Suggestion Using One vs Rest

2021 ◽  
pp. 245-253
Author(s):  
Dyapa Sravan Reddy ◽  
Lakshmi Prasanna Reddy ◽  
Kandibanda Sai Santhosh ◽  
Virrat Devaser
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

SEO Analyst pays a lot of time finding relevant tags for their articles and in some cases, they are unaware of the content topics. The current proposed ML model will recommend content-related tags so that the Content writers/SEO analyst will be having an overview regarding the content and minimizes their time spent on unknown articles. Machine Learning algorithms have a plethora of applications and the extent of their real-life implementations cannot be estimated. Using algorithms like One vs Rest (OVR), Long Short-Term Memory (LSTM), this study has analyzed how Machine Learning can be useful for tag suggestions for a topic. The training of the model with One vs Rest turned out to deliver more accurate results than others. This Study certainly answers how One vs Rest is used for tag suggestions that are needed to promote a website and further studies are required to suggest keywords required.

scholarly journals A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

2020 ◽  
Vol 10 (19) ◽  
pp. 6755
Author(s):  
Carlos Iturrino Garcia ◽  
Francesco Grasso ◽  
Antonio Luchetta ◽  
Maria Cristina Piccirilli ◽  
Libero Paolucci ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
Short Term ◽  
Current Time ◽  
Term Memory ◽  
Long Short Term Memory

The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

scholarly journals Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1923
Author(s):  
Feng Liu ◽  
Xiaodi Huang ◽  
Weidong Huang ◽  
Sophia Xiaoxia Duan
Keyword(s):  
Short Term Memory ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Keyword Extraction ◽  
Online Environment ◽  
Short Term ◽  
Online Comments ◽  
Student Comment ◽  
Long Short Term Memory

Topic keyword extraction (as a typical task in information retrieval) refers to extracting the core keywords from document topics. In an online environment, students often post comments in subject forums. The automatic and accurate extraction of keywords from these comments are beneficial to lecturers (particular when it comes to repeatedly delivered subjects). In this paper, we compare the performance of traditional machine learning algorithms and two deep learning methods in extracting topic keywords from student comments posted in subject forums. For this purpose, we collected student comment data from a period of two years, manually tagging part of the raw data for our experiments. Based on this dataset, we comprehensively compared the five typical algorithms of naïve Bayes, logistic regression, support vector machine, convolutional neural networks, and Long Short-Term Memory with Attention (Att-LSTM). The performances were measured by the four evaluation metrics. We further examined the keywords by visualization. From the results of our experiment and visualization, we conclude that the Att-LSTM method is the best approach for topic keyword extraction from student comments. Further, the results from the algorithms and visualization are symmetry, to some degree. In particular, the extracted topics from the comments posted at the same stages of different teaching sessions are, almost, reflection symmetry.

ClickbaitTR: Dataset for clickbait detection from Turkish news sites and social media with a comparative analysis via machine learning algorithms

2021 ◽  
pp. 016555152110077
Author(s):  
Şura Genç ◽  
Elif Surer
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Logistic Regression ◽  
Random Forest ◽  
Short Term Memory ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Clickbait is a strategy that aims to attract people’s attention and direct them to specific content. Clickbait titles, created by the information that is not included in the main content or using intriguing expressions with various text-related features, have become very popular, especially in social media. This study expands the Turkish clickbait dataset that we had constructed for clickbait detection in our proof-of-concept study, written in Turkish. We achieve a 48,060 sample size by adding 8859 tweets and release a publicly available dataset – ClickbaitTR – with its open-source data analysis library. We apply machine learning algorithms such as Artificial Neural Network (ANN), Logistic Regression, Random Forest, Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Ensemble Classifier on 48,060 news headlines extracted from Twitter. The results show that the Logistic Regression algorithm has 85% accuracy; the Random Forest algorithm has a performance of 86% accuracy; the LSTM has 93% accuracy; the ANN has 93% accuracy; the Ensemble Classifier has 93% accuracy; and finally, the BiLSTM has 97% accuracy. A thorough discussion is provided for the psychological aspects of clickbait strategy focusing on curiosity and interest arousal. In addition to a successful clickbait detection performance and the detailed analysis of clickbait sentences in terms of language and psychological aspects, this study also contributes to clickbait detection studies with the largest clickbait dataset in Turkish.

scholarly journals Alzheimer’s Disease Prediction Using Long Short-Term Memory with Early-Phase 18F-Florbetaben Imaging Data

2021 ◽  
Author(s):  
Hyeon Kang ◽  
Kyung Won Park ◽  
Do-Young Kang
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Dual Phase ◽  
Imaging Data ◽  
Short Term ◽  
Term Memory ◽  
Imaging Test ◽  
Long Short Term Memory

Abstract Single amyloid-beta (Aβ) imaging test is not enough to rise to the challenge of making AD diagnosis because of Aβ-negative AD or positive cognitively normal (CN). We aimed to distinguish AD from CN with dual-phase 18F-Florbetaben (FBB) via machine learning algorithms and evaluate the AD positivity scores compared to delay-phase FBB (dFBB) which is currently adopted for AD diagnosis.A total of 264 patients (74 CN and 190 AD), who underwent FBB imaging test and neuropsychological tests were retrospectively analyzed. We compared three kinds of machine learning-based models and evaluated their performance with 4-fold cross validation.AD positivity scores estimated from dual-phase FBB showed better accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection (ACC: 84.091 %, AUROC: 0.900) than those from dFBB imaging (ACC: 81.364 %, AUROC: 0.890). The association between predicted AD positivity and the AD occurrence were compared, the use of dual-phase FBB was highest (OR: 56.333), followed by dFBB (OR: 35.182).These results show that the combined model which interpret dual-phase FBB with long short-term memory can be used to provide a more accurate AD positivity score, which shows a closer association with AD, than the prediction with only single-phase FBB.

scholarly journals Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation

2021 ◽  
Vol 11 (24) ◽  
pp. 12019
Author(s):  
Chia-Chun Chuang ◽  
Chien-Ching Lee ◽  
Chia-Hong Yeng ◽  
Edmund-Cheung So ◽  
Yeou-Jiunn Chen
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Blood Pressure Estimation ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Monitoring people’s blood pressure can effectively prevent blood pressure-related diseases. Therefore, providing a convenient and comfortable approach can effectively help patients in monitoring blood pressure. In this study, an attention mechanism-based convolutional long short-term memory (LSTM) neural network is proposed to easily estimate blood pressure. To easily and comfortably estimate blood pressure, electrocardiogram (ECG) and photoplethysmography (PPG) signals are acquired. To precisely represent the characteristics of ECG and PPG signals, the signals in the time and frequency domain are selected as the inputs of the proposed NN structure. To automatically extract the features, the convolutional neural networks (CNNs) are adopted as the first part of neural networks. To identify the meaningful features, the attention mechanism is used in the second part of neural networks. To model the characteristic of time series, the long short-term memory (LSTM) is adopted in the third part of neural networks. To integrate the information of previous neural networks, the fully connected networks are used to estimate blood pressure. The experimental results show that the proposed approach outperforms CNN and CNN-LSTM and complies with the Association for the Advancement of Medical Instrumentation standard.

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