scholarly journals Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Anselim M. Mwaura ◽  
Yong-Kuo Liu
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Radial Basis Function Network ◽  
Machine Learning Techniques ◽  
Feed Water ◽  
Inference System ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
Adaptive Neurofuzzy Inference System ◽  
Function Network

Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named \newline SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping further spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can predict on small dataset with higher accuracy.Methods: In this research, we have used the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75. Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

scholarly journals Analysis and Prediction of COVID-19 Pandemic in Bangladesh by using Long short-term memory network (LSTM) and Adaptive neuro fuzzy inference system(ANFIS)

2020 ◽  
Author(s):  
Anjir Ahmed Chowdhury ◽  
Khandaker Tabin Hasan ◽  
Khadija Kubra Shahjalal Hoque
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Inference System ◽  
Learning Techniques ◽  
Neuro Fuzzy ◽  
The World ◽  
Long Short Term Memory

Abstract Objectives: The dangerously contagious virus named SARS-CoV-2 has hit the world hard that has locked downed billion people in their homes for stopping fur- ther spread. All the researchers and scientists in various fields are working around the clock to come up with a vaccine and prevention methods to save the world from this invisible pathogen. However, reliable prediction of the epidemic may help contain the contagion until cure becomes available. The machine learning techniques is one of the frontier in predicting the future trend and behavior of this outbreak. Our research is focused on finding a suitable machine learning model that can pre- dict on small dataset with higher accuracy.Methods: In this research, we have used the Adap- tive Neuro-Fuzzy Inference System (ANFIS) and the long short-term memory[LSTM] to foresee the newly infected cases in Bangladesh. We have compared both the results of the experiments and it can be forenamed that LSTM has shown more satisfactory results.Results: Upon study and testing on several models, we have showed that LSTM works better on scenario based model for Bangladesh with MAPE 4.51, RMSE 6.55 and Correlation Coefficient 0.75.Conclusion: This study is expected to shade light on Covid-19 prediction models for researchers working with machine learning techniques and help avoid proven failures specially for small imprecise dataset.

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.

scholarly journals Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Jurgita Kapočiūtė-Dzikienė ◽  
Robertas Damaševičius ◽  
Marcin Woźniak
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Full Dataset ◽  
Learning Techniques ◽  
Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

scholarly journals COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

10.6036/10007 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 528-533
Author(s):  
XAVIER LARRIVA NOVO ◽  
MARIO VEGA BARBAS ◽  
VICTOR VILLAGRA ◽  
JULIO BERROCAL
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
New Technologies ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

scholarly journals An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search

2020 ◽  
Vol 10 (22) ◽  
pp. 8123
Author(s):  
Yong-Wook Nam ◽  
Hwi-Yeon Cho ◽  
Do-Youn Kim ◽  
Seung-Hyun Moon ◽  
Yong-Hyuk Kim
Keyword(s):  
Machine Learning ◽  
Flow Velocity ◽  
Wind Velocity ◽  
Prediction Models ◽  
Short Term Memory ◽  
Radial Basis Function Network ◽  
Absolute Error ◽  
Evolutionary Search ◽  
Long Short Term Memory ◽  
Function Network

In this study, we estimated drifter tracking over seawater using machine learning and evolutionary search techniques. The parameters used for the prediction are the hourly position of the drifter, the wind velocity, and the flow velocity of each drifter position. Our prediction model was constructed through cross-validation. Trajectories were affected by wind velocity and flow velocity from the starting points of drifters. Mean absolute error (MAE) and normalized cumulative Lagrangian separation (NCLS) were used to evaluate various prediction models. Radial basis function network showed the lowest MAE of 0.0556, an improvement of 35.20% over the numerical model MOHID. Long short-term memory showed the highest NCLS of 0.8762, an improvement of 6.24% over MOHID.

scholarly journals The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3692
Author(s):  
Xinyu Gu ◽  
KW See ◽  
Yunpeng Wang ◽  
Liang Zhao ◽  
Wenwen Pu
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Sliding Window ◽  
Processing Technique ◽  
State Of Charge ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Learning Techniques ◽  
Memory Network ◽  
Long Short Term Memory

The state of charge (SOC) prediction for an electric vehicle battery pack is critical to ensure the reliability, efficiency, and life of the battery pack. Various techniques and statistical systems have been proposed in the past to improve the prediction accuracy, reduce complexity, and increase adaptability. Machine learning techniques have been vigorously introduced in recent years, to be incorporated into the existing prediction algorithms, or as a stand-alone system, with a large amount of recorded past data to interpret the battery characteristics, and further predict for the present and future. This paper presents an overview of the machine learning techniques followed by a proposed pre-processing technique employed as the input to the long short-term memory network (LSTM) algorithm. The proposed pre-processing technique is based on the time-based sliding window algorithm (SW) and the Shapley additive explanation theory (SHAP). The proposed technique showed improvement in accuracy, adaptability, and reliability of SOC prediction when compared to other conventional machine learning models. All the data employed in this investigation were extracted from the actual driving cycle of five different electric vehicles driven by different drivers throughout a year. The computed prediction error, as compared to the original SOC data extracted from the vehicle, was within the range of less than 2%. The proposed enhanced technique also demonstrated the feasibility and robustness of the prediction results through the persistent computed output from a random selection of the data sets, consisting of different driving profiles and ambient conditions.

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