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

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

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.

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

10.21203/rs.3.rs-55617/v2 ◽

2020 ◽

Author(s):

Anjir Ahmed Chowdhury ◽

Khandaker Tabin Hasan ◽

Khadija Kubra Shahjalal Hoque

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Fuzzy Inference ◽

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

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.

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

10.21203/rs.3.rs-55617/v3 ◽

2020 ◽

Author(s):

Anjir Ahmed Chowdhury ◽

Khandaker Tabin Hasan ◽

Khadija Kubra Shahjalal Hoque

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Fuzzy Inference ◽

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

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.

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

10.21203/rs.3.rs-55617/v5 ◽

2020 ◽

Author(s):

Anjir Ahmed Chowdhury ◽

Khandaker Tabin Hasan ◽

Khadija Kubra Shahjalal Hoque

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Fuzzy Inference ◽

Short Term ◽

Inference System ◽

Learning Techniques ◽

Neuro Fuzzy ◽

The World ◽

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

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

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.

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

DYNA INGENIERIA E INDUSTRIA ◽

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 ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

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.

Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting

International Journal of Green Energy ◽

10.1080/15435075.2020.1865375 ◽

2020 ◽

pp. 1-17

Author(s):

Mehmet Bilgili ◽

Alper Yildirim ◽

Arif Ozbek ◽

Kerimcan Celebi ◽

Firat Ekinci

Keyword(s):

Electricity Generation ◽

Short Term Memory ◽

Fuzzy Inference ◽

Renewable Electricity ◽

Short Term ◽

Inference System ◽

Term Memory ◽

Neuro Fuzzy ◽

Long Short Term Memory ◽

Renewable Electricity Generation

Short-term air temperature prediction by adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) network

Meteorology and Atmospheric Physics ◽

10.1007/s00703-021-00791-4 ◽

2021 ◽

Author(s):

Aliihsan Sekertekin ◽

Mehmet Bilgili ◽

Niyazi Arslan ◽

Alper Yildirim ◽

Kerimcan Celebi ◽

...

Keyword(s):

Short Term Memory ◽

Fuzzy Inference ◽

Short Term ◽

Temperature Prediction ◽

Inference System ◽

Term Memory ◽

Neuro Fuzzy ◽

Long Short Term Memory ◽

Lstm Network ◽

Air Temperature Prediction

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

Science and Technology of Nuclear Installations ◽

10.1155/2021/5511735 ◽

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 ◽

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.

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 ◽

10.3390/en14123692 ◽

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 ◽

Short Term ◽

Learning Techniques ◽

Memory Network ◽

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.