Application of Machine Learning Techniques to Predict Software Reliability

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

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Application of Machine Learning Techniques to Predict Software Reliability

Machine Learning ◽

10.4018/978-1-60960-818-7.ch301 ◽

2012 ◽

pp. 354-370

Author(s):

Ramakanta Mohanty ◽

V. Ravi ◽

M. R. Patra

Keyword(s):

Neural Network ◽

Machine Learning ◽

Software Reliability ◽

Fuzzy Inference ◽

Back Propagation ◽

Multivariate Adaptive Regression Splines ◽

Machine Learning Techniques ◽

Group Method ◽

Learning Techniques ◽

Trained Neural Network

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

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Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Sustainability ◽

10.3390/su10010203 ◽

2018 ◽

Vol 10 (1) ◽

pp. 203 ◽

Cited By ~ 8

Author(s):

Xianming Dou ◽

Yongguo Yang ◽

Jinhui Luo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Eddy Covariance ◽

Carbon Flux ◽

Carbon Fluxes ◽

Forest Carbon ◽

Machine Learning Techniques ◽

Support Vector ◽

Inference System ◽

Learning Techniques

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

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Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

Energies ◽

10.3390/en11081975 ◽

2018 ◽

Vol 11 (8) ◽

pp. 1975 ◽

Cited By ~ 7

Author(s):

Wei Dong ◽

Qiang Yang ◽

Xinli Fang

Keyword(s):

Machine Learning ◽

Phase Space ◽

Fuzzy Inference ◽

Weather Prediction ◽

Wind Farms ◽

Machine Learning Techniques ◽

Multiple Time ◽

Inference System ◽

Learning Techniques ◽

Benchmark Solutions

Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.

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Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Modern Physics Letters B ◽

10.1142/s0217984918501324 ◽

2018 ◽

Vol 32 (11) ◽

pp. 1850132 ◽

Cited By ~ 9

Author(s):

Harpreet Singh ◽

Prashant Singh Rana ◽

Urvinder Singh

Keyword(s):

Machine Learning ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Machine Learning Techniques ◽

Support Vector ◽

Prediction Errors ◽

Learning Approaches ◽

Inference System ◽

Drug Synergy ◽

Learning Techniques

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug–drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

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Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Advances in Meteorology ◽

10.1155/2018/1824317 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Xianming Dou ◽

Yongguo Yang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Water Vapor ◽

Data Driven ◽

Machine Learning Techniques ◽

Support Vector ◽

Group Method ◽

Generalized Regression Neural Network ◽

Learning Approaches ◽

Inference System

Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.

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Handoff Decisions in Hetnets using Fuzzy Logic and Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9583.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 474-478

Keyword(s):

Neural Network ◽

Decision Tree ◽

Classification Accuracy ◽

Communication Systems ◽

Fuzzy Inference ◽

Back Propagation ◽

Cost Effective ◽

Machine Learning Techniques ◽

Inference System ◽

Training Time

Handover is a vital part of any wireless Mobile Communication Network. Efficient handover algorithms provide a gain in monetary cost effective method for enhancement in the capacity and QOS(quality of services) of cellular system. The work reported here presents a handover decision support system for wireless heterogenous communication. Fuzzy inference system along with neural network techniques in Matlab has been used for the development of the system.Results for performance of five different neural network techniques have been drawn on basis of their training time,classification accuracy and number of neurons taken. The neural tools used here are Back propagation, Radial Basis Function, Nave Bayes, Bayes net and C4.5 decision tree. On the basis of their performance, a best neural tool has been selected for the use in making handover decisions in wireless communication systems and it is seen that among all the neural network tools C 4.5 decision tree gives the better results in terms of classification accuracy and training time for handover decisions

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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/v1 ◽

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.

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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/v4 ◽

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.

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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 ◽

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.

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