scholarly journals New approach for gas identification using supervised learning methods (SVM and LVQ)

2019 ◽  
Vol 261 ◽  
pp. 06004
Author(s):  
Aref Harakeh ◽  
Samia Mellah ◽  
Mustapha Ouladsine ◽  
Rafic Younes ◽  
Catherine Bellet
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Supervised Learning ◽  
Learning Rate ◽  
Multi Layer Perceptron ◽  
Learning Methods ◽  
New Approach ◽  
Validation Rate ◽  
Gas Identification ◽  
Carbon Dioxide Co2

This article proposes a new approach for gas identification, this approach relies on applying supervised learning methods to identify a single gas as well as a mixture of two gases. The gas is trapped in a gas discharge tube, it is then ionized at a relatively low pressure using an HV transformer. The images captured after the ionization of each single gas is then captured and transformed into a database after being treated in order to be classified. The obtained results were very satisfying for SVM as well as for LVQ. For the case of identification of a single gas, the learning rate as well as the validation rate for both methods were 100%. However, for the case of mixture of two gases, a Multi-Layer Perceptron neural network was used to identify the gases, the learning rate as well as the validation rate were 98.59% and 98.77% respectively. The program developed on MATLAB takes the captured image as an input and outputs the identified gases for the user. The gases used in the experiments are Argon (Ar), oxygen (O2), Helium (He) and carbon dioxide (CO2).

scholarly journals Improved supervised learning methods for EoR parameters reconstruction

2019 ◽  
Vol 490 (1) ◽  
pp. 371-384 ◽  
Author(s):  
Aristide Doussot ◽  
Evan Eames ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Supervised Learning ◽  
Confidence Level ◽  
Thermal Noise ◽  
Model Parameters ◽  
Learning Methods ◽  
Parameter Values

ABSTRACT Within the next few years, the Square Kilometre Array (SKA) or one of its pathfinders will hopefully detect the 21-cm signal fluctuations from the Epoch of Reionization (EoR). Then, the goal will be to accurately constrain the underlying astrophysical parameters. Currently, this is mainly done with Bayesian inference. Recently, neural networks have been trained to perform inverse modelling and, ideally, predict the maximum-likelihood values of the model parameters. We build on these by improving the accuracy of the predictions using several supervised learning methods: neural networks, kernel regressions, or ridge regressions. Based on a large training set of 21-cm power spectra, we compare the performances of these methods. When using a noise-free signal generated by the model itself as input, we improve on previous neural network accuracy by one order of magnitude and, using a local ridge kernel regression, we gain another factor of a few. We then reach an accuracy level on the reconstruction of the maximum-likelihood parameter values of a few per cents compared the 1σ confidence level due to SKA thermal noise (as estimated with Bayesian inference). For an input signal affected by an SKA-like thermal noise but constrained to yield the same maximum-likelihood parameter values as the noise-free signal, our neural network exhibits an error within half of the 1σ confidence level due to the SKA thermal noise. This accuracy improves to 10$\, {\rm per\, cent}$ of the 1σ level when using the local ridge kernel. We are thus reaching a performance level where supervised learning methods are a viable alternative to determine the maximum-likelihood parameters values.

scholarly journals Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

2019 ◽  
Vol 11 (24) ◽  
pp. 7020 ◽  
Author(s):  
Amjed Hassan ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Absolute Error ◽  
Analytical Models ◽  
Co2 Injection ◽  
Co2 Flooding ◽  
Estimation Errors ◽  
Heterogeneous Reservoirs ◽  
Minimum Miscibility Pressure ◽  
Carbon Dioxide Co2

Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP.

scholarly journals A Ratiometric Optical Dual Sensor for the Simultaneous Detection of Oxygen and Carbon Dioxide

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4057
Author(s):  
Divyanshu Kumar ◽  
Cheng-Shane Chu
Keyword(s):  
Carbon Dioxide ◽  
Quantum Dots ◽  
Fluorescence Intensity ◽  
Simultaneous Detection ◽  
Phenol Red ◽  
New Approach ◽  
Dual Sensor ◽  
Response And Recovery ◽  
Carbon Dioxide Co2 ◽  
Spectral Intensities

Simultaneous detection of carbon dioxide (CO2) and oxygen (O2) has attracted considerable interest since CO2 and O2 play key roles in various industrial and domestic applications. In this study, a new approach based on a fluorescence ratiometric referencing method was reported to develop an optical dual sensor where platinum (II) meso-tetrakis(pentafluorophenyl)porphyrin (PtTFPP) complex used as the O2-sensitive dye, CdSe/ZnS quantum dots (QDs) combined with phenol red used as the CO2-sensitive dye, and CdSe/ZnS QDs used as the reference dye for the simultaneous detection of O2 and CO2. All the dyes were immobilized in a gas-permeable matrix poly (isobutyl methacrylate) (PolyIBM) and subjected to excitation using a 380 nm LED. The as-obtained distinct fluorescence spectral intensities were alternately exposed to analyte gases to observe changes in the fluorescence intensity. In the presence of O2, the fluorescence intensity of the Pt (II) complex was considerably quenched, while in the presence of CO2, the fluorescence intensity of QDs was increased. The corresponding ratiometric sensitivities of the optical dual sensor for O2 and CO2 were approximately 13 and 144, respectively. In addition, the response and recovery for O2 and CO2 were calculated to be 10 s/35 s and 20 s/60 s, respectively. Thus, a ratiometric optical dual gas sensor for the simultaneous detection of O2 and CO2 was successfully developed. Effects of spurious fluctuations in the intensity of external and excitation sources were suppressed by the ratiometric sensing approach.

scholarly journals SUPERVISED MACHINE LEARNING ALGORITHMS FOR DETECTING CREDIT CARD FRAUD

2021 ◽  
pp. 131-134
Author(s):  
G.Bhargav Chowdari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Learning ◽  
Credit Card ◽  
Learning Algorithms ◽  
Learning Methods ◽  
Credit Card Fraud

One of the most serious ethical challenges in the credit card industry is fraud. Our paper’s major goal is to identify credit card theft and offer a reasonable solution to the problem. Credit card fraud has cost customers and banks billions of dollars around the world. Fraudsters are constantly attempting to come up with new ways and tricks to commit fraud, despite the fact that there are several measures in place to prevent it. Fraud detection is extremely important in the banking and finance industries. For detection purposes, we will use an artificial neural network. As a result, in order to prevent it, we will develop a system that will not only detect fraud, but will also detect it before it occurs. In order to detect new scams, our system will learn from previous frauds. Mining algorithms were used to detect fraud, but they failed miserably. We use machine learning methods to detect fraud in credit card transactions in our paper. The research employs supervised learning methods that are applied to a kaggle dataset that is severely skewed and imbalanced. We used robust scalar to balance the set, resulting in 51 percent non-fraud cases and 49 percent fraud ones. Logistic regression, random forest, decision tree, and KNN have all been implemented, with additional learning curves displaying which algorithm performs best. Accuracy, specificity, precision, and sensitivity are the evaluation criteria, and a comparative chart is created to show the comparative analysis of various supervised learning algorithms. KEYWORDS: KNN,Neural network,Logistic regression,Random forest,Decision tree

scholarly journals Enhanced Error Reduction of Signal Power Loss During Electromagnetic Propagation: Architectural Composition and Learning Rate Selection

2021 ◽  
pp. 450-456
Author(s):  
Virginia C. Ebhota ◽  
◽  
Viranjay M. Srivastava
Keyword(s):  
Neural Network ◽  
Power Loss ◽  
Single Layer ◽  
Signal Propagation ◽  
Learning Rate ◽  
Signal Power ◽  
Multi Layer Perceptron ◽  
Coefficient Of Correlation ◽  
Network Training ◽  
Hidden Layer

This research work analyses the effect of the architectural composition of Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) combined with the effect of the learning rate for effective prediction of signal power loss during electromagnetic signal propagation. A single hidden layer and two hidden layers of MLP ANN have been considered. Different configurations of the neural network architecture ranging from 4 to 100 for both MLP networks have been analyzed. The required hidden layer neurons for optimal training of a single layer multi-layer network were 40 neurons with 0.99670 coefficient of correlation and 1.28020 standard deviations, while [68 72] trained two hidden layers multi-layer perceptron with 0.98880 coefficient of correlation and standard deviation of 1.42820. Different learning rates were also adopted for the network training. The results further validate better MLP neural network training for signal power loss prediction using single-layer perceptron network compared to two hidden layers perceptron network with the coefficient of correlation of 0.99670 for single-layer network and 0.9888 for two hidden layers network. Furthermore, the learning rate of 0.003 shows the best training capability with lower mean squared error and higher training regression compared to other values of learning rate used for both single layer and two hidden layers perceptron MLP networks.

scholarly journals A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Farah Aqilah Bohani ◽  
Azizah Suliman ◽  
Mulyana Saripuddin ◽  
Sera Syarmila Sameon ◽  
Nur Shakirah Md Salleh ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Predictive Accuracy ◽  
Research Direction ◽  
Future Research ◽  
Learning Methods ◽  
Electricity Theft ◽  
Supervised Learning Algorithms ◽  
Artificial Neural

There are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. A public dataset from the State Grid Corporation of China (SGCC) was used for this study. The dataset consisted of power consumption in kWh unit. Based on the analysis results, the DANN outperforms compared to other supervised learning classifiers such as ANN, AdaBoost, and DT in recall, F1-Score, and AUC. A future research direction is the experiments can be performed on other supervised learning algorithms with different types of datasets and suitable preprocessing methods can be applied to produce better performance.

scholarly journals Kajian kemampuan metode neural network untuk klasifikasi penutup lahan dengan menggunakan Citra Landsat-8 OLI (kasus di Kota Yogyakarta dan sekitarnya)

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Melania Swetika Rini
Keyword(s):  
Neural Network ◽  
Input Parameter ◽  
Learning Rate ◽  
Landsat 8 ◽  
Parameter Learning ◽  
Multi Layer Perceptron ◽  
Landsat 8 Oli ◽  
Hidden Layer

Penelitian ini bertujuan: Mengkaji akurasi metode neural network untuk klasifikasi penutup lahan dengan menggunakan citra multispectral Landsat 8 dan pengaruh input parameter pada metode neural network terhadap hasil akurasi klasifikasi penutup lahan. Penelitian ini merupakan penelitian berbasis data penginderaan jauh yang mencakup analisis digital dan kerja lapangan. Analisis digital mencakup kegiatan menguji kemampuan metode neural network/Multi Layer Perceptron (MLP) dengan algoritma backpropagation untuk klasifikasi penutup lahan berbasis citra penginderaan jauh Landsat 8. Kerja lapangan dilakukan untuk mengambil sampel penelitian dan menguji hasil akurasi klasifikasi penutup lahan dengan metode jaringan syaraf. Uji akurasi menggunakan akurasi keseluruhan, akurasi produser, akurasi pemakai dan analisis kappa accuracy. Hasil penelitian menunjukan (1) nilai akurasi terbaik yang diperoleh pada metode MLP dengan 7 kelas penutup lahan yaitu overall accuracy 76,69%, kappa accuracy 0,722 serta waktu eksekusi 1,25 menit, dengan kombinasi parameter yaitu 1 hidden layer; 0,001 learning rate; 0,5 momentum factor; 0,001 RMS; dan 15000 iterasi; (2) Nilai parameter learning rate 0,001 memberikan pengaruh nilai overall accuracy yang rendah sedangkan nilai learning rate 0,01 memberikan nilai overall accuracy yang baik. Iterasi 15000 lebih optimal dibandingkan nilai iterasi 10000 dan 20000 dalam pengaruhnya terhadap nilai akurasi hasil klasifikasi penutup lahan.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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