scholarly journals Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources

2019 ◽  
Vol 490 (4) ◽  
pp. 4770-4777 ◽  
Author(s):  
M Kovačević ◽  
G Chiaro ◽  
S Cutini ◽  
G Tosti
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gamma Ray ◽  
Classification Performance ◽  
Previous Method ◽  
Machine Learning Method ◽  
Learning Method ◽  
Large Area ◽  
Γ Ray ◽  
Automatic Technique

ABSTRACT Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to develop an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope γ-ray instrument. The final result of this study increased the classification performance by about 80 ${{\ \rm per\ cent}}$ with respect to previous method, leaving only 15 unclassified blazars out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.

Determination of thermodynamic state variables of liquids from its microscopic structure using an artificial neural network

Soft Matter ◽  
2020 ◽  
Author(s):  
Ulices Que-Salinas ◽  
Pedro Ezequiel Ramirez-Gonzalez ◽  
Alexis Torres-Carbajal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Distribution Function ◽  
Microscopic Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
State Variables ◽  
Thermodynamic State

In this work we implement a machine learning method to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function (RDF). The...

scholarly journals An investigation on the factors affecting machine learning classifications in gamma-ray astronomy

2020 ◽  
Vol 492 (4) ◽  
pp. 5377-5390 ◽  
Author(s):  
Shengda Luo ◽  
Alex P Leung ◽  
C Y Hui ◽  
K L Li
Keyword(s):  
Machine Learning ◽  
Gamma Ray ◽  
Small Sample Size ◽  
Classification Performance ◽  
Small Sample ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Large Area ◽  
Actual Performance ◽  
Statistical Fluctuations

ABSTRACT We have investigated a number of factors that can have significant impacts on the classification performance of gamma-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of automatic feature selection can construct a simple model with a small set of features that yields better performance over previous results. Secondly, because of the small sample size of the training/test sets of certain classes in gamma-ray, nested re-sampling and cross-validations are suggested for quantifying the statistical fluctuations of the quoted accuracy. We have also constructed a test set by cross-matching the identified active galactic nuclei (AGNs) and the pulsars (PSRs) in the Fermi-LAT 8-yr point source catalogue (4FGL) with those unidentified sources in the previous 3rd Fermi-LAT Source Catalog (3FGL). Using this cross-matched set, we show that some features used for building classification model with the identified source can suffer from the problem of covariate shift, which can be a result of various observational effects. This can possibly hamper the actual performance when one applies such model in classifying unidentified sources. Using our framework, both AGN/PSR and young pulsar (YNG)/millisecond pulsar (MSP) classifiers are automatically updated with the new features and the enlarged training samples in 4FGL catalogue incorporated. Using a two-layer model with these updated classifiers, we have selected 20 promising MSP candidates with confidence scores $\gt 98{{\ \rm per\ cent}}$ from the unidentified sources in 4FGL catalogue that can provide inputs for a multiwavelength identification campaign.

An Useful Review on Optical Character Recognition for Smart Era Generation

2021 ◽  
pp. 1-41
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Learning Method ◽  
Result Section ◽  
Optical Character

In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.

scholarly journals ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

2019 ◽  
Vol 15 (2) ◽  
pp. 141-148
Author(s):  
Sri Rahayu ◽  
Fitra Septia Nugraha ◽  
Muhammad Ja’far Shidiq
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Side Effects ◽  
20Th Century ◽  
High Accuracy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Good Classification ◽  
Auc Value

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.

scholarly journals Model Selection For Forecasting Rainfall Dataset

2021 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Amri Muhaimin ◽  
Hendri Prabowo ◽  
Suhartono
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Seasonal Pattern ◽  
Information Criteria ◽  
Rainfall Time Series ◽  
Machine Learning Method ◽  
Learning Method ◽  
Hourly Rainfall

The objective of this research is to obtain the best method for forecasting rainfall in the Wonorejo reservoir in Surabaya. Time series and causal approaches using statistical methods and machine learning will be compared to forecast rainfall. Time series regression (TSR), autoregressive integrated moving average (ARIMA), linear regression (LR), and transfer function (TF) are used as a statistical method. Feedforward neural network (FFNN) and deep feed-forward neural network (DFFNN) is used as a machine learning method. Statistical methods are used to capture linear patterns, whereas the machine learning method is used to capture nonlinear patterns. Data about hourly rainfall in the Wonorejo reservoir is used as a case study. The data has a seasonal pattern, i.e. monthly seasonality. Based on the cross-validation and information criteria, the results showed that DFFNN using the time series approach has a more accurate forecast than other methods. In general, machine learning methods have better accuracy than statistical methods. Furthermore, additional information is obtained, through this research the parameter that best to make a neural network model is known. Moreover, these results are also not in line with the results of M3 and M4 competition, i.e. more complex methods do not necessarily produce better forecasts than simpler methods.

Digraph states and their neural network representations

2021 ◽  
Author(s):  
Ying Yang ◽  
Huaixin Cao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Body Problem ◽  
Theoretical Foundation ◽  
Rapid Development ◽  
Quantum States ◽  
Machine Learning Method ◽  
Learning Method ◽  
Many Body ◽  
Graph State

Abstract With the rapid development of machine learning, artificial neural networks provide a powerful tool to represent or approximate many-body quantum states. It was proved that every graph state can be generated by a neural network. In this paper, we aim to introduce digraph states and explore their neural network representations (NNRs). Based on some discussions about digraph states and neural network quantum states (NNQSs), we construct explicitly the NNR for any digraph state, implying every digraph state is an NNQS. The obtained results will provide a theoretical foundation for solving the quantum many-body problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.

scholarly journals Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network

2021 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Diny Melsye Nurul Fajri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Environmentally Friendly ◽  
Machine Learning Method ◽  
Learning Method ◽  
Industrial Products ◽  
Kenaf Fiber ◽  
Plant Pests

Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.

scholarly journals Comparing deep learning with several typical methods in prediction of assessing chlorophyll-a by remote sensing: a case study in Taihu Lake, China

2021 ◽  
Author(s):  
Xiaolan Zhao ◽  
Haoli Xu ◽  
Zhibin Ding ◽  
Daqing Wang ◽  
Zhengdong Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Curve Fitting ◽  
Taihu Lake ◽  
Machine Learning Method ◽  
Learning Method ◽  
Chl A ◽  
A Value ◽  
Fitting Model

Abstract Chlorophyll-a (Chl-a) is an important index in water quality assessment by remote sensing technology. For the study of Chl-a value measurement in rivers or lakes, there were many classical methods, such as curve fitting, back propagation (BP) neural network and radial basis function (RBF) neural network, and all of them had some corresponding applications. With the rise of computer power and deep learning, this study intended to analyze the measurement of water quality and Chl-a in deep learning (DL) and to compare with several classical methods, so as to explore and develop better methods. Taking Taihu Lake of China as the case, this study adopted the measured data of Chl-a in Taihu Lake in 2017 and the data corresponding to the same time of Landsat8. In this study, the four methods were used to inverse the distribution of Chl-a value in Taihu Lake. From the results of inversion, power curve fitting model with the ∑Residual2 of fitting of 90.469 and inverse curve fitting model with the one of 602156.608 had the better results than other curve fitting models, however, were not as accurate as the machine learning method from segmentation results images. The machine learning method had better accuracy than the curve fitting methods from segmentation results images. The mean squared error of testing of the three methods of machine learning (BP, RBF, DL) were respectively 1.436, 4.479, 4.356. Thus, the BP method and DL method had better results in this study.

scholarly journals Analysis of Generalization Ability for Different AdaBoost Variants Based on Classification and Regression Trees

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Shuqiong Wu ◽  
Hiroshi Nagahashi
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Mathematical Analysis ◽  
Classification Performance ◽  
Optimal Combination ◽  
Machine Learning Method ◽  
Learning Method ◽  
Generalization Ability ◽  
Classification And Regression ◽  
Real Adaboost

As a machine learning method, AdaBoost is widely applied to data classification and object detection because of its robustness and efficiency. AdaBoost constructs a global and optimal combination of weak classifiers based on a sample reweighting. It is known that this kind of combination improves the classification performance tremendously. As the popularity of AdaBoost increases, many variants have been proposed to improve the performance of AdaBoost. Then, a lot of comparison and review studies for AdaBoost variants have also been published. Some researchers compared different AdaBoost variants by experiments in their own fields, and others reviewed various AdaBoost variants by basically introducing these algorithms. However, there is a lack of mathematical analysis of the generalization abilities for different AdaBoost variants. In this paper, we analyze the generalization abilities of six AdaBoost variants in terms of classification margins. The six compared variants are Real AdaBoost, Gentle AdaBoost, Modest AdaBoost, Parameterized AdaBoost, Margin-pruning Boost, and Penalized AdaBoost. Finally, we use experiments to verify our analyses.

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