scholarly journals Improving galaxy morphologies for SDSS with Deep Learning

2018 ◽  
Vol 476 (3) ◽  
pp. 3661-3676 ◽  
Author(s):  
H Domínguez Sánchez ◽  
M Huertas-Company ◽  
M Bernardi ◽  
D Tuccillo ◽  
J L Fischer
Keyword(s):  
Deep Learning ◽  
Test Sample ◽  
Sloan Digital Sky Survey ◽  
Support Vector ◽  
Type Model ◽  
Visual Classification ◽  
Hubble Sequence ◽  
Sky Survey ◽  
Vector Machines ◽  
The One

Abstract We present a morphological catalogue for ∼670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-types and a series of GZ2 type questions (disc/features, edge-on galaxies, bar signature, bulge prominence, roundness, and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-type model is not so efficient. For the T-type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (>97 per cent), precision and recall values (>90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.

scholarly journals State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Ramin Keivani ◽  
Sina Faizollahzadeh Ardabili ◽  
Farshid Aram
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Smart Cities ◽  
Model Development ◽  
Urban Sustainability ◽  
Urban Transport ◽  
Support Vector ◽  
Neuro Fuzzy ◽  
Vector Machines

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

scholarly journals Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit

2021 ◽  
Vol 39 (4) ◽  
pp. 1190-1197
Author(s):  
Y. Ibrahim ◽  
E. Okafor ◽  
B. Yahaya
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Face Recognition ◽  
Regularization Parameter ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Support Vector ◽  
Vector Machines ◽  
Linear Svm ◽  
Learning Architectures

Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance. Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector  Machines.

scholarly journals Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1753 ◽  
Author(s):  
Hassan El-Khatib ◽  
Dan Popescu ◽  
Loretta Ichim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Classical Method ◽  
Skin Lesions ◽  
Support Vector ◽  
Dice Similarity Coefficient ◽  
Final Decision ◽  
Decision System ◽  
The One ◽  
Sensitivity Specificity

The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning–based methods. We propose a new decision system based on multiple classifiers like neural networks and feature–based methods. Each classifier (method) gives the final decision system a certain weight, depending on the calculated accuracy, helping the system make a better decision. First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process. Some biostatistic parameters, such as accuracy, specificity, sensitivity, and Dice coefficient are calculated. Then, we developed three other methods based on convolutional neural networks (CNNs). The CNNs were pre-trained using large ImageNet and Places365 databases. GoogleNet, ResNet-101, and NasNet-Large, were used in the enumeration order. CNN architectures were fine-tuned in order to distinguish the different types of skin lesions using transfer learning. The accuracies of the classifications were determined. The last proposed method uses the classical method of image object detection, more precisely, the one in which some features are extracted from the images, followed by the classification step. In this case, the classification was done by using a support vector machine. Just as in the first method, the sensitivity, specificity, Dice similarity coefficient and accuracy are determined. A comparison of the obtained results from all the methods is then done. As mentioned above, the novelty of this paper is the integration of these methods in a global fusion-based decision system that uses the results obtained by each individual method to establish the fusion weights. The results obtained by carrying out the experiments on two different free databases shows that the proposed system offers higher accuracy results.

scholarly journals Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Longjun Dong ◽  
Xibing Li ◽  
Gongnan Xie
Keyword(s):  
Predictive Power ◽  
Seismic Event ◽  
Naive Bayes ◽  
Nuclear Explosion ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Classifier ◽  
Vector Machines ◽  
The One ◽  
Calculated Distance

The discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Naïve Bayes Classifier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were discussed and compared. The result of RF method clearly shows the best predictive power with a maximum area of 0.975 under the ROC among RF, SVM, and NBC. The discriminant accuracies of RF, SVM, and NBC for test samples are 92.86%, 85.71%, and 92.86%, respectively. It has been demonstrated that the presented RF model can not only identify seismic event automatically with high accuracy, but also can sort the discriminant indicators according to calculated values of weights.

scholarly journals Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

2018 ◽  
Vol 611 ◽  
pp. A97 ◽  
Author(s):  
J. Pasquet-Itam ◽  
J. Pasquet
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Sloan Digital Sky Survey ◽  
Light Curves ◽  
Support Vector ◽  
Learning Approach ◽  
Photometric Redshifts ◽  
K Nearest Neighbors ◽  
Sky Survey ◽  
Extraction Step

We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope.

Least Squares Support Vector Machine for Fault Diagnosis Optimization

2013 ◽  
Vol 347-350 ◽  
pp. 505-508
Author(s):  
Si Yang Liang ◽  
Jian Hong Lv
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Learning Algorithm ◽  
Test Sample ◽  
Digital Circuit ◽  
Training Sample ◽  
Support Vector ◽  
Vector Machines ◽  
Sample Data ◽  
Diagnosis Method

In order to improve the diagnostic accuracy of digital circuit, the fault diagnosis method based on support vector machines (SVM) is proposed. The input is fault characteristics of digital circuit; the output is the fault style. The connection of fault characteristics and style was established. Network learning algorithm using least squares, the training sample data is formed by the simulation, the test sample data is formed by the untrained simulation. The method achieved the classification of faulted digital circuits, and the results show that the method has the features of fast and high accuracy.

Deep learning of support vector machines with class probability output networks

2015 ◽  
Vol 64 ◽  
pp. 19-28 ◽  
Author(s):  
Sangwook Kim ◽  
Zhibin Yu ◽  
Rhee Man Kil ◽  
Minho Lee
Keyword(s):  
Deep Learning ◽  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Class Probability

scholarly journals Sentiment Analysis towards Actionable Intelligence via Deep Learning

TEM Journal ◽  
2020 ◽  
pp. 1663-1668
Author(s):  
Shorouq Fathi Eletter
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Service Providers ◽  
Unstructured Data ◽  
Classification Model ◽  
Online Review ◽  
Support Vector ◽  
Competitive Advantages ◽  
Accuracy Rate ◽  
Vector Machines

The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.

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