Detection of weather images by using spiking neural networks of deep learning models

Toğaçar, Mesut; Ergen, Burhan; Cömert, Zafer

doi:10.1007/s00521-020-05388-3

Cited by 31 publications

(21 citation statements)

References 43 publications

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“…Many research works have been carried out related to the WIs processing and analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Some of the works are described as follows.…”

Section: Related Workmentioning

confidence: 99%

“…Purwandari et al [6] emphasized on ML mechanism for multi-class weather forecasting from twitter. Togacar et al [7] emphasized on spiking NN of DL mechanisms for the detection of WIs. Chandrayan et al [8] emphasized on ML mechanism to predict the atmospheric weather fluctuation. Tao et al [9] focused on ML and extreme gradient boosting feature selection mechanism for the prediction of weather relative humidity.…”

Section: Related Workmentioning

confidence: 99%

“…Weather information [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] is an important concern throughout the globe. It has the capability of lowering the weather related losses and enhancing societal benefits such as the protection of life, health, property, etc., if the weather information is shared among the entire human society at the right time properly.…”

Section: Introductionmentioning

confidence: 99%

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Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Jena

2022

IJAEML

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Purpose: Weather information plays a crucial role in the human society. It helps to lower the weather related losses and enhance the societal benefits such as the protection of life, health, property, etc., It is very much essential for the proper classification of weather images (WIs) into several categories such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow, etc. so that appropriate information can be provided to the people as well as organizations for further analysis. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of WIs into the dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1604 WIs having 149, 141, 146, 150, 144, 146, 142, 147, 149, 147, 143 numbers of dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types respectively are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of WIs into several types such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow type. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

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“…Many research works have been carried out related to the WIs processing and analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Some of the works are described as follows.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Jena

2022

IJAEML

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“…Toğaçar et al [37] detect skin cancer by using disease images and also [38] make a weather forecast using aerial images by employing a method used in this study. Four different weather conditions images were exploited with GoogleNet and VggNet CNN models.…”

Section: Introductionmentioning

confidence: 99%

SpiCoNET: A Hybrid Deep Learning Model to Diagnose COVID-19 and Pneumonia Using Chest X-Ray Images

Tümen¹

2022

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Using deep learning techniques on radiological lung images for detecting COVID-19 is a promising technique in shortening the diagnosis time. In this study, we propose a hybrid deep learning model, detecting the COVID-19 and Pneumonia virus using Chest X-ray images. The proposed model, named SpiCoNET, first runs multiple well-known deep learning models combined with Spiking Neural Network (SNN) in order to identify the models with higher accuracy rates. Then, SpiCoNET combines the features of the two models with the highest accuracy rates among the well-known models and hands the combined features over to a different SNN layer as an input. Finally, the features are classified by using the SEFRON learning algorithm. The proposed hybrid deep learning model takes advantage of the features of the well-known models combined with SNN providing the highest accuracy rate. Moreover, the proposed model makes use of the SEFRON learning algorithm to provide better classification. The proposed model provides an accuracy rate of 97.09% for the classification of images of the COVID-19, Pneumonia and Normal, which outperforms AlexNet (91.27%) and DenseNet201 (90.40%). The results reveal that deep learning based systems for the identification of COVID-19 and Pneumonia can help healthcare professionals control the COVID-19 pandemic in an effective manner.

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“…In addition, developing computer technologies and arti cial intelligence methods [2] [3][4] [5] have also begun to gain popularity in diagnosing this disease. Image processing [6] [7], machine learning [8][9] [10], deep learning methods [2][11] [12][13] and optimization methods [14][15] [16] frequently used of the methods.…”

Section: Introductionmentioning

confidence: 99%

Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

Başaran

Çelik

2022

Preprint

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If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years many diseases have been rapidly detected with high accuracy with artificial intelligence methods and the treatment process has accelerated. Convolutional neural networks one of the artificial intelligence methods provide very detailed information about images and extremely successful results are obtained in classifying images. In this study firstly the data set was trained with the EfficientNetB0 model which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting with PSO and GA optimization algorithms, and different feature combinations were created. Each of these selected feature sets were classified by the SVM method and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89,17%.

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Detection of weather images by using spiking neural networks of deep learning models

Cited by 31 publications

References 43 publications

Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

SpiCoNET: A Hybrid Deep Learning Model to Diagnose COVID-19 and Pneumonia Using Chest X-Ray Images

Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

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