Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems

Arıcan, Murat; Polat, Kemal

doi:10.33969/ais.2020.21003

Cited by 38 publications

(17 citation statements)

References 17 publications

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“…In our applications, we have used the C4.5 decision tree classifier. For more information about the decision tree classifier, the readers can refer to [35] , [36] , [37] , [38] , [39] , [40] .…”

Section: Methodsmentioning

confidence: 99%

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour

Cömert

Polat

2020

Applied Soft Computing

Self Cite

276

215

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A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.

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Section: Methodsmentioning

confidence: 99%

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour

Cömert

Polat

2020

Applied Soft Computing

Self Cite

276

215

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“…Considering these advantages, FsBoost may be a commonly used algorithm soon. FsBoost algorithms are also suitable for use in biomedical signal processing, deep learning, and communication [35][36][37].…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Noor

Uçar

Polat

et al. 2020

Mathematical Problems in Engineering

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In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

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“…By using this information, pixels can be dissociated. A basic signal processing has four steps of signal acquisition, preprocessing, feature extraction and selection, classification [32]. To acquire hyperspectral images, customized special 'Hyperspectral Camera' is used.…”

Section: Figure 1 Electromagnetic Spectrummentioning

confidence: 99%

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

Özdemir¹,

Polat²

2020

JIEC

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Since the acquisition of digital images, scientific studies on these images have been making significant progress. The sizes and quality of the images obtained have increased greatly from past to present. However, when the information contained in these images remains on the visible band (RGB band), the results that can be obtained are limited. For this reason, the need to acquire images with more broadband information has emerged. Hyperspectral Imaging (HSI) method has been developed to meet this need. A hyperspectral image consists of reflections in hundreds of different bands of the electromagnetic spectrum. Each object exhibits a unique reflection characteristic. Due to this characteristic, objects can be separated from each other using hyperspectral imaging. Hyperspectral cameras are used to obtain this image. The information it contains is much more than an RGB image, so deeper results can be achieved than the human eye can see. In this respect, it has great importance. Artificial intelligence technologies are used extensively in image processing as well as in many other fields. As a result, classification studies are carried out on hyperspectral images with machine learning methods. Machine learning methods can be considered as the most general terms of supervised machine learning, unsupervised machine learning, and reinforced machine learning. Supervised machine learning methods mainly: Support Vector Machines(SVM), k-Nearest Neighborhood(k-NN), Decision Trees, Random Forest, Linear Regression and Neural Networks(NN). Neural networks are also used as an unsupervised learning method. However, Deep Learning, a specialized method of artificial neural networks, is highly preferred due to its unique structure. Artificial intelligence methods have been widely used in recent years, especially for the classification of hyperspectral images containing complex information. Considering the studies, it is seen that especially deep learning is used intensively. At this point, studies have revealed different types of models. The number of models and their successes are increasing day by day.

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Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems

Cited by 38 publications

References 17 publications

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

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