Emotion Recognition Based on Eeg Features With Various Brain Regions

Wagh, Kalyani P.; Vasanth, K.; Shinde, Sagar

doi:10.21817/indjcse/2022/v13i1/221301095

Cited by 4 publications

(4 citation statements)

References 3 publications

(3 reference statements)

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“…This method can also detect variations in brightness to discover the boundaries of the objects in photos. Statistical patterns or structures in a raw dataset that are recurrent are referred to as such (Wagh et al 2022). Different methods can be utilized to obtain these features, like transformations and statistical procedures (Zende et al 2017).…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Identification of fake currency using soft computing

Shinde,

Wadhwa,

Bhalke

et al. 2023

Multidiscip. Sci. J.

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Advancements in color printing techniques have led to a surge in the creation and duplication of counterfeit currency notes, which has become prevalent in India. Although it was previously difficult to carry out this type of operation, anyone with a laser printer now has the capability to do it. This issue is considered a major problem due to the country's various problems, such as frauds, corruption and black money. It is needed to develop the system for the detection of fake currency. A system that can quickly identify counterfeit money is being developed. The proposed solution will utilize image processing to authenticate Indian currency notes. The proposed system will involve extracting different features from the notes, such as the security thread, Bleed Line, edges, shapes, textures, colors and Fluorescence. It is being developed using the software known as MATLAB. The supervised learning model of support vector machine for the extraction of the notes' features is also being used. This is utilized for analyzing the data related to classification and regression. Once the statistical features have been extracted, they were matched with the features stored in MAT file using SVM (Support Vector Machine) classifier. We have also conducted a test of other methods, such as the black box algorithm. The proposed solution is very fast and simple to implement, allowing it to identify if the notes are fake or real. The outcome of proposed system is the remarkable working of proposed system for the detection of fake currency and accuracy has been measured with the help of confusion matrix. The accuracy, precision & F- score are best by using SVM as compare to SVC (support Vector Classifier) & GBC (Genetic Bee Colony) algorithm.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Identification of fake currency using soft computing

Shinde,

Wadhwa,

Bhalke

et al. 2023

Multidiscip. Sci. J.

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“…This proposed compiler optimization model comprises three working phases: model training, feature extraction [24,25], as well as model exploitation (feature selection). First, the inputs were fed into the model training phase, which tries to match the right weights as well as bias to a learning algorithm [26,27] in order to minimize a loss function throughout the validation range. The retrieved characteristics, such as static, dynamic, as well as improved entropy, were then transferred to the model exploitation phase [21], where the optimal features were chosen utilizing the improved chaos game optimization.…”

Section: Proposed Compiler Optimization Prediction Modelmentioning

confidence: 99%

“…Outputs from model training phase were given to the feature extraction phase to extract the static, dynamic and improved entropy features [26,27].…”

Section: B Feature Extractionmentioning

confidence: 99%

Compiler Optimization Prediction with New Self-Improved Optimization Model

Shewale¹,

Shinde²,

Gurav³

et al. 2023

IJACSA

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Users may now choose from a vast range of compiler optimizations. These optimizations interact in a variety of sophisticated ways with one another and with the source code. The order in which optimization steps are applied can have a considerable influence on the performance obtained. As a result, we created a revolutionary compiler optimization prediction model. Our model comprises three operational phases: model training, feature extraction, as well as model exploitation. The model training step includes initialization as well as the formation of candidate sample sets. The inputs were then sent to the feature extraction phase, which retrieved static, dynamic, and improved entropy features. These extracted features were then optimized by the feature exploitation phase, which employs an improved hunger games search algorithm to choose the best features. In this work, we used a Convolutional Neural Network to predict compiler optimization based on these selected characteristics, and the findings show that our innovative compiler optimization model surpasses previous approaches.

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“…In order to enhance or extract information from an image, various techniques are used, which are referred to as image processing. This type of signal processing involves taking a picture as its input, and then coming up with another image or feature, depending on its associated elements (Wagh et al 2022;. Image processing is a promising area of research within the fields of computer science and engineering.…”

Section: Image Processingmentioning

confidence: 99%

Soft computing approach for feature extraction of palm biometric

Shende,

Shinde,

Wadhwa

et al. 2023

Multidiscip. Sci. J.

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The method for developing a secure and reliable identification system relies on the use of biometrics. In this case, the palm vein is used as a security measure. The proposed system is powered by a convolutional neural network, which is a type of neural network that is commonly utilized for image recognition. The palm vein's visual features are extracted using a convolutional neural network. This method can improve the recognition rate and its performance parameters. The results of an experiment conducted with this method were better than those obtained with conventional techniques.

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Emotion Recognition Based on Eeg Features With Various Brain Regions

Cited by 4 publications

References 3 publications

Identification of fake currency using soft computing

Identification of fake currency using soft computing

Compiler Optimization Prediction with New Self-Improved Optimization Model

Soft computing approach for feature extraction of palm biometric

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