BPSO ve SVM'ye Dayalı Yüzde Duygu Tanıma için  Derin Özellik Seçimi

Donuk, Kenan; Arı, Ali; Özdemir, Mehmet Fatih; Hanbay, Davut

doi:10.2339/politeknik.992720

Cited by 6 publications

(6 citation statements)

References 81 publications

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“…Several academics have looked at the usefulness of deep learning in automatically learning emotional information from signal data in the field of voice emotion detection [5]. Emotion identification from face photos might be simplified using a three-stage technique developed by Donuk et al [6]. e Fer+ data set is used to train the convolutional neural network (CNN)-based network in the first step.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Serbaya

2022

Security and Communication Networks

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Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.

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Section: Introductionmentioning

confidence: 99%

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Serbaya

2022

Security and Communication Networks

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“…By applying the Sigmoidal function to the velocities of the particles, values between 0-1 are obtained. Then, the obtained values are compared with a randomly determined number between 0-1 and the new positions of the units of the particle are updated as "0" or "1" [15]. Position calculation formulas in BPSO optimization are given in Equation 8 and Equation 9.…”

Section: Resultsmentioning

confidence: 99%

“…The index values of the units with the value "1" of the particle are marked and the features with these index values in the feature vector form the new vector representing the emotion. The resulting new feature vectors are classified by SVM and an error value is obtained [15]. This process ends with finding the solution candidate with the lowest error rate as a result of SVM classification.…”

Section: Resultsmentioning

confidence: 99%

CREMA-D: Improving Accuracy with BPSO-Based Feature Selection for Emotion Recognition Using Speech

Donuk

2022

Journal of Soft Computing and Artificial Intelligence

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People mostly communicate through speech or facial expressions. People's feelings and thoughts are reflected in their faces and speech. This phenomenon is an important tool for people to empathize when communicating with each other. Today, human emotions can be recognized automatically with the help of artificial intelligence systems. Automatic recognition of emotions can increase productivity in all areas including virtual reality, psychology, behavior modeling, in short, human-computer interaction. In this study, we propose a method based on improving the accuracy of emotion recognition using speech data. In this method, new features are determined using convolutional neural networks from MFCC coefficient matrices of speech records in Crema-D dataset. By applying particle swarm optimization to the features obtained, the accuracy was increased by selecting the features that are important for speech emotion classification. In addition, 64 attributes used for each record were reduced to 33 attributes. In the test results, 62.86% accuracy was obtained with CNN, 63.93% accuracy with SVM and 66.01% accuracy with CNN+BPSO+SVM.

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“…ONCE) YOLO algoritması, nesne tespit problemlerinde sonuca hızla ulaşabilmesi sebebiyle günümüze kadar dikkat çekmiş ve birçok çalışmada tercih edilmiş bir nesne tespit yöntemidir. Geçmişten günümüze geleneksel ve modern derin öğrenme algoritmaları incelendiğinde, çok katmanlı ağ yapısı sayesinde CNN (Evrişimsel Sinir Ağı (Convolutional Neural Network)) tabanlı mimarilerin farklı alanlardaki bilimsel çalışmalarda kullanıldığı gözlemlenmiştir [16,17]. CNN modeli, verilen görüntü üzerindeki nitelik çıkarımında ortaya koyduğu başarı ile nesne tespit problemlerinde tercih edilen bir ağ modelidir [18].…”

Section: Yalnizca Bi̇r Kere Bak (You Only Lookunclassified

Akıllı Mobil Cihazlarda YOLOv7 Modeli ile Nesne Tespiti

Karadağ

Arı

2023

Politeknik Dergisi

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Derin öğrenmeye dayalı güncel nesne tespit algoritmalarından biri olan YOLOv7 modelinin Microsoft COCO verisetinde aldığı %51.2’lik ortalama kesinlik başarısı, diğer nesne tespit yöntemlerinin ilerisinde olduğunu kanıtlamıştır. YOLO ilk sunulduğu dönemden itibaren, hız ve doğruluk açısından etkili olması sebebiyle ticari alandaki nesne tespit problemlerinde tercih edilen bir model olmuştur. Genellikle derin öğrenmeye dayalı sistemlerin çalıştırılabilmesi için yüksek kapasitede donanımlara ihtiyaç duyulmaktadır. Bu çalışmada, günümüzde ticaretin önemli araçlarından biri haline gelen akıllı mobil cihazlarda nesne tespiti yapılabilmesi için YOLOv7 modelinin sunucuda aktif edilmesi ile akıllı mobil cihazlarda grafik işlemci birimi kullanılmadan nesne tespiti yapılabilmesi amaçlanmıştır. Yapılan çalışma ile YOLOv7 nesne tespit algoritması, iOS işletim sistemine sahip mobil cihazlarda başarı ile çalıştırılmıştır. Bu sayede mobil cihazlarda çekilen bir görüntü veya halihazırda galeride bulunan herhangi bir görüntü sunucuya aktarıldıktan sonra, doğruluk ve hız açısından etkili bir şekilde görüntü içerisinde bulunan nesnelerin tespitinin gerçekleştirilmesi sağlanmıştır.

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BPSO ve SVM'ye Dayalı Yüzde Duygu Tanıma için Derin Özellik Seçimi

Cited by 6 publications

References 81 publications

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)

CREMA-D: Improving Accuracy with BPSO-Based Feature Selection for Emotion Recognition Using Speech

Akıllı Mobil Cihazlarda YOLOv7 Modeli ile Nesne Tespiti

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