scholarly journals Emotion Recognition using Convolutional Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 1748-1765
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Medical Science ◽  
Recognition System ◽  
Activation Function ◽  
State Observation ◽  
Human Needs ◽  
Hidden Layer

Emotion recognition system place the important role in many fields, particularly image processing, medical science, machine learning. As per human needs, the effect and potential use of programmed emotion recognition have been developing in a wide scope of utilizations, including human-PC communication, robot control and driver state observation. In any case, to date, vigorous acknowledgment of outward appearances from pictures and recordings is yet a testing errand because of the trouble in precisely extricating the helpful passionate highlights. These highlights are regularly spoken to in various structures, for example, static, dynamic, point-based geometric or area based appearance. Facial development highlights, which incorporate component position and shape changes, are by and large brought about by the developments of facial components and muscles on the face of enthusiastic manner. Emotion recognition system has many applications. and it plays a vital part in fault detection and in gaming application. In this project the emotion recognition is of dynamic way and not like uploading the image and finding the emotion. And this is achieved with the help of the concept of machine learning called Convolutional Neural Network. This is one of the most familiar deep learning concept. The main moto of using this concept is to maintain accuracy. The CNN consists of many intermediate state which plays the important role in producing the accurate output. The layers of CNN are input layer, hidden layer and output layer. The hidden layer is used to update weight, bias and activation function. If we use the CNN methodology the unwanted parts which is un necessary for the emotion recognition will be eliminated accurately. The CNN helps to reduce our elimination task in easier way and with minimal steps.

scholarly journals Applying of machine learning in the construction of a voice-controlled interface on the example of a music player

2019 ◽  
Vol 13 ◽  
pp. 302-309
Author(s):  
Jakub Basiakowski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Feedforward Neural Network ◽  
Hidden Layer ◽  
Music Player ◽  
The Impact

The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

scholarly journals Emotion Recognition using Convolutional Neural Network in Virtual Meeting Environment

2021 ◽  
Vol 13 (1) ◽  
pp. 30-38
Author(s):  
Nabila Husna Shabrina ◽  
Julando Omar ◽  
Akmal Nusa Bhakti ◽  
Axel Patria
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Recognition System ◽  
Cascade Classifier ◽  
Training Process ◽  
Training Set ◽  
Test Set ◽  
Virtual Meeting ◽  
Haar Cascade

This study is done in order to propose an Emotion Recognition System that uses Convolutional Neural Network in a Virtual Meeting Environment to detect non-verbal feedback that emerge when communicating. This study starts with the training process of the CNN model with version 2.3.0 of tensorflow-gpu library, along with FER-2013 dataset, where only 80% of the data is used as the training set, and the other 20% is used as the test set. The model is trained for 430 epochs that results in 73.86% rate of accuracy with a loss of 1.42. In the classification process, a Haar-Cascade Classifier algorithm is used to detect faces within an image that has been inputted using OpenCV. Next the already developed model is used to predict the image that has been pre-processed. Based on the results shown, it can be concluded that the study has provided satisfactory results and is expected to help in understanding non-verbal input given when communicating and among other various things.

scholarly journals KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

2020 ◽  
Vol 9 (3) ◽  
pp. 273-282
Author(s):  
Isna Wulandari ◽  
Hasbi Yasin ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Training Data ◽  
Young Generation ◽  
Testing Data ◽  
Filter Size ◽  
Hidden Layer ◽  
Herbs And Spices

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 

scholarly journals Recognition of Face Emotion using Convolutional Neural Network

2020 ◽  
Vol 9 (9) ◽  
pp. 364-376
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Recognition System ◽  
Video Stream ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Recognition of face emotion has been a challenging task for many years. This work uses machine learning algorithms for both, a real-time image or a stored database image in the area of facial emotion recognition system. So it is very clear that, deep learning technology becomes important for Human-computer interaction (HCI) applications. The proposed system has two parts, real-time based facial emotion recognition system and also the image based facial emotion recognition system. A Convolutional Neural Network (CNN) model is used to train and test different facial emotion images in this research work. This work was executed successfully using Python 3.7.6 platform. The input Face image of a person was taken using the webcam video stream or from the standard database available for research. The five different facial emotions considered in this work are happy, surprise, angry, sad and neutral. The best recognition accuracy with the proposed system for the webcam video stream is found to be 91.2%, whereas for the input database images is found to be 90.08%.

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

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