scholarly journals Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron

2021 ◽  
Vol 10 (10) ◽  
pp. 686
Author(s):  
Emily Evenden ◽  
Robert Gilmore Pontius
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Closed Interval ◽  
Machine Learning Algorithms ◽  
Smoothing Function ◽  
Multi Layer Perceptron ◽  
Categorical Variable ◽  
Empirical Probability ◽  
Independent Variable

The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s Land Change Modeler proposes encoding a categorical variable onto the continuous closed interval from 0 to 1 based on each category’s Population Evidence Likelihood (PEL) for input to the Multi-Layer Perceptron, which is a type of neural network. We designed examples to test the wisdom of these encodings. The results show that encoding a categorical variable based on each category’s Sample Empirical Probability (SEP) produces results similar to binary encoding and superior to PEL encoding. The Multi-Layer Perceptron’s sigmoidal smoothing function can cause PEL encoding to produce nonsensical results, while SEP encoding produces straightforward results. We reveal the encoding methods by illustrating how a dependent variable gains across an independent variable that has four categories. The results show that PEL can differ substantially from SEP in ways that have important implications for practical extrapolations. If users must encode a categorical variable for input to a neural network, then we recommend SEP encoding, because SEP efficiently produces outputs that make sense.

EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

2021 ◽  
pp. 44-51
Author(s):  
E. Yu. Shchetinin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Deep Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Audio Recordings ◽  
Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Sentiment Perception in Articulation

2021 ◽  
Vol 9 (VII) ◽  
pp. 2803-2812
Author(s):  
Ganesh NagaVenkataSai Mohan Kancherla
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Daily Life ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Multi Layer Perceptron ◽  
Working Model ◽  
Initial Stage ◽  
Perception System

Emotion is quite prevalent aspect in daily life. Every individual has a inequity levels of anxiety in the finding the concealed emotion present in a speech or talk. So we had decided to procreate a new methodology in which every emotion which is present in a speech can be detected. The system we developed can detect any emotion with a great extent of efficiency. Any type of emotion will be detected using Machine learning algorithms in a effective way. We will utilize Multi-Layer Perceptron in the initial stage and then we will compare this with working model of Convolution Neural Networks. We want to develop an Artificial Intelligence perception system which leads to detection of emotion in any articulation

scholarly journals DEVELOPMENT A SYSTEM FOR CLASSIFYING AND RECOGNIZING PERSON’S FACE

2020 ◽  
Vol 4 (4(73)) ◽  
pp. 15-24
Author(s):  
S.N. Boranbayev ◽  
M.S. Amirtayev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Machine Learning Algorithms ◽  
Facial Features ◽  
Histogram Of Oriented Gradients ◽  
Image Encoding ◽  
Linear Classifier ◽  
The Face ◽  
Special Points

The purpose of this article is to summarize the knowledge gained in the development and implementation of a neural network for facial recognition. Neural networks are used to solve complex tasks that require analytical calculations similar to what the human brain does. Machine learning algorithms are the foundation of a neural network. As input, the algorithm receives an image with people's faces, then searches for faces in this image using HOG (Histogram of oriented gradients). The result is images with explicit face structures. To determine unique facial features, the Face landmark algorithm is used, which finds 68 special points on the face. These points can be used to centerthe eyes and mouth for more accurate encoding. To get an accurate “face map” consisting of 128 dimensions, you need to use image encoding. Using the obtained data, the convolutional neural network can determine people's faces using the SVM linear classifier algorithm.

scholarly journals Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5823
Author(s):  
Krzysztof Przybył ◽  
Krzysztof Koszela ◽  
Franciszek Adamski ◽  
Katarzyna Samborska ◽  
Katarzyna Walkowiak ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Machine Learning ◽  
Neural Networks ◽  
Scanning Electron Microscopy ◽  
Fourier Transform ◽  
High Efficiency ◽  
Multi Layer Perceptron ◽  
Neural Models ◽  
Scanning Electron

In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.

scholarly journals TO THE QUESTION OF LEARNING MODELING A NEURAL NETWORK IN SCHOOL COURSE OF INFORMATICS

2020 ◽  
Vol 72 (4) ◽  
pp. 180-187
Author(s):  
D.N. Issabayeva ◽  
◽  
G.A. Abdulkarimova ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Computer Modeling ◽  
Machine Learning Algorithms ◽  
Distinctive Features ◽  
Design Task ◽  
Recognition Of Objects ◽  
Simulated Object

The article describes an approach to computer modeling of a neural network. To simulate a neural network, it is necessary to collect a large amount of data about the simulated object, identify its features, create an algorithm that is a set of operations performed on a computer. The function of neural networks is based on classification (sorting), prediction and recognition of objects or events. Modeling neural networks can be carried out by collecting data about an object, highlighting its distinctive features and creating an algorithm using the example of Iris-Fisher flowers. The article also includes a description of the execution of a neural network design task and the use of machine learning algorithms for data mining in an MS Excel spreadsheet processor for teaching lessons.

scholarly journals Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

2021 ◽  
Vol 5 (4 (113)) ◽  
pp. 55-63
Author(s):  
Beimbet Daribayev ◽  
Aksultan Mukhanbet ◽  
Yedil Nurakhov ◽  
Timur Imankulov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning Algorithms ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Machine Learning Classifiers ◽  
Oil Displacement ◽  
Learning Classifiers

The problem of oil displacement was solved using neural networks and machine learning classifiers. The Buckley-Leverett model is selected, which describes the process of oil displacement by water. It consists of the equation of continuity of oil, water phases and Darcy’s law. The challenge is to optimize the oil displacement problem. Optimization will be performed at three levels: vectorization of calculations; implementation of classical algorithms; implementation of the algorithm using neural networks. A feature of the method proposed in the work is the identification of the method with high accuracy and the smallest errors, comparing the results of machine learning classifiers and types of neural networks. The research paper is also one of the first papers in which a comparison was made with machine learning classifiers and neural and recurrent neural networks. The classification was carried out according to three classification algorithms, such as decision tree, support vector machine (SVM) and gradient boosting. As a result of the study, the Gradient Boosting classifier and the neural network showed high accuracy, respectively 99.99 % and 97.4 %. The recurrent neural network trained faster than the others. The SVM classifier has the lowest accuracy score. To achieve this goal, a dataset was created containing over 67,000 data for class 10. These data are important for the problems of oil displacement in porous media. The proposed methodology provides a simple and elegant way to instill oil knowledge into machine learning algorithms. This removes two of the most significant drawbacks of machine learning algorithms: the need for large datasets and the robustness of extrapolation. The presented principles can be generalized in countless ways in the future and should lead to a new class of algorithms for solving both forward and inverse oil problems

scholarly journals Cyberbullying Detection: Hybrid Models Based on Machine Learning and Natural Language Processing Techniques

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2810
Author(s):  
Chahat Raj ◽  
Ayush Agarwal ◽  
Gnana Bharathy ◽  
Bhuva Narayan ◽  
Mukesh Prasad
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Classification Methods ◽  
Cyberbullying Detection

The rise in web and social media interactions has resulted in the efortless proliferation of offensive language and hate speech. Such online harassment, insults, and attacks are commonly termed cyberbullying. The sheer volume of user-generated content has made it challenging to identify such illicit content. Machine learning has wide applications in text classification, and researchers are shifting towards using deep neural networks in detecting cyberbullying due to the several advantages they have over traditional machine learning algorithms. This paper proposes a novel neural network framework with parameter optimization and an algorithmic comparative study of eleven classification methods: four traditional machine learning and seven shallow neural networks on two real world cyberbullying datasets. In addition, this paper also examines the effect of feature extraction and word-embedding-techniques-based natural language processing on algorithmic performance. Key observations from this study show that bidirectional neural networks and attention models provide high classification results. Logistic Regression was observed to be the best among the traditional machine learning classifiers used. Term Frequency-Inverse Document Frequency (TF-IDF) demonstrates consistently high accuracies with traditional machine learning techniques. Global Vectors (GloVe) perform better with neural network models. Bi-GRU and Bi-LSTM worked best amongst the neural networks used. The extensive experiments performed on the two datasets establish the importance of this work by comparing eleven classification methods and seven feature extraction techniques. Our proposed shallow neural networks outperform existing state-of-the-art approaches for cyberbullying detection, with accuracy and F1-scores as high as ~95% and ~98%, respectively.

Convolutional Neural Network

2021 ◽  
pp. 12-26
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

RANDOM MULTI-MODAL DEEP LEARNING IN THE PROBLEM OF IMAGE RECOGNITION

2021 ◽  
pp. 21-26
Author(s):  
А.И. Паршин ◽  
М.Н. Аралов ◽  
В.Ф. Барабанов ◽  
Н.И. Гребенникова
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Recognition ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
Machine Learning Algorithms ◽  
Complex Data ◽  
Advantages And Disadvantages

Задача распознавания изображений - одна из самых сложных в машинном обучении, требующая от исследователя как глубоких знаний, так и больших временных и вычислительных ресурсов. В случае использования нелинейных и сложных данных применяются различные архитектуры глубоких нейронных сетей, но при этом сложным вопросом остается проблема выбора нейронной сети. Основными архитектурами, используемыми повсеместно, являются свёрточные нейронные сети (CNN), рекуррентные нейронные сети (RNN), глубокие нейронные сети (DNN). На основе рекуррентных нейронных сетей (RNN) были разработаны сети с долгой краткосрочной памятью (LSTM) и сети с управляемыми реккурентными блоками (GRU). Каждая архитектура нейронной сети имеет свою структуру, свои настраиваемые и обучаемые параметры, обладает своими достоинствами и недостатками. Комбинируя различные виды нейронных сетей, можно существенно улучшить качество предсказания в различных задачах машинного обучения. Учитывая, что выбор оптимальной архитектуры сети и ее параметров является крайне трудной задачей, рассматривается один из методов построения архитектуры нейронных сетей на основе комбинации свёрточных, рекуррентных и глубоких нейронных сетей. Показано, что такие архитектуры превосходят классические алгоритмы машинного обучения The image recognition task is one of the most difficult in machine learning, requiring both deep knowledge and large time and computational resources from the researcher. In the case of using nonlinear and complex data, various architectures of deep neural networks are used but the problem of choosing a neural network remains a difficult issue. The main architectures used everywhere are convolutional neural networks (CNN), recurrent neural networks (RNN), deep neural networks (DNN). Based on recurrent neural networks (RNNs), Long Short Term Memory Networks (LSTMs) and Controlled Recurrent Unit Networks (GRUs) were developed. Each neural network architecture has its own structure, customizable and trainable parameters, and advantages and disadvantages. By combining different types of neural networks, you can significantly improve the quality of prediction in various machine learning problems. Considering that the choice of the optimal network architecture and its parameters is an extremely difficult task, one of the methods for constructing the architecture of neural networks based on a combination of convolutional, recurrent and deep neural networks is considered. We showed that such architectures are superior to classical machine learning algorithms

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