scholarly journals Neural Machine Translation using Recurrent Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1395-1400
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Neural Machine Translation ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Translation Systems

In this era of globalization, it is quite likely to come across people or community who do not share the same language for communication as us. To acknowledge the problems caused by this, we have machine translation systems being developed. Developers of several reputed organizations like Google LLC, have been working to bring algorithms to support machine translations using machine learning algorithms like Artificial Neural Network (ANN) in order to facilitate machine translation. Several Neural Machine Translations have been developed in this regard, but Recurrent Neural Network (RNN), on the other hand, has not grown much in this field. In our work, we have tried to bring RNN in the field of machine translations, in order to acknowledge the benefits of RNN over ANN. The results show how RNN is able to perform machine translations with proper accuracy.

scholarly journals ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network

Molecules ◽  
2017 ◽  
Vol 22 (10) ◽  
pp. 1732 ◽  
Author(s):  
Renzhi Cao ◽  
Colton Freitas ◽  
Leong Chan ◽  
Miao Sun ◽  
Haiqing Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Neural Machine Translation

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.

scholarly journals Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1576 ◽  
Author(s):  
Li Zhu ◽  
Lianghao Huang ◽  
Linyu Fan ◽  
Jinsong Huang ◽  
Faming Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Environmental Factors ◽  
Recurrent Neural Network ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Rate

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

scholarly journals Micro Clustering Methodology for Document Objects using Deep Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 1546-1552
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Large Data ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Volume Data ◽  
Data Set ◽  
Clustering And Classification

Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

scholarly journals A Document Classification using NLP and Recurrent Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 632-636
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recurrent Neural Network ◽  
Time Complexity ◽  
Learning Task ◽  
Document Classification ◽  
Machine Learning Algorithms ◽  
Class Label ◽  
Abstract Section ◽  
Document Models

The classification technique is most important for supervised and semi supervised base machine learning task. Many classification algorithms has introduced already for existing systems. Class-label classification is an important machine learning task wherein one assigns a subset of candidate without label to an object. Classification of various document models based on short text, metadata, heading levels these are the existing techniques which are introduced in literature survey. Sometime whole data reading and processing might be take a much time for classification, so it increase the time complexity for entire system. We proposed a new document classification method based on deep learning using NLP and machine learning approach. In this work system has several attractive properties: it captures some metadata from entire abstract section and built the training set first. Once complete all document process, it deals with optimization algorithm. Recurrent Neural Network has used to categories the individual object according to their weights. And it provides final class label for entire test dataset. Based on the various experimental analysis system provides data classification accuracy as well as minimum time complexity than classical machine learning algorithms.

scholarly journals Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Ali Najah Ahmed ◽  
Nur’atiah Zaini ◽  
Arif Razzaq ◽  
Pavitra Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Unseen Data ◽  
Temperature And Humidity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Transfer Learning Based Recurrent Neural Network Algorithm for Linguistic Analysis

2021 ◽  
Vol 20 (3) ◽  
pp. 1-16
Author(s):  
Peipei Jiang ◽  
Liailun Chen ◽  
Min-Feng Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
The Core ◽  
Machine Learning Methods ◽  
Neural Network Algorithm ◽  
Comparative Results

Each language is a system of understanding and skills that allows language users to interact, express thoughts, hypotheses, feelings, wishes, and all that needs to be expressed. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine Learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of Big Data. This article provides a collaborative effort (transfer learning integrated into Recurrent Neural Network) to analyze the distinct kinds of writing between the language's linear and non-computational sides, and to enhance granularity. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.

Neural Machine Translation Using Sequence Modeling

2021 ◽  
Vol 9 (8) ◽  
pp. 2153-2169
Author(s):  
N Revathi
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Machine Translation ◽  
Term Memory ◽  
Sequence Modeling

Abstract: Language is a main mode of communication, and translation is a critical tool for understanding information in a foreign language. Without the help of human translators, machine translation allows users to absorb unfamiliar linguistic material. The main goal of this project is to create a practical language translation from English to Hindi. Given its relevance and potential in the English-Hindi translation, machine translation is an efficient way to turn content into a new language without employing people. Among all available translation machines, Neural Machine Translation (NMT) is one of the most efficient ways. So, in this case, we're employing Sequence to Sequence Modeling, which includes the Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM), and Encoder-Decoder methods. Deep Neural Network (DNN) comprehension and principles of deep learning, i.e. machine translation, are disclosed in the field of Natural Language Processing (NLP). In machine reclining techniques, DNN plays a crucial role. Keywords: Sequence to Sequence, Encoder-Decoder, Recurrent Neural Network, Long & Short term Memory, Deep Neural Network.

scholarly journals Gaze Gesture Recognition by Graph Convolutional Networks

2021 ◽  
Vol 8 ◽  
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Eye Tracking ◽  
Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Research Areas ◽  
Artificial Neural Network Ann

Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success of applying machine learning techniques for gaze gesture recognition. More recently, graph neural networks (GNNs) have shown great potential applications in several research areas such as image classification, action recognition, and text classification. However, GNNs are less applied in eye tracking researches. In this work, we propose a graph convolutional network (GCN)–based model for gaze gesture recognition. We train and evaluate the GCN model on the HideMyGaze! dataset. The results show that the accuracy, precision, and recall of the GCN model are 97.62%, 97.18%, and 98.46%, respectively, which are higher than the other compared conventional machine learning algorithms, the artificial neural network (ANN) and the convolutional neural network (CNN).

scholarly journals Predicting Protein Thermostability Upon Mutation Using Molecular Dynamics Timeseries Data

2016 ◽  
Author(s):  
Noah Fleming ◽  
Benjamin Kinsella ◽  
Christopher Ing
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Protein Structure ◽  
Protein Stability ◽  
Recurrent Neural Network ◽  
Molecular Basis ◽  
Sequence Data ◽  
Structural Features ◽  
Machine Learning Algorithms

AbstractA large number of human diseases result from disruptions to protein structure and function caused by missense mutations. Computational methods are frequently employed to assist in the prediction of protein stability upon mutation. These methods utilize a combination of protein sequence data, protein structure data, empirical energy functions, and physicochemical properties of amino acids. In this work, we present the first use of dynamic protein structural features in order to improve stability predictions upon mutation. This is achieved through the use of a set of timeseries extracted from microsecond timescale atomistic molecular dynamics simulations of proteins. Standard machine learning algorithms using mean, variance, and histograms of these timeseries were found to be 60-70% accurate in stability classification based on experimental ΔΔGor protein-chaperone interaction measurements. A recurrent neural network with full treatment of timeseries data was found to be 80% accurate according the F1 score. The performance of our models was found to be equal or better than two recently developed machine learning methods for binary classification as well as two industry-standard stability prediction algorithms. In addition to classification, understanding the molecular basis of protein stability disruption due to disease-causing mutations is a significant challenge that impedes the development of drugs and therapies that may be used treat genetic diseases. The use of dynamic structural features allows for novel insight into the molecular basis of protein disruption by mutation in a diverse set of soluble proteins. To assist in the interpretation of machine learning results, we present a technique for determining the importance of features to a recurrent neural network using Garson’s method. We propose a novel extension of neural interpretation diagrams by implementing Garson’s method to scale each node in the neural interpretation diagram according to its relative importance to the network.

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