Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)—the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)—are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.

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Sentiment Analysis in the Light of LSTM Recurrent Neural Networks

International Journal of Synthetic Emotions ◽

10.4018/ijse.2018010103 ◽

2018 ◽

Vol 9 (1) ◽

pp. 33-39 ◽

Cited By ~ 12

Author(s):

Subarno Pal ◽

Soumadip Ghosh ◽

Amitava Nag

Keyword(s):

Neural Networks ◽

Sentiment Analysis ◽

Recurrent Neural Networks ◽

Deep Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Different Types ◽

Long Short Term Memory ◽

Increase In Accuracy ◽

Temporal Sequences

Long short-term memory (LSTM) is a special type of recurrent neural network (RNN) architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more accurately. In this article, the authors work with different types of LSTM architectures for sentiment analysis of movie reviews. It has been showed that LSTM RNNs are more effective than deep neural networks and conventional RNNs for sentiment analysis. Here, the authors explore different architectures associated with LSTM models to study their relative performance on sentiment analysis. A simple LSTM is first constructed and its performance is studied. On subsequent stages, the LSTM layer is stacked one upon another which shows an increase in accuracy. Later the LSTM layers were made bidirectional to convey data both forward and backward in the network. The authors hereby show that a layered deep LSTM with bidirectional connections has better performance in terms of accuracy compared to the simpler versions of LSTM used here.

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Application of Deep Learning Methods to Forecasting Changes in Short-Term Currency Trends

Scientific Bulletin of Mukachevo State University Series “Economics” ◽

10.52566/msu-econ.7(2).2020.75-86 ◽

2020 ◽

Vol 7 (2) ◽

pp. 75-86

Author(s):

Vasily D. Derbentsev ◽

Vitalii S. Bezkorovainyi ◽

Iryna V. Luniak

Keyword(s):

Neural Networks ◽

Time Series ◽

Deep Learning ◽

Deep Neural Networks ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Better Than

This study investigates the issues of forecasting changes in short-term currency trends using deep learning models, which is relevant for both the scientific community and for traders and investors. The purpose of this study is to build a model for forecasting the direction of change in the prices of currency quotes based on deep neural networks. The developed architecture was based on the model of valve recurrent node, which is a modification of the model of “Long Short-Term Memory”, but is simpler in terms of the number of parameters and learning time. The forecast calculations of the dynamics of quotations of the currency pair euro/dollar and the most capitalised cryptocurrency Bitcoin/dollar were performed using daily, four-hour and hourly datasets. The obtained results of binary classification (forecast of the direction of trend change) when applying daily and hourly quotations turned out to be generally better than those of time series models or models of neural networks of other architecture (in particular, multilayer perceptron or “Long Short-Term Memory” models). According to the study results, the highest accuracy of classification was for the model of daily quotations for both euro/dollar – about 72%, and for Bitcoin/ dollar – about 69%. For four-hour and hourly time series, the accuracy of classification decreased, which can be explained both by the increase in the impact of “market noise” and the probable overfitting. Computer simulation has demonstrated that models predict a rising trend better than a declining one. The study confirmed the prospects for the application of deep learning models for short-term forecasting of time series of currency quotes. The use of the developed models proved to be effective for both fiat and cryptocurrencies. The proposed system of models based on deep neural networks can be used as a basis for developing an automated trading system in the foreign exchange market

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Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification

Remote Sensing ◽

10.3390/rs13173504 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3504

Author(s):

Jing Shen ◽

Chao Tao ◽

Ji Qi ◽

Hao Wang

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Time Series ◽

Land Cover ◽

Classification Accuracy ◽

Short Term Memory ◽

Remote Sensing Images ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Time series images with temporal features are beneficial to improve the classification accuracy. For abstract temporal and spatial contextual information, deep neural networks have become an effective method. However, there is usually a lack of sufficient samples in network training: one is the loss of images or the discontinuous distribution of time series data because of the inevitable cloud cover, and the other is the lack of known labeled data. In this paper, we proposed a Semi-supervised convolutional Long Short-Term Memory neural network (SemiLSTM) for time series remote sensing images, which was validated on three data sets with different time distributions. It achieves an accurate and automated land cover classification via a small number of labeled samples and a large number of unlabeled samples. Besides, it is a robust classification algorithm for time series optical images with cloud coverage, which reduces the requirements for cloudless remote sensing images and can be widely used in areas that are often obscured by clouds, such as subtropical areas. In conclusion, this method makes full advantage of spectral-spatial-temporal characteristics under the condition of limited training samples, especially expanding time context information to enhance classification accuracy.

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Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽

10.3390/electronics10040495 ◽

2021 ◽

Vol 10 (4) ◽

pp. 495

Author(s):

Imayanmosha Wahlang ◽

Arnab Kumar Maji ◽

Goutam Saha ◽

Prasun Chakrabarti ◽

Michal Jasinski ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Support Vector ◽

Variational Autoencoder ◽

Different Types ◽

Static Images ◽

Long Short Term Memory ◽

2D And 3D ◽

Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

The Scientific World JOURNAL ◽

10.1155/2015/205749 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Eva Volna ◽

Martin Kotyrba ◽

Hashim Habiballa

Keyword(s):

Neural Networks ◽

Time Series ◽

Ecg Signals ◽

Detection Systems ◽

Language Interpretation ◽

Different Types ◽

Time Courses ◽

Natural Language Interpretation ◽

Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

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A Review on Recent Advancements in FOREX Currency Prediction

Algorithms ◽

10.3390/a13080186 ◽

2020 ◽

Vol 13 (8) ◽

pp. 186 ◽

Cited By ~ 2

Author(s):

Md. Saiful Islam ◽

Emam Hossain ◽

Abdur Rahman ◽

Mohammad Shahadat Hossain ◽

Karl Andersson

Keyword(s):

Selection Criteria ◽

Short Term Memory ◽

Time Series Prediction ◽

Optimization Techniques ◽

Selection Algorithm ◽

Relevant Research ◽

Short Term ◽

Different Types ◽

Long Short Term Memory ◽

Gated Recurrent Unit

In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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