scholarly journals Analyzing Impact of Social Media Sentiments on Financial Markets

2021 ◽  
Vol 10 (10) ◽  
pp. 113-120
Author(s):  
Poorna Chandra Vemula* ◽  
◽  
Santhosh Reddy Chilaka ◽  
Mullapudi Raghu Vamsi ◽  
Jonnalagadda Praveen Reddy ◽  
...  
Keyword(s):  
Financial Markets ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Short Term ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
The Impact ◽  
Gated Recurrent Unit ◽  
Exchange Data

This paper analyzes the impact of continuously changing sentiments on apparently unstable stock exchange. Right when a monetary supporter decides to buy or sell stock, his decision is very much dependent on to rise or fall in price of the stock. In this paper, we look at the possibility of using notion attitudes (good versus negative) and moreover sentiments (delight, feel sorry for, etc) isolated from finance related news or tweets to help predict stock worth turns of events. This examination uses a model-self-ruling approach to manage uncover the mysterious components of stock exchange data using distinctive significant learning techniques like Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU).

Sequence Model based Cloudburst Prediction for the Indian State of Uttarakhand

2021 ◽  
Vol 14 (7) ◽  
pp. 1-9
Author(s):  
M. Sivagami ◽  
P. Radha ◽  
A. Balasundaram
Keyword(s):  
Predictive Power ◽  
Short Term Memory ◽  
Geographic Location ◽  
Short Term ◽  
The Past ◽  
Learning Techniques ◽  
Indian State ◽  
Long Short Term Memory ◽  
Life Challenge ◽  
Gated Recurrent Unit

Predicting the phenomenon of cloudburst has been a larger than life challenge to many weather and rain scientists. The very nature of cloudburst occurrence itself complicates the prediction of cloudburst. Since, cloudburst downpour occurs over a short span of time and is confined to very narrow geographic location, it is highly difficult for weather scientists to make any cloudburst predictions. In this work, the authors propose a cloudburst prediction model that leverages deep learning techniques to predict the occurrence of cloudburst in a location. The authors have collected the data pertaining to the cloudburst events that have occurred in the Indian State of Uttarakhand over the past decade and developed the model. Experiments were conducted using time series sequence models namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Predictive Power Score (PPS) has been used to extract the essential features that are fed as input to these sequence models. The performance of sequence models has been discussed in terms of loss function and accuracy and the results are promising for GRU based model in comparison with other sequence models.

scholarly journals Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 214
Author(s):  
Shipeng Han ◽  
Zhen Meng ◽  
Xingcheng Zhang ◽  
Yuepeng Yan
Keyword(s):  
Noise Reduction ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Dynamic Experiments ◽  
Long Short Term Memory ◽  
Reduction Effect ◽  
Mems Imu ◽  
Gated Recurrent Unit

Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

scholarly journals Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 62-67
Author(s):  
Faisal Dharma Adhinata ◽  
Diovianto Putra Rakhmadani
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
The Impact

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

Recognition of Musical Instrument Using Deep Learning Techniques

2021 ◽  
Vol 11 (4) ◽  
pp. 41-60
Author(s):  
Sangeetha Rajesh ◽  
Nalini N. J.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Instrumental Music ◽  
Short Term Memory ◽  
Short Term ◽  
Mel Frequency Cepstral Coefficients ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
The Impact

The proposed work investigates the impact of Mel Frequency Cepstral Coefficients (MFCC), Chroma DCT Reduced Pitch (CRP), and Chroma Energy Normalized Statistics (CENS) for instrument recognition from monophonic instrumental music clips using deep learning techniques, Bidirectional Recurrent Neural Networks with Long Short-Term Memory (BRNN-LSTM), stacked autoencoders (SAE), and Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM). Initially, MFCC, CENS, and CRP features are extracted from instrumental music clips collected as a dataset from various online libraries. In this work, the deep neural network models have been fabricated by training with extracted features. Recognition rates of 94.9%, 96.8%, and 88.6% are achieved using combined MFCC and CENS features, and 90.9%, 92.2%, and 87.5% are achieved using combined MFCC and CRP features with deep learning models BRNN-LSTM, CNN-LSTM, and SAE, respectively. The experimental results evidence that MFCC features combined with CENS and CRP features at score level revamp the efficacy of the proposed system.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

scholarly journals Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

2021 ◽  
Vol 13 (8) ◽  
pp. 4418
Author(s):  
Miraj Ahmed Bhuiyan ◽  
Jaehyung An ◽  
Alexey Mikhaylov ◽  
Nikita Moiseev ◽  
Mir Sayed Shah Danish
Keyword(s):  
Renewable Energy ◽  
Short Term Memory ◽  
External Environment ◽  
Energy Markets ◽  
Energy Market ◽  
Short Term ◽  
The United Kingdom ◽  
Long Short Term Memory ◽  
Restrictive Measures ◽  
The Impact

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level

2021 ◽  
Vol 20 (6) ◽  
pp. 1-17
Author(s):  
B. Premjith ◽  
K. P. Soman
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Short Term ◽  
Long Short Term Memory ◽  
Target Languages ◽  
Main Components ◽  
Learning Architectures ◽  
Gated Recurrent Unit ◽  
Character Sequence

Morphological synthesis is one of the main components of Machine Translation (MT) frameworks, especially when any one or both of the source and target languages are morphologically rich. Morphological synthesis is the process of combining two words or two morphemes according to the Sandhi rules of the morphologically rich language. Malayalam and Tamil are two languages in India which are morphologically abundant as well as agglutinative. Morphological synthesis of a word in these two languages is challenging basically because of the following reasons: (1) Abundance in morphology; (2) Complex Sandhi rules; (3) The possibilty in Malayalam to form words by combining words that belong to different syntactic categories (for example, noun and verb); and (4) The construction of a sentence by combining multiple words. We formulated the task of the morphological generation of nouns and verbs of Malayalam and Tamil as a character-to-character sequence tagging problem. In this article, we used deep learning architectures like Recurrent Neural Network (RNN) , Long Short-Term Memory Networks (LSTM) , Gated Recurrent Unit (GRU) , and their stacked and bidirectional versions for the implementation of morphological synthesis at the character level. In addition to that, we investigated the performance of the combination of the aforementioned deep learning architectures and the Conditional Random Field (CRF) in the morphological synthesis of nouns and verbs in Malayalam and Tamil. We observed that the addition of CRF to the Bidirectional LSTM/GRU architecture achieved more than 99% accuracy in the morphological synthesis of Malayalam and Tamil nouns and verbs.

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