Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

MapReduce and Optimized Deep Network for Rainfall Prediction in Agriculture

2020 ◽  
Vol 63 (6) ◽  
pp. 900-912
Author(s):  
Oswalt Manoj S ◽  
Ananth J P
Keyword(s):  
Deep Learning ◽  
Gradient Descent ◽  
Prediction Models ◽  
Short Term Memory ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
Rainfall Prediction ◽  
Gradient Descent Algorithm ◽  
Major Factors

Abstract Rainfall prediction is the active area of research as it enables the farmers to move with the effective decision-making regarding agriculture in both cultivation and irrigation. The existing prediction models are scary as the prediction of rainfall depended on three major factors including the humidity, rainfall and rainfall recorded in the previous years, which resulted in huge time consumption and leveraged huge computational efforts associated with the analysis. Thus, this paper introduces the rainfall prediction model based on the deep learning network, convolutional long short-term memory (convLSTM) system, which promises a prediction based on the spatial-temporal patterns. The weights of the convLSTM are tuned optimally using the proposed Salp-stochastic gradient descent algorithm (S-SGD), which is the integration of Salp swarm algorithm (SSA) in the stochastic gradient descent (SGD) algorithm in order to facilitate the global optimal tuning of the weights and to assure a better prediction accuracy. On the other hand, the proposed deep learning framework is built in the MapReduce framework that enables the effective handling of the big data. The analysis using the rainfall prediction database reveals that the proposed model acquired the minimal mean square error (MSE) and percentage root mean square difference (PRD) of 0.001 and 0.0021.

Analysis on Indian Stock Market Prediction Using Deep Learning Models

2021 ◽  
pp. 80-90
Author(s):  
Kalaivani Karuppiah ◽  
Umamaheswari N. ◽  
Venkatesh R.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Stock Market ◽  
Multilayer Perceptron ◽  
Stock Exchange ◽  
Market Price ◽  
Learning Models ◽  
Stock Market Prediction ◽  
Indian Stock Market

The neural network is one of the best data mining techniques that have been used by researchers in different areas for the past 10 years. Analysis on Indian stock market prediction using deep learning models plays a very important role in today's economy. In this chapter, various deep learning architectures such as multilayer perceptron, recurrent neural networks, long short -term memory, and convolutional neural network help to predict the stock market prediction. There are two different stock market price companies, namely National Stock Exchange and New York Stock Exchange, are used for analyzing the day-wise closing price used for comparing different techniques such as neural network, multilayer perceptron, and so on. Both the NSE and NYSE share their common details, and they are compared with various existing models. When compared with the previous existing models, neural networks obtain higher accuracy, and their experimental result is shown in betterment compared with existing techniques.

scholarly journals Design and Implementation of MCNN for Better Prediction of Stock Price Movement

2018 ◽  
pp. 238-243
Author(s):  
Anshul Sahu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Processing Technique ◽  
Stochastic Gradient Descent ◽  
Stock Price Prediction ◽  
Price Prediction

The stock market prediction is problematic subsequently the stock price is active in environment. To decrease the inappropriate predictions of the stock market and evolution the ability to predict the market actions. To escape the risk and the challenging in predicting stock price. Predicting stock market prices is a difficult task that conventionally contains extensive neural network. Owed to the linked environment of stock prices, conventional batch processing technique cannot be developed competently for stock market analysis. We propose an efficient Learning algorithm that develops a kind of Modified Computational Neural Networks (MCNN) based on BPNN (Back Propagation neural network) filter in training to increase the stock price prediction. Where the weights are adjusted for separate data points using stochastic gradient descent. This will distribute extra precise outcomes when linked to existing stock price prediction algorithms. The network is trained and evaluated for accurateness complete numerous sizes of data, and the results are organized.

scholarly journals Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network

2019 ◽  
Vol 2 (1) ◽  
pp. 41
Author(s):  
Heri Darmanto
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Discriminant Analysis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Graphical Processing

Hasil sensus kehidupan laut pada tahun 2013 di seluruh dunia terdapat lebih dari 23.000 spesies dan masih banyak sekali spesies ikan yang belum diidentifikasi. Otolith merupakan organ yang sangat penting di belakang telinga ikan, karena melalui otolith ini dapat diketahui jenis ikan, pertumbuhan dan lingkungan, serta sejarah kehidupannya,  misalnya, umur, reproduksi, dan migrasi. Dengan semakin  canggihnya komputer dan pengolahan di bidang citra,  diharapkan  kemampuan  mengidentifikasi jenis  ikan  yang dimiliki oleh manusia bisa diadopsi  dan diterapkan pada perangkat komputer. Deep Learning saat ini semakin berkembang memanfaatkan sumber daya perangkat keras yang semakin canggih termasuk penggunaan GPU (Graphical Processing Unit) untuk perhitungan proses komputasi dengan akurasi yang lebih baik dan proses yang lebih cepat. Pada penelitian ini metode yang diusulkan, untuk keperluan klasifikasi ikan menggunakan metode Convolutional Neural Network dengan teknik Transfer Learning dari model Alexnet dan optimasi Momentum Stochastic Gradient Descent. Hasil eksperimen diperoleh akurasi sebesar 95.4% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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