scholarly journals Deep Learning Methods for Modeling Bitcoin Price

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1245 ◽  
Author(s):  
Prosper Lamothe-Fernández ◽  
David Alaminos ◽  
Prosper Lamothe-López ◽  
Manuel A. Fernández-Gámez
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Poor Performance ◽  
Asset Valuation ◽  
Prediction Horizon ◽  
Explanatory Factors ◽  
Robust Model ◽  
Future Prediction ◽  
Precise Prediction ◽  
Potential Impact

A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.

scholarly journals Reinforcement based Multi-Model Deep Learning Algorithm for Classification

2020 ◽  
Vol 9 (2) ◽  
pp. 1704-1710
Keyword(s):  
Deep Learning ◽  
Maximum Entropy ◽  
Learning Algorithm ◽  
Dimensional Space ◽  
Image Data ◽  
Space Policy ◽  
Model Classification ◽  
Deep Learning Algorithm ◽  
Robust Model ◽  
Neural Network Algorithm

Nowadays researchers are focused on processing the multi-media data for classifying the queries of end users by using search engines. The hybrid combination of a powerful classifier and deep feature extractor are used to develop a robust model, which is performed in a high dimensional space. In this research, a three different types of algorithms are combined to attain a stochastic belief space policy, where these algorithms include generative adversary modelling, maximum entropy Reinforcement Learning (RL) and belief space planning which leads to develop a multi-model classification algorithm. In the simulation framework, different adversarial behaviours are used to minimize the agent's action predictability, which has resulted the proposed method to attain robustness, while comparing with unmodelled adversarial strategies. The proposed reinforcement based Deep Learning (DL) algorithm can be used as multi-model classification purpose. The single neural network algorithm can perform the classification on text data and image data. The RL learns the appropriate belief space policy from the feature extracted information of the text and image data, the belief space policy is generated based on the maximum entropy computation

scholarly journals The Impact of Culture on the Indian Construction Industry

2013 ◽  
Vol 5 (2) ◽  
pp. 153
Author(s):  
Syed Nihas ◽  
Kristen Barlish ◽  
Jacob Kashiwagi ◽  
Dean Kashiwagi
Keyword(s):  
Construction Industry ◽  
Poor Performance ◽  
Industry Structure ◽  
Cultural Issues ◽  
Major Research ◽  
Indian Culture ◽  
Cost Overruns ◽  
Deductive Logic ◽  
Potential Impact ◽  
The Impact

The Indian construction industry has been characterized by poor performance. This paper analyzes the potential impact of the Indian culture on the poor performance. If the culture is a major cause in the construction industry subpar performance, can the cultural influence be overridden to minimize construction project delays and cost overruns. The authors propose to identify the  unique cultural issues, identify using the Construction Industry Structure (CIS) model the impact of the cultural issues on the construction industry, and identify a potential solution to the problem. The paper proposes to test the solution in actual tests with industry participants. What makes this research unique is the approach of using deductive logic to create a simple solution, and then convincing a major research client to test the proposal.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

scholarly journals An Empirical Assessment of the Sources of Police Job Satisfaction

2019 ◽  
Vol 23 (1) ◽  
pp. 55-81
Author(s):  
Eugene A. Paoline ◽  
Jacinta M. Gau
Keyword(s):  
Job Satisfaction ◽  
Police Officers ◽  
Empirical Studies ◽  
Police Department ◽  
Poor Performance ◽  
Job Turnover ◽  
Work Related ◽  
Explanatory Factors ◽  
Work Avoidance ◽  
Police Job Satisfaction

Dissatisfied workers are at risk for negative occupational behaviors such as job turnover, poor performance, work avoidance, decreased morale among coworkers, and physical or legal liability. Relying heavily on demographic (e.g., sex, race, education) and occupational (e.g., rank, experience, assignment) explanatory factors, early empirical studies failed to effectively model the statistical correlates of police officer job satisfaction. Recent inquiries have found more success in explaining the variation in job satisfaction by examining a variety of work-related attitudes. The current study adds to this burgeoning area of research by assessing the role of internal and external dimensions of the work environment, as well as views of fairness and effectiveness, on the job satisfaction of police officers. Based on survey data from a midsized municipal police department in Florida, the multivariate analysis reveals a number of successful predictors of job satisfaction, especially for those officers with a street-level assignment. A second analysis, based on qualitative coding of open-ended survey questions, finds differences in positive and negative features of the occupation across varying levels of satisfied and dissatisfied respondents. Implications of these findings for police practitioners and researchers are discussed.

scholarly journals Using deep learning to detect price change indications in financial markets

2017 ◽  
Author(s):  
Avraam Tsantekidis ◽  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Financial Markets ◽  
Price Change
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