Prospective Stock Analysis Model to improve the investment chances using Machine Learning

2021 ◽  
Author(s):  
Sudhanshu Kushwaha ◽  
Nirbhay Singh Naruka ◽  
S. Ramamoorthy
Keyword(s):  
Machine Learning ◽  
Analysis Model ◽  
Stock Analysis

Regional effect of monetary policy based on GVAR model and machine learning

2020 ◽  
pp. 1-13
Author(s):  
Zengming Zhao ◽  
Wenting Chen
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Monetary Policy ◽  
Stress Test ◽  
Analysis Model ◽  
Conduction Model ◽  
Regional Effect ◽  
Macroeconomic Factors ◽  
Model Based ◽  
Gray Correlation

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry’s own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good.

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

scholarly journals Machine Learning Based Analysis Model for Early Stroke Detection

2019 ◽  
Vol 8 (1) ◽  
pp. 59-70
Author(s):  
Jae-Seoung Kim ◽  
Umirzakova Sabina ◽  
Taeg-Keun Whangbo
Keyword(s):  
Machine Learning ◽  
Analysis Model ◽  
Early Stroke

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals Assessment of bowel cleansing quality in colon capsule endoscopy using machine learning: a pilot study

2018 ◽  
Vol 06 (08) ◽  
pp. E1044-E1050 ◽  
Author(s):  
Maria Magdalena Buijs ◽  
Mohammed Hossain Ramezani ◽  
Jürgen Herp ◽  
Rasmus Kroijer ◽  
Morten Kobaek-Larsen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Capsule Endoscopy ◽  
Analysis Model ◽  
Specific Analysis ◽  
Colon Capsule Endoscopy ◽  
Bowel Cleansing ◽  
Colon Capsule ◽  
Pixel Analysis ◽  
Almost All

Abstract Background and study aims The aim of this study was to develop a machine learning-based model to classify bowel cleansing quality and to test this model in comparison to a pixel analysis model and assessments by four colon capsule endoscopy (CCE) readers. Methods A pixel analysis and a machine learning-based model with four cleanliness classes (unacceptable, poor, fair and good) were developed to classify CCE videos. Cleansing assessments by four CCE readers in 41 videos from a previous study were compared to the results both models yielded in this pilot study. Results The machine learning-based model classified 47 % of the videos in agreement with the averaged classification by CCE readers, as compared to 32 % by the pixel analysis model. A difference of more than one class was detected in 12 % of the videos by the machine learning-based model and in 32 % by the pixel analysis model, as the latter tended to overestimate cleansing quality. A specific analysis of unacceptable videos found that the pixel analysis model classified almost all of them as fair or good, whereas the machine learning-based model identified five out of 11 videos in agreement with at least one CCE reader as unacceptable. Conclusions The machine learning-based model was superior to the pixel analysis in classifying bowel cleansing quality, due to a higher sensitivity to unacceptable and poor cleansing quality. The machine learning-based model can be further improved by coming to a consensus on how to classify cleanliness of a complete CCE video, by means of an expert panel.

scholarly journals Student Feedback Sentiment Analysis Model Using Various Machine Learning Schemes A Review

2019 ◽  
Vol 14 (12) ◽  
pp. 1-9 ◽  
Author(s):  
Irfan Ali Kandhro ◽  
Muhammad Ameen Chhajro ◽  
Kamlesh Kumar ◽  
Haque Nawaz Lashari ◽  
Usman Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Student Feedback ◽  
Analysis Model ◽  
Learning Schemes

A Review of Regression Models in Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
Sunil Kumar ◽  
Vaibhav Bhatnagar
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Analysis Model ◽  
Data Set ◽  
Data Regression ◽  
Different Types

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.

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