scholarly journals INVESTMENT USING CRYPTOCURRENCY FOR A MACHINE LEARNING BASED STOCK PREDICTION MODEL

2020 ◽  
Vol 07 (05) ◽  
pp. 70-110
Author(s):  
Swastik Das ◽  
Dr.Murali S
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Stock Prediction

scholarly journals Stock Prediction Model Using TensorFlow

2021 ◽  
Vol 9 (12) ◽  
pp. 99-103
Author(s):  
Gourav Jaiswal
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Data Visualization ◽  
Recent Trend ◽  
Stock Exchange ◽  
Stock Prediction ◽  
Future Value ◽  
A Company ◽  
Stock Values ◽  
Learning Data

Abstract: In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend available in market prediction technologies is that the use of machine learning that makes predictions on the basis of values of current stock exchange indices by training on their previous values. Machine learning itself employs completely different models to create prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Considering the factors are open, close, low, high and volume. Keywords: Stock Prediction, Machine Learning, Data Visualization, Yahoo Finance Dataset

Prediction Model for Diabetes Mellitus Using Machine Learning Techniques

2018 ◽  
Vol 6 (3) ◽  
pp. 292-296
Author(s):  
N.A. Farooqui ◽  
◽  
Ritika . ◽  
A. Tyagi ◽  
◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Prediction Model ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

Machine learning‐based individualized survival prediction model for total knee replacement in osteoarthritis: Data from the Osteoarthritis Initiative

2021 ◽  
Author(s):  
Afshin Jamshidi ◽  
Jean‐Pierre Pelletier ◽  
Aurelie Labbe ◽  
François Abram ◽  
Johanne Martel‐Pelletier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Total Knee Replacement ◽  
Prediction Model ◽  
Knee Replacement ◽  
Survival Prediction ◽  
Osteoarthritis Initiative ◽  
Total Knee

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

Constructing a prediction model for difficult intubation of obese patients based on machine learning

2021 ◽  
Vol 72 ◽  
pp. 110278
Author(s):  
Cheng-Mao Zhou ◽  
Qiong Xue ◽  
Hao-Tian Ye ◽  
Ying Wang ◽  
Jianhua Tong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Difficult Intubation ◽  
Obese Patients
Sign in / Sign up

Export Citation Format

Share Document