scholarly journals INVESTIGATION OF THE PROCESS OF PENETRATION INTO THE SOIL OF THE WORKING BODY OF A MINING MACHINE

2021 ◽  
pp. 29-34
Author(s):  
Grigoriy G. Buriy
Keyword(s):  
Mining Machine

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.

Effect of Operator Position on the Incidence of Continuous Mining Machine/Worker Collisions

2007 ◽  
Vol 51 (20) ◽  
pp. 1416-1420
Author(s):  
John R. Bartels ◽  
Dean H. Ambrose ◽  
Sean Gallagher
Keyword(s):  
Response Times ◽  
Health And Safety ◽  
Mining Machine ◽  
Workplace Relationships ◽  
Continuous Miner ◽  
Work Posture ◽  
Close Proximity ◽  
Continuous Mining ◽  
Machine Speed ◽  
Machine Work

Remote operation of continuous miners has enhanced the health and safety of underground miners in many respects; however, numerous fatal and non-fatal continuous miner struck-by accidents have occurred when using remote controls. In an effort to prevent these injuries, NIOSH researchers at Pittsburgh Research Laboratory examined the workplace relationships between continuous miner operators and various tramming modes of the equipment using motion captured data, predicted operator response times, and field- of- view data to determine causes of operator-machine struck-by events in a virtual mine environment. Factors studied included machine speed, direction of escape, operator facing orientation relative to the machine, work posture, distance from machine, and operator anthropometry. Close proximity to the machine, high machine tramming speeds, a right-facing orientation and operator positioning near the tail all resulted in high risk of being struck. It is hoped that this data will provide an improved rationale for operator positioning for remotely operated continuous miners.

Power Balance Control for Two Motors Driving Shearer Base on ANFIS

2013 ◽  
Vol 694-697 ◽  
pp. 2228-2232
Author(s):  
Yuan Hua Zhou ◽  
Hong Wei Ma
Keyword(s):  
Fuzzy Inference ◽  
Balance Control ◽  
Control Model ◽  
Mining Machine ◽  
Power Balance ◽  
Motor Power ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Control Scheme ◽  
Individual Motor

Considering the power balance control in two motors driving shearer, a novel multi-motor power balance control scheme based on ANFIS (Adaptive Neuro-Fuzzy Inference System) is presented. The scheme avoided to modeling the control model of the coal mining machine and could meet the demand of the control system and prevent individual motor from overloading. In MATLAB, use the filed data to simulate and the simulation verify the proposed scheme is valid．

Data Mining Approach to Analyze COVID-19 Clinical Dataset

2021 ◽  
Vol 15 (6) ◽  
pp. 1812-1819
Author(s):  
Azita Yazdani ◽  
Ramin Ravangard ◽  
Roxana Sharifian
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Clinical Signs ◽  
Study Data ◽  
Mining Machine ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Approach

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

scholarly journals Methods and means for the in-house training of mining machine operators

2017 ◽  
Vol 129 ◽  
pp. 06004 ◽  
Author(s):  
Vladimir Velikanov ◽  
Natalja Dyorina ◽  
Azat Abdrakhmanov
Keyword(s):  
Mining Machine ◽  
Machine Operators
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