mining methods
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2022 ◽  
Vol 14 (2) ◽  
pp. 635
Author(s):  
Ahmed M. A. Shohda ◽  
Mahrous A. M. Ali ◽  
Gaofeng Ren ◽  
Jong-Gwan Kim ◽  
Mohamed Abd-El-Hakeem Mohamed

Decision-making is very important in many fields, such as mining engineering. In addition, there has been a growth of computer applications in all fields, especially mining operations. One of these application fields is mine design and the selection of suitable mining methods, and computer applications can help mine engineers to decide upon and choose more satisfactory methods. The selection of mining methods depends on the rock-layer specification. All rock characteristics should be classified in terms of technical and economic concerns related to mining rock specifications, such as mechanical and physical properties, and evaluated according to their weights and ratings. Methodologically, in this study, the criteria considered in the University of British Columbia (UBC) method were used as references to establish general criteria. These criteria consist of general shape, ore thickness, ore plunge, and grade distribution, in addition to the rock quality designation (ore zone, hanging wall, and foot wall) and rock substance strength (ore zone, hanging wall, and foot wall). The technique for order of preference by similarity to ideal solution (TOPSIS) was adopted, and an improved TOPSIS method was developed based on experimental testing and checked by means of the application of cascade forward backpropagation neural networks in mining method selection. The results provide indicators that decision makers can use to choose between different mining methods based on the total points given to all ore properties. The best mining method is cut and fill stopping, with a rank of 0.70, and the second is top slicing, with a rank of 0.67.


Author(s):  
Ganesh K. Shinde

Abstract: Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.


2021 ◽  
Vol 9 (2) ◽  
pp. 208-215
Author(s):  
Luky Fabrianto ◽  
Novianti Madhona Faizah ◽  
Johan Hendri Prasetyo ◽  
Bobby Suryo Prakoso ◽  
Gani Wiharso

The popular data mining methods to find the relationship between an item and another item is the association rule method using A Priori algorithm, this method is precise to generate a pattern of relationship rules between the types of items sold based on sales data. Support values ​​on frequent items and confidence in the rules obtained can be an actionable insight that can be follow up by minimarket managers, cooperatives and etc. The categorization of product types in minimarkets is much while the total number of transactions in one year is also very large, but the number of types of items sold in a transaction is very few, thus the threshold value cannot be high. In this study, the association rule method was carried out per event or certain period related to Muslim holidays, the highest rule was obtained is Makanan ringan => Sembako with 46% confidence and 16% support which occurred in the month of Ramadan.


2021 ◽  
Vol 12 (5-2021) ◽  
pp. 91-103
Author(s):  
Olga V. Fridman ◽  

The article provides a brief overview of Data Mining methods and algorithms which are used in solving various tasks where both quantitative and qualitative data have to be processed. The purpose of the review is a brief description of the methods and algorithms, as well as a list of sources in which they are described in detail. The features of existing approaches to solving such problems are considered, the analysis of modern methods for solving Data Mining problems is carried out.


2021 ◽  
Vol 6 (4) ◽  
pp. 252-258
Author(s):  
Sh. I. Khakimov ◽  
Sh. R. Urinov

In the process of underground mining of deep levels rock pressure can appear in any form, creating a serious threat to the lives of miners, disrupting the normal course of mining works and reducing the efficiency of mining production. The solution of the problem of rock pressure control becomes very urgent for underground mines developing vein deposits at a depth of more than 250 m. The aim of the study is the development and justification of mining methods to provide safe and efficient mining of deposits in complicated mining and mechanical conditions. In this paper, the factors of redistribution and dangerous concentration of stresses in the mined ore mass were identified, the methods of rock mass management in complicated geotechnical conditions were studied, and their advantages and disadvantages were revealed. It was determined that the sublevel stoping with the combined use of existing methods of rock pressure control and applying selfpropelled mining machinery is currently one of the most promising method finding widening application scope. In the context of Zarmitan gold ore zone the options of technological schemes of the sublevel stoping method were considered, providing for a combination of different methods of rock pressure control, allowing to minimize the disadvantages of one method through using the advantages of other ones. We proposed sublevel stoping options with artificial polygonal pillars and with artificial columnar pillars, which allowed to reduce ore losses in inter-stope pillars, arch pillars, and secondary dilution. In addition, artificial pillars, taking compressive/tensile stresses, prevent their concentration and create safe conditions for extraction at adjacent and underlying levels.


2021 ◽  
pp. 001698622110618
Author(s):  
Selcuk Acar ◽  
Kelly Berthiaume ◽  
Katalin Grajzel ◽  
Denis Dumas ◽  
Charles “Tedd” Flemister ◽  
...  

In this study, we applied different text-mining methods to the originality scoring of the Unusual Uses Test (UUT) and Just Suppose Test (JST) from the Torrance Tests of Creative Thinking (TTCT)–Verbal. Responses from 102 and 123 participants who completed Form A and Form B, respectively, were scored using three different text-mining methods. The validity of these scoring methods was tested against TTCT’s manual-based scoring and a subjective snapshot scoring method. Results indicated that text-mining systems are applicable to both UUT and JST items across both forms and students’ performance on those items can predict total originality and creativity scores across all six tasks in the TTCT-Verbal. Comparatively, the text-mining methods worked better for UUT than JST. Of the three text-mining models we tested, the Global Vectors for Word Representation (GLoVe) model produced the most reliable and valid scores. These findings indicate that creativity assessment can be done quickly and at a lower cost using text-mining approaches.


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