Search results for: mining methods

Authors: Muhammad Usman

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Pattern extraction has been done in past to extract hidden and interesting patterns from large datasets. Recently, advancements are being made in these techniques by providing the ability of multi-level mining, effective dimension reduction, advanced evaluation and visualization support. This paper focuses on reviewing the current techniques in literature on the basis of these parameters. Literature review suggests that most of the techniques which provide multi-level mining and dimension reduction, do not handle mixed-type data during the process. Patterns are not extracted using advanced algorithms for large datasets. Moreover, the evaluation of patterns is not done using advanced measures which are suited for high-dimensional data. Techniques which provide visualization support are unable to handle a large number of rules in a small space. We present a theoretical model to handle these issues. The implementation of the model is beyond the scope of this paper.

Keywords: association rule mining, data mining, data warehouses, visualization of association rules

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Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

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This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

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Authors: Mingming Guo, Xiaozan Wang

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Introduction: The purpose of this study is to examine the correlation between the physical fitness of Chinese college students and their daily running habits (RH). Methods: A total of 718 college students from East China Normal University participated in this study (385 boys and 333 girls). Each participant participated in the Chinese Students’ Physical Fitness Test during the 2018-2019 school year. In addition, each student is also required to use the app to record all their running results during each run during the 2018-2019 school year. Researchers can query and export all running records through the app's management platform. Results: (1) The total number of kilometers run by the students showed a significant negative correlation with their vital capacity (VC), sitting body flexion (SBF), and long jump (LJ) (rᵥ

Keywords: college students, physical fitness, running habits, data mining

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Authors: S. McLean, J. A. Scott

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The research topic is focusing on improving sustainable operation through recovery and reuse of waste heat in process water streams, an area in the mining industry that is often overlooked. There are significant advantages to the application of this topic, including economic and environmental benefits. The smelting process in the mining industry presents an opportunity to recover waste heat and apply it to alternative uses, thereby enhancing the overall process. This applied research has been conducted at the Sudbury Integrated Nickel Operations smelter site, in particular on the water cooling towers. The aim was to determine and optimize methods for appropriate recovery and subsequent upgrading of thermally low-grade heat lost from the water cooling towers in a manner that makes it useful for repurposing in applications, such as within an acid plant. This would be valuable to mining companies as it would be an opportunity to reduce the cost of the process, as well as decrease environmental impact and primary fuel usage. The waste heat from the cooling towers needs to be upgraded before it can be beneficially applied, as lower temperatures result in a decrease of the number of potential applications. Temperature and flow rate data were collected from the water cooling towers at an acid plant over two years. The research includes process control strategies and the development of a model capable of determining if the proposed heat recovery technique is economically viable, as well as assessing any environmental impact with the reduction in net energy consumption by the process. Therefore, comprehensive cost and impact analyses are carried out to determine the best area of application for the recovered waste heat. This method will allow engineers to easily identify the value of thermal resources available to them and determine if a full feasibility study should be carried out. The rapid scoping model developed will be applicable to any site that generates large amounts of waste heat. Results show that heat pumps are an economically viable solution for this application, allowing for reduced cost and CO₂ emissions.

Keywords: environment, heat recovery, mining engineering, sustainability

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: client classification, loan suitability, risk rating, CART analysis

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Authors: Meng-Hui Chen, Chen-Yu Kao, Chia-Yu Hsu, Pei-Chann Chang

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The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems.

Keywords: combinatorial problems, sequential pattern mining, estimationof distribution algorithms, artificial chromosomes

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Authors: Rajkumar Ghosh

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The industrialization of coal mining and related activities has a significant impact on groundwater in the surrounding areas of the Damodar River. The Damodar River basin, located in eastern India, is known as the "Ruhr of India" due to its abundant coal reserves and extensive coal mining and industrial operations. One of the major consequences of collieries on groundwater is the contamination of water sources. Coal mining activities often involve the excavation and extraction of coal through underground or open-pit mining methods. These processes can release various pollutants and chemicals into the groundwater, including heavy metals, acid mine drainage, and other toxic substances. As a result, the quality of groundwater in the Damodar River region has deteriorated, making it unsuitable for drinking, irrigation, and other purposes. The high concentration of heavy metals, such as arsenic, lead, and mercury, in the groundwater has posed severe health risks to the local population. Prolonged exposure to contaminated water can lead to various health problems, including skin diseases, respiratory issues, and even long-term ailments like cancer. The contamination has also affected the aquatic ecosystem, harming fish populations and other organisms dependent on the river's water. Moreover, the excessive extraction of groundwater for industrial processes, including coal washing and cooling systems, has resulted in a decline in the water table and depletion of aquifers. This has led to water scarcity and reduced availability of water for agricultural activities, impacting the livelihoods of farmers in the region. Efforts have been made to mitigate these issues through the implementation of regulations and improved industrial practices. However, the historical legacy of coal industrialization continues to impact the groundwater in the Damodar River area. Remediation measures, such as the installation of water treatment plants and the promotion of sustainable mining practices, are essential to restore the quality of groundwater and ensure the well-being of the affected communities. In conclusion, the coal industrialization in the Damodar River surrounding has had a detrimental impact on groundwater. This research focuses on soil subsidence induced by the over-exploitation of ground water for dewatering open pit coal mines. Soil degradation happens in arid and semi-arid regions as a result of land subsidence in coal mining region, which reduces soil fertility. Depletion of aquifers, contamination, and water scarcity are some of the key challenges resulting from these activities. It is crucial to prioritize sustainable mining practices, environmental conservation, and the provision of clean drinking water to mitigate the long-lasting effects of collieries on the groundwater resources in the region.

Keywords: coal mining, groundwater, soil subsidence, water table, damodar river

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Authors: Walid Moudani, Ahmad Shahin

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This research presents a study to handle the recruitment services system. It aims to enhance a business intelligence system by embedding data mining in its core engine and to facilitate the link between job searchers and recruiters companies. The purpose of this study is to present an intelligent management system for supporting recruitment services based on data mining methods. It consists to apply segmentation on the extracted job postings offered by the different recruiters. The details of the job postings are associated to a set of relevant features that are extracted from the web and which are based on critical criterion in order to define consistent clusters. Thereafter, we assign the job searchers to the best cluster while providing a ranking according to the job postings of the selected cluster. The performance of the proposed model used is analyzed, based on a real case study, with the clustered job postings dataset and classified job searchers dataset by using some metrics.

Keywords: job postings, job searchers, clustering, genetic algorithms, business intelligence

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Authors: Khaleel Saleh Al-Rababah

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Amyloid fibril forming regions, which are known as protein aggregates, in sequences of some protein families are associated with a number of diseases known as amyloidosis. Mutations play a role in forming fibrils by accelerating the fibril formation process. In this paper we want to extract diseases that caused by those mutations as a result of the impact of the mutations on structural and functional properties of the aggregated protein. We propose a text mining system, to automatically extract mutations, diseases and relations between mutations and diseases. We presented an algorithm based on finite state to cluster mutations found in the same sentence as a sentence could contain different mutation cause different diseases. Also, we presented a co reference algorithm that enables cross-link sentences.

Keywords: amyloid, amyloidosis, co reference, protein, text mining

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Ulas Cinar, Omer Faruk Ugurlu, Selcuk Cebi

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Occupational safety risk management is the most important element of a safe working environment. Effective risk management can only be possible with accurate analysis and evaluations. Scoring-based risk assessment methods offer considerable ease of application as they convert linguistic expressions into numerical results. It can also be easily adapted to any field. Contrary to all these advantages, important problems in scoring-based methods are frequently discussed. Effective measurability is one of the most critical problems. Existing methods allow experts to choose a score equivalent to each parameter. Therefore, experts prefer the score of the most likely outcome for risk. However, all other possible consequences are neglected. Assessments of the existing methods express the most probable level of risk, not the real risk of the enterprises. In this study, it is aimed to develop a method that will present a more comprehensive evaluation compared to the existing methods by evaluating the probability and severity scores, all sub-parameters, and potential results, and a new scoring-based method is proposed in the literature.

Keywords: occupational health and safety, risk assessment, scoring based risk assessment method, underground mining, weighted risk scores

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Authors: G. Gevorgyan, S. Khudaverdyan, A. Vaseashta

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The Lake Sevan basin is situated in the eastern part of the Republic of Armenia (Gegharquniq marz/district). The heavy metal pollution of the some tributaries of Lake Sevan was investigated. Water sampling was performed in August and December, 2014 from the 4 observation sites: 1) Sotq river upstream (about 600 meters upstream from the Sotq gold mine); 2) Sotq river mouth; 3) Masrik river mouth; 4) Dzknaget river mouth. Heavy metal (V, Fe, Ni, Cu, As, Mo, Pb) concentrations in the water samples were determined by the standard methods using an atomic absorption spectrophotometer. The results of the study showed that heavy metal content mainly increased from the upstream of the Sotq river to the mouth of the Masrik river which may have been conditioned by the influence of gold mining activity as the Masrik and its tributary-Sotq rivers passing through the gold mining area were exposed to heavy metal pollution. The observation sites can be ranked by pollution degree as follows: №3> №2> №1> №4. The highest heavy metal pollution degree was observed in the Masrik river mouth which may have been conditioned by the direct impact of gold mining activity and the pressure of its tributary–the Sotq river which flows through the gold mining area. The lowest heavy metal pollution degree was registered in the Dzknaget river mouth which flowing through rural areas wasn’t subject to significant heavy metal pollution. According to the observation sites of the Sotq and Masrik rivers, high positive correlation was mainly observed between the concentrations of the investigated heavy metals (except nickel) which indicated that all the heavy metals except the nickel had the same anthropogenic pollution source which was the activity of the Sotq gold mine. In general, it is possible to state that the activity of the Sotq gold mine in the Lake Sevan basin caused the heavy metal pollution of the Sotq and Masrik rivers which may have posed environmental hazards. Heavy metals are nondegradable substances, and heavy metal pollution of freshwater systems may pose risks to the environment and human health through accumulation in the tissues of aquatic organisms, water-food chain as well as oral ingestion and dermal contact.

Keywords: Armenia, Lake Sevan basin, gold mining activity, river ecosystems, heavy metal pollution

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Authors: Rachida Malaoui, Leila Arabet , Asma Benbouza

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The use of mining waste rock as a material for construction is one of the biggest concerns grabbing the attention of many mining countries. As these materials are abandoned, more effective solutions have been made to offset some of the building materials, and to avoid environmental pollution. The sands of the Djemi Djema deposit mines of the Djebel Onk mines are sedimentary materials of several varieties of layers with varying thicknesses and are worth far more than 300m deep. The sands from the Djemi Djema business area are medium to coarse and are discharged and accumulated, generating a huge estimated quantity of more than 77424250 tonnes. This state of "resource" is of great importance so as to be oriented towards the fields of public works and civil engineering after having reached the acceptable properties of this resource

Keywords: reuse, sands, shear tests, waste rock

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Authors: Diego Mauricio Murillo Gomez, Enney Leon Gonzalez Ramirez, Hugo Piedrahita, Jairo Yate

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This paper proposes a methodology for the management of noise in open mining industries based on an integral concept, which takes into consideration occupational and environmental noise as a whole. The approach relies on the characterization of sources, the combination of several measurements’ techniques and the use of acoustic prediction software. A discussion about the difference between frequently used acoustic indicators such as Leq and LAV is carried out, aiming to establish common ground for homologation. The results show that the correct integration of this data not only allows for a more robust technical analysis but also for a more strategic route of intervention as several departments of the company are working together. Noise control measurements can be designed to provide a healthy acoustic surrounding in which the exposure workers but also the outdoor community is benefited.

Keywords: environmental noise, noise control, occupational noise, open mining

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Authors: Rosendo Mendoza, María Carmen Hidalgo, María José Campos-Suñol, Julián Martínez, Javier Rey

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The impact of mining environmental liabilities and mine drainage on surface water quality has been investigated in the hydrographic basin of the La Carolina mining district (southern Spain). This abandoned mining district is characterized by the existence of important mineralizations of sulfoantimonides of Pb - Ag, and sulfides of Cu - Fe. All surface waters reach the main river of this mining area, the Grande River, which ends its course in the Rumblar reservoir. This waterbody is intended to supply 89,000 inhabitants, as well as irrigation and livestock. Therefore, the analysis and control of the metal(loid) concentration that exists in these surface waters is an important issue because of the potential pollution derived from metallic mining. A hydrogeochemical campaign consisting of 20 water sampling points was carried out in the hydrographic network of the Grande River, as well as two sampling points in the Rumbler reservoir and at the main tailings impoundment draining to the river. Although acid mine drainage (pH below 4) is discharged into the Grande river from some mine adits, the pH values in the river water are always neutral or slightly alkaline. This is mainly the result of a dilution process of the small volumes of mine waters by net alkaline waters of the river. However, during the dry season, the surface waters present high mineralization due to a constant discharge from the abandoned flooded mines and a decrease in the contribution of surface runoff. The concentrations of dissolved Cd and Pb in the water reach values of 2 and 81 µg/l, respectively, exceeding the limit established by the Environmental Quality Standard for surface water. In addition, the concentrations of dissolved As, Cu, and Pb in the waters of the Rumblar reservoir reached values of 10, 20, and 11 µg/l, respectively. These values are higher than the maximum allowable concentration for human consumption, a circumstance that is especially alarming.

Keywords: environmental quality, hydrogeochemistry, metal mining, surface water

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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Authors: Ketan Mahawer, Abeer Mutto, S. K. Gupta

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The mining wastewater contains inorganic metal salts, which makes it saline and additionally contributes to contaminating the surface and underground freshwater reserves that exist nearby mineral processing industries. Therefore, treatment of wastewater and water recovery is obligatory by any available technology before disposing it into the environment. Currently, reverse osmosis (RO) is the commercially acceptable conventional membrane process for saline wastewater treatment, but consumes an enormous amount of energy and makes the process expensive. To solve this industrial problem with minimum energy consumption, we tested the feasibility of forward osmosis-nanofiltration (FO-NF) hybrid process for the mining wastewater treatment. The FO-NF process experimental results for 0.029M concentration of saline wastewater treated by 0.42 M sodium-sulfate based draw solution shows that specific energy consumption of the FO-NF process compared with standalone NF was slightly above (between 0.5-1 kWh/m3) from conventional process. However, average freshwater recovery was 30% more from standalone NF with same feed and operating conditions. Hence, FO-NF process in place of RO/NF offers a huge possibility for treating mining industry wastewater and concentrates the metals as the by-products without consuming an excessive/large amount of energy and in addition, mitigates the fouling in long periods of treatment, which also decreases the maintenance and replacement cost of the separation process.

Keywords: forward osmosis, nanofiltration, mining, draw solution, divalent solute

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: G. A. Gevorgyan, K. A. Ghazaryan, T. H. Derdzyan

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The heavy metal pollution of the soils around the mining area near Shamlugh town and related risks to human health were assessed. The investigations showed that the soils were polluted with heavy metals that can be ranked by anthropogenic pollution degree as follows: Cu>Pb>As>Co>Ni>Zn. The main sources of the anthropogenic metal pollution of the soils were the copper mining area near Shamlugh town, the Chochkan tailings storage facility and the trucks transferring are from the mining area. Copper pollution degree in some observation sites was unallowable for agricultural production. The total non-carcinogenic chronic hazard index (THI) values in some places, including observation sites in Shamlugh town, were above the safe level (THI<1) for children living in this territory. Although the highest heavy metal enrichment degree in the soils was registered in case of copper, the highest health risks to humans especially children were posed by cobalt which is explained by the fact that heavy metals have different toxicity levels and penetration characteristics.

Keywords: Armenia, copper mine, heavy metal pollution of soil, health risks

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Erika Ruíz, Luis Amaya, Diego Carreño

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The main objective of this study is to design a mathematical model for the logistics of mining collection centers in the northern region of the department of Boyacá (Colombia), determining the structure that facilitates the flow of products along the supply chain. In order to achieve this, it is necessary to define a suitable design of the distribution network, taking into account the products, customer’s characteristics and the availability of information. Likewise, some other aspects must be defined, such as number and capacity of collection centers to establish, routes that must be taken to deliver products to the customers, among others. This research will use one of the operation research problems, which is used in the design of distribution networks known as Location Routing Problem (LRP).

Keywords: location routing problem, logistic, mining collection, model

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Nidhin Dani Abraham, T. K. Sri Shilpa

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Asset liability management is an important aspect in banking business. Moreover, the today’s banking is based on BASEL III which strictly regulates on the counterparty default. This paper focuses on prediction and analysis of counter party default risk, which is a type of risk occurs when the customers fail to repay the amount back to the lender (bank or any financial institutions). This paper proposes an approach to reduce the counterparty risk occurring in the financial institutions using an appropriate data mining technique and thus predicts the occurrence of NPA. It also helps in asset building and restructuring quality. Liability management is very important to carry out banking business. To know and analyze the depth of liability of bank, a suitable technique is required. For that a data mining technique is being used to predict the dormant behaviour of various deposit bank customers. Various models are implemented and the results are analyzed of saving bank deposit customers. All these data are cleaned using data cleansing approach from the bank data warehouse.

Keywords: data mining, asset liability management, BASEL III, banking

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Yusra Abdulsalam Alqamati, Ahmed Alkilany

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The teaching staff is a distinct group whose impact is on the educational process and which plays an important role in enhancing the quality of the academic education process. To improve the management effectiveness of the academy, the Teaching Staff Management System (TSMS) proposes that all teacher processes be digitized. Since the BPMN approach can accurately describe the processes, it lacks a clear picture of the process flow map, something that the process mining approach has, which is extracting information from event logs for discovery, monitoring, and model enhancement. Therefore, these two methodologies were combined to create the most accurate representation of system operations, the ability to extract data records and mining processes, recreate them in the form of a Petri net, and then generate them in a BPMN model for a more in-depth view of process flow. Additionally, the TSMS processes will be orchestrated to handle all requests in a guaranteed small-time manner thanks to the integration of the Google Cloud Platform (GCP), the BPM engine, and allowing business owners to take part throughout the entire TSMS project development lifecycle.

Keywords: process mining, BPM, business process model and notation, Petri net, teaching staff, Google Cloud Platform

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Authors: Zunaira Asif, Zhi Chen

Abstract:

The sustainable measures on air quality management are recognized as one of the most serious environmental concerns in the mining region. The mining operations emit various types of pollutants which have significant impacts on the environment. This study presents a stochastic control strategy by developing the air pollution control model to achieve a cost-effective solution. The optimization method is formulated to predict the cost of treatment using linear programming with an objective function and multi-constraints. The constraints mainly focus on two factors which are: production of metal should not exceed the available resources, and air quality should meet the standard criteria of the pollutant. The applicability of this model is explored through a case study of an open pit metal mine, Utah, USA. This method simultaneously uses meteorological data as a dispersion transfer function to support the practical local conditions. The probabilistic analysis and the uncertainties in the meteorological conditions are accomplished by Monte Carlo simulation. Reasonable results have been obtained to select the optimized treatment technology for PM_2.5, PM₁₀, NO_x, and SO₂. Additional comparison analysis shows that baghouse is the least cost option as compared to electrostatic precipitator and wet scrubbers for particulate matter, whereas non-selective catalytical reduction and dry-flue gas desulfurization are suitable for NO_x and SO₂ reduction respectively. Thus, this model can aid planners to reduce these pollutants at a marginal cost by suggesting control pollution devices, while accounting for dynamic meteorological conditions and mining activities.

Keywords: air pollution, linear programming, mining, optimization, treatment technologies

Procedia PDF Downloads 170

Authors: Jaishree Ranganathan, MuthuPriya Shanmugakani Velsamy, Shamika Kulkarni, Angelina Tzacheva

Abstract:

Emotions play an important role in everyday life. An-alyzing these emotions or feelings from social media platforms like Twitter, Facebook, blogs, and forums based on user comments and reviews plays an important role in various factors. Some of them include brand monitoring, marketing strategies, reputation, and competitor analysis. The opinions or sentiments mined from such data helps understand the current state of the user. It does not directly provide intuitive insights on what actions to be taken to benefit the end user or business. Actionable Pattern Mining method provides suggestions or actionable recommendations on what changes or actions need to be taken in order to benefit the end user. In this paper, we propose automatic classification of emotions in Twitter data using Recurrent Neural Network - Gated Recurrent Unit. We achieve training accuracy of 87.58% and validation accuracy of 86.16%. Also, we extract action rules with respect to the user emotion that helps to provide actionable suggestion.

Keywords: emotion mining, twitter, recurrent neural network, gated recurrent unit, actionable pattern mining

Procedia PDF Downloads 133