Search results for: mining activities

Authors: Victor Radich, Tania Basso, Regina Moraes

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Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis or opinion mining can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results obtained so far have shown that it is possible to extract data from social networks and combine the techniques for a more complete classification.

Keywords: lead qualification, sentiment analysis, opinion mining, machine learning, CRM, lead scoring

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: social media, text mining, knowledge discovery, predictive analysis, machine learning

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: Katsue Edo, Yu Hiroi

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T Increasing attention has been paid to using pictures and photographs in research since the beginning of the 21th century in social sciences. Meanwhile we have been studying the usefulness of Picture mining, which is one of the new ways for a these picture using researches. Picture Mining is an explorative research analysis method that takes useful information from pictures, photographs and static or moving images. It is often compared with the methods of text mining. The Picture Mining concept includes observational research in the broad sense, because it also aims to analyze moving images (Ochihara and Edo 2013). In the recent literature, studies and reports using pictures are increasing due to the environmental changes. These are identified as technological and social changes (Edo et.al. 2013). Low price digital cameras and i-phones, high information transmission speed, low costs for information transferring and high performance and resolution of the cameras of mobile phones have changed the photographing behavior of people. Consequently, there is less resistance in taking and processing photographs for most of the people in the developing countries. In these studies, this method of collecting data from respondents is often called as ‘participant-generated photography’ or ‘respondent-generated visual imagery’, which focuses on the collection of data and its analysis (Pauwels 2011, Snyder 2012). But there are few systematical and conceptual studies that supports it significance of these methods. We have discussed in the recent years to conceptualize these picture using research methods and formalize theoretical findings (Edo et. al. 2014). We have identified the most efficient fields of Picture mining in the following areas inductively and in case studies; 1) Research in Consumer and Customer Lifestyles. 2) New Product Development. 3) Research in Fashion and Design. Though we have found that it will be useful in these fields and areas, we must verify these assumptions. In this study we will focus on the field of fashion and design, to determine whether picture mining methods are really reliable in this area. In order to do so we have conducted an empirical research of the respondents’ attitudes and behavior concerning pictures and photographs. We compared the attitudes and behavior of pictures toward fashion to meals, and found out that taking pictures of fashion is not as easy as taking meals and food. Respondents do not often take pictures of fashion and upload their pictures online, such as Facebook and Instagram, compared to meals and food because of the difficulty of taking them. We concluded that we should be more careful in analyzing pictures in the fashion area for there still might be some kind of bias existing even if the environment of pictures have drastically changed in these years.

Keywords: empirical research, fashion and design, Picture Mining, qualitative research

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Authors: Adesuwa Omorede, Rufai Haruna Kilu

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Affective motivation (motivation that is emotionally laden usually related to affect, passion, emotions, moods) in the workplace stimulates individuals to reinforce, persist and commit to their task, which leads to the individual and organizational performance. This leads individuals to reach goals especially in situations where task are highly challenging and hostile. In such situations, individuals are more disposed to be more creative, innovative and see new opportunities from the loopholes in their workplace. However, when individuals feel displaced and less important, an adverse reaction may suffice which may be detrimental to the organization and its performance. One sector where affective motivation is eminently present and relevant, is the mining industry. Due to its intense work environment; mostly dominated by men and masculinity cultures; and deliberate exclusion of women in this environment which, makes the women working in these environments to feel marginalized. In Ghana, the mining industry is mostly seen as a very physical environment especially underground and mostly considerd as 'no place for a woman'. Despite the fact that these women feel less 'needed' or 'appreciated' in such environments, they still have to juggle between intense work shifts; face violence and other health risks with their families, which put a strain on their affective motivational reaction. Beyond these challenges, however, several mining companies in Ghana today are working towards providing a fair and equal working situation for both men and women miners, by recognizing them as key stakeholders, as well as including them in the stages of mining projects from the planning and designing phase to the evaluation and implementation stage. Drawing from the psychology and gender literature, this study takes a narrative approach to identify and understand the shifting gender dynamics within the mine works in Ghana, occasioning a change in background disposition of miners, which leads to more women taking up mine jobs in the country. In doing so, a qualitative study was conducted using semi-structured interviews from Ghana. Several women working within the mining industries in Ghana shared their experiences and how they felt and still feel in their workplace. In addition, archival documents were gathered to support the findings. The results suggest a change in enrolment regimes in a mining and technology university in Ghana, making room for a more gender equal enrolments in the university. A renowned university that train and feed mine work professional into the industry. The results further acknowledge gender equal and diversity recruitment policies and initiatives among the mining companies of Ghana. This study contributes to the psychology and gender literature by highlighting the hindrances women face in the mining industry as well as highlighting several of their affective reactions towards gender inequality. The study also provides several suggestions for decision makers in the mining industry of what can be done in the future to reduce the gender inequality gap within the industry.

Keywords: affective motivation, gender shape shifting, mining industry, women miners

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Authors: Gholamhassan Kakha, Mohsen Jami, Daniel Alex Merino Natorce

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Sustainable development refers to the creation of a balance between the development and the environment too; it consists of three key principles namely environment, society and economy. These three parameters are related to each other and the imbalance occurs in each will lead to the disparity of the other parts. Mining is one of the most important tools of the economic growth and social welfare in many countries. Meanwhile, assessment of the environmental impacts has directed to the attention of planners toward the natural environment of the areas surrounded by mines and allowing for monitoring and controlling of the current situation by the designers. In this look upon, a semi-quantitative model using a matrix method is presented for assessing the environmental impacts in the BOOG Granite Mine located in Sistan and Balouchestan, one of the provinces of Iran for determining the effective factors and environmental components. For accomplishing this purpose, the initial data are collected by the experts at the next stage; the effect of the factors affects each environmental component is determined by specifying the qualitative viewpoints. Based on the results, factors including air quality, ecology, human health and safety along with the environmental damages resulted from mining activities in that area. Finally, the results gained from the assessment of the environmental impact are used to evaluate the sustainability by using Philips mathematical model. The results show that the sustainability of this area is weak, so environmental preventive measures are recommended to reduce the environmental damages to its components.

Keywords: sustainable development, environmental impacts' assessment, BOOG granite, Philips mathematical model

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Authors: Ayuko Itsuki, Sachiyo Aburatani

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Soil enzyme activities in Kasuga-yama Hill Primeval Forest (Nara, Japan) were examined to determine levels of mineralization and metabolism. Samples were selected from the soil surrounding laurel-leaved (B_B-1) and Carpinus japonica (B_B-2 and P_w) trees for analysis. Cellulase, β-xylosidase, and protease activities were higher in B_B-1 samples those in B_B-2 samples. These activity levels corresponded to the distribution of cellulose and hemicellulose in the soil horizons. Cellulase, β-xylosidase, and chymotrypsin activities were higher in soil from the P_w forest than in that from the B_B-2 forest. The relationships between the soil enzymes calculated by Spearman’s rank correlation indicate that the interactions between enzymes in B_B-2 samples were more complex than those in P_w samples.

Keywords: comparative analysis, enzyme activities, forest soil, Spearman's rank correlation

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung

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In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.

Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining

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Authors: Jureerat Kilsomporn, Chularat Howharn

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The aim of this qualitative study was to describe the meaning of leisure and the role of leisure among elderly people aged more than 80. Participants were purposive selected. Inclusion criteria were age more than 80 year, has no disease, and independent for daily life activities. Data were collected by in-depth interview. Each participant was interviewed two times and 45-60 minutes in each time of interview. Content analyses were conducted to describe the findings. The findings were as followed. Ten active aging participated in this study. All of them are Buddhist with age between 83-89 years old. Most of them completed the primary school. All of them were active members of community club such as elderly club, women club, and funeral association. Active aging described leisure as activities that connected them with their communities. Leisure could be categorized into three groups which were religious activities, entertainment activities, and hobbies. Examples of religious activities were ordination, house-blessing ceremony, and wedding ceremony. Examples of entertainment activities were Thai dance, beat a drum, and sang local songs. Participants described more that they joined and did in leisure for a sake of pleasure. These leisure played significant roles; significantly affect were their minds and their spiritual. Religious activities play a significant role in spiritual well-being while entertainment activities play a significant role in maintaining good status of mental health. Effect of hobby activities which most of these were payable activities were increased their self-valued. Although it was not high incomes but they can use their own money as they designed. Moreover, participants describe that they have a feeling of use their time wisely since they can earn the money, they can have an exercise, and they can share the time with their neighborhood with these activities. Suggestions from this study were that authorized personnel should promote leisure for aging regularly and continuously. Any facilities that meet with their need and their desired should be promoted since these can facilitate aging to maintain their good health status.

Keywords: active aging, activities, elderly, leisure

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

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Authors: Rofem Inyang Bassey, Pfarelo Matshidze, Dolphin Mabale, Ishmael Iwara

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This article strove to establish the factors responsible for the activities of the transitional rites of passage on youth’s morality. South Africa, among other countries, has serious effects on the moral systems of youths. Factors responsible for the activities of the transitional rites of passage on youth’s morality has been brought to the fore by on-going transitional rites depicted by weekend’s social activities with the practice of substance use and abuse among the youths at various recreational spots. The transitional rites are structured under the guise of “freaks” as an evolving culture among the youths. The freaks culture is a counterculture of the usual initiation’s schools for transitional rites of passage which instil morality among youths. Qualitative design was used to obtain data from custodians, parents of the youths, and the youths themselves. The findings widely show that the factors responsible for the activities of the transitional rites is the act of the current on-going transitional rites effect as an inappropriate behaviour towards youth morality. This study concluded that to establish the factors responsible for the activities of the transitional rites of passage on youth’s morality, activities and practices evolved as a current socialization standard for quick maturity status, as a result of that, it will be harder to provide a complete change of this evolving culture. However, what will work is to build on the existing transitional rites of passage to moderate appropriate youths’ morality in Thulamela communities.

Keywords: behaviour, morality, transitional rites, youths, socials activities, freak culture

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Authors: Zhang Yuqi

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Service facilities are the boosters for the cultivation and development of innovative functions in innovative cluster areas. At the same time, reasonable service facilities planning can better link the internal functional blocks. This paper takes Zhejiang University Zijin Co-Maker Town as the research object, based on the combination of network data mining and field research and verification, combined with the needs of its internal innovative groups. It studies the distribution characteristics and existing problems of service facilities and then proposes a targeted planning suggestion. The main conclusions are as follows: (1) From the perspective of view, the town is rich in general life-supporting services, but lacking of provision targeted and distinctive service facilities for innovative groups; (2) From the perspective of scale structure, small-scale street shops are the main business form, lack of large-scale service center; (3) From the perspective of spatial structure, service facilities layout of each functional block is too fragile to fit the characteristics of 2aggregation- distribution' of innovation and entrepreneurial activities; (4) The goal of optimizing service facilities planning should be guided for fostering function of innovation and entrepreneurship and meet the actual needs of the innovation and entrepreneurial groups.

Keywords: the cultivation of innovative function, Zhejiang University Zijin Co-Maker Town, service facilities, network data mining, space optimization advice

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Authors: Isaura Esther Solano Núñez, David Suarez

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The main purpose of this research is to discover what causes death in children of the Wayuu community, and deeply analyze those results in order to take corrective measures to properly control infant mortality. We consider important to determine the reasons that are producing early death in this specific type of population, since they are the most vulnerable to high risk environmental conditions. In this way, the government, through competent authorities, may develop prevention policies and the right measures to avoid an increase of this tragic fact. The methodology used to develop this investigation is data mining, which consists in gaining and examining large amounts of data to produce new and valuable information. Through this technique it has been possible to determine that the child population is dying mostly from malnutrition. In short, this technique has been very useful to develop this study; it has allowed us to transform large amounts of information into a conclusive and important statement, which has made it easier to take appropriate steps to resolve a particular situation.

Keywords: malnutrition, data mining, analytical, descriptive, population, Wayuu, indigenous

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Authors: Alissa Jane S. Mondejar, Florifern C. Paglinawan, Nanette Hope N. Sumaya, Joey Genevieve T. Martinez, Mylah Villacorte-Tabelin

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Mining is associated with increased heavy metals in the environment, and heavy metal contamination disrupts the activities of soil fauna, such as nematodes, causing changes in the function of the soil ecosystem. Previous studies found that nematode community composition and diversity indices were strongly affected by heavy metals (e.g., Pb, Cu, and Zn). In this study, the influence of heavy metals on nematode survivability and reproduction were investigated. Life history analysis of the free-living nematodes, Heterocephalobellus sp. and Cephalobus sp. (Rhabditida: Cephalobidae) were assessed using the hanging drop technique, a technique often used in life history trait experiments. The nematodes were exposed to different temperatures, i.e.,20°C, 25°C, and 30°C, in different groups (control and heavy metal exposed) and fed with the same bacterial density of 1×109 Escherichia coli cells ml-1 for 30 days. Results showed that increasing temperature and exposure to heavy metals had a significant influence on the survivability and egg production of both species. Heterocephalobellus sp. and Cephalobus sp., when exposed to 20°C survived longer and produced few numbers of eggs but without subsequent hatching. Life history parameters of Heterocephalobellus sp. showed that the value of parameters was higher in the control group under net production rate (R0), fecundity (mx) which is also the same value for the total fertility rate (TFR), generation times (G0, G₁, and Gh) and Population doubling time (PDT). However, a lower rate of natural increase (rm) was observed since generation times were higher. Meanwhile, the life history parameters of Cephalobus sp. showed that the value of net production rate (R0) was higher in the exposed group. Fecundity (mx) which is also the same value for the TFR, G0, G1, Gh, and PDT, were higher in the control group. However, a lower rate of natural increase (rm) was observed since generation times were higher. In conclusion, temperature and exposure to heavy metals had a negative influence on the life history of the nematodes, however, further experiments should be considered.

Keywords: artisanal and small-scale gold mining (ASGM), hanging drop method, heavy metals, life history trait.

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Authors: Jae Min Lee

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Presence of people and intensity of activities have been widely accepted as an indicator for successful public spaces in urban design literature. This study attempts to predict the qualitative indicators, presence of people and intensity of activities, with the quantitative measurements of pedestrian counting. We conducted participant observation in Dilworth Park, Philadelphia to collect the total number of people and activities in the park. Then, the participant observation data is compared with detailed pedestrian counts at 10 exit locations to estimate the number of park users. The study found that there is a clear correlation between the intensity of social activities and automated pedestrian counts.

Keywords: automated pedestrian count, computer vision, public space, urban design

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Authors: Ilona Mewes, Helena Jenzer, Farshideh Einsele

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Objective: The objective of the study was to integrate two big databases from Swiss nutrition national survey (menuCH) and Swiss health national survey 2012 for data mining purposes. Each database has a demographic base data. An integrated Swiss database is built to later discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Swiss nutrition national survey (menuCH) with approx. 2000 respondents from two different surveys, one by Phone and the other by questionnaire along with Swiss health national survey 2012 with 21500 respondents were pre-processed, cleaned and finally integrated to a unique relational database. Results: The result of this study is an integrated relational database from the Swiss nutritional and health databases.

Keywords: health informatics, data mining, nutritional and health databases, nutritional and chronical databases

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Authors: Rusudan Chagelishvili, Nino Sulava, Tamar Beridze, Nana Rezesidze, Nikoloz Tatuashvili

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The South Caucasus is one of the most important centers of prehistoric metallurgy, known for its Colchian bronze culture. Modern Lechkhumi – historical Mountainous Colchis where the existence of prehistoric metallurgy is confirmed by the discovery of many artifacts is a part of this area. Studies focused on prehistoric smelting sites, related artefacts, and ore deposits have been conducted during last ten years in Lechkhumi. More than 20 prehistoric smelting sites and artefacts associated with metallurgical activities (ore roasting furnaces, slags, crucible, and tuyères fragments) have been identified so far. Within the framework of integrated studies was established that these sites were operating in 13-9 centuries B.C. and used for copper smelting. Palynological studies of slags revealed that chestnut (Castanea sativa) and hornbeam (Carpinus sp.) wood were used as smelting fuel. Geological exploration-analytical studies revealed that copper ore mining, processing, and smelting sites were distributed close to each other. Despite recent complex data, the signs of prehistoric mines (trenches) haven’t been found in this part of the study area so far. Since 2018 the archaeological-geological exploration has been focused on the southern part of Lechkhumi and covered the areas of villages Okureshi and Opitara. Several copper smelting sites (Okureshi 1 and 2, Opitara 1), as well as a Colchian Bronze culture settlement, have been identified here. Three mine workings have been found in the narrow gorge of the river Rtkhmelebisgele in the vicinities of the village Opitara. In order to establish a link between the Opitara-Okureshi archaeometallurgical sites, Late Bronze Age settlements, and mines, various scientific analytical methods -mineralized rock and slags petrography and atomic absorption spectrophotometry (AAS) analysis have been applied. The careful examination of Opitara mine workings revealed that there is a striking difference between the mine #1 on the right bank of the river and mines #2 and #3 on the left bank. The first one has all characteristic features of the Soviet period mine working (e. g. high portal with angular ribs and roof showing signs of blasting). In contrast, mines #2 and #3, which are located very close to each other, have round-shaped portals/entrances, low roofs, and fairly smooth ribs and are filled with thick layers of river sediments and collapsed weathered rock mass. A thorough review of the publications related to prehistoric mine workings revealed some striking similarities between mines #2 and #3 with their worldwide analogues. Apparently, the ore extraction from these mines was conducted by fire-setting applying primitive tools. It was also established that mines are cut in Jurassic mineralized volcanic rocks. Ore minerals (chalcopyrite, pyrite, galena) are related to calcite and quartz veins. The results obtained through the petrochemical and petrography studies of mineralized rock samples from Opitara mines and prehistoric slags are in complete correlation with each other, establishing the direct link between copper mining and smelting within the study area. Acknowledgment: This work was supported by the Shota Rustaveli National Science Foundation of Georgia (grant # FR-19-13022).

Keywords: archaeometallurgy, Mountainous Colchis, mining, ore minerals

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Authors: Ahmad Afif, Teuku Yuri M. Zagloel

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Sustainable supply chain management is a new pattern that has emerged recently in industry and companies. The procurement process is one of the key factors for efficiency in supply chain management practices. Partnership is one of the procurement strategies for strategic items. The success factors of the partnership must be determined to avoid things that endanger the financial and operational status of the company. The current supplier partnership research focuses on the selection of general criteria and sustainable supplier selection. Currently, there is still limited research on the success factors of supplier partnerships that focus on strategic items in the coal mining industry. Meanwhile, the procurement of coal mining has its own characteristics, and there are regulations related to the procurement of goods. Therefore, this research was conducted to determine the categories of goods that are included in the strategic items and to design the success factors of supplier partnerships. The main factors studied are general, financial, production, reputation, synergies, and sustainable. The research was conducted using the Kraljic method to determine the categories of goods that are included in the strategic items. To design a supplier partnership success factor using the Hybrid Multi Criteria Decision Making method. Integrated Fuzzy AHP-Fuzzy TOPSIS is used to determine the weight of the success factors of supplier partnerships and to rank suppliers on the factors used.

Keywords: supplier, partnership, strategic item, success factors, and coal mining industry

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Authors: Lindie Grebe

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This paper reports on a study that explored the strategizing practices of professional accountants in the mining industry, based on Jarratt and Stiles’ dominant strategizing practice models framework. Drawing on a strategy-as-practice perspective, the paper recognises qualified professional accountants in strategic management such as Chief Executive Officers, as strategy practitioners that perform their strategizing practices and praxis within a specific context. The main findings of this paper were produced through semi-structured individual interviews with accountants that perform strategy on a business level in the South African mining industry. Qualitative data were analysed through conversation analysis over two coding-cycles. Findings describe accountant strategists as practitioners who challenge the dominant business model when a disconnect seems to exist between international corporate level strategy and business level strategy in the South African mining industry. Accountant strategy practitioners described their dominant strategizing practice model as incremental change during strategic planning and as a lived experience during strategy implementation. Findings portrayed these strategists as taking initiative as strategy leaders in a dynamic and volatile environment to combine their accounting background with strategic management and challenge the dominant business model. Understanding how accountant strategists perform strategizing offers insight into the social practice of strategic management. This understanding contributes to the body of knowledge on strategizing in the South African mining industry. In addition, knowledge on the transformation of accountants as strategists could provide valuable practice relevant insights for accounting educators and the accounting profession alike.

Keywords: accountant strategists, dominant strategizing practice models framework, mining industry, strategy-as-practice

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Authors: Doug Specht

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This research project sought to explore the links between digital media, PGIS and social movement organisations in Tolima, Colombia. The primary aim of the research was to examine how knowledge is created and disseminated through digital media and GIS in the region, and whether there exists the infrastructure to allow for this. The second strand was to ascertain if this has had a significant impact on the way grassroots movements work and produce collective actions. The third element is a hypothesis about how digital media and PGIS could play a larger role in activist activities, particularly in reference to the extractive industries. Three theoretical strands have been brought together to provide a basis for this research, namely (a) the politics of knowledge, (b) spatial management and inclusion, and (c) digital media and political engagement. Quantitative data relating to digital media and mobile internet use was collated alongside qualitative data relating to the likelihood of using digital media in activist campaigns, with particular attention being given to grassroots movements working against extractive industries in the Tolima region of Colombia. Through interviews, surveys and GIS analysis it has been possible to build a picture of online activism and the role of PPGIS within protest movement in the region of Tolima, Colombia. Results show a gap between the desires of social movements to use digital media and the skills and finances required to implement programs that utilise it. Maps and GIS are generally reserved for legal cases rather than for informing the lay person. However, it became apparent that the combination of digital/social media and PPGIS could play a significant role in supporting the work of grassroots movements.

Keywords: PGIS, GIS, social media, digital media, mining, colombia, social movements, protest

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Authors: Ntloana Hlabishi Peter

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The availability of public transportation modes and qualitative infrastructure is a burning issue that affects urban sustainability. Public transportation is indispensable in providing adequate transportation means to people at an affordable price, and it promotes public transport reliance. Burgersfort town has a critical condition on the urban public transportation infrastructure which affects the bus and taxi public transport modes and the existing infrastructure. The municipality is regarded as one of the mining towns in Limpopo Province considering the availability of mining activities and proposal on establishment of a Special Economic Zone (SEZ). The study aim is to assess the efficacy of current public transportation infrastructure and to propose relevant recommendations that will unlock the possibility of future supportable public transportation systems. The Key Informant Interview (KII) was used to acquire data on the views from commuters and stakeholders involved. There KII incorporated three relevant questions in relation to services rendered in public transportation. Relevant literature relating to public transportation modes and infrastructure revealed the imperatives of public transportation infrastructure, and relevant legislation was reviewed concerning public transport infrastructure. The finding revealed poor conditions on the public transportation ranks and also inadequate parking space for public transportation modes. The study reveals that 100% of people interviewed were not satisfied with the condition of public transportation infrastructure and 100% are not satisfied with the services offered by public transportation sectors. The findings revealed that the municipality is the main player who can upgrade the existing conditions of public transportation. The study recommended that an intermodal transportation facility must be established to resolve the emerging challenges.

Keywords: public transportation, modes, infrastructure, urban sustainability

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Authors: Ben Salah Nahla

Abstract:

The problem of water in Tunisia affects the quality and quantity. Tunisia is in a situation of water shortage. It was estimated that 4.6 Mm3/an. Moreover, the quality of water in Tunisia is also mediocre. In fact, 50% of the water has a high salinity (> 1.5g/l). There are several parameters which affect water quality such as sodium, fluoride. An excess of this parameter may induce some human health. Furthermore, the mining basin area has a problem of industrial waste. This problem may affect the water quality of the groundwater. Therefore, the purpose of this work is to assess the water quality in Basin Mining and the impact of fluorine. For this research, some water samples were done in the field and specific water analysis was implemented in the laboratory. Sampling is carried out on eight drilling in the area of the mining region. In the following, we will look at water view composition, physical and chemical quality. A physical-chemical analysis of water from a survey of the Mining area of Tunisia was performed and showed an excess for the following items: fluorine, sodium, sulfate. So many chemicals may be present in water. However, only a small number of them immediately concern in terms of health in all circumstances. Fluorine (F) is one particular chemical that is considered both necessary for the human body, but an excess of the rate of this chemical causes serious diseases. Sodium fluoride and sodium silicofluoride are more soluble and may spread in animals and plants where their toxicity largest organizations. The more complex particles such as cryolite and fluorite, almost insoluble, are more stable and less toxic. Thereafter, we will study the problem of excess fluorine in the water. The latter intended for human consumption must always comply with the limits for microbiological quality parameters and physical-chemical parameters defined by European standards (1.5 mg/l) and Tunisian (2 mg/l).

Keywords: water, minier basin, fluorine, silicofluoride

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Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

Abstract:

De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

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Authors: Andrii Shalaginov, Katrin Franke, Xiongwei Huang

Abstract:

One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities.

Keywords: malware detection, network security, targeted attack, computational intelligence

Procedia PDF Downloads 233

Authors: Valentina Marutyan

Abstract:

Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

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Authors: Galo D. Soria, Jenny S. Casame, Eddy F. Pazmino

Abstract:

In Ecuador, mining operations have significantly impacted water sources. Artisanal mining extensively relies in mercury amalgamation. Mercury is a neurotoxic substance even at low concentrations. The objective of this investigation is to exploit Hg-removal capacity of sulfur-limonene polysulfide (SLP), which is a low-cost polymer, in order to prepare granular media (sand) coated with SLP to be used in laboratory scale column-filtration systems. Preliminary results achieved 85% removal of Hg⁺⁺ from synthetic effluents using 20-cm length and 5-cm diameter columns at 119m/day average pore water velocity. During elution of the column, the SLP-coated sand indicated that Hg⁺⁺ is permanently fixed to the collector surface, in contrast, uncoated sand showed reversible retention in Hg⁺⁺ in the solid phase. Injection of 50 pore volumes decreased Hg⁺⁺ removal to 46%. Ongoing work has been focused in optimizing the synthesis of SLP and the polymer content in the porous media coating process to improve Hg⁺⁺ removal and extend the lifetime of the column-filter.

Keywords: column-filter, mercury, mining, polysulfide, water treatment

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Authors: Carla Silva

Abstract:

Recent development of information and communication technology enables us to acquire, collect, analyse data in various fields of socioeconomic – technological systems. Along with the increase of economic globalization and the evolution of information technology, data mining has become an important approach for economic data analysis. As a result, there has been a critical need for automated approaches to effective and efficient usage of massive amount of educational data, in order to support institutions to a strategic planning and investment decision-making. In this article, we will address data from several different perspectives and define the applied data to sciences. Many believe that 'big data' will transform business, government, and other aspects of the economy. We discuss how new data may impact educational policy and educational research. Large scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe educational activity and educational impact. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in educational and furthermore in economics. Finally, we highlight a number of challenges and opportunities for future research.

Keywords: data mining, research analysis, investment decision-making, educational research

Procedia PDF Downloads 331