Search results for: mining methods

Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

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Authors: Abdelaziz Fellah, Allaoua Maamir

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We propose a domain-independent merging-cluster filter approach complemented with a set of algorithms for identifying approximate duplicate entities efficiently and accurately within a single and across multiple data sources. The near-optimal merging-cluster filter (MCF) approach is based on the Monge-Elkan well-tuned algorithm and extended with an affine variant of the Smith-Waterman similarity measure. Then we present constant, variable, and function threshold algorithms that work conceptually in a divide-merge filtering fashion for detecting near duplicates as hierarchical clusters along with their corresponding representatives. The algorithms take recursive refinement approaches in the spirit of filtering, merging, and updating, cluster representatives to detect approximate duplicates at each level of the cluster tree. Experiments show a high effectiveness and accuracy of the MCF approach in detecting approximate duplicates by outperforming the seminal Monge-Elkan’s algorithm on several real-world benchmarks and generated datasets.

Keywords: data mining, data cleaning, approximate duplicates, near-duplicates detection, data mining applications and discovery

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Authors: S. H. Liao, J. M. Huang

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The delivery service business model is the final link in logistics for both online-and-offline businesses. The online-and-offline business model focuses on the entire customer purchasing process online and offline, placing greater emphasis on the importance of data to optimize overall retail operations. For the retail industry, it is an important task of information and management to strengthen the collection and investigation of consumers' online and offline purchasing data to better understand customers and then recommend products. This study implements two-stage data mining analytics for clustering and association rules analysis to investigate Taiwanese consumers' (n=2,209) preferences for delivery service. This process clarifies online-and-offline purchasing behaviors and preferences to find knowledge profiles/patterns/rules for cross-channel collaborative recommendations. Finally, theoretical and practical implications for methodology and enterprise are presented.

Keywords: delivery service, online-and-offline purchasing, retail cross-channel, collaborative recommendations, data mining analytics

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Authors: Francis Alterado, Jennifer Enriquez

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Semantic Based Analysis in Complaint Management System with Analytics is an enhanced tool of providing complaints by the clients as well as a mechanism for Palawan Polytechnic College to gather, process, and monitor status of these complaints. The study has a mobile application that serves as a remote facility of communication between the students and the school management on the issues encountered by the student and the solution of every complaint received. In processing the complaints, text mining and clustering algorithms were utilized. Every module of the systems was tested and based on the results; these are 100% free from error before integration was done. A system testing was also done by checking the expected functionality of the system which was 100% functional. The system was tested by 10 students by forwarding complaints to 10 departments. Based on results, the students were able to submit complaints, the system was able to process accordingly by identifying to which department the complaints are intended, and the concerned department was able to give feedback on the complaint received to the student. With this, the system gained 4.7 rating which means Excellent.

Keywords: technology adoption, emerging technology, issues challenges, algorithm, text mining, mobile technology

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Authors: M. Inmaculada Alvarez-Fernández, Celestino González-Nicieza, M. Belén Prendes-Gero, Fernando López-Gayarre

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Among many factors affecting the stability of mining excavations, rock-bursts and tremors play a special role. These dynamic loads occur practically always and have different sources of generation. The most important of them is the commonly used mining technique, which disintegrates a certain area of the rock mass not only in the area of the planned mining, but also creates waves that significantly exceed this area affecting the structural elements. In this work it is analysed the consequences of dynamic loads over the structural elements in an underground room and pillar mine to avoid roof instabilities. With this end, dynamic loads were evaluated through in situ and laboratory tests and simulated with numerical modelling. Initially, the geotechnical characterization of all materials was carried out by mean of large-scale tests. Then, drill holes were done on the roof of the mine and were monitored to determine possible discontinuities in it. Three seismic stations and a triaxial accelerometer were employed to measure the vibrations from blasting tests, establish the dynamic behaviour of roof and pillars and develop the transmission laws. At last, computer simulations by FLAC3D software were done to check the effect of vibrations on the stability of the roofs. The study shows that in-situ tests have a greater reliability than laboratory samples because of eliminating the effect of heterogeneities, that the pillars work decreasing the amplitude of the vibration around them, and that the tensile strength of a beam and depending on its span is overcome with waves in phase and delayed. The obtained transmission law allows designing a blasting which guarantees safety and prevents the risk of future failures.

Keywords: dynamic modelling, long term instability risks, room and pillar, seismic collapse

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Authors: Felicite Ann Fairer-Wessels

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There has always been a disparity between mining and tourism mainly due to the socio-economic and environmental impacts of mines on both the adjacent resident communities and the areas taken up by the mining operation. Although heritage mining tourism has been actively and successfully pursued and developed in the UK, largely Wales, and Scandinavian countries, the debate whether active mining and tourism can have a mutually beneficial relationship remains imminent. This pilot study explores the relationship between the ‘to be developed’ future Nokeng Mine and its adjacent community, the rural community of Moloto, will be investigated in terms of whether sustainable tourism and livelihood activities can potentially be developed with the support of the mine. Concepts such as social entrepreneur, corporate social responsibility, sustainable development and triple bottom line are discussed. Within the South African context as a mineral rich developing country, the government has a statutory obligation to empower disenfranchised communities through social and labour plans and policies. All South African mines must preside over a Social and Labour Plan according to the Mineral and Petroleum Resources Development Act, No 28 of 2002. The ‘social’ component refers to the ‘social upliftment’ of communities within or adjacent to any mine; whereas the ‘labour’ component refers to the mine workers sourced from the specific community. A qualitative methodology is followed using the case study as research instrument for the Nokeng Mine and Moloto community with interviews and focus group discussions. The target population comprised of the Moloto Tribal Council members (8 in-depth interviews), the Moloto community members (17: focus groups); and the Nokeng Mine representatives (4 in-depth interviews). In this pilot study two disparate ‘worlds’ are potentially linked: on the one hand, the mine as social entrepreneur that is searching for feasible and sustainable ideas; and on the other hand, the community adjacent to the mine, with potentially sustainable tourism entrepreneurs that can tap into the resources of the mine should their ideas be feasible to build their businesses. Being an exploratory study the findings are limited but indicate that the possible success of tourism and sustainable livelihood activities lies in the fact that both the Mine and Community are keen to work together – the mine in terms of obtaining labour and profit; and the community in terms of improved and sustainable social and economic conditions; with both parties realizing the importance to mitigate negative environmental impacts. In conclusion, a relationship of trust is imperative between a mine and a community before a long term liaison is possible. However whether tourism is a viable solution for the community to engage in is debatable. The community could initially rather pursue the sustainable livelihoods approach and focus on life-supporting activities such as building, gardening, etc. that once established could feed into possible sustainable tourism activities.

Keywords: community development, mining tourism, sustainability, South Africa

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Authors: Abraham Addo-Bediako

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Many freshwater ecosystems are facing immense pressure from anthropogenic activities, such as agricultural, industrial and mining. Trace metal pollution in freshwater ecosystems has become an issue of public health concern due to its toxicity and persistence in the environment. Trace elements pose a serious risk not only to the environment and aquatic biota but also humans. Chromium is one of such trace elements and its pollution in surface waters and groundwaters represents a serious environmental problem. In South Africa, agriculture, mining, industrial and domestic wastes are the main contributors to chromium discharge in rivers. The common forms of chromium are chromium (III) and chromium (VI). The latter is the most toxic because it can cause damage to human health. The aim of the study was to assess the contamination of chromium in the water and sediments of two rivers in the Steelpoort River sub-catchment of the Olifants River Basin, South Africa and human health risk. The concentration of Cr was analyzed using inductively coupled plasma–optical emission spectrometry (ICP-OES). The concentration of the metal was found to exceed the threshold limit, mainly in areas of high human activities. The hazard quotient through ingestion exposure did not exceed the threshold limit of 1 for adults and children and cancer risk for adults and children computed did not exceed the threshold limit of 10-4. Thus, there is no potential health risk from chromium through ingestion of drinking water for now. However, with increasing human activities, especially mining, the concentration could increase and become harmful to humans who depend on rivers for drinking water. It is recommended that proper management strategies should be taken to minimize the impact of chromium on the rivers and water from the rivers should properly be treated before domestic use.

Keywords: land use, health risk, metal pollution, water quality

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Authors: Hamed Homaei, Iraj Mahdavi, Ali Tajdin

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One of the main concerns of miners is to increase the quality level of their products because the mining metals price depends on their quality level; however, increasing the quality level of these products has different costs at different levels of the supply chain. These costs usually increase after extractor level. This paper studies the coordination issue of a decentralized three-level supply chain with one supplier (extractor), one mineral processor and one manufacturer in which the increasing product quality level cost at the processor level is higher than the supplier and at the level of the manufacturer is more than the processor. We identify the optimal product quality level for each supply chain member by designing a revenue sharing contract. Finally, numerical examples show that the designed contract not only increases the final product quality level but also provides a win-win condition for all supply chain members and increases the whole supply chain profit.

Keywords: three-stage supply chain, product quality improvement, channel coordination, revenue sharing

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Authors: Javier Sedano-Cibrián, Julio Manuel de Luis-Ruiz, Rubén Pérez-Álvarez, Raúl Pereda-García, Beatriz Malagón-Picón

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Structural inspection activities are necessary to ensure the correct functioning of infrastructures. Unmanned Aerial Vehicle (UAV) techniques have become more popular than traditional techniques. Specifically, UAV Photogrammetry allows time and cost savings. The development of this technology has permitted the use of low-cost thermal sensors in UAVs. The representation of 3D thermal models with this type of equipment is in continuous evolution. The direct processing of thermal images usually leads to errors and inaccurate results. A methodology is proposed for the generation of 3D thermal models using dual sensors, which involves the application of visible Red-Blue-Green (RGB) and thermal images in parallel. Hence, the RGB images are used as the basis for the generation of the model geometry, and the thermal images are the source of the surface temperature information that is projected onto the model. Mining/industrial facilities representations that are obtained can be used for inspection activities.

Keywords: aerial thermography, data processing, drone, low-cost, point cloud

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: O. E. Obayemi, M. A. Ayoade, O. O. Komolafe

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Fish survey was carried out for an annual cycle covering both rainy and dry seasons using cast nets, gill nets and traps at two different reservoirs. The objective was to examined the fish assemblages of the reservoirs and provide more additional information on the reservoir. The fish species in the reservoirs comprised of twelve species of six families. The results of the study also showed that five species of fish were caught in reservoir five while ten fish species were captured in reservoir six. Species such as Malapterurus electricus, Ctenopoma kingsleyae, Mormyrus rume, Parachanna obscura, Sarotherodon galilaeus, Tilapia mariae, C. guntheri, Clarias macromystax, Coptodon zilii and Clarias gariepinus were caught during the sampling period. There was a significant difference (p=0.014, t = 1.711) in the abundance of fish species in the two reservoirs. Seasonally, reservoirs five (p=0.221, t = 1.859) and six (p=0.453, t = 1.734) showed there was no significant difference in their fish populations. Also, despite being impacted with gold mining the diversity indices were high when compared to less disturbed waterbodies. The study concluded that the environments recorded low abundant fish species which suggests the influence of mining on the abundance and diversity of fish species.

Keywords: Igun, fish, Shannon-Wiener Index, Simpson index, Pielou index

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Authors: Fiona S. Motswaiso, Kengo Nakamura, Takeshi Komai

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Heavy metals may occur naturally in rocks and soils, but elevated quantities of them are being gradually released into the environment by anthropogenic activities such as mining. In order to address issues of heavy metal water and soil pollution, a distinction needs to be made between natural and anthropogenic anomalies. The current study aims at characterizing the spatial distribution of trace elements and evaluate site-specific geochemical background concentrations of trace elements in the mine soils examined, and also to discriminate between lithogenic and anthropogenic sources of enrichment around a copper-nickel mining town in Selibe-Phikwe, Botswana. A total of 20 Soil samples, 11 river sediment, and 9 river water samples were collected from an area of 625m² within the precincts of the mine and the smelter. The concentrations of metals (Cu, Ni, Pb, Zn, Cr, Ni, Mn, As, Pb, and Co) were determined by using an ICP-MS after digestion with aqua regia. Major elements were also determined using ED-XRF. Water pH and EC were measured on site and recorded while soil pH and EC were also determined in the laboratory after performing water elution tests. The highest Cu and Ni concentrations in soil are 593mg/kg and 453mg/kg respectively, which is 3 times higher than the crustal composition values and 2 times higher than the South African minimum allowable levels of heavy metals in soils. The level of copper contamination was higher than that of nickel and other contaminants. Water pH levels ranged from basic (9) to very acidic (3) in areas closer to the mine/smelter. There is high variation in heavy metal concentration, eg. Cu suggesting that some sites depict regional natural background concentrations while other depict anthropogenic sources.

Keywords: contamination, geochemical baseline, heavy metals, soils

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Authors: Mai Ghazal, Ahmed Hammad

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Cost overruns in construction projects are considered as worldwide challenges since the cost performance is one of the main measures of success along with schedule performance. To overcome this problem, studies were conducted to investigate the cost overruns' factors, also projects' historical data were analyzed to extract new and useful knowledge from it. This research is studying and analyzing the effect of some factors causing cost overruns using the historical data from completed construction projects. Then, using these factors to estimate the probability of cost overrun occurrence and predict its percentage for future projects. First, an intensive literature review was done to study all the factors that cause cost overrun in construction projects, then another review was done for previous researcher papers about mining process in dealing with cost overruns. Second, a proposed data warehouse was structured which can be used by organizations to store their future data in a well-organized way so it can be easily analyzed later. Third twelve quantitative factors which their data are frequently available at construction projects were selected to be the analyzed factors and suggested predictors for the proposed model.

Keywords: construction management, construction projects, cost overrun, cost performance, data mining, data warehousing, knowledge discovery, knowledge management

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Authors: Hamd Rezaeifar, Hamid Reza Sahriari

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Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

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Authors: J. P. Chollom, G. M. Kumleng, S. Longwap

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The search for higher order A-stable linear multi-step methods has been the interest of many numerical analysts and has been realized through either higher derivatives of the solution or by inserting additional off step points, supper future points and the likes. These methods are suitable for the solution of stiff differential equations which exhibit characteristics that place a severe restriction on the choice of step size. It becomes necessary that only methods with large regions of absolute stability remain suitable for such equations. In this paper, high order block implicit multi-step methods of the hybrid form up to order twelve have been constructed using the multi-step collocation approach by inserting one or more off step points in the multi-step method. The accuracy and stability properties of the new methods are investigated and are shown to yield A-stable methods, a property desirable of methods suitable for the solution of stiff ODE’s. The new High Order Block Implicit Multistep methods used as block integrators are tested on stiff differential systems and the results reveal that the new methods are efficient and compete favourably with the state of the art Matlab ode23 code.

Keywords: block linear multistep methods, high order, implicit, stiff differential equations

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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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: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Carlos Gonzalez, Neil Slatcher, Marcus Properzi, Kan Seah

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In many underground mining operations, shotcrete is used for permanent rock support. Shotcrete thickness is a critical measure of the success of this process. 3D Laser Mapping, in conjunction with Jetcrete, has developed a 3D laser scanning system specifically for measuring the thickness of shotcrete. The system is mounted on the shotcrete spraying machine and measures the rock faces before and after spraying. The calculated difference between the two 3D surface models is measured as the thickness of the sprayed concrete. Typical work patterns for the shotcrete process required a rapid and automatic system. The scanning takes place immediately before and after the application of the shotcrete so no convergence takes place in the interval between scans. Automatic alignment of scans without targets was implemented which allows for the possibility of movement of the spraying machine between scans. Case studies are presented where accuracy tests are undertaken and automatic audit reports are calculated. The use of 3D imaging data for the calculation of shotcrete thickness is an important tool for geotechnical engineers and contract managers, and this could become the new state-of-the-art methodology for the mining industry.

Keywords: 3D imaging, shotcrete, surface model, tunnel stability

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Authors: Wagner Pereira, Alphonse Kelecom

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Mining can impact the environment, and the major impact of some mining activities is the radiological impact. In human populations, such impact is well studied and regulated. For biota, this assessment always had as focus the protection of human food chain. The protection of biota itself is a new approach, still developing. In order to contribute to this new approach, fish collecting was carried out in areas of naturally occurring radioactive materials (NORM), where a uranium mine is in decommissioning phase. The activity concentrations were analyzed, in Bq/kg wet weight, for Uranium (Unat), Th-232 and Ra-226 in the lambari fish Astyanax bimaculatus L. (omnivorous fish) and in the traíra fish Hoplias malabaricus Bloch, 1794 (carnivorous fish). Seven composite samples (that is: a sufficient number of individuals to reach at least 2 kg of fresh weight) were collected every six months between 2013 and 2015. The mean activity concentrations (AC) for uranium ranged from 1.12 (lambari) to 0.60 (lungfish). For Th, variations ranged from 0.30 to 0.05 (lambari and traíra, respectively). Finally, the Ra-226 means ranged between 0.08 and 0.03. No temporal trends of accumulation could be identified. Systematically, the AC values of radionuclides were higher in omnivorous fish when compared to the carnivore ones.

Keywords: biota dose, NORM, fish, environmental protection

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Authors: Livhuwani Muthelo

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The health and safety of the miners in the South African mining industry are guided by the regulations and standards which are anticipated to promote a healthy work environment and fatalities. It is of utmost importance for the miners to comply with these regulations/standards to protect themselves from potential occupational health and safety risks, accidents, and fatalities. The purpose of this study was to develop and validate strategies to enhance compliance with the Health and safety standards within the mining industries of Limpopo province in South Africa. A mixed-method exploratory sequential research design was adopted. The population consisted of 5350 miners. Purposive sampling was used to select the participants in the qualitative strand and stratified random sampling in the quantitative strand. Semi-structured interviews were conducted among the occupational health nurse practitioners and the health and safety team. Thematic analysis was used to generate an understanding of the interviews. In the quantitative strand, a survey was conducted using a self-administered questionnaire. Data were analysed using SPSS version 26.0. A descriptive statistical test was used in the analysis of data including frequencies, means, and standard deviation. Cronbach's alpha test was used to measure internal consistency. The integrated results revealed that there are diverse experiences related to health and safety standards compliance among the mineworkers. The main findings were challenges related to leadership compliance and also related to the cost of maintaining safety, Miner's behavior-related challenges; the impact of non-compliance on the overall health of the miners was also described, the conflict between production and safety. Health and safety compliance is not just mere compliance with regulations and standards but a culture that warrants the miners and organization to take responsibility for their behavior and actions towards health and safety. Thus taking responsibility for your well-being and other miners.

Keywords: perceptions, compliance, health and safety, legislation, standards, miners

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Authors: Krunal Thakar

Abstract:

Wire ropes combine high tensile strength and flexibility as compared to other general steel products. They are used in various application areas such as cranes, mining, elevators, bridges, cable cars, etc. The earliest reported use of wire ropes was for mining hoist application in 1830s. Over the period, there have been substantial advancement in the design of wire ropes for various application areas. Under operational conditions, wire ropes are subjected to varying tensile loads and bending loads resulting in material wear and eventual structural failure due to fretting fatigue. The conventional inspection methods to determine wire failure is only limited to outer wires of rope. However, till date, there is no effective mathematical model to examine the inter wire contact forces and wear characteristics. The scope of this paper is to present a computational simulation technique to evaluate inter wire contact forces and wear, which are in many cases responsible for rope failure. Two different type of ropes, IWRC-6xFi(29) and U3xSeS(48) were taken for structural strength evaluation and wear prediction. Both ropes have a double helix twisted wire profile as per JIS standards and are mainly used in cranes. CAD models of both ropes were developed in general purpose design software using in house developed formulation to generate double helix profile. Numerical simulation was done under two different load cases (a) Axial Tension and (b) Bending over Sheave. Different parameters such as stresses, contact forces, wear depth, load-elongation, etc., were investigated and compared between both ropes. Numerical simulation method facilitates the detailed investigation of inter wire contact and wear characteristics. In addition, various selection parameters like sheave diameter, rope diameter, helix angle, swaging, maximum load carrying capacity, etc., can be quickly analyzed.

Keywords: steel wire ropes, numerical simulation, material wear, structural strength, axial tension, bending over sheave

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Authors: Shafiq Alam, Yen Ning Lee

Abstract:

Conventional hydrometallurgical processing of metals involves the use of large quantities of toxic chemicals. Realizing a need to develop sustainable technologies, extensive research studies are being carried out to recover and recycle base, precious and rare earth metals from their pregnant leach solutions (PLS) using green chemicals/biomaterials prepared from biomass wastes derived from agriculture, marine and forest resources. Our innovative research showed that bio-adsorbents prepared from such biomass wastes can effectively adsorb precious metals, especially gold after conversion of their functional groups in a very simple process. The highly effective ‘Adsorption-coupled-Reduction’ phenomenon witnessed appears promising for the potential use of this gold biosorption process in the mining industry. Proper management and effective use of biomass wastes as value added green chemicals will not only reduce the volume of wastes being generated every day in our society, but will also have a high-end value to the mining and mineral processing industries as those biomaterials would be cheap, but very selective for gold recovery/recycling from low grade ore, leach residue or e-wastes.

Keywords: biosorption, hydrometallurgy, gold, adsorption, reduction, biomass, sustainability

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Authors: C. B. Le, V. N. Pham

Abstract:

In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Saadoun Abd Errazak, Hafssaoui Abdallah, Fredj Mohamed

Abstract:

Mining operations open induce risks of instability that can cause landslides and collapse at the bleachers slope. These risks may occur both during and after the operation phase. The magnitude of these risks depends on the mechanical and physical characteristics of the rock mass, the geometrical dimensions of ore bodies, their spatial arrangement, and the state of the operated area. If security and technology measures are not taken into account for this purpose, the environment will be affected. The main objective of this work is to assess these risks by analytical and numerical methods. The study is based on the geological, hydrogeological and geotechnical rock mass of the open pit quarry of Chouf Amar M'sila. The results obtained have allowed us to obtain an acceptable factor of safety and stability study of the open pit.

Keywords: stability, land sliding, numerical modeling, safety factor, open-pit quarry

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Authors: Jay N. Vyas, Sachin Daxini

Abstract:

Numerical simulation techniques are widely used now in product development and testing instead of expensive, time-consuming and sometimes dangerous laboratory experiments. Numerous numerical methods are available for performing simulation of physical problems of different engineering fields. Grid based methods, like Finite Element Method, are extensively used in performing various kinds of static, dynamic, structural and non-structural analysis during product development phase. Drawbacks of grid based methods in terms of discontinuous secondary field variable, dealing fracture mechanics and large deformation problems led to development of a relatively a new class of numerical simulation techniques in last few years, which are popular as Meshless methods or Meshfree Methods. Meshless Methods are expected to be more adaptive and flexible than Finite Element Method because domain descretization in Meshless Method requires only nodes. Present paper introduces Meshless Methods and differentiates it with Finite Element Method in terms of following aspects: Shape functions used, role of weight function, techniques to impose essential boundary conditions, integration techniques for discrete system equations, convergence rate, accuracy of solution and computational effort. Capabilities, benefits and limitations of Meshless Methods are discussed and concluded at the end of paper.

Keywords: numerical simulation, Grid-based methods, Finite Element Method, Meshless Methods

Procedia PDF Downloads 384

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

Abstract:

Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

Procedia PDF Downloads 137