Search results for: basalt mining

Authors: Fuad Ali Mohammed Al-Yarimi

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Many algorithms have been proposed to provide privacy preserving in data mining. These protocols are based on two main approaches named as: the perturbation approach and the Cryptographic approach. The first one is based on perturbation of the valuable information while the second one uses cryptographic techniques. The perturbation approach is much more efficient with reduced accuracy while the cryptographic approach can provide solutions with perfect accuracy. However, the cryptographic approach is a much slower method and requires considerable computation and communication overhead. In this paper, a new scalable protocol is proposed which combines the advantages of the perturbation and distortion along with cryptographic approach to perform privacy preserving in distributed frequent itemset mining on horizontally distributed data. Both the privacy and performance characteristics of the proposed protocol are studied empirically.

Keywords: anonymity data, data mining, distributed frequent itemset mining, gaussian perturbation, perturbation approach, privacy preserving data mining

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Authors: J. K. Kavitha, D. Manjula, U. Kanimozhi

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Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance.

Keywords: data sets, recommendation system, utility item sets, frequent item sets mining

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Authors: Ashok Tejankar, Rohan K. Pathrikar

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Maharashtra is covered by heterogeneous flows of Deccan basaltic terrains of upper cretaceous to lower Eocene age. It consist mainly different types of basalt flow, having heterogeneous Geohydrological characters. The study area Aurangabad dist. lies in the central part of Maharashtra. The study area is typically covered by Deccan traps formation mainly basalt type of igneous volcanic rock. The area is located in the survey of India toposheet No. 47M and laying between 19° to 20° north latitudes and 74° to 76° east longitudes. Groundwater is the primary source for fresh water in the study area. There has been a growing demand for fresh water in domestic & agriculture sectors. Due to over exploitation and rainfall failure has been created an irrecoverable stress on groundwater in study area. In an effort to maintain the water table condition in balance, artificial recharge is being implemented. The selection of site for artificial recharge is a very important task in recharge basalt. The present study aims at sitting artificial recharge structure at village Pachod in basaltic terrain of the Godavari-Purna river basin in Aurangabad district of Maharashtra, India. where the average annual rainfall is 650mm. In this investigation, integrated remote sensing and GIS techniques were used and various parameters like lithology, structure, etc. aspect of drainage basins, landforms and other parameters were extracted from visual interpretation of IRS P6 Satellite data and Survey of India (SIO) topographical sheets, aided by field checks by carrying well inventory survey. The depth of weathered material, water table conditions, and rainfall data were been considered. All the thematic information layers were digitized and analyzed in Arc-GIS environment and the composite maps produced show suitable site, depth of bed rock flows for successful artificial recharge in village Pachod to increase groundwater potential of low laying area.

Keywords: hard rock, artificial recharge, remote sensing, GIS

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Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

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Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

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Authors: Manvar Sagar, Nikul Virpariya

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The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the over-heads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed Association Rule mining using Shamir’s secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and require only one time a trusted third party. We use secret sharing for privately sharing the information and code based identification scheme to add support against malicious adversaries.

Keywords: Privacy, Privacy Preservation in Data Mining (PPDM), horizontally partitioned database, EMHS, MFI, shamir secret sharing

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Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

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Authors: A. Argiri, A. Molla, Tzouvalekas, E. Skoufogianni, N. Danalatos

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The release of heavy metals into the environment has increased over the last years. In this study, 25 soil samples (0-15 cm) from the fields near the mining area in Drama region were selected. The samples were analyzed in the laboratory for their physicochemical properties and for seven “pseudo-total’’ heavy metals content, namely Pb, Zn, Cd, Cr, Cu, Ni, and Mn. The total metal concentrations (Pb, Zn, Cd, Cr, Cu, Ni and Mn) in digests were determined by using the atomic absorption spectrophotometer. According to the results, the mean concentration of the listed heavy metals in 25 soil samples are Cd 1.1 mg/kg, Cr 15 mg/kg, Cu 21.7 mg/kg, Ni 30.1 mg/kg, Pd 50.8 mg/kg, Zn 99.5 mg/kg and Mn 815.3 mg/kg. The results show that the heavy metals remain in the soil even if the mining closed many years ago.

Keywords: Greece, heavy metals, mining, pollution

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Authors: Muhammet Dursun Kaya, Hasan Asil

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One of the most important factors of research and development is the deep insight into the evolutions of scientific development. The state-of-the-art tools and instruments can considerably assist the researchers, and many of the world organizations have become aware of the advantages of data mining for the acquisition of the knowledge required for the unstructured data. This paper was an attempt to review the articles on the information technology published in the past five years with the aid of data mining. A clustering approach was used to study these articles, and the research results revealed that three topics, namely health, innovation, and information systems, have captured the special attention of the researchers.

Keywords: information technology, data mining, scientific development, clustering

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Authors: Aroma Elmina Martha

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Environment affects the continuity of life and human well-being and the bodies of other living. Environmental quality is very closely related to the quality of life. Sustainability must be protected from damage due to the use of natural resources, such as tin mining in Bangka island. This research is a descriptive study, which identifies the environmental damage caused by mining land and sea in Bangka district. The approach used is juridical, social and economic. The study uses primary legal materials, secondary, and tertiary, equipped with field research. The analysis technique used is qualitative analysis. The impacts of mining on land among other physical and chemical damage, erosion and widening the depth of the river, a pool of micro-climate, the quality and feasibility, vegetation, wildlife and biodiversity, land values, social and economic. This mining causes damage to the soil structure, and puddles in the former digs which were not backfilled again. The impact of mining on the ocean such as changes in current surge, erosion and abrasion basic coastal waters, shoreline change, marine water quality changes, and changes in marine communities. The findings of the research show that tin mining in the sea also potentially have a significant impact on the life of the reef, populations of marine organisms. However, mining on land needs to consider the impact of the damage, so that the damage can be minimized. In the recovery process needs to be pursued by exploiting the rest of the pile of tin. Thus, mining activities should take into account the distance of beach sediment size, wave height, wave length, wave period, and the acceleration of gravity. The process of the tin washing should be done in a fairly safe area, thus avoiding damage to the coral reefs that will eventually reduce the population of marine life.

Keywords: abration, environmental damage, mining, shoreline

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Authors: Rabia Bashir, Sajjad Akbar

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There is an inevitable demand of web mining due to rapid increase of huge information on the Internet, but the striking variety of web structures has made required content retrieval a difficult task. To counter this issue, Web Content Mining (WCM) emerges as a potential candidate which extracts and integrates suitable resources of data to users. In past few years, research has been done on several extraction techniques for WCM i.e. agent-based, template-based, assumption-based, statistic-based, wrapper-based and machine learning. However, it is still unclear that either these approaches are efficiently tackling the significant challenges of WCM or not. To answer this question, this paper identifies these challenges such as language independency, structure flexibility, performance, automation, dynamicity, redundancy handling, intelligence, relevant content retrieval, and privacy. Further, mapping of these challenges is done with existing extraction mechanisms which helps to adopt the most suitable WCM approach, given some conditions and characteristics at hand.

Keywords: content mining challenges, web content mining, web content extraction approaches, web information retrieval

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Authors: S. Candamano, A. Iorfida, F. Crea, A. Macario

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Recently, basalt fabric reinforced cementitious composites (FRCM) have attracted great attention because they result to be effective in structural strengthening and cost/environment efficient. In this study, authors investigate their mechanical behavior when an inorganic matrix, belonging to the family of alkali-activated binders, is used. In particular, the matrix has been designed to contain high amounts of industrial by-products and waste, such as Ground Granulated Blast Furnace Slag (GGBFS) and Fly Ash. Fresh state properties, such as workability, mechanical properties and shrinkage behavior of the matrix have been measured, while microstructures and reaction products were analyzed by Scanning Electron Microscopy and X-Ray Diffractometry. Reinforcement is made up of a balanced, coated bidirectional fabric made out of basalt fibres and stainless steel micro-wire, with a mesh size of 8x8 mm and an equivalent design thickness equal to 0.064 mm. Mortars mixes have been prepared by maintaining constant the water/(reactive powders) and sand/(reactive powders) ratios at 0.53 and 2.7 respectively. An appropriate experimental campaign based on direct tensile tests on composite specimens and single-lap shear bond test on brickwork substrate has been thus carried out to investigate their mechanical behavior under tension, the stress-transfer mechanism and failure modes. Tensile tests were carried out on composite specimens of nominal dimensions equal to 500 mm x 50 mm x 10 mm, with 6 embedded rovings in the loading direction. Direct shear tests (DST) were carried out on brickwork substrate using an externally bonded basalt-FRCM composite strip 10 mm thick, 50 mm wide and a bonded length of 300 mm. Mortars exhibit, after 28 days of curing, an average compressive strength of 32 MPa and flexural strength of 5.5 MPa. Main hydration product is a poorly crystalline aluminium-modified calcium silicate hydrate (C-A-S-H) gel. The constitutive behavior of the composite has been identified by means of direct tensile tests, with response curves showing a tri-linear behavior. Test results indicate that the behavior is mainly governed by cracks development (II) and widening (III) up to failure. The ultimate tensile strength and strain were respectively σᵤ = 456 MPa and ɛᵤ= 2.20%. The tensile modulus of elasticity in stage III was EIII= 41 GPa. All single-lap shear test specimens failed due to composite debonding. It occurred at the internal fabric-to-matrix interface, and it was the result of a fracture of the matrix between the fibre bundles. For all specimens, transversal cracks were visible on the external surface of the composite and involved only the external matrix layer. This cracking appears when the interfacial shear stresses increase and slippage of the fabric at the internal matrix layer interface occurs. Since the external matrix layer is bonded to the reinforcement fabric, it translates with the slipped fabric. Average peak load around 945 N, peak stress around 308 MPa and global slip around 6 mm were measured. The preliminary test results allow affirming that Alkali-Activated Materials can be considered a potentially valid alternative to traditional mortars in designing FRCM composites.

Keywords: Alkali-activated binders, Basalt-FRCM composites, direct shear tests, structural strengthening

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Bernard Kumi-Boateng, Kofi Bonsu

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The clearing of vegetation in the Tarkwa Mining Area (TMA) for the purposes of mining, lumbering and development of settlement for the increasing population has caused a large scale denudation of the forest cover and erosion of the top soil thereby degrading the agriculture land. It is, therefore, essential to know the current status of land degradation in TMA so as to facilitate land conservation policy-making. The types of degradation, the extents of the degradations and their various degrees were combined to develop a composite land degradation index to assess the current status of land degradation in TMA using GIS based techniques. The assessment revealed that the most significant types of degradation in TMA were open pit and quarry mining; urbanisation and other construction projects; and surface scraping during land clearing. It was found that 21.62 % of the total area of TMA (353.07 km2) had high degradation index rating. It is recommended that decision makers use this assessment as a reference point for future initiatives that will be taken in order to develop land conservation policy.

Keywords: degradation, GIS, land, mining

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Paulinah Oyindamola Fasanmi, Matthew Omoniyi Isinkaye

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In this study, the activity concentration of ²²²Rn in groundwater samples collected from gold and kaolin mining communities within the Ife-Ilesha schist belt, southwestern Nigeria, with their corresponding annual effective doses have been determined using the Durridge RAD-7, radon-in-water detector. The mean concentration of ²²²Rn in all the groundwater samples was 13.83 Bql-¹. In borehole water, ²²²Rn had a mean value of 20.68 Bql-¹, while it had a mean value of 11.67 Bql-¹ in well water samples. The mean activity concentration of radon obtained from the gold mining communities ranged from 1.6 Bql-¹ from Igun town to 4.8 Bql-¹ from Ilesha town. A higher mean value of 41.8 Bql-¹ was, however, obtained from Ijero, which is the kaolin mining community. The mean annual effective dose due to ingestion and inhalation of radon from groundwater samples was obtained to be 35.35 μSvyr-¹ and 34.86 nSvyr-¹, respectively. The mean annual ingestion dose estimated for well water samples was 29.90 μSvyr-¹, while 52.85 μSvyr-¹ was obtained for borehole water samples. On the other hand, the mean annual inhalation dose for well water was 29.49 nSvyr-¹, while for borehole water, 52.13 nSvyr-¹ was obtained. The mean annual effective dose due to ingestion of radon in groundwater from the gold mining communities ranged from 4.10 μSvyr-¹ from Igun to 13.1 μSvyr-¹ from Ilesha, while a mean value of 106.7 μSvyr-¹ was obtained from Ijero kaolin mining community. For inhalation, the mean value varied from 4.0 nSvyr-¹ from Igun to 12.9 nSvyr-¹ from Ilesha, while 105.2 nSvyr-¹ was obtained from the kaolin mining community. The mean annual effective dose due to ingestion and inhalation is lower than the reference level of 100 μSvyr-¹ recommended by World Health Organization except for values obtained from Ijero kaolin mining community, which exceeded the reference levels. It has been concluded that as far as radon-related health risks are concerned, groundwater from gold mining communities is generally safe, while groundwater from kaolin mining communities needs mitigation and monitoring. It has been discovered that Kaolin mining impacts groundwater with ²²²Rn than gold mining. Also, the radon level in borehole water exceeds its level in well water.

Keywords: 222Rn, Groundwater, Radioactivity, Annual Effective Dose, Mining.

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Jerome Blackman, Pamela Franklin, Volha Roshchanka

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The US coal mining sector accounts for 6% of total US Methane emissions (2021). 60% of US coal mining methane emissions come from active underground mine ventilation systems. Abandoned mines contribute about 13% of methane emissions from coal mining. While there are thousands of abandoned underground coal mines in the US, the Environmental Protection Agency (EPA) estimates that fewer than 100 have sufficient methane resources for viable methane recovery and use projects. Many abandoned mines are in remote areas far from potential energy customers and may be flooded, further complicating methane recovery. Because these mines are no longer active, recovery projects can be simpler to implement.

Keywords: abandoned mines, coal mine methane, coal mining, methane emissions, methane mitigation, recovery and use

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Authors: Faisal Alshuwaier, Ali Areshey

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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

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Authors: Sabri Serkan Güllüoğlu

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Analysis can be defined as the process of gathering, recording and researching data related to products and services, in order to learn something. But for marketers, analyses are not only used for learning but also an essential and critical part of the business, because this allows companies to offer products or services which are focused and well targeted. Market analysis also identify market trends, demographics, customer’s buying habits and important information on the competition. Data mining is used instead of traditional research, because it extracts predictive information about customer and sales from large databases. In contrast to traditional research, data mining relies on information that is already available. Simply the goal is to improve the efficiency of supermarkets. In this study, the purpose is to find dependency on products. For instance, which items are bought together, using association rules in data mining. Moreover, this information will be used for improving the profitability of customers such as increasing shopping time and sales of fewer sold items.

Keywords: data mining, association rule mining, market basket analysis, purchasing

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Authors: Franklin Nantui Mabe, Robert Osei

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Activities and operations of artisanal small scale mining (ASM) have recently appealed to the attention of policymakers, researchers, and the general public in Ghana. This stems from the negative impacts of ASM operations on the environment and livelihoods of local inhabitants, as well as the disregard for available ASM mining policies. This study, therefore, investigates whether or not artisanal small-scale miners have enough knowledge of the mining policies and their implementations. The study adopted the Knowledge, Attitudes, and Practices (KAP) framework approach to design the research, collect and analyze primary data. The most aware ASM policy provision is the one that mandates the government to reserve demarcated ASM areas for Ghanaians, whilst the least aware provision is the one that admonishes the government to promote co-operative saving among ASM. The awareness index is lower than the attitude index towards the policy provisions. In terms of practices, miners continued to use bad practices with the associated negative impacts on the environment and rural livelihoods. It is therefore important for the government through mineral commission, district, municipal and metropolitan assemblies to intensify the education on the ASM policies. These could be done with the help of ASM associations. The current systems where a cluster of districts have a single Mineral Commission Office should be restructured to make sure that each mining district has an office.

Keywords: mining policies, KAP, awareness, artisanal small-scale mining

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Authors: Simon Mensah Ofosu

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Illicit surface mining activities pollutes agricultural lands and water bodies and results in accumulation of heavy metals in vegetables cultivated in such areas. Heavy metal (HM) accumulation in vegetables is a serious food safety issues due to the adverse effects of metal toxicities, hence the need to investigate the levels of these metals in cultivated vegetables in the eastern region. Cocoyam leaves, cabbage and cucumber were sampled from selected farms in mining areas (Atiwa District) and non -mining areas (Yilo Krobo and East Akim District) of the region for the study. Levels of Cadmium, Lead, Mercury and Arsenic were investigated in the vegetables with Atomic Absorption Spectrometer, and the results statistically analyzed with Microsoft Office Excel (2013) Spread Sheet and ANOVA. Cadmium (Cd) and arsenic (As) were the highest and least concentrated HM in the vegetables sampled, respectively. The mean concentrations of Cd and Pb in cabbage (0.564 mg/kg, 0.470 mg/kg), cucumber (0.389 mg/kg, 0.190 mg/kg), cocoyam leaves (0.410 mg/kg, 0.256 mg/kg) respectively from the mining areas exceeded the permissible limits set by Joint FAO/WHO. The mean concentrations of the metals in vegetables from the mining and non-mining areas varied significantly (P<0.05). The Target Hazard Quotient (THQ) was used to assess the health risk posed to the human population via vegetable consumption. The THQ values of cadmium, mercury, and lead in adults and children through vegetable consumption in the mining areas were greater than 1 (THQ >1). This indicates the potential health risk that the children and adults may be facing. The THQ values of adults and children in the non-mining areas were less than the safe limit of 1 (THQ<1), hence no significant health risk posed to the population from such areas.

Keywords: food safety, risk assessment, illicit mining, public health, contaminated vegetables

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Authors: Fadlan Atmaja Nursiwan, Ugi Kurnia Gusti

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Garba Hill, located in Tekana Village, South Sumatera Province is comprised to South Sumatra Basin and classified as back arc basin. This area is entered as an active margin of Sundaland which experiences subduction several times since Mesozoic to recent time. The traces of Mesozoic subduction in the southern part of Sumatra island are exposed in Garba Hill area. The aim of this investigation is to study the tectonic changes in the first phase in Mesozoic era at the active margin of Sundaland which causes the rocks assemblage in Garba hill consist of continental and oceanic plate rocks which the correlation between those rocks show indistinct relation. This investigation is conducted by field observation in Tekana village and Lubar Village, Muara Dua, South Sumatra along with laboratory analysis included fossil and geochemistry analysis of radiolarian chert, petrography analysis of granite and basalt, and structural modelling. Fossil and geochemistry analysis of radiolarian chert and geochemistry of granite rocks shown the relation between the two rocks and Mesozoic subduction of Woyla terrane on western margin of Sundaland. Petrography analysis from granite and basalt depict the tectonic affinity of rocks. Moreover, structural analysis showed the changes of lineation direction from N-S to WNW-ESE.

Keywords: granite, mesozoic, radiolarian, subduction traces

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

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Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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Authors: G. Surendra Babu

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We all know the importance of mineral wealth, which has been buried inside the layers of the earth for decades. These are the natural energy sources that are used in our day to day life like fuel, electricity, construction, etc. but the process of extraction causes damage to the nature that can’t be returned back and which are left over after completion of mining we can see these are barren from decades these remain unused degraded land. Most of them are covered with vegetation before the start during mining which damages the native vegetation of the region and disturbs the watershed boundary of the regions and it also disturbs the biodiversity of the reign. The major motto of the study is to understand the various issues that are found and to understand various methods of reclamations process that are suitable for revegetating and also variously practiced which are carried out in the different case studies and government guidelines procedure of lease licenses which includes the environmental clearances and also to study the vegetation pattern according to the major issues identified. And finally suggesting the new guidelines with respect to the old guidelines which helps in the revegetation of the mine-sites which helps in establishing of its own sustainable ecosystem in future.

Keywords: reclamation, open-pit mining, revegetation, reclamation methods

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Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

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Authors: Karima Qayumi, Alex Norta

Abstract:

The rapid generation of high volume and a broad variety of data from the application of new technologies pose challenges for the generation of business-intelligence. Most organizations and business owners need to extract data from multiple sources and apply analytical methods for the purposes of developing their business. Therefore, the recently decentralized data management environment is relying on a distributed computing paradigm. While data are stored in highly distributed systems, the implementation of distributed data-mining techniques is a challenge. The aim of this technique is to gather knowledge from every domain and all the datasets stemming from distributed resources. As agent technologies offer significant contributions for managing the complexity of distributed systems, we consider this for next-generation data-mining processes. To demonstrate agent-based business intelligence operations, we use agent-oriented modeling techniques to develop a new artifact for mining massive datasets.

Keywords: agent-oriented modeling (AOM), business intelligence model (BIM), distributed data mining (DDM), multi-agent system (MAS)

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Authors: Marcin Michalak

Abstract:

Due to the increasing efforts on saving our natural environment a change in the structure of energy resources can be observed - an increasing fraction of a renewable energy sources. In many countries traditional underground coal mining loses its significance but there are still countries, like Poland or Germany, in which the coal based technologies have the greatest fraction in a total energy production. This necessitates to make an effort to limit the costs and negative effects of underground coal mining. The longwall complex is as essential part of the underground coal mining. The safety and the effectiveness of the work is strongly dependent of the diagnostic state of powered roof supports. The building of a useful and reliable diagnostic system requires a lot of data. As the acquisition of a data of any possible operating conditions it is important to have a possibility to generate a demanded artificial working characteristics. In this paper a new approach of modelling a leg pressure in the single unit of powered roof support. The model is a result of the analysis of a typical working cycles.

Keywords: machine modelling, underground mining, coal mining, structure

Procedia PDF Downloads 338