Search results for: mining operations

Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: Kyle De Freitas, Margaret Bernard

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Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

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Authors: Zunaira Asif, Zhi Chen

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

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

Procedia PDF Downloads 171

Authors: Odinah Cuartero-Enteria, Kyla Rita Mercado, Jason Salamanes, Aian Pecasales, Sherwin Sabado

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The northern part of Mindanao Island, Philippines has sizable reserve of mineral resources. Years ago, mining activities have been flourishing which resulted to both local economic gain but with environmental concerns. This study investigates the prevalent diseases by mining activities in these areas. The study was done using the secondary data gathered from the Rural Health Units (RHU) of the selected areas. The study further determined the prevalent diseases that existed in the three areas from years 2005, 2010 and 2015 indicating before the mining activities and when mining activities are present. The results show that areas which are far from mining activities have fewer cases of patients suffering from air-borne diseases. The top ten most common diseases such as pneumonia, tuberculosis, influenza, upper respiratory tract infection (URTI) and skin diseases were caused by air-borne due to air pollution. Hence, the places where mining activities are present contribute to the prevalent diseases. Thus, addressing the air pollution caused by mining activities is very important.

Keywords: Philippines, Mindanao Island, mining activities, pollution, prevalent diseases

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Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

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Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.

Keywords: cloud computing, data mining, computing models, cloud services

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Authors: Sohail Imran, Tariq Mahmood

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In complex healthcare diagnostic investigation process, medical practitioners have to focus on ways to standardize their processes to perform high quality care and optimize the time and costs. Process mining techniques can be applied to extract process related knowledge from data without considering causal and dynamic dependencies in business domain and processes. The application of process mining is effective in diagnostic investigation. It is very helpful where a treatment gives no dispositive evidence favoring it. In this paper, we applied process mining to discover important process flow of diagnostic investigation for hepatitis patients. This approach has some benefits which can enhance the quality and efficiency of diagnostic investigation processes.

Keywords: process mining, healthcare, diagnostic investigation process, process flow

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Authors: Adam Kok Wey Leong

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Special operations were supposedly a new way of irregular warfare that was officially formed during World War 2. For example, the famous British Special Operations Executive (SOE) and the Americans’ Office for Strategic Services (OSS) – the forerunners of modern day CIA were born in World War 2. These special operations units were tasked with the conduct of sabotage and subversion activities behind enemy lines, placing great importance in forming Fifth Column activities and supporting resistance movements. This pointed to a paradoxical argument that modern day special operations is a product of Western modern military innovation but utilizing Eastern ways of ‘ungentlemanly’ warfare. This thesis is superfluous as special operations had been well practised by both ancient Western empires such as the Greeks and Romans, and around the same time in the East, such as in China, and Japan. This paper will describe the practice of special operations, first from the Western military history of the Greeks during the Peloponnesian war. It will then highlight the similar practice of special operations by the Near Eastern Assassins and Eastern militaries by using examples from the Chinese and the Japanese. This paper propounds that special operations, or ways of warfare as a whole, has no cultural and geographical divide, but rather very similarly practiced by men from all over the world. Ideas of fighting, killing and ultimately winning a war have similar undertones – attempts to find ways to win economically and at the least time.

Keywords: special operations, strategic culture, ways of warfare, Sun Tzu, Frontinus

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Authors: Anurag Savarnya

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The reliability of the various parts of electric mining shovel has been assessed through the application of Weibull Model. The study was initiated to find reliability of components of electric mining shovel. The paper aims to optimize the reliability of components and increase the life cycle of component. A multilevel decomposition of the electric mining shovel was done and maintenance records were used to evaluate the failure data and appropriate system characterization was done to model the system in terms of reasonable number of components. The approach used develops a mathematical model to assess the reliability of the electric mining shovel components. The model can be used to predict reliability of components of the hydraulic mining shovel and system performance. Reliability is an inherent attribute to a system. When the life-cycle costs of a system are being analyzed, reliability plays an important role as a major driver of these costs and has considerable influence on system performance. It is an iterative process that begins with specification of reliability goals consistent with cost and performance objectives. The data were collected from an Indian open cast coal mine and the reliability of various components of the electric mining shovel has been assessed by following a Weibull Model.

Keywords: reliability, Weibull model, electric mining shovel

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Authors: Adewale O. Ogunde, Olusegun Folorunso, Adesina S. Sodiya

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Most existing Distributed Association Rule Mining (DARM) systems are still facing several challenges. One of such challenges that have not received the attention of many researchers is the inability of existing systems to adapt to constantly changing databases and mining environments. In this work, an Adaptive Incremental Mining Algorithm (AIMA) is therefore proposed to address these problems. AIMA employed multiple mobile agents for the entire mining process. AIMA was designed to adapt to changes in the distributed databases by mining only the incremental database updates and using this to update the existing rules in order to improve the overall response time of the DARM system. In AIMA, global association rules were integrated incrementally from one data site to another through Results Integration Coordinating Agents. The mining agents in AIMA were made adaptive by defining mining goals with reasoning and behavioral capabilities and protocols that enabled them to either maintain or change their goals. AIMA employed Java Agent Development Environment Extension for designing the internal agents’ architecture. Results from experiments conducted on real datasets showed that the adaptive system, AIMA performed better than the non-adaptive systems with lower communication costs and higher task completion rates.

Keywords: adaptivity, data mining, distributed association rule mining, incremental mining, mobile agents

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

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

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

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Authors: Sidharth Talan, Sanjay Kumar Sharma, Eoin Joseph Wallace, Nikita Agrawal

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Underground mining is at the core of rapidly evolving metals and minerals sector due to the increasing mineral consumption globally. Even though the surface mines are still more abundant on earth, the scales of industry are slowly tipping towards underground mining due to rising depth and complexities of orebodies. Thus, the efficient and productive functioning of underground operations depends significantly on the synchronized performance of key elements such as operating site, mining equipment, manpower and mine services. Production drilling is the process of conducting long hole drilling for the purpose of charging and blasting these holes for the production of ore in underground metal mines. Thus, production drilling is the crucial segment in the underground metal mining value chain. This paper presents the process mapping tool to evaluate the production drilling process in the underground metal mining operation by dividing the given process into three segments namely Input, Process and Output. The three segments are further segregated into factors and sub-factors. As per the study, the major input factors crucial for the efficient functioning of production drilling process are power, drilling water, geotechnical support of the drilling site, skilled drilling operators, services installation crew, oils and drill accessories for drilling machine, survey markings at drill site, proper housekeeping, regular maintenance of drill machine, suitable transportation for reaching the drilling site and finally proper ventilation. The major outputs for the production drilling process are ore, waste as a result of dilution, timely reporting and investigation of unsafe practices, optimized process time and finally well fragmented blasted material within specifications set by the mining company. The paper also exhibits the drilling loss matrix, which is utilized to appraise the loss in planned production meters per day in a mine on account of availability loss in the machine due to breakdowns, underutilization of the machine and productivity loss in the machine measured in drilling meters per unit of percussion hour with respect to its planned productivity for the day. The given three losses would be essential to detect the bottlenecks in the process map of production drilling operation so as to instigate the action plan to suppress or prevent the causes leading to the operational performance deficiency. The given tool is beneficial to mine management to focus on the critical factors negatively impacting the production drilling operation and design necessary operational and maintenance strategies to mitigate them. 

Keywords: process map, drilling loss matrix, SIPOC, productivity, percussion rate

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Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: G. Edwards, N. Dybro, L. J. Munkholm, C. G. Sørensen

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Planning agricultural operations requires an understanding of when fields are ready for operations. However determining a field’s readiness is a difficult process that can involve large amounts of data and an experienced farm manager. A consequence of this is that operations are often executed when fields are unready, or partially unready, which can compromise results incurring environmental impacts, decreased yield and increased operational costs. In order to assess timeliness of operations’ execution, a new scheme is introduced to quantify the aptitude of farm managers to plan operations. Two criteria are presented by which the execution of operations can be evaluated as to their exploitation of a field’s readiness window. A dataset containing the execution dates of spring and autumn operations on 93 fields in Iowa, USA, over two years, was considered as an example and used to demonstrate how operations’ executions can be evaluated. The execution dates were compared with simulated data to gain a measure of how disparate the actual execution was from the ideal execution. The presented tool is able to evaluate the spring operations better than the autumn operations as required data was lacking to correctly parameterise the crop model. Further work is needed on the underlying models of the decision support tool in order for its situational knowledge to emulate reality more consistently. However the assessment methods and evaluation criteria presented offer a standard by which operations' execution proficiency can be quantified and could be used to identify farm managers who require decisional support when planning operations, or as a means of incentivising and promoting the use of sustainable farming practices.

Keywords: operation management, field readiness, sustainable farming, workability

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: M. Sai Veerendra

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project on developing a new data mining solution for AML Units in an international investment bank in Ireland, we survey recent data mining approaches for AML. In this paper, we present not only these approaches but also give an overview on the important factors in building data mining solutions for AML activities.

Keywords: data mining, clustering, money laundering, anti-money laundering solutions

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Authors: Linus O. Asuquo

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Mining in Nigeria and development effort of metallurgical technologies at National Metallurgical Development Centre Jos has been addressed in this paper. The paper has looked at the history of mining in Nigeria, the impact of mining on social and industrial development, and the contribution of the mining sector to Nigeria’s Gross Domestic Product (GDP). The paper clearly stated that Nigeria’s mining sector only contributes 0.5% to the nation’s GDP unlike Botswana that the mining sector contributes 38% to the nation’s GDP. Nigeria Bureau of Statistics has it on record that Nigeria has about 44 solid minerals awaiting to be exploited. Clearly highlighted by this paper is the abundant potentials that exist in the mining sector for investment. The paper made an exposition on the extensive efforts made at National Metallurgical Development Center (NMDC) to develop metallurgical technologies in various areas of the metals sector; like mineral processing, foundry development, nonferrous metals extraction, materials testing, lime calcination, ANO (Trade name for powder lubricant) wire drawing lubricant, refractories and many others. The paper went ahead to draw a conclusion that there is a need to develop the mining sector in Nigeria and to give a sustainable support to the efforts currently made at NMDC to develop metallurgical technologies which are capable of transforming the metals sector in Nigeria, which will lead to industrialization. Finally the paper made some recommendations which traverse the topic for the best expectation.

Keywords: mining, minerals, technologies, value addition

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Awatef Hicheur Cairns, Billel Gueni, Hind Hafdi, Christian Joubert, Nasser Khelifa

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Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to discover, analyze and provide a visual representation of complete educational processes. In this paper, we present our distributed computation platform which allows different education centers and institutions to load their data and access to advanced data mining and process mining services. To achieve this, we present also a comparative study of the different clustering techniques developed in the context of process mining to partition efficiently educational traces. Our goal is to find the best strategy for distributing heavy analysis computations on many processing nodes of our platform.

Keywords: educational process mining, distributed process mining, clustering, distributed platform, educational data mining, ProM

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Authors: Seema Vaidya

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Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient.

Keywords: association rule, data mining, IWI mining, infrequent item set, frequent pattern growth

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Authors: K. -H. Yang

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In the past, the quay crane operations have been well studied. There were a certain number of scheduling algorithms for quay crane operations, but without considering some nuisance factors that might disrupt the quay crane operations. For example, bad grapples make a crane unable to load or unload containers or a sudden strong breeze stops operations temporarily. Although these disruptive conditions randomly occur, they influence the efficiency of quay crane operations. The disruption is not considered in the operational procedures nor is evaluated in advance for its impacts. This study applies simulation and optimization approaches to develop structures of pre-analysis and post-analysis for the Quay Crane Scheduling Problem to deal with disruptive scenarios for quay crane operation. Numerical experiments are used for demonstrations for the validity of the developed approaches.

Keywords: disruptive quay crane scheduling, pre-analysis, post-analysis, disruption

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Authors: Thalia Viveros-Uehara

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The virtues of Corporate Social Responsibility (CSR) are explored widely in the academic literature. However, few studies address its link to human rights, per se; specifically, the right to a safe, clean and healthy environment. Fewer still are the research works in this area that relate to developing countries, where a number of areas are biodiversity hotspots. In Mexico, despite the rise and evolution of CSR schemes, grave episodes of pollution persist, especially those caused by the mining industry. These cases set up the question of the correspondence between the current CSR practices of mining companies in the country and their responsibility to respect the right to a safe, clean and healthy environment. The present study approaches precisely such a bridge, which until now has not been fully tackled in light of Mexico's 2011 constitutional human rights amendment and the United Nation's Guiding Principles on Business and Human Rights (UN Guiding Principles), adopted by the Human Rights Council in 2011. To that aim, it initially presents a contextual framework; it then explores qualitatively the adoption of human rights’ language in the CSR strategies of the three main mining companies in Mexico, and finally, it examines their standing with respect to the UN Guiding Principles. The results reveal that human rights are included in the RSE strategies of the analysed businesses, at least at the rhetoric level; however, they do not embrace the right to a safe, clean and healthy environment as such. Moreover, we conclude that despite the finding that corporations publicly express their commitment to respect human rights, some operational weaknesses that hamper the exercise of such responsibility persist; for example, the systematic lack of human rights impact assessments per mining unit, the denial of actual and publicly-known negative episodes on the environment linked directly to their operations, and the absence of effective mechanisms to remediate adverse impacts.

Keywords: corporate social responsibility, environmental impacts, human rights, right to a safe, clean and healthy environment, mining industry

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Authors: Yazan Khalid Abed-Allah Migdadi

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The aim of this study is identifying the effective operations competitive advantages of mobile phone service providers across countries. All Arab countries in the Middle East region were surveyed except Syria, and 27 out of 31 service providers were surveyed. Data collected from corporations’ annual reports, websites and other professional institutions published sources. Multiple linear regression analysis test was used to identify the relationship between operations competitive advantages and market share. The effective operations competitive advantages were; diversity of offers and service accessibility

Keywords: competitive advantage, mobile telecommunication operations, Middle East, service provider

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Authors: Abbas Fazelinia

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Terrorism is not a new phenomenon to the world, yet it remains difficult to define and to counter. Countering terrorism requires several measures that must be taken at the same time. Counterterrorism strategies of most countries depend on military measures. However, those strategies should also focus on nonlethal measures, such as economic, political, and social measures. The psychological dimensions of terrorism must be understood, evaluated, and used in countering terrorism. This study suggests that psychological operations, as nonlethal military operations, can be used to influence individuals not to join terrorist organizations and to facilitate defections from terrorist organizations. However, in order to implement effective psychological operations, one has to have appropriate intelligence about terrorist organizations. Examining terrorist organizations help us to identify their vulnerabilities and obtain this intelligence. This article concludes that terrorists’ motivations, terrorist organizations’ radicalization, recruitment, and conversion processes, ideology, goals, strategies, and general structure form the intelligence requirement for psychological operations in counterterrorism. The methodology used in this article is a mixed method.

Keywords: psychological operations, terrorist, counterterrorism, terrorism

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Authors: Mohammad Aljuhani, Yasamina Aljuhani

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The Mining Engineering Department at College of Engineering in NBU is under establishment. It is essential to establish such department in NBU. This is because, it is the only university in the region. Moreover, the mining industry is very active in the northern borders region. However, there is no mining engineering department in KSA except one in King Abdulziz University, which is 1400 km from the mining industry in the northern borders. As a result, department graduates from KAU find difficulties to get suitable jobs in their specialization in spite of their few numbers graduated per year and the presence of many jobs vacancies at the local mining sector. Therefore, the objectives of this research are to identify, measure and analyze the above mentioned problem from educational point of view. One more objective is to add a contribution towards solving such vital, society affecting problem. For achieving the first task of the research, that is problem size identification and analyses, a questionnaire was designed. The questionnaire was directed towards experienced engineers, in the mining and related industries, including the ministry of petroleum and minerals, Saudi Geological Survey, and Ma’aden Company as being prospective employers for the mining sector. The questionnaire target was to evaluate the Saudi mining engineers from an industrial point of view and to detect the main reasons behind their failure to find jobs. In addition, the study focuses in the demand of mining engineers in the northern borders region. Moreover, the study plan of the suggested department is designed based on the requirements of the mining industry. The feedback received from the industry reflected major educational shortcomings. In order to overcome the revealed defects, the second objective of the research was achieved where a suggested study plan “curriculum” has been prepared to take into consideration all the points of weakness so as to improve the graduates’ quality to fit the local mining work market.

Keywords: mining engineering, labor market, qualifications, curriculum, mining industry, mining engineers

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Authors: Davidson E. Egirani, Nanfe R. Poyi, Napoleon Wessey

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This paper would report the environmental challenges faced by stakeholders in the development and management of mineral resources in Mindanao mining region of the Philippines. The paper would proffer solutions via the development and management of integrated mineral resource framework. This is by interfacing the views of government, operating mining companies and the mining host communities. The project methods involved the desktop review of existing local, regional, national environmental and mining legislation. This was followed up with visits to mining sites and discussions were held with stakeholders in the mineral sector. The findings from a 2-year investigation would reveal lack of information, education, and communication campaign by stakeholders on environmental, health, political, and social issues in the mining industry. Small-scale miners lack the professional muscles for a balance shift of emphasis to sustainable and responsible mining to avoid environmental degradation and human health effect. Therefore, there is a need to balance ecological requirements, sustainability of the environment and development of mineral resources. This paper would provide an environmentally friendly mineral resource development framework.

Keywords: ecological requirements, environmental degradation, human health, mining legislation, responsible mining

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Authors: J. Suneetha, Vijayalaxmi

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Sequential Pattern Mining involves applying data mining methods to large data repositories to extract usage patterns. Sequential pattern mining methodologies used to analyze the data and identify patterns. The patterns have been used to implement efficient systems can recommend on previously observed patterns, in making predictions, improve usability of systems, detecting events, and in general help in making strategic product decisions. In this paper, identified performance of approximate sequential pattern mining defines as identifying patterns approximately shared with many sequences. Approximate sequential patterns can effectively summarize and represent the databases by identifying the underlying trends in the data. Conducting an extensive and systematic performance over synthetic and real data. The results demonstrate that ApproxMAP effective and scalable in mining large sequences databases with long patterns.

Keywords: multiple data, performance analysis, sequential pattern, sequence database scalability

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Tanvi P. Patel, Warish D. Patel

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Association rule mining forms the core of data mining and it is termed as one of the well-known methodologies of data mining. Objectives of mining is to find interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. For efficient targeted query processing, finding frequent patterns and itemset mining, there is an efficient way to generate an itemset tree structure named Memory Efficient Itemset Tree. Memory efficient IT is efficient for storing itemsets, but takes more time as compare to traditional IT. The proposed strategy generates maximal frequent itemsets from memory efficient itemset tree by using levelwise pruning. For that firstly pre-pruning of items based on minimum support count is carried out followed by itemset tree reconstruction. By having maximal frequent itemsets, less number of patterns are generated as well as tree size is also reduced as compared to MEIT. Therefore, an enhanced approach of memory efficient IT proposed here, helps to optimize main memory overhead as well as reduce processing time.

Keywords: association rule mining, itemset mining, itemset tree, meit, maximal frequent pattern

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