Search results for: educational process mining

Authors: E. Fathallahi Aghdam, V. Hosseini

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Environmental pollution is not limited to a specific region or country; that is why sustainable development, as a necessary process for improvement, pays attention to issues such as destruction of natural resources, degradation of biological system, global pollution, and climate change in the world, especially in the developing countries. According to the World Health Organization, as a developing city, Tehran (capital of Iran) is one of the most polluted cities in the world in terms of air pollution. In this study, three pollutants including particulate matter less than 10 microns, nitrogen oxides, and sulfur dioxide were evaluated in Tehran using data mining techniques and through Crisp approach. The data from 21 air pollution measuring stations in different areas of Tehran were collected from 1999 to 2013. Commercial softwares Clementine was selected for this study. Tehran was divided into distinct clusters in terms of the mentioned pollutants using the software. As a data mining technique, clustering is usually used as a prologue for other analyses, therefore, the similarity of clusters was evaluated in this study through analyzing local conditions, traffic behavior, and industrial activities. In fact, the results of this research can support decision-making system, help managers improve the performance and decision making, and assist in urban studies.

Keywords: data mining, clustering, air pollution, crisp approach

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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: Shafiq Alam, Yen Ning Lee

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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: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

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Authors: Hyun-Woo Cho

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Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

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Authors: Konstantinos Sofianos, Michail Stefanidakis

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Nowadays, despite the great advances in technology, most educational frameworks lack a strong educational design basis. E-learning has become prevalent, but it faces various challenges such as student isolation and lack of quality in the learning process. An intelligent learning system provides a student with educational material according to their learning background and learning preferences. It records full information about the student, such as demographic information, learning styles, and academic performance. This information allows the system to be fully adapted to the student’s needs. In this paper, we propose a framework and an ontology for music education, consisting of the learner model and all elements of the learning process (learning objects, teaching methods, learning activities, assessment). This framework can be integrated into an intelligent learning system and used for music education in schools for the development of professional skills and beyond.

Keywords: intelligent learning systems, e-learning, music education, ontology, semantic web

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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: Rezky Rifaini, Raden Bagus Fajriya Hakim

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This research was conducted at the Bolo primary health Care in Bima Regency. The purpose of the research is to find out the association pattern that is formed of medical record database from Bolo Primary health care’s patient. The data used is secondary data from medical records database PHC. Sequential pattern mining technique is the method that used to analysis. Transaction data generated from Patient_ID, Check_Date and diagnosis. Sequential Pattern Discovery Algorithms Using Equivalent Classes (SPADE) is one of the algorithm in sequential pattern mining, this algorithm find frequent sequences of data transaction, using vertical database and sequence join process. Results of the SPADE algorithm is frequent sequences that then used to form a rule. It technique is used to find the association pattern between items combination. Based on association rules sequential analysis with SPADE algorithm for minimum support 0,03 and minimum confidence 0,75 is gotten 3 association sequential pattern based on the sequence of patient_ID, check_Date and diagnosis data in the Bolo PHC.

Keywords: diagnosis, primary health care, medical record, data mining, sequential pattern mining, SPADE algorithm

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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: Kuznetsova Tamara, Sametova Fauziya

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Modern period of educational development is characterized by various attempts in higher quality and effective result provision. Having acquired the modernized educational paradigm, our academic community placed the personality development through language and culture under the focus of primary research. The competency-based concept claims for professionally ready specialists who are capable of solving practical problems. In this sense, under the circumstances of the current development of Kazakhstani society, it is inevitable to form the ability to conduct argumentative discourse as the crucial element of intercultural communicative competence. This article particularly states the necessity of the culture of argumentative discourse formation presents theoretical background of its organization and aims at identifying important argumentative skills within educational process.

Keywords: argumentative discourse, teaching process, skills, competency

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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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: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data 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: Ruth Donkoh, Wing On Lee, Solomon A. Boateng, Portia Oware Twerefoo, Josephine Donkor

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In this paper we defend the value of theories and approaches to educational policy and planning in enhancing the educational developments in Ghana. This mission is achieved by enumerating the recent educational developments in Ghana and juxtaposing it with some educational theories, approaches to policy making, and policy planning to see if the educational developments conform with the theory principles as well as policy making and planning processes. Data collection for the research was made through textual analysis of policy documents as well as review of relevant literatures. The findings reveled that educational developments in Ghana are unable to attain its objectives due to the policies not conforming with the policy formation and planning principles. In addition, was that education planning in Ghana does not follow the policy-administration dichotomy theory principles and likewise the distribution of educational needs goes contrary to the equity theory. We recommend that educational policies in Ghana should be in conformity with the principles of theories as well as the approaches to educational policy making and planning to help meet the needs of learners, attain educational quality, and to help in the accomplishment of educational development objectives.

Keywords: Ghana education, equity theories, politics- administration dichotomy theory, educational policies, educational planning

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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: Saeidreza Radpour, Md. Ahiduzzaman, Amit Kumar

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Alberta’s mining sector consumed 871.3 PJ in 2012, which is 67.1% of the energy consumed in the industry sector and about 40% of all the energy consumed in the province of Alberta. Natural gas, petroleum products, and electricity supplied 55.9%, 20.8%, and 7.7%, respectively, of the total energy use in this sector. Oil sands mining and upgrading to crude oil make up most of the mining energy sector activities in Alberta. Crude oil is produced from the oil sands either by in situ methods or by the mining and extraction of bitumen from oil sands ore. In this research, the factors affecting oil sands production have been assessed and a framework has been developed for market penetration of new efficient technologies in this sector. Oil sands production amount is a complex function of many different factors, broadly categorized into technical, economic, political, and global clusters. The results of developed and implemented statistical analysis in this research show that the importance of key factors affecting on oil sands production in Alberta is ranked as: Global energy consumption (94% consistency), Global crude oil price (86% consistency), and Crude oil export (80% consistency). A framework for modeling oil sands energy technologies’ market penetration (OSETMP) has been developed to cover related technical, economic and environmental factors in this sector. It has been assumed that the impact of political and social constraints is reflected in the model by changes of global oil price or crude oil price in Canada. The market share of novel in situ mining technologies with low energy and water use are assessed and calculated in the market penetration framework include: 1) Partial upgrading, 2) Liquid addition to steam to enhance recovery (LASER), 3) Solvent-assisted process (SAP), also called solvent-cyclic steam-assisted gravity drainage (SC-SAGD), 4) Cyclic solvent, 5) Heated solvent, 6) Wedge well, 7) Enhanced modified steam and Gas push (emsagp), 8) Electro-thermal dynamic stripping process (ET-DSP), 9) Harris electro-magnetic heating applications (EMHA), 10) Paraffin froth separation. The results of the study will show the penetration profile of these technologies over a long term planning horizon.

Keywords: appliances efficiency improvement, diffusion models, market penetration, residential sector

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Authors: Kalu Uka Orji, Nasiman Sapari, Khamaruzaman W. Yusof

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Exposure of galena (PbS), tealite (PbSnS2), and other associated minerals during mining activities release lead (Pb) and other heavy metals into the mining water through oxidation and dissolution. Heavy metal pollution has become an environmental challenge. Lead, for instance, can cause toxic effects to human health, including brain damage. Ex-mining pond water was reported to contain lead as high as 69.46 mg/L. Conventional treatment does not easily remove lead from water. A promising and emerging treatment technology for lead removal is the application of the electrocoagulation (EC) process. However, some of the problems associated with EC are systematic reactor design, selection of maximum EC operating parameters, scale-up, among others. This study investigated an EC process for the removal of lead from synthetic ex-mining pond water using a batch reactor and Fe electrodes. The effects of various operating parameters on lead removal efficiency were examined. The results obtained indicated that the maximum removal efficiency of 98.6% was achieved at an initial PH of 9, the current density of 15mA/cm2, electrode spacing of 0.3cm, treatment time of 60 minutes, Liquid Motion of Magnetic Stirring (LM-MS), and electrode arrangement = BP-S. The above experimental data were further modeled and optimized using a 2-Level 4-Factor Full Factorial design, a Response Surface Methodology (RSM). The four factors optimized were the current density, electrode spacing, electrode arrangements, and Liquid Motion Driving Mode (LM). Based on the regression model and the analysis of variance (ANOVA) at 0.01%, the results showed that an increase in current density and LM-MS increased the removal efficiency while the reverse was the case for electrode spacing. The model predicted the optimal lead removal efficiency of 99.962% with an electrode spacing of 0.38 cm alongside others. Applying the predicted parameters, the lead removal efficiency of 100% was actualized. The electrode and energy consumptions were 0.192kg/m3 and 2.56 kWh/m3 respectively. Meanwhile, the adsorption kinetic studies indicated that the overall lead adsorption system belongs to the pseudo-second-order kinetic model. The adsorption dynamics were also random, spontaneous, and endothermic. The higher temperature of the process enhances adsorption capacity. Furthermore, the adsorption isotherm fitted the Freundlish model more than the Langmuir model; describing the adsorption on a heterogeneous surface and showed good adsorption efficiency by the Fe electrodes. Adsorption of Pb2+ onto the Fe electrodes was a complex reaction, involving more than one mechanism. The overall results proved that EC is an efficient technique for lead removal from synthetic mining pond water. The findings of this study would have application in the scale-up of EC reactor and in the design of water treatment plants for feed-water sources that contain lead using the electrocoagulation method.

Keywords: ex-mining water, electrocoagulation, lead, adsorption kinetics

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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: Edo O. Ekanem, Eme S. Ndeh, Ubong M. Nkok

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The role of educational technology in teaching is of great importance because of its systematic way of conceptualizing the execution and evaluation of Educational process. This study therefore examines the use of Educational Technology for teaching in Colleges of Education in south south geo-political zone of Nigeria. Four specific purposes, four research questions and four null hypotheses guided the study. The study adopted descriptive research design of the survey type. A sample of 295 lecturers from six colleges of education was selected using stratified and simple random sampling techniques. The data for this study were collected through a self-designed questionnaire and was analyzed using frequency counts, percentage scores and t-test statistics. The hypotheses for the study were tested at 0.05 significance. Findings from the study reveal that Educational Technology facilities such as Internet, electronic notice boards and projectors were not adequately used for teaching in the Colleges. It was also found that most lecturers use more of visual media than electronic/digital media in the classrooms. Moreover, the study shows that lecturers’ use of educational technology is influenced by their highest academic qualification while their level of awareness about the value of technology in education is not gender based. Lecturers’ lack of competence, inadequate Educational Technology facilities and Power are among the factors that inhibit the adequate use of the facilities. Based on the findings, recommendations were made on how to ensure effective use of Educational Technology for teaching in the Colleges in Nigeria.

Keywords: colleges of education, educational technology, teaching, Nigeria

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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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: Victor Radich, Tania Basso, Regina Moraes

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

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

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Authors: Mohammed Amraouy, Mostafa Bellafkih, Abdellah Bennane, Aziza Benomar

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Distance education has become popular due to their ability to provide learning from almost anywhere and anytime. COVID-19 forced educational institutions to urgently introduce distance education to ensure pedagogical continuity, so all stakeholders were invited to adapt to this new paradigm. In order to identify strengths and weaknesses, the research focuses on the need to create an effective mechanism for evaluating distance education. The aims of this research were to explore and evaluate the use of digital media in general and official platforms in particular in distance education practices. To this end, we have developed and validated a questionnaire before administering it to a sample of 431 teachers in Morocco. Teachers reported lower knowledge and skills in the didactic use of ICT in the distance education process. In addition, although age and educative experience of the teachers continue to modulate the level of instrumental skills. Therefore, resources (digital resources and infrastructure) and the teachers’ ICT training present serious limitations, which require a training more focused on the distance educational paradigm and educational environments that allow teachers to create educational activities able to promote and facilitate the distance learning process.

Keywords: distance education, e-learning, teachers’ perceptions, assessment

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Authors: Zeba Mahmood

Abstract:

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

Abstract:

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: 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