Search results for: graph mining

Authors: Valentina Marutyan

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

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

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

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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: Carla Silva

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

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

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Authors: Arie Pujiwati, Kengo Nakamura, Noriaki Watanabe, Takeshi Komai

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Coal mining is well known to cause considerable environmental impacts, including trace element contamination of soil. This study aimed to assess the trace element (As, Cd, Co, Cu, Ni, Pb, Sb, and Zn) contamination of soil in the vicinity of coal mining activities, using the case study of Asam-asam River basin, South Kalimantan, Indonesia, and to assess the human health risk, incorporating total and bioavailable (water-leachable and acid-leachable) concentrations. The results show the enrichment of As and Co in soil, surpassing the background soil value. Contamination was evaluated based on the index of geo-accumulation, I_geo and the pollution index, PI. I_geo values showed that the soil was generally uncontaminated (I_geo ≤ 0), except for elevated As and Co. Mean PI for Ni and Cu indicated slight contamination. Regarding the assessment of health risks, the Hazard Index, HI showed adverse risks (HI > 1) for Ni, Co, and As. Further, Ni and As were found to pose unacceptable carcinogenic risk (risk > 1.10^-5). Farming, settlement, and plantation were found to present greater risk than coal mines. These results show that coal mining activity in the study area contaminates the soils by particular elements and may pose potential human health risk in its surrounding area. This study is important for setting appropriate countermeasure actions and improving basic coal mining management in Indonesia.

Keywords: coal mine, risk, trace elements, soil

Procedia PDF Downloads 256

Authors: Po-Jen Chen, Jian-Jiun Ding, Hung-Wei Hsu, Chien-Yao Wang, Jia-Ching Wang

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A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved.

Keywords: convolutional neural network, label relation, hierarchy neural network, scene classification

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Authors: Ndivhudzannyi Rembuluwani, Francis Dacosta, Emmanuel Mhlongo

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The small-scale rock aggregate sector of the mining industry is a major source of employment for a significant number of people, particularly in remote rural areas, where alternative livelihoods are rare. It contributes to local economy by generating income and producing major and essential materials for the building, construction, and other industries. However, the sector is confronted with many challenges that hamper productivity and growth. The problems that confront this sector includes: health and safety, environmental impacts, low production and low adherence to mining legislations. This study investigated the challenges confronting selected small-scale rock aggregate mines in the Vhembe District of Limpopo province of South Africa, assesses the health, safety, low production and environmental impacts associated with aggregate production and to develop an integrated approach of addressing the multi-faceted challenges.

Keywords: health and safety, legislative framework, productivity, rock aggregate, small-scale mining

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Olaniyi Awe, Adelana R. Adetunji, Abraham Adeleke

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Mining of alluvial gold ore by artisanal miners has been going on for decades at Itagunmodi, Southwestern Nigeria. In order to optimize the traditional panning gravity separation method commonly used in the area, a mineral particle size analysis study is critical. This study analyzed alluvial gold ore samples collected at identified five different locations in the area with a view to determine the ore particle size distributions. 500g measured of as-received alluvial gold ore sample was introduced into the uppermost sieve of an electrical sieve shaker consisting of sieves arranged in the order of decreasing nominal apertures of 5600μm, 3350μm, 2800μm, 355μm, 250μm, 125μm and 90μm, and operated for 20 minutes. The amount of material retained on each sieve was measured and tabulated for analysis. A screen analysis graph using the Gaudin Schuman method was drawn for each of the screen tests on the alluvial samples. The study showed that the percentages of fine particle size -125+90 μm fraction were 45.00%, 36.00%, 39.60%, 43.00% and 36.80% for the selected samples. These primary ore characteristic results provide reference data for the alluvial gold ore processing method selection, process performance measurement and optimization.

Keywords: alluvial gold ore, sieve shaker, particle size, Gaudin Schumann

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Authors: Waheeda Jabbar, Ali Gul, Rida Noor, Sania Kurd, Saba Gulzar

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Hundreds of miners die from mining accidents each year due to poisonous gases found underground mining areas. This paper proposed an idea to protect the precious lives of mining workers. A supervising system is designed which is based on ZigBee wireless technique along with the smart protective helmets to detect real-time surveillance and it gives early warnings on presence of different poisonous gases in order to save mineworkers from any danger caused by these poisonous gases. A wireless sensor network is established using ZigBee wireless technique by integrating sensors on the helmet, apart from this helmet have embedded heartbeat sensor to detect the pulse rate and be aware of the physical or mental strength of a mineworker to increase the potential safety. Radio frequency identification (RFID) technology is used to find the location of workers. A ZigBee based base station is set-upped to control the communication. The idea is implemented and results are verified through experiment.

Keywords: Arduino, gas sensor (MQ7), RFID, wireless ZigBee

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Yusif Habib

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The Volta River Basin (VRB) is a transboundary resource shared by Six (6) the West African States. It’s utilization spans across irrigation, hydropower generation, domestic/household water use, transportation, industrial processing, among others. Simultaneously, mineral resources such as gold are mined within the VRB catchment. Typically, the extraction/mining operation is earth-surface excavation; known as Artisanal and Small-scale mining. We developed a conceptual framework in the context of Water-Energy-Food (WEF) Nexus to delineate the trade-offs and synergies between the mineral extractive operation’s impact on Agricultural systems, specifically, cereal crops (e.g. Maize, Millet, and Rice) and the environment (water and soil quality, deforestation, etc.) on the VRB. Thus, the study examined the trade-offs and synergies through the WEF nexus lens to explore the extent of an eventual overarching mining preference for gold exploration with high economic returns as opposed to the presumably low yearly harvest and household income from food crops production to inform intervention prioritization. Field survey (household, expert, and stakeholder consultation), bibliometric analysis/literature review, scenario, and simulation models, including land-use land cover (LULC) analyses, were conducted. The selected study area(s) in Ghana was the location where the mineral extractive operation’s presence and impact are widespread co-exist with the Agricultural systems. Overall, the study proposes mechanisms of the virtuous cycle through FEW Nexus instead of the presumably existing vicious cycle to inform decision making and policy implementation.

Keywords: agriculture, environmental sustainability, gold Mining, synergies, trade-off, water-energy-food nexus

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Authors: Da Tao, Mingfu Qin, Wenkai Li, Tieyan Wang

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While the use of electronic personal health portals has gained increasing popularity in the healthcare industry, users usually experience difficulty in comprehending and correctly responding to personal health information, partly due to inappropriate or poor presentation of the information. The way personal health information is visualized may affect how users perceive and assess their personal health information. This study was conducted to examine the effects of information visualization format and visualization mode on the comprehension and perceptions of personal health information among personal health information users with eye tracking techniques. A two-factor within-subjects experimental design was employed, where participants were instructed to complete a series of personal health information comprehension tasks under varied types of visualization mode (i.e., whether the information visualization is static or dynamic) and three visualization formats (i.e., bar graph, instrument-like graph, and text-only format). Data on a set of measures, including comprehension performance, perceptions, and eye movement indicators, were collected during the task completion in the experiment. Repeated measure analysis of variance analyses (RM-ANOVAs) was used for data analysis. The results showed that while the visualization format yielded no effects on comprehension performance, it significantly affected users’ perceptions (such as perceived ease of use and satisfaction). The two graphic visualizations yielded significantly higher favorable scores on subjective evaluations than that of the text format. While visualization mode showed no effects on users’ perception measures, it significantly affected users' comprehension performance in that dynamic visualization significantly reduced users' information search time. Both visualization format and visualization mode had significant main effects on eye movement behaviors, and their interaction effects were also significant. While the bar graph format and text format had similar time to first fixation across dynamic and static visualizations, instrument-like graph format had a larger time to first fixation for dynamic visualization than for static visualization. The two graphic visualization formats yielded shorter total fixation duration compared with the text-only format, indicating their ability to improve information comprehension efficiency. The results suggest that dynamic visualization can improve efficiency in comprehending important health information, and graphic visualization formats were favored more by users. The findings are helpful in the underlying comprehension mechanism of visualized personal health information and provide important implications for optimal design and visualization of personal health information.

Keywords: eye tracking, information comprehension, personal health information, visualization

Procedia PDF Downloads 102

Authors: Ariungerel Jargal

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: Cave mining is a bulk underground mining method, which allows large low-grade deposits to be mined underground. This method involves undermining the orebody to make it collapse under its own weight into a series of chambers from which the ore extracted. It is a useful technique to extend the life of large deposits previously mined by open pits, and it is a method increasingly proposed for new mines around the world. We plan to conduct a feasibility study using Electrical impedance tomography (EIT) technology to show how much subsidence there is at the intersection with the cave mining surface. EIT is an imaging technique which uses electrical measurements at electrodes attached on the body surface to yield a cross-sectional image of conductivity changes within the object. EIT has been developed in several different applications areas as a simpler, cheaper alternative to many other imaging methods. A low frequency current is injected between pairs of electrodes while voltage measurements are collected at all other electrode pairs. In the difference EIT, images are reconstructed of the change in conductivity distribution (σ) between the acquisition of the two sets of measurements. Image reconstruction in EIT requires the solution of an ill-conditioned nonlinear inverse problem on noisy data, typically requiring make simpler assumptions or regularization. It is noted that the ratio of current to voltage represents a complex value according to Ohm’s law, and that it is theoretically possible to re-express EIT. The results of the experiment were presented on the simulation, and it was concluded that it is possible to conduct further real experiments. Drill a certain number of holes in the top wall of the cave to attach the electrodes, flow a current through them, and measure and acquire the potential through these electrodes. Appropriate values should be selected depending on the distance between the holes, the frequency and duration of the measurements, the surface characteristics and the size of the study area using an EIT device.

Keywords: impedance tomography, cave mining, soil, EIT device

Procedia PDF Downloads 122

Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

Procedia PDF Downloads 109

Authors: Z. Abdullah, A. R. Hamdan

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This paper is meant to analyze the ranking of University of Malaysia Terengganu, UMT’s website in the World Wide Web. There are only few researches have been done on comparing the ranking of universities’ websites so this research will be able to determine whether the existing UMT’s website is serving its purpose which is to introduce UMT to the world. The ranking is based on hub and authority values which are accordance to the structure of the website. These values are computed using two web-searching algorithms, HITS and SALSA. Three other universities’ websites are used as the benchmarks which are UM, Harvard and Stanford. The result is clearly showing that more work has to be done on the existing UMT’s website where important pages according to the benchmarks, do not exist in UMT’s pages. The ranking of UMT’s website will act as a guideline for the web-developer to develop a more efficient website.

Keywords: algorithm, ranking, website, web structure mining

Procedia PDF Downloads 508

Authors: Akshay Agrawal, Anand K. Srivastava

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The increasing amount of XML datasets available to casual users increases the necessity of investigating techniques to extract knowledge from these data. Data mining is widely applied in the database research area in order to extract frequent correlations of values from both structured and semi-structured datasets. The increasing availability of heterogeneous XML sources has raised a number of issues concerning how to represent and manage these semi structured data. In recent years due to the importance of managing these resources and extracting knowledge from them, lots of methods have been proposed in order to represent and cluster them in different ways.

Keywords: XML, similarity measure, clustering, cluster quality, semantic clustering

Procedia PDF Downloads 373

Authors: Khaled Benyounes

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Determination of the rock mechanical properties such as unconfined compressive strength UCS, Young’s modulus E, and tensile strength by the Brazilian test Rtb is considered to be the most important component in drilling and mining engineering project. Research related to establishing correlation between strength and physical parameters of rocks has always been of interest to mining and reservoir engineering. For this, many rock blocks of limestone were collected from the quarry located in Meftah(Algeria), the cores were crafted in the laboratory using a core drill. This work examines the relationships between mechanical properties and some physical properties of limestone. Many empirical equations are established between UCS and physical properties of limestone (such as dry bulk density, velocity of P-waves, dynamic Young’s modulus, alteration index, and total porosity). Others correlations UCS-tensile strength, dynamic Young’s modulus-static Young’s modulus have been find. Based on the Mohr-Coulomb failure criterion, we were able to establish mathematical relationships that will allow estimating the cohesion and internal friction angle from UCS and indirect tensile strength. Results from this study can be useful for mining industry for resolve range of geomechanical problems such as slope stability.

Keywords: limestone, mechanical strength, Young’s modulus, porosity

Procedia PDF Downloads 448

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

Procedia PDF Downloads 347

Authors: Mouslem Damkhi

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This paper focuses on possible states of a football tournament final table according to the number of participating teams. Each team holds a position in the table with which it is possible to determine the highest and lowest points for that team. This paper proposes an optimized search space based on the minimum and maximum number of points which can be gained by each team to produce and enumerate the possible states for a football tournament final table. The proposed search space minimizes producing the invalid states which cannot occur during a football tournament. The generated states are filtered by a validity checking algorithm which seeks to reach a tournament graph based on a generated state. Thus, the algorithm provides a way to determine which team’s wins, draws and loses values guarantee a particular table position. The paper also presents and discusses the experimental results of the approach on the tournaments with up to eight teams. Comparing with a blind search algorithm, our proposed approach reduces generating the invalid states up to 99.99%, which results in a considerable optimization in term of the execution time.

Keywords: combinatorics, enumeration, graph, tournament

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Authors: Vivian W. L. Tsang, Karen Lockhart, Samuel Spiegel, Annalee Yassi

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Background: Workers in the informal economy often incur exposure to well-documented occupational health hazards. Insufficient attention has been afforded to rigorously evaluating intervention programs to reduce the risks, especially in artisanal and small-scale gold mining (ASGM). Objectives: This systematic review, conducted as part of the World Health Organization’s Global Plan of Action for Workers’ Health, sought to assess the state of knowledge on occupational health programs and interventions for the informal artisanal and small-scale gold mining (ASGM) sector, an occupation which directly employs at least 50 million people. Methods: We used a comprehensive search strategy for four well-known databases relevant to health outcomes: PubMed, Engineering Village, OVID Medline, and Web of Science, and employed the PRISMA framework for our analysis. Findings: Ten studies met the inclusion criteria of a primary study focused on assessing the impact of interventions addressing occupational health concerns in ASGM. There were no studies evaluating or even identifying comprehensive occupational health and safety programs for this sector, although target interventions addressing specific hazards exist. Major areas of intervention –education and introduction of mercury-reducing/eliminating technology were identified, and the challenges and limitations of each intervention taken into the assessment. Even for these, however, there was a lack of standardization for measuring outcome or impact, let alone long-term health outcomes for miners and mining communities. Conclusion: There is an urgent need for research on comprehensive occupational health programs addressing the array of hazards faced by artisanal and small-scale miners.

Keywords: informal economy, artisanal and small-scale gold mining, occupational health, health and safety, workplace safety

Procedia PDF Downloads 148

Authors: I. Inthanongsone, C. Drebenstedt, J. C. Bongaerts, P. Sontamino

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The major concern in evaluating the value of mining projects related to the deficiency of the traditional discounted cash flow (DCF) method. This method does not take uncertainties into account and, hence it does not allow for an economic assessment of managerial flexibility and operational adaptability, which are increasingly determining long-term corporate success. Such an assessment can be performed with the real options valuation (ROV) approach, since it allows for a comparative evaluation of unforeseen uncertainties in a project life cycle. This paper presents an economic evaluation model for open pit mining projects based on real options valuation approach. Uncertainties in the model are caused by metal prices and cost uncertainties and the system dynamics (SD) modeling method is used to structure and solve the real options model. The model is applied to a case study. It can be shown that that managerial flexibility reacting to uncertainties may create additional value to a mining project in comparison to the outcomes of a DCF method. One important insight for management dealing with uncertainty is seen in choosing the optimal time to exercise strategic options.

Keywords: DCF methods, ROV approach, system dynamics modeling methods, uncertainty

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

Procedia PDF Downloads 140

Authors: E Tambo, O. O. Olalubi, E. C. Ugwu, J. Y. Ngogang

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Despite the World Health Organization (WHO) declaration of international health emergency concern, the status quo of responses and efforts to stem the worst-recorded Ebola epidemic Ebola outbreak is still precariously inadequate in most of the affected in West. Mining natural resources have been shown to play a key role in both motivating and fuelling ethnic, civil and armed conflicts that have plagued a number of African countries over the last decade. Revenues from the exploitation of natural resources are not only used in sustaining the national economy but also armies, personal enrichment and building political support. Little is documented on the mining and ecological impact on the emergence and geographical distribution of Ebola in Africa over time and space. We aimed to provide a better understanding of the interconnectedness among issues of mining natural, resource management, mining conflict and post-conflict on Ebola outbreak and how wealth generated from abundant natural resources could be better managed in promoting research and development towards strengthening environmental, socioeconomic and health systems sustainability on Ebola outbreak and other emerging diseases surveillance and responses systems prevention and control, early warning alert, durable peace and sustainable development rather than to fuel conflicts, resurgence and emerging diseases epidemics in the perspective of community and national/regional approach. Our results showed the first assessment of systematic impact of all major minerals conflict events diffusion over space and time and mining activities on nine Ebola genesis and geo-distribution in affected countries across Africa. We demonstrate how, where and when mining activities in Africa increase ecological degradation, conflicts at the local level and then spreads violence across territory and time by enhancing the financial capacities of fighting groups/ethnics and diseases onset. In addition, led process of developing minimum standards for natural resource governance; improving governmental and civil society capacity for natural resource management, including the strengthening of monitoring and enforcement mechanisms; understanding the post-mining and conflicts community or national reconstruction and rehabilitation programmes in strengthening or developing community health systems and regulatory mechanisms. In addition the quest for the control over these resources and illegal mining across the landscape forest incursion provided increase environmental and ecological instability and displacement and disequilibrium, therefore affecting the intensity and duration of mining and conflict/wars and episode of Ebola outbreaks over time and space. We highlight the key findings and lessons learnt in promoting country or community-led process in transforming natural resource wealth from a peace liability to a peace asset. The imperative necessity for advocacy and through facilitating intergovernmental deliberations on critical issues and challenges affecting Africa community transforming exploitation of natural resources from a peace liability to outbreak prevention and control. The vital role of mining in increasing government revenues and expenditures, equitable distribution of wealth and health to all stakeholders, in particular local communities requires coordination, cooperative leadership and partnership in fostering sustainable developmental initiatives from mining context to outbreak and other infectious diseases surveillance responses systems in prevention and control, and judicious resource management.

Keywords: mining, mining conflicts, mines, ecological, Ebola, outbreak, mining companies, miners, impact

Procedia PDF Downloads 299

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: Stellina Jolly

Abstract:

July 18th to August 19th 2013 has gone down in the history of India for undertaking the country’s first environment referendum. The Supreme Court had ruled that the Vedanta Group's bauxite mining project in the Niyamgiri Hills of Orissa will have to get clearance from the gram sabha, which will consider the cultural and religious rights of the tribals and forest dwellers living in Rayagada and Kalahandi districts. In the Niyamgiri hills, people of small tribal hamlets were asked to voice their opinion on bauxite mining in their habitat. The ministry has reiterated its stand that mining cannot be allowed on the Niyamgiri hills because it will affect the rights of the Dongria Kondhs. The tribal person who occupies the Niyamgiri Hills in Eastern India accomplished their first success in 2010 in their struggle to protect and preserve their existence, culture and land against Vedanta a London-based mining giant. In August, 2010 Government of India revoked permission for Vedanta Resources to mine bauxite from hills in Orissa State where the Dongria Kondh live as forest dwellers. This came after various protests and reports including amnesty report wherein it highlighted that an alumina refinery in eastern India operated by a subsidiary of mining company. Vedanta was accused of causing air and water pollution that threatens the health of local people and their access to water. The abuse of human rights by corporate is not a new issue it has occurred in Africa, Asia and other parts of the world. Paper focuses on the instances and extent of human right especially in terms of environment violations by corporations. Further Paper details on corporations and sustainable development. Paper finally comes up with certain recommendation including call for a declaration by United Nations on Corporate environment Human Rights Liability.

Keywords: environment, corporate, human rights, sustainable development

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Authors: Vishwa Joshi

Abstract:

The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

Procedia PDF Downloads 492

Authors: M. Ndeley

Abstract:

The performance of global manufacturing supply chains depends on the interaction of production and transport processes. Currently, the scheduling of these processes is done separately without considering mutual requirements, which leads to no optimal solutions. An integrated scheduling of both processes enables the improvement of supply chain performance. The integrated production and transport scheduling problem (PTSP) is NP-hard, so that heuristic methods are necessary to efficiently solve large problem instances as in the case of global manufacturing supply chains. This paper presents a heuristic scheduling approach which handles the integration of flexible production processes with intermodal transport, incorporating flexible land transport. The method is based on a graph that allows a reformulation of the PTSP as a shortest path problem for each job, which can be solved in polynomial time. The proposed method is applied to a supply chain scenario with a manufacturing facility in South Africa and shipments of finished product to customers within the Country. The obtained results show that the approach is suitable for the scheduling of large-scale problems and can be flexibly adapted to different scenarios.

Keywords: production and transport scheduling problem, graph based scheduling, integrated scheduling

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Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

Abstract:

Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Diana Codat

Abstract:

Tweets' analysis is part of text mining. Each document is a written text. It's possible to apply the usual text search techniques, in particular by switching to the bag-of-words representation. But the tweets induce peculiarities. Some may enrich the analysis. Thus, their length is calibrated (at least as far as public messages are concerned), special characters make it possible to identify authors (@) and themes (#), the tweet and retweet mechanisms make it possible to follow the diffusion of the information. Conversely, other characteristics may disrupt the analyzes. Because space is limited, authors often use abbreviations, emoticons to express feelings, and they do not pay much attention to spelling. All this creates noise that can complicate the task. The tweets carry a lot of potentially interesting information. Their exploitation is one of the main axes of the analysis of the social networks. We show how to access Twitter-related messages. We will initiate a study of the properties of the tweets, and we will follow up on the exploitation of the content of the messages. We will work under R with the package 'twitteR'. The study of tweets is a strong focus of analysis of social networks because Twitter has become an important vector of communication. This example shows that it is easy to initiate an analysis from data extracted directly online. The data preparation phase is of great importance.

Keywords: data mining, language R, social networks, Twitter

Procedia PDF Downloads 182

Authors: Khaled Benyounes

Abstract:

Determination of the rock mechanical properties such as unconfined compressive strength UCS, Young’s modulus E, and tensile strength by the Brazilian test Rtb is considered to be the most important component in drilling and mining engineering project. Research related to establishing correlation between strength and physical parameters of rocks has always been of interest to mining and reservoir engineering. For this, many rock blocks of limestone were collected from the quarry located in Meftah (Algeria), the cores were crafted in the laboratory using a core drill. This work examines the relationships between mechanical properties and some physical properties of limestone. Many empirical equations are established between UCS and physical properties of limestone (such as dry bulk density, velocity of P-waves, dynamic Young’s modulus, alteration index, and total porosity). Other correlations, UCS - tensile strength, dynamic Young’s modulus - static Young’s modulus have been find. Based on the Mohr-Coulomb failure criterion, we were able to establish mathematical relationships that will allow estimating the cohesion and internal friction angle from UCS and indirect tensile strength. Results from this study can be useful for mining industry for resolve range of geomechanical problems such as slope stability.

Keywords: limestone, mechanical strength, Young’s modulus, porosity

Procedia PDF Downloads 633