Search results for: emotion mining

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: K. Jahani, J. Razavi

Abstract:

Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone

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Authors: Mohit Kumar, Mayur Betharia

Abstract:

Healthcare domain, with the emergence of Electronic Medical Record (EMR), collects a lot of data which have been attracting Data Mining expert’s interest. In the past, doctors have relied on their intuition while making critical clinical decisions. This paper presents the means to analyze the Medical workflows to get business insights out of huge dumped medical databases. Market Basket Analysis (MBA) which is a special data mining technique, has been widely used in marketing and e-commerce field to discover the association between products bought together by customers. It helps businesses in increasing their sales by analyzing the purchasing behavior of customers and pitching the right customer with the right product. This paper is an attempt to demonstrate Market Basket Analysis applications in healthcare. In particular, it discusses the Market Basket Analysis Algorithm ‘Apriori’ applications within healthcare in major areas such as analyzing the workflow of diagnostic procedures, Up-selling and Cross-selling of Healthcare Systems, designing healthcare systems more user-friendly. In the paper, we have demonstrated the MBA applications using Angiography Systems, but can be extrapolated to other modalities as well.

Keywords: data mining, market basket analysis, healthcare applications, knowledge discovery in healthcare databases, customer relationship management, healthcare systems

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Authors: Issa Qabaja, Fadi Thabtah

Abstract:

Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: K. H. Oyuntungalag, Scott Kenner, O. Erdenetuya

Abstract:

The life cycle phases of mining projects are described in this guidance document, and includes initial phases (exploration, feasibility and planning), mine development (construction and operations), closure and reclamation. Initial phases relate to field programs and desktop studies intended to build the data and knowledge base, including the design of water management infrastructure and development during these initial phases. Such a model is essential to demonstrate that the water management plan (WMP) will provide adequate water for the mine operations and sufficient capacity for anticipated flows and volumes, and minimize environmental impacts on the receiving environment. The water and mass balance model must cover the whole mine life cycle, from the start of mine development to a date sufficiently far in the future where the reclaimed landscape is considered self- sustaining following complete closure of the mine (i.e., post- closure). The model simulates the movement of water within the components of the water management infrastructure and project operating areas, and calculates chemical loadings to each mine component. At Erdenet Mining company an initial water balance model reflecting the tailings dam, groundwater seepage and mine process water was developed in collaboration with Dr. Scott Kenner (visiting Fulbright scholar). From this preliminary study the following recommendations were made: 1. Develop a detailed groundwater model to simulate seepage from the tailings dam, 2. Establish an evaporation pan for improving evapotranspiration estimates, and 3. Measure changes in storage of water within the tailings dam and other water storage components within the mine processing.

Keywords: evapotranspiration , monitoring program, Erdenet mining, tailings dam

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

Abstract:

Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Aloysius Hardoko, Susilo

Abstract:

The root cause of environmental damage post coal mining activities as determined by the province of East Kalimantan as a corridor of economic activity masterplan acceleration of economic development expansion (MP3EI) is the behavior of adults. Adult behavior can be changed through knowledge management and attitude. Based on the root of the problem, the objective of the research is to apply knowledge management and attitude based on holistic approach in learning andragogy as an effort to solve environmental problems after coal mining activities. Research methods to achieve the objective of using quantitative research with pretest posttest group design. Knowledge management and attitudes based on a holistic approach in adult learning are applied through initial learning activities, core and case-based cover of environmental damage. The research instrument is a description of the case of environmental damage. The data analysis uses t-test to see the effect of knowledge management attitude based on holistic approach before and after adult learning. Location and sample of representative research of adults as many as 20 people in Kutai Kertanegara District, one of the districts in East Kalimantan province, which suffered the worst environmental damage. The conclusion of the research result is the application of knowledge management and attitude in adult learning influence to adult knowledge and attitude to overcome environmental problem post coal mining activity.

Keywords: knowledge management and attitude, holistic approach, andragogy learning, environmental damage

Procedia PDF Downloads 241

Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Rajkumar Ghosh

Abstract:

The industrialization of coal mining and related activities has a significant impact on groundwater in the surrounding areas of the Damodar River. The Damodar River basin, located in eastern India, is known as the "Ruhr of India" due to its abundant coal reserves and extensive coal mining and industrial operations. One of the major consequences of collieries on groundwater is the contamination of water sources. Coal mining activities often involve the excavation and extraction of coal through underground or open-pit mining methods. These processes can release various pollutants and chemicals into the groundwater, including heavy metals, acid mine drainage, and other toxic substances. As a result, the quality of groundwater in the Damodar River region has deteriorated, making it unsuitable for drinking, irrigation, and other purposes. The high concentration of heavy metals, such as arsenic, lead, and mercury, in the groundwater has posed severe health risks to the local population. Prolonged exposure to contaminated water can lead to various health problems, including skin diseases, respiratory issues, and even long-term ailments like cancer. The contamination has also affected the aquatic ecosystem, harming fish populations and other organisms dependent on the river's water. Moreover, the excessive extraction of groundwater for industrial processes, including coal washing and cooling systems, has resulted in a decline in the water table and depletion of aquifers. This has led to water scarcity and reduced availability of water for agricultural activities, impacting the livelihoods of farmers in the region. Efforts have been made to mitigate these issues through the implementation of regulations and improved industrial practices. However, the historical legacy of coal industrialization continues to impact the groundwater in the Damodar River area. Remediation measures, such as the installation of water treatment plants and the promotion of sustainable mining practices, are essential to restore the quality of groundwater and ensure the well-being of the affected communities. In conclusion, the coal industrialization in the Damodar River surrounding has had a detrimental impact on groundwater. This research focuses on soil subsidence induced by the over-exploitation of ground water for dewatering open pit coal mines. Soil degradation happens in arid and semi-arid regions as a result of land subsidence in coal mining region, which reduces soil fertility. Depletion of aquifers, contamination, and water scarcity are some of the key challenges resulting from these activities. It is crucial to prioritize sustainable mining practices, environmental conservation, and the provision of clean drinking water to mitigate the long-lasting effects of collieries on the groundwater resources in the region.

Keywords: coal mining, groundwater, soil subsidence, water table, damodar river

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Authors: Jose Daniel Braga Alves, Joaquim Gois, Alexandre Leite

Abstract:

This abstract presents the responsible and sustainable mining case study of a Portuguese mine operation, highlighting how mine exploitation can sustainably exist in balance with the environment, aligned with all stakeholders. The mining operation is remotely located in a United Nations (UN) biodiversity reserve, away from major industrial centers or logistical ports, and presents an interesting investigation to assess the balanced mine operation in alignment with all key stakeholders, which presents unique opportunities as well as challenges. Based on the sustainable mining framework, it is intended to detail examples of best practices from Sociedade Mineira de Neves-Corvo (SOMINCOR), demonstrating social acceptance by the local community, health, and safety at work, reduction of environmental impacts and management of mining waste, which directly influence the acceptance and recognition of a sustainable operation. The case study aims to present the SOMINCOR approach to sustainable mining, focusing on social responsibility, considering materials provided by Lundin Mining Corporation (LMC) and SOMINCOR and the socially responsible approach of the mining operations., referencing related international guidelines, UN Sustainable Development Goals. The researchers reviewed LMC's annual Sustainability Reports (2019, 2020 and 2021) and updated information regarding material topics of the most significant interest to internal and external stakeholders. These material topics formed the basis of the corporation-wide sustainability strategy. LMC's Responsible Mining Policy (RMP) was reviewed, focusing on the commitment that guides the approach to responsible operation and management of the Company's business. Social performance, compliance, environmental management, governance, human rights, and economic contribution are principles of the RMP. The Human Rights Risk Impact Assessment (HRRIA), based on frameworks including UN Guiding Principles (UNGP), Voluntary Principles on Security and Human Rights, and a community engagement program implemented (SLO index), was part of this research. The program consists of ongoing surveys and perceptions studies using behavioural science insights, data from which was not available within the timeframe of completing this research. LMC stakeholder engagement standards and grievance mechanisms were also reviewed. Stakeholder engagement and the community's perception are key to this operation to ensure social license to operate (SLO). Preliminary surveys with local communities provided input data for the local development strategy. After the implementation of several initiatives, subsequent surveys were performed to assess acceptance and trust from the local communities and changes to the SLO index. SOMINCOR's operation contributes to 12 out of 17 sustainable development goals. From the assessed and available data, local communities and social engagement are priorities to SOMINCOR. Experience to date shows that the continual engagement with local communities and the grievance mechanisms in place are respected and followed for all concerns presented by any stakeholder. It can be concluded that this underground mine in Portugal complies with applicable regulations and goes beyond them with regard to sustainable development and engagement with key stakeholders.

Keywords: sustainable mining, development goals, portuguese mining, zinc copper

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

Abstract:

In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Safa Adi

Abstract:

This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms). We will compare two previous approaches GTC and PSP, that resumes the general principles of GSP. Furthermore this paper will discuss PG-hybrid algorithm, that using PSP and GTC. The results show that PSP and GTC are more efficient than GSP. On the other hand, the GTC algorithm performs better than PSP. The PG-hybrid algorithm use PSP algorithm for the two first passes on the database, and GTC approach for the following scans. Experiments show that the hybrid approach is very efficient for short, frequent sequences.

Keywords: database, GTC algorithm, PSP algorithm, sequential patterns, time constraints

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Authors: Philip T. Broughton, Mark P. Bettney, Isla L. Smail

Abstract:

Effective treatment of ground instability is essential when managing the impacts associated with historic mining. A field trial was undertaken by the Coal Authority to investigate the geotechnical performance and potential use of composite materials comprising resin and fill or stone to safely treat surface collapses, such as crown-holes, associated with shallow mining. Test pits were loosely filled with various granular fill materials. The fill material was injected with commercially available silicate and polyurethane resin foam products. In situ and laboratory testing was undertaken to assess the geotechnical properties of the resultant composite materials. The test pits were subsequently excavated to assess resin permeation. Drilling and resin injection was easiest through clean limestone fill materials. Recycled building waste fill material proved difficult to inject with resin; this material is thus considered unsuitable for use in resin composites. Incomplete resin permeation in several of the test pits created irregular ‘blocks’ of composite. Injected resin foams significantly improve the stiffness and resistance (strength) of the un-compacted fill material. The stiffness of the treated fill material appears to be a function of the stone particle size, its associated compaction characteristics (under loose tipping) and the proportion of resin foam matrix. The type of fill material is more critical than the type of resin to the geotechnical properties of the composite materials. Resin composites can effectively support typical design imposed loads. Compared to other traditional treatment options, such as cement grouting, the use of resin composites is potentially less disruptive, particularly for sites with limited access, and thus likely to achieve significant reinstatement cost savings. The use of resin composites is considered a suitable option for the future treatment of shallow mining collapses.

Keywords: composite material, ground improvement, mining legacy, resin

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Authors: María R. Contreras, Diana Endara

Abstract:

Cyanide leaching is the most used technology for gold mining industry, which produces large amounts of effluents requiring treatment. In Ecuador the development of gold mining industry has increased, causing significant environmental impacts due to the highly use of cyanide, it is estimated that 10 gr of extracted gold generates 7000 liters of water contaminated with 300mg/L of free cyanide. The most common methods used nowadays are the treatment with peroxodisulfuric acid, ozonation, H₂O₂ and other reactants which are expensive and present disadvantages. Several methods have been developed to treat this contaminant such as heterogeneous catalysts. Layered double hydroxides (LDHs) have received much attention due to their wide applications like a catalysis support. Therefore, in this study, Mg-Al/ LDH was synthetized by coprecipitation method and then platinum was impregned on it, in order to enhance its catalytic activity. Two methods of impregnation were used, the first one, called incipient wet impregnation and the second one was developed by continuous agitation of LDH in contact with chloroplatinic acid solution for 24 h. The support impregnated was analyzed by X-ray diffraction, FTIR and SEM. Finally, the oxidation of cyanide ion was performed by preparing synthetic solutions of sodium cyanide (NaCN) with an initial concentration of 500 mg/L at pH 10,5 and air flow of 180 NL/h. After 8 hours of treatment, an 80% of oxidation of ion cyanide was achieved.

Keywords: catalysis, cyanide, LDHs, mining

Procedia PDF Downloads 145

Authors: Adesola O. Orimoloye, Edward Gobina

Abstract:

Membrane separation technology is still considered as an emerging technology in the mining sector and does not yet have the widespread acceptance that it has in other industrial sectors. Underground Coal Gasification (UCG), wherein coal is converted to gas in-situ, is a safer alternative to mining method that retains all pollutants underground making the process environmentally friendly. In-situ combustion of coal for power generation allows access to more of the physical global coal resource than would be included in current economically recoverable reserve estimates. Where mining is no longer taking place, for economic or geological reasons, controlled gasification permits exploitation of the deposit (again a reaction of coal to form a synthesis gas) of coal seams in situ. The oxygen supply stage is one of the most expensive parts of any gasification project but the use of membranes is a potentially attractive approach for producing oxygen-enriched air. In this study, a variety of cost-effective membrane materials that gives an optimal amount of oxygen concentrations in the range of interest was designed and tested at diverse operating conditions. Oxygen-enriched atmosphere improves the combustion temperature but a decline is observed if oxygen concentration exceeds optimum. Experimental result also reveals the preparatory method, apparatus and performance of the fabricated membrane.

Keywords: membranes, oxygen-enrichment, gasification, coal

Procedia PDF Downloads 322

Authors: Iftikhar U. Sikder

Abstract:

This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

Procedia PDF Downloads 531

Authors: Aloysius Hardoko, Susilo

Abstract:

The root cause of the problem after the environmental damage due to coal mining activities defined as the province of East Kalimantan corridor masterplan economic activity accelerated the expansion of Indonesia's economic development (MP3EI) is the behavior of adults. Adult behavior can be changed through knowledge management and attitude. Based on the root of the problem, the objective of the research is to apply knowledge management and attitude based on holistic approach in learning andragogy as an effort to solve environmental problems after coal mining activities. Research methods to achieve the objective of using quantitative research with pretest postes group design. Knowledge management and attitudes based on a holistic approach in adult learning are applied through initial learning activities, core and case-based cover of environmental damage. The research instrument is a description of the case of environmental damage. The data analysis uses t-test to see the effect of knowledge management attitude based on holistic approach before and after adult learning. Location and sample of representative research of adults as many as 20 people in Kutai Kertanegara District, one of the districts in East Kalimantan province, which suffered the worst environmental damage. The conclusion of the research result is the application of knowledge management and attitude in adult learning influence to adult knowledge and attitude to overcome environmental problem post-coal mining activity.

Keywords: knowledge management and attitude, holistic approach, andragogy learning, environmental Issue

Procedia PDF Downloads 207

Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 420

Authors: Wusu Conteh

Abstract:

Since the 1990s, neo-liberalization has become a global agenda. The free market ushered in an unprecedented drive by Multinational Corporations (MNCs) to secure mineral rights in resource-rich countries. Several governments in the Global South implemented a liberalized mining policy with support from the International Financial Institutions (IFIs). MNCs have maintained that voluntary Corporate Social Responsibility (CSR) has engendered socio-economic development in mining-affected communities. However, most resource-rich countries are struggling to transform the resources into sustainable socio-economic development. They are trapped in what has been widely described as the ‘resource curse.’ In an attempt to address this resource conundrum, the African Mining Vision (AMV) of 2009 developed a model on resource governance. The advent of the AMV has engendered the introduction of mandatory community development agreement (CDA) into the legal framework of many countries in Africa. In 2009, Sierra Leone enacted the Mines and Minerals Act that obligates mining companies to invest in Primary Host Communities. The study employs interviews and field observation techniques to explicate the dynamics of the CDA program. A total of 25 respondents -government officials, NGOs/CSOs and community stakeholders were interviewed. The study focuses on a case study of the Sierra Rutile CDA program in Sierra Leone. Extant scholarly works have extensively explored the resource curse and voluntary CSR. There are limited studies to uncover the mandatory CDA and its impact on socio-economic development in mining-affected communities. Thus, the purpose of this study is to explicate the impact of the CDA in Sierra Leone. Using the theory of change helps to understand how the availability of mandatory funds can empower communities to take an active part in decision making related to the development of the communities. The results show that the CDA has engendered a predictable fund for community development. It has also empowered ordinary members of the community to determine the development program. However, the CDA has created a new ground for contestations between the pre-existing local governance structure (traditional authority) and the newly created community development committee (CDC) that is headed by an ordinary member of the community.

Keywords: community development agreement, impact, mandatory, participation

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Authors: Ravindra N. Singh, Dheerendra P. Singh

Abstract:

Recent discoveries of Bronze Age artefacts, tin slag, furnaces and crucibles, together with new geological evidence on tin deposits in Tosham area of Bhiwani district in Haryana (India) provide the opportunity to survey the evidence for possible sources of tin and the use of bronze in the Harappan sites of north western India. Earlier, Afghanistan emerged as the most promising eastern source of tin utilized by Indus Civilization copper-smiths. Our excavations conducted at Khanak near Tosham mining area during 2014 and 2016 revealed ample evidence of metallurgical activities as attested by the occurrence of slag, ores and evidences of ashes and fragments of furnaces in addition to the bronze objects. We have conducted petrological, XRD, EDAX, TEM, SEM and metallography on the slag, ores, crucible fragments and bronze objects samples recovered from Khanak excavations. This has given positive indication of mining and metallurgy of poly-mettalic Tin at the site; however, it can only be ascertained after the detailed scientific examination of the materials which is underway. In view of the importance of site, we intend to excavate the site horizontally in future so as to obtain more samples for scientific studies.

Keywords: archaeometallurgy, problem of tin, metallography, indus civilization

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Authors: Swati Kishor Zode, Rahul Ambekar

Abstract:

Data mining is the extraction of fascinating examples on the other hand information from enormous measure of information and choice is made as indicated by the applicable information extracted. As of late, with the dangerous advancement in internet, stockpiling of information and handling procedures, privacy preservation has been one of the major (higher) concerns in data mining. Various techniques and methods have been produced for protection saving data mining. In the situation of Clinical Decision Support System, the choice is to be made on the premise of the data separated from the remote servers by means of Internet to diagnose the patient. In this paper, the fundamental thought is to build the precision of Decision Support System for multiple diseases for different maladies and in addition protect persistent information while correspondence between Clinician side (Client side) also, the Server side. A privacy preserving protocol for clinical decision support network is proposed so that patients information dependably stay scrambled amid diagnose prepare by looking after the accuracy. To enhance the precision of Decision Support System for various malady C5.0 classifiers and to save security, a Homomorphism encryption algorithm Paillier cryptosystem is being utilized.

Keywords: classification, homomorphic encryption, clinical decision support, privacy

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Authors: M. Sumana, K. S. Hareesha

Abstract:

Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

Procedia PDF Downloads 412

Authors: Adesola O. Orimoloye, Edward Gobina

Abstract:

Membrane separation technology is still considered as an emerging technology in the mining sector and does not yet have the widespread acceptance that it has in other industrial sectors. Underground Coal Gasification (UCG), wherein coal is converted to gas in-situ, is a safer alternative to mining method that retains all pollutants underground making the process environmentally friendly. In-situ combustion of coal for power generation allows access to more of the physical global coal resource than would be included in current economically recoverable reserve estimates. Where mining is no longer taking place, for economic or geological reasons, controlled gasification permits exploitation of the deposit (again a reaction of coal to form a synthesis gas) of coal seams in situ. The oxygen supply stage is one of the most expensive parts of any gasification project but the use of membranes is a potentially attractive approach for producing oxygen-enriched air. In this study, a variety of cost-effective membrane materials that gives an optimal amount of oxygen concentrations in the range of interest was designed and tested at diverse operating conditions. Oxygen-enriched atmosphere improves the combustion temperature but a decline is observed if oxygen concentration exceeds optimum. Experimental result also reveals the preparatory method, apparatus and performance of the fabricated membrane.

Keywords: membranes, oxygen-enrichment, gasification, coal

Procedia PDF Downloads 460

Authors: Arundhuti Devi, Gitumani Devi, Krishna G. Bhattacharyya

Abstract:

The goal of this study was to assess the characteristics of the airborne dust created by opencast coal mining and its relation to population hospitalization risk for skin and lung diseases in Margherita Coalfield, Assam, India. Air samples were collected for 24 h in three 8-h periods. For the collection of particulate matter (PM10) and total suspended particulate matter (SPM) samples, respiratory dust samplers with glass microfiber filter papers were used. PM10 was analyzed for Cu, Cd, Cr, Mn, Zn, Ni, Fe and Pb with Flame Atomic Absorption Spectrophotometer (FAAS). SPM and PM10 concentrations were respectively found to be as high as 1,035 and 265.85 μg/m³ in work zone air. The concentration of metals associated with PM10 showed values higher than the permissible limits. It was observed that the average concentrations of the metals Fe, Pb, Ni, Zn, and Cu were very high during the winter month of December, those of Cd and Cr were high during the month of May and Mn was high during February. The morphology of the particles studied with scanning electron microscopy (SEM) gave significant results. Due to opencast coal mining, the air in the work zone, as well as the general ambient air, was found to be highly polluted with respect to dust. More than 8000 patient records maintained by the hospital authority were collected from three hospitals in the area. The highest percentage of people suffering from lung diseases are found in Margherita Civil Hospital (~26.77%) whereas most people suffering from skin diseases reported for treatment in the ESIC hospital (47.47%). Both PM10 and SPM were alarmingly high, and the results were in conformity with the high incidence of lung and other respiratory diseases in the study area.

Keywords: heavy metals, open cast coal mining, PM10, respiratory diseases

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Authors: Jianzhou Ni, Hua Fan

Abstract:

It has been determined that supervisor support has a major influence on postgraduate students' academic emotions and is considered a method of successfully anticipating postgraduates' good psychological well-being levels. As a result, by assessing the mediating influence upon academic emotions for contemporary postgraduates in China, this study investigated the tight reciprocal relationship between psychological empowerment and positive mental well-being among postgraduates. To that end, a help enables a theoretical analysis of role clarity, academic emotion, and positive psychological health was developed, and its validity and reliability were demonstrated for the first time using the normalized postgrad relationship with supervisor scale, academic emotion scale, and positive mental scale, as well as questionnaire data from Chinese postgraduate students. This study used the cross-lagged (ARCL) panel model data to longitudinally measure 798 valid data from two survey questions polls taken in 2019 (T1) and 2021 (T2) to investigate the link between supervisor support and positive graduate student mental well-being in a bidirectional relationship of influence. The study discovered that mentor assistance could have a considerable beneficial impact on graduate students' academic emotions and, as a result, indirectly help learners attain positive mental health development. This verifies the theoretical premise that academic emotions partially mediate the effect of mentor support on positive mental health development and argues for the coexistence of the two. The outcomes of this study can help researchers gain a better knowledge of the dynamic interplay among three different research variables: supervisor support, academic emotions, and positive mental health, as well as fill gaps in previous research. In this regard, the study indicated that mentor assistance directly stimulates students' academic drive and assists graduate students in developing good academic emotions, which contributes to the development of positive mental health. However, given the restricted measurement time in this study's cross-lagged panel data and the potential effect of moderating effects other than academic mood on graduate students' good mental health, the results of this study need to be more fully understood and validated.

Keywords: supervisor support, academic emotions, positive mental health, interaction effects, longitudinal cross-lagged measurements

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Authors: Hina Kausher, Sangita Srivastava

Abstract:

In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: S. McLean, J. A. Scott

Abstract:

The research topic is focusing on improving sustainable operation through recovery and reuse of waste heat in process water streams, an area in the mining industry that is often overlooked. There are significant advantages to the application of this topic, including economic and environmental benefits. The smelting process in the mining industry presents an opportunity to recover waste heat and apply it to alternative uses, thereby enhancing the overall process. This applied research has been conducted at the Sudbury Integrated Nickel Operations smelter site, in particular on the water cooling towers. The aim was to determine and optimize methods for appropriate recovery and subsequent upgrading of thermally low-grade heat lost from the water cooling towers in a manner that makes it useful for repurposing in applications, such as within an acid plant. This would be valuable to mining companies as it would be an opportunity to reduce the cost of the process, as well as decrease environmental impact and primary fuel usage. The waste heat from the cooling towers needs to be upgraded before it can be beneficially applied, as lower temperatures result in a decrease of the number of potential applications. Temperature and flow rate data were collected from the water cooling towers at an acid plant over two years. The research includes process control strategies and the development of a model capable of determining if the proposed heat recovery technique is economically viable, as well as assessing any environmental impact with the reduction in net energy consumption by the process. Therefore, comprehensive cost and impact analyses are carried out to determine the best area of application for the recovered waste heat. This method will allow engineers to easily identify the value of thermal resources available to them and determine if a full feasibility study should be carried out. The rapid scoping model developed will be applicable to any site that generates large amounts of waste heat. Results show that heat pumps are an economically viable solution for this application, allowing for reduced cost and CO₂ emissions.

Keywords: environment, heat recovery, mining engineering, sustainability

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Authors: Alexander Scott English, Yilin Ma, Xiaoying Liu

Abstract:

The widespread use of artificial intelligence in everyday life has triggered a fervent discussion covering a wide range of areas. However, to date, research on the influence of gender in various segments and factors from a social science perspective is still limited. This study aims to explore whether there are gender differences in human trust in AI for its application in basic everyday life and correlates with human perceived similarity, perceived emotions (including competence and warmth), and attractiveness. We conducted a study involving 321 participants using a two-subject experimental design with a two-factor (masculinized vs. feminized voice of the AI) multiplied by a two-factor (pitch level of the AI's voice) between-subject experimental design. Four contexts were created for the study and randomly assigned. The results of the study showed significant gender differences in perceived similarity, trust, and perceived emotion of the AIs, with females rating them significantly higher than males. Trust was higher in relation to AIs presenting the same gender (e.g., human female to female AI, human male to male AI). Mediation modeling tests indicated that emotion perception and similarity played a sufficiently mediating role in trust. Notably, although trust in AIs was strongly correlated with human gender, there was no significant effect on the gender of the AI. In addition, the study discusses the effects of subjects' age, job search experience, and job type on the findings.

Keywords: artificial intelligence, gender differences, human-robot trust, mediation modeling

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Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

Abstract:

In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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Authors: Farhood Rismanchian, Seong Hyeon Park, Young Hoon Lee

Abstract:

During the recent years, demand for healthcare services has dramatically increased. As the demand for healthcare services increases, so does the necessity of constructing new healthcare buildings and redesigning and renovating existing ones. Increasing demands necessitate the use of optimization techniques to improve the overall service efficiency in healthcare settings. However, high complexity of care processes remains the major challenge to accomplish this goal. This study proposes a method based on process mining results to address the high complexity of care processes and to find the optimal layout of the various medical centers in an emergency department. ProM framework is used to discover clinical pathway patterns and relationship between activities. Sequence clustering plug-in is used to remove infrequent events and to derive the process model in the form of Markov chain. The process mining results served as an input for the next phase which consists of the development of the optimization model. Comparison of the current ED design with the one obtained from the proposed method indicated that a carefully designed layout can significantly decrease the distances that patients must travel.

Keywords: Mixed Integer programming, Facility layout problem, Process Mining, Healthcare Operation Management

Procedia PDF Downloads 339