Search results for: mining collection

Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Livhuwani Muthelo, Tebogo Maria Mothiba, Rambelani Nancy Malema

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Background: Despite occupational legislation/standards being in place in the industry, there are many reported health and safety incidents, including both occupational injuries and illnesses in the South African mining industry. Purpose: This systematic literature review aimed to describe and identify the existing gaps in health and safety compliance within the South African mining industry and propose future research areas. Methodology: A systematic literature review was conducted using the key concepts of health and safety, compliance, standards, and mining. A total of 102 papers issued from 1994 to April 2020 were extracted from an online database search, which included a combination of South African and international government OHS legislation documents, policies, standards, reports from the mineral departments and international labour office, qualitative and quantitative journal articles, dissertations, seminars and conference proceedings. Results: The literature review revealed that, though there are laws, regulations, standards to guide the industry on health and safety issues in South Africa, the main challenge is with the compliance with the existing health and safety systems, wherein systems are not being implemented. Conclusion: Gaps between research, policy, and implementation in occupational health practice in the South African mining industry were also identified.

Keywords: circumstances, non-compliance, health and safety, standards, mining industry

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Authors: Sujata Upgupta, Prasoon Kumar Singh

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The expansion of large scale coal mining into forest areas is a potential hazard for the local biodiversity and wildlife. The objective of this study is to provide a picture of the threat that coal mining poses to the forests of the East Bokaro landscape. The vulnerable forest areas at risk have been assessed and the priority areas for conservation have been presented. The forested areas at risk in the current scenario have been assessed and compared with the past conditions using classification and buffer based overlay approach. Forest vulnerability has been assessed using an analytical framework based on systematic indicators and composite vulnerability index values. The results indicate that more than 4 km² of forests have been lost from 1973 to 2016. Large patches of forests have been diverted for coal mining projects. Forests in the northern part of the coal field within 1-3 km radius around the coal mines are at immediate risk. The original contiguous forests have been converted into fragmented and degraded forest patches. Most of the collieries are located within or very close to the forests thus threatening the biodiversity and hydrology of the surrounding regions. Based on the vulnerability values estimated, it was concluded that more than 90% of the forested grids in East Bokaro are highly vulnerable to mining. The forests in the sub-districts of Bermo and Chandrapura have been identified as the most vulnerable to coal mining activities. This case study would add to the capacity of the forest managers and mine managers to address the risk and vulnerability of forests at a small landscape level in order to achieve sustainable development.

Keywords: forest, coal mining, indicators, vulnerability

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

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Authors: Pascal Rey, Anaïs Weber

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Many mining projects are being developed in Africa the last decades. Due to the economic opportunities they offer, these projects result in a massive and rapid influx of migrants to the surrounding area. In areas where central government representation is low and local administration lack financial resources, urban development is often anarchical, beyond all public control. It leads to socio-spatial segregation, insecurity and the risk of social conflicts rising. Aware that their economic development is very correlated with local situation, mining companies get more and more involved in regional planning in setting up tools and Strategic Directions document. One of the commonly used tools in this regard is the “Influx Management Plan”. It consists in looking at the region’s absorption capacities in order to ensure its coherent development and by developing several urban centers than one macrocephalic city. It includes many other measures such as urban governance support, skills transfer, creation of strategic guidelines, financial support (local taxes, mining taxes, development funds etc.) local development projects. Through various examples of mining projects in Guinea, A country that is host to many large mining projects, we will look at the implications of regional and urban planning of which mining companies are key playor as well as public authorities. While their investment capacity offers advantages and accelerates development, their actions raise questions of the unilaterality of interests and local governance. By interfering in public affairs are mining companies not increasing the risk of central and local government shirking their responsibilities in terms of regional development, or even calling their legitimacy into question? Is such public-private collaboration really sustainable for the region as a whole and for all stakeholders?

Keywords: Africa, guinea, mine, urban planning

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Authors: Javad Someehneshin, Weizhou Quan, Abdelsalam Abugharara, Stephen Butt

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Narrow vein mining (NVM) is exploiting very thin but valuable ore bodies that are uneconomical to extract by conventional mining methods. NVM applies the technique of Sustainable Mining by Drilling (SMD). The SMD method is used to mine stranded, steeply dipping ore veins, which are too small or isolated to mine economically using conventional methods since the dilution is minimized. This novel mining technique uses drilling rigs to extract the ore through directional drilling surgically. This paper is focusing on utilizing the run of the mine tailings and Portland cement as backfill material to support the hanging wall for providing safe mine operation. Cemented paste backfill (CPB) is designed by mixing waste tailings, water, and cement of the precise percentage for optimal outcomes. It is a non-homogenous material that contains 70-85% solids. Usually, a hydraulic binder is added to the mixture to increase the strength of the CPB. The binder fraction mostly accounts for 2–10% of the total weight. In the mining industry, CPB has been improved and expanded gradually because it provides safety and support for the mines. Furthermore, CPB helps manage the waste tailings in an economical method and plays a significant role in environmental protection.

Keywords: backfilling, cement backfill, tailings, Portland cement

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Authors: Oshin Anand, Atanu Rakshit

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The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

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Authors: Sergio Perez Rodriguez

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Coltan Artisanal and Small-Scale Mining (ASM) production from Africa's Great Lakes region has previously been addressed at large scales, notably from regional to country levels. The current findings address the unresolved issue of a production model of ASM of coltan ore by an average Democratic Republic of Congo (DRC) mineworker, which can be used as a reference for a similar characterization of the daily labor of counterparts from other countries in the region. To that end, the Fundamental Equation of Mineral Production has been applied, considering a miner's average daily output of coltan, estimated in the base of gross statistical data gathered from reputable sources. Results indicate daily yields of individual miners in the order of 300 g of coltan ore, with hourly peaks of production in the range of 30 to 40 g of the mineral. Yields are expected to be in the order of 5 g or less during the least productive hours. These outputs are expected to be achieved during the halves of the eight to ten hours of daily working sessions that these artisanal laborers can attend during the mining season.

Keywords: coltan, mineral production, production to reserve ratio, artisanal mining, small-scale mining, ASM, human work, Great Lakes region, Democratic Republic of Congo

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Authors: Kim Dongwon, Yu Songjin

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This study identifies the business value of VOC (Voice of Customer) on the business. Precisely, we intend to demonstrate how much negative and positive sentiment of VOC has an influence on car sales market share in the unites states. We extract 7 emotions such as sadness, shame, anger, fear, frustration, delight and satisfaction from the VOC data, 23,204 pieces of opinions, that had been posted on car-related on-line community from 2007 to 2009(a part of data collection from 2007 to 2015), and intend to clarify the correlation between negative and positive sentimental keywords and contribution to market share. In order to develop a lexicon for each category of negative and positive sentiment, we took advantage of Corpus program, Antconc 3.4.1.w and on-line sentimental data, SentiWordNet and identified the part of speech(POS) information of words in the customers' opinion by using a part-of-speech tagging function provided by TextAnalysisOnline. For the purpose of this present study, a total of 45,741 pieces of customers' opinions of 28 car manufacturing companies had been collected including titles and status information. We conducted an experiment to examine whether the inclusion, frequency and intensity of terms with negative and positive emotions in each category affect the adoption of customer opinions for vehicle organizations' market share. In the experiment, we statistically verified that there is correlation between customer ideas containing negative and positive emotions and variation of marker share. Particularly, "Anger," a domain of negative domains, is significantly influential to car sales market share. The domain "Delight" and "Satisfaction" increased in proportion to growth of market share.

Keywords: data mining, opinion mining, sentiment analysis, VOC

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Authors: Dhvanidevi N. Jadeja, Daya S. Kaul, Anurag A. Kandya

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Municipal Solid Waste (MSW) collection in a city is performed in less effective manner which results in the poor management of the environment and natural resources. Municipal corporation does not possess efficient waste management and recycling programs because of the complex task involving many factors. Solid waste collection system depends upon various factors such as manpower, number and size of vehicles, transfer station size, dustbin size and weight, on-road traffic, and many others. These factors affect the collection cost, energy and overall municipal tax for the city. Generally, different types of waste are scattered throughout the city in a heterogeneous way that poses changes for efficient collection of solid waste. Efficient waste collection and transportation strategy must be effectively undertaken which will include optimization of routes, volume of waste, and manpower. Being these optimized, the overall cost can be reduced as the fuel and energy requirements would be less and also the municipal waste taxes levied will be less. To carry out the optimization study of collection system various data needs to be collected from the Ahmedabad municipal corporation such as amount of waste generated per day, number of workers, collection schedule, road maps, number of transfer station, location of transfer station, number of equipment (tractors, machineries), number of zones, route of collection etc. The ArcGis Network Analyst is introduced for the best routing identification applied in municipal waste collection. The simulation consists of scenarios of visiting loading spots in the municipality of Ahmedabad, considering dynamic factors like network traffic changes, closed roads due to natural or technical causes. Different routes were selected in a particular area of Ahmedabad city, and present routes were optimized to reduce the length of the routes, by using ArcGis Network Analyst. The result indicates up to 35% length minimization in the routes.

Keywords: collection routes, efficiency, municipal solid waste, optimization

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: Kateryna Zharan, Jan C. Bongaerts

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In recent years, economic attractiveness of renewable energy (RE) for the mining industry, especially for off-grid mines, and a negative environmental impact of fossil energy are stimulating to use RE for mining needs. Being that remote area mines have higher energy expenses than mines connected to a grid, integration of RE may give a mine economic benefits. Regarding the literature review, there is a lack of business models for adopting of RE at mine. The main aim of this paper is to develop an optimized model of RE integration into the German mining industry (GMI). Hereby, the GMI with amount of around 800 mill. t. annually extracted resources is included in the list of the 15 major mining country in the world. Accordingly, the mining potential of Germany is evaluated in this paper as a perspective market for RE implementation. The GMI has been classified in order to find out the location of resources, quantity and types of the mines, amount of extracted resources, and access of the mines to the energy resources. Additionally, weather conditions have been analyzed in order to figure out where wind and solar generation technologies can be integrated into a mine with the highest efficiency. Despite the fact that the electricity demand of the GMI is almost completely covered by a grid connection, the hybrid energy system (HES) based on a mix of RE and fossil energy is developed due to show environmental and economic benefits. The HES for the GMI consolidates a combination of wind turbine, solar PV, battery and diesel generation. The model has been calculated using the HOMER software. Furthermore, the demonstrated HES contains a forecasting model that predicts solar and wind generation in advance. The main result from the HES such as CO2 emission reduction is estimated in order to make the mining processing more environmental friendly.

Keywords: diesel generation, German mining industry, hybrid energy system, hybrid optimization model for electric renewables, optimized model, renewable energy

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Authors: José Eugenio Ortiz, Pierre Plaza, Josefa Herrero, Iván Cabria, José Luis Blanco, Javier Gavilanes, José Ignacio Escavy, Ignacio López-Cilla, Virginia Yagüe, César Pérez, Silvia Rodríguez, Jorge Rico, Cecilia Serrano, Jesús Bernat

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The development of Artificial Intelligence services in mining processes, specifically in aggregate quarries, is facilitating automation and improving numerous aspects of operations. Ultimately, AI is transforming the mining industry by improving efficiency, safety and sustainability. With the ability to analyze large amounts of data and make autonomous decisions, AI offers great opportunities to optimize mining operations and maximize the economic and social benefits of this vital industry. Within the framework of the European DIGIECOQUARRY project, various services were developed for the identification of material quality, production estimation, detection of anomalies and prediction of consumption and production automatically with good results.

Keywords: aggregates, artificial intelligence, automatization, mining operations

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Authors: Nakache Radouane, M. Boukelloul, M. Fredj

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Method room and pillar sizes are key factors for safe mining and their recovery in open-stop mining. This method is advantageous due to its simplicity and requirement of little information to be used. It is probably the most representative method among the total load approach methods although it also remains a safe design method. Using a finite element software (PLAXIS 3D), analyses were carried out with an elasto-plastic model and comparisons were made with methods based on the total load approach. The results were presented as the optimization for improving the ore recovery rate while maintaining a safe working environment.

Keywords: room and pillar, mining, total load approach, elasto-plastic

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Authors: Seyed Mohsen Samaei, Shirley Gato-Trinidad

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A pre-treatment stage prior to reverse osmosis (RO) is very important to ensure the long-term performance of the RO membranes in any wastewater treatment using RO. This study aims to evaluate the application of the Actiflo^® clarifier as part of a pre-treatment unit in mining operations. It involves performing analytical testing on RO feed water before and after installation of Actiflo^® unit. Water samples prior to RO plant stage were obtained on different dates from Costerfield mining operations in Victoria, Australia. Tests were conducted in an independent laboratory to determine the concentration of various compounds in RO feed water before and after installation of Actiflo^® unit during the entire evaluated period from December 2015 to June 2018. Water quality analysis shows that the quality of RO feed water has remarkably improved since installation of Actiflo^® clarifier. Suspended solids (SS) and turbidity removal efficiencies has been improved by 91 and 85 percent respectively in pre-treatment system since the installation of Actiflo^®. The Actiflo^® clarifier proved to be a valuable part of pre-treatment system prior to RO. It has the potential to conveniently condition the mining wastewater prior to RO unit, and reduce the risk of RO physical failure and irreversible fouling. Consequently, reliable and durable operation of RO unit with minimum requirement for RO membrane replacement is expected with Actiflo^® in use.

Keywords: ACTIFLO ® clarifier, mining wastewater, reverse osmosis, water treatment

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Authors: İbrahi̇m Kara, Seher Arslankaya

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Frequently preferred in the field of engineering in particular, data mining has now begun to be used in the field of health as well since the data in the health sector have reached great dimensions. With data mining, it is aimed to reveal models from the great amounts of raw data in agreement with the purpose and to search for the rules and relationships which will enable one to make predictions about the future from the large amount of data set. It helps the decision-maker to find the relationships among the data which form at the stage of decision-making. In this study, it is aimed to determine the risk of heart attack at the first stage, to control it, and to make its resource planning with the method of data mining. Through the early and correct diagnosis of heart attacks, it is aimed to reveal the factors which affect the diseases, to protect health and choose the right treatment methods, to reduce the costs in health expenditures, and to shorten the durations of patients’ stay at hospitals. In this way, the diagnosis and treatment costs of a heart attack will be scrutinized, which will be useful to determine the risk of the disease at the first stage, to control it, and to make its resource planning.

Keywords: data mining, decision support systems, heart attack, health sector

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Authors: Salamatu Shaibu, Jan Hernning Sommer

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Integrating biodiversity conservation practices in mining landscapes ensures the continual provision of various ecosystem services to the dependent communities whilst serving as ecological insurance for corporate mining when purchasing reclamation security bonds. Climate hazards such as long dry seasons, erratic rainfall patterns, and extreme weather events contribute to biodiversity loss in addition to the impact due to mining. Both corporate mining and mine-fringe communities perceive the effect of climate on biodiversity from the context of the benefits they accrue, which motivate their conservation practices. In this study, pragmatic approaches including semi-structured interviews, field visual observation, and review were used to collect data on corporate mining employees and households of fringing communities in the southwestern mining hub. The perceived changes in the local climatic conditions and the consequences on environmental management practices that promote biodiversity conservation were examined. Using a thematic content analysis tool, the result shows that best practices such as concurrent land rehabilitation, reclamation ponds, artificial wetlands, land clearance, and topsoil management are directly affected by prolonging long dry seasons and erratic rainfall patterns. Excessive dust and noise generation directly affect both floral and faunal diversity coupled with excessive fire outbreaks in rehabilitated lands and nearby forest reserves. Proposed adaptive measures include engaging national conservation authorities to promote reforestation projects around forest reserves. National government to desist from using permit for mining concessions in forest reserves, engaging local communities through educational campaigns to control forest encroachment and burning, promoting community-based resource management to promote community ownership, and provision of stricter environmental legislation to compel corporate, artisanal, and small scale mining companies to promote biodiversity conservation.

Keywords: biodiversity conservation, climate hazards, corporate mining, mining landscapes

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Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

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In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: S. Abera, T. Gidey, W. Terefe

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Introduction: Testing for HIV is the key entry point to HIV prevention, treatment, and care and support services. Hence, predictive data mining techniques can greatly benefit to analyze and discover new patterns from huge datasets like that of EDHS 2011 data. Objectives: The objective of this study is to build a predictive modeling for HIV testing and identify determinants of HIV testing for adults with age above fourteen years using data mining techniques. Methods: Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to predict the model for HIV testing and explore association rules between HIV testing and the selected attributes among adult Ethiopians. Decision tree, Naïve-Bayes, logistic regression and artificial neural networks of data mining techniques were used to build the predictive models. Results: The target dataset contained 30,625 study participants; of which 16, 515 (53.9%) were women. Nearly two-fifth; 17,719 (58%), have never been tested for HIV while the rest 12,906 (42%) had been tested. Ethiopians with higher wealth index, higher educational level, belonging 20 to 29 years old, having no stigmatizing attitude towards HIV positive person, urban residents, having HIV related knowledge, information about family planning on mass media and knowing a place where to get testing for HIV showed an increased patterns with respect to HIV testing. Conclusion and Recommendation: Public health interventions should consider the identified determinants to promote people to get testing for HIV.

Keywords: data mining, HIV, testing, ethiopia

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24 percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: behavior, data mining technique, a priori algorithm, knowledge discovery

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Authors: Shahin Shabani, Pouya Pourmadadi

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Tunnel boring machines (TBMs) have been used for the construction of various tunnels for mining projects for the purpose of access, conveyance of ore and waste, drainage, exploration, water supply and water diversion. Several mining projects have seen the successful and economic beneficial use of TBMs, and there is an increasing awareness of the benefits of TBMs for mining projects. Key technical considerations for the use of TBMs for the construction of tunnels for mining projects include geological issues (rock type, rock alteration, rock strength, rock abrasivity, durability, ground water inflows), depth of cover and the potential for overstressing/rockbursts, site access and terrain, portal locations, TBM constraints, minimum tunnel size, tunnel support requirements, contractor and labor experience, and project schedule demands. This study focuses on tunnelling mining, with the goal to develop methods and tools to be used to gain understanding of these processes, and to analyze metro of Tehran. The Metro Line 7 of Tehran is one of the Longest (26 Km) and deepest (27m) of projects that’s under implementation. Because of major differences like passing under all geotechnical layers of the town and encountering part of it with underground water table and also using mechanized excavation system, is one of special metro projects.

Keywords: TBM, tunnel boring machines economic, metro, line 7

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Authors: Shivam Dwivedi, Sumit Prakash Gupta, Durga Toshniwal

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It is a challenging task in today’s date to keep defence forces in the highest state of combat readiness with budgetary constraints. A huge amount of time and money is squandered in the unnecessary and expensive traditional maintenance activities. To overcome this limitation Defence Intelligent Mission Readiness and Maintenance Scheduling System has been proposed, which ameliorates the maintenance system by diagnosing the condition and predicting the maintenance requirements. Based on new data mining algorithms, this system intelligently optimises mission readiness for imminent operations and maintenance scheduling in repair echelons. With modified data mining algorithms such as Weighted Feature Ranking Genetic Algorithm and SVM-Random Forest Linear ensemble, it improves the reliability, availability and safety, alongside reducing maintenance cost and Equipment Out of Action (EOA) time. The results clearly conclude that the introduced algorithms have an edge over the conventional data mining algorithms. The system utilizing the intelligent condition-based maintenance approach improves the operational and maintenance decision strategy of the defence force.

Keywords: condition based maintenance, data mining, defence maintenance, ensemble, genetic algorithms, maintenance scheduling, mission capability

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Babawale Akin Adeyemi, Esan Temitayo, Adeyemi Olabisi Omowumi

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The paper analyzed waste disposal system in Akure, Ondo State using GIS techniques. Specifically, the study identified the spatial distribution of collection points and existing dumpsite; evaluated the accessibility of waste collection points and their proximity to each other with the view of enhancing better performance of the waste disposal system. Data for the study were obtained from both primary and secondary sources. Primary data were obtained through the administration of questionnaire. From field survey, 35 collection points were identified in the study area. 10 questionnaires were administered around each collection point making a total of 350 questionnaires for the study. Also, co-ordinates of each collection point were captured using a hand-held Global Positioning System (GPS) receiver which was used to analyze the spatial distribution of collection points. Secondary data used include administrative map collected from Akure South Local Government Secretariat. Data collected was analyzed using the GIS analytical tools which is neighborhood function. The result revealed that collection points were found in all parts of Akure with the highest concentration around the central business district. The study also showed that 80% of the collection points enjoyed efficient waste service while the remaining 20% does not. The study further revealed that most collection points in the core of the city were in close proximity to each other. In conclusion, the paper revealed the capability of Geographic Information System (GIS) as a technique in management of waste collection and disposal technique. The application of Geographic Information System (GIS) in the evaluation of the solid waste management in Akure is highly invaluable for the state waste management board which could also be beneficial to other states in developing a modern day solid waste management system. Further study on solid waste management is also recommended especially for updating of information on both spatial and non-spatial data.

Keywords: assessment, geospatial, system, waste disposal

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Authors: Majdah Alnefaie

Abstract:

The ‘netnographic study’ and data mining have been used to monitor the public interaction on Social Media Sites (SMSs) to understand what the motivational factors influence the Saudi intentions regarding allowing women driving in Saudi Arabia in 2018. The netnographic study monitored the publics’ textual and visual communications in Twitter, Snapchat, and YouTube. SMSs users’ communications method is also known as electronic word of mouth (eWOM). Netnography methodology is still in its initial stages as it depends on manual extraction, reading and classification of SMSs users text. On the other hand, data mining is come from the computer and physical sciences background, therefore it is much harder to extract meaning from unstructured qualitative data. In addition, the new development in data mining software does not support the Arabic text, especially local slang in Saudi Arabia. Therefore, collaborations between social and computer scientists such as ‘netnographic study’ and data mining will enhance the efficiency of this study methodology leading to comprehensive research outcome. The eWOM communications between individuals on SMSs can promote a sense that sharing their preferences and experiences regarding politics and social government regulations is a part of their daily life, highlighting the importance of using SMSs as assistance in promoting participation in political and social. Therefore, public interactions on SMSs are important tools to comprehend people’s intentions regarding the new government regulations in the country. This study aims to answer this question, "What factors influence the Saudi Arabians' intentions of Saudi female's car-driving in 2018". The study utilized qualitative method known as netnographic study. The study used R studio to collect and analyses 27000 Saudi users’ comments from 25th May until 25th June 2018. The study has developed data collection model that support importing and analysing the Arabic text in the local slang. The data collection model in this study has been clustered based on different type of social networks, gender and the study main factors. The social network analysis was employed to collect comments from SMSs owned by governments’ originations, celebrities, vloggers, social activist and news SMSs accounts. The comments were collected from both males and females SMSs users. The sentiment analysis shows that the total number of positive comments Saudi females car driving was higher than negative comments. The data have provided the most important factors influenced the Saudi Arabians’ intention of Saudi females car driving including, culture and environment, freedom of choice, equal opportunities, security and safety. The most interesting finding indicted that women driving would play a role in increasing the individual freedom of choice. Saudi female will be able to drive cars to fulfill her daily life and family needs without being stressed due to the lack of transportation. The study outcome will help Saudi government to improve woman quality of life by increasing the ability to find more jobs and studies, increasing income through decreasing the spending on transport means such as taxi and having more freedom of choice in woman daily life needs. The study enhances the importance of using use marketing research to measure the public opinions on the new government regulations in the country. The study has explained the limitations and suggestions for future research.

Keywords: netnographic study, data mining, social media, Saudi Arabia, female driving

Procedia PDF Downloads 129

Authors: Ebrahim Fataei, Seyed Ali Hosseini, Zahra Arabi, Habib farhadi, Mehdi Aalipour Erdi, Seiied Taghi Seiied Safavian

Abstract:

Solid waste production is an integral part of human life and management of waste require full scientific approach and essential planning. The allocation of most management cost to collection and transportation and also the necessity of operational efficiency in this system, by limiting time consumption, and on the other hand optimum collection system and transportation is the base of waste design and management. This study was done to optimize the exits collection and transportation system of solid waste in Meshkinshahr city. So based on the analyzed data of municipal solid waste components in seven zones of Meshkinshahr city, and GIS software, applied to design storage place based on origin recycling and a route to collect and transport. It was attempted to represent an appropriate model to store, collect and transport municipal solid waste. The result shows that GIS can be applied to locate the waste container and determine a waste collection direction in an appropriate way.

Keywords: municipal solid waste management, transportation, optimizing, GIS, Iran

Procedia PDF Downloads 502

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

Abstract:

The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 61

Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

Abstract:

In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

Procedia PDF Downloads 172

Authors: Archana Singh, Jyoti Agarwal, Ajay Rana

Abstract:

Data Mining is an efficient technology to discover patterns in large databases. Association Rule Mining techniques are used to find the correlation between the various item sets in a database, and this co-relation between various item sets are used in decision making and pattern analysis. In recent years, the problem of finding association rules from large datasets has been proposed by many researchers. Various research papers on association rule mining (ARM) are studied and analyzed first to understand the existing algorithms. Apriori algorithm is the basic ARM algorithm, but it requires so many database scans. In DIC algorithm, less amount of database scan is needed but complex data structure lattice is used. The main focus of this paper is to propose a new optimized algorithm (Friendly Algorithm) and compare its performance with the existing algorithms A data set is used to find out frequent itemsets and association rules with the help of existing and proposed (Friendly Algorithm) and it has been observed that the proposed algorithm also finds all the frequent itemsets and essential association rules from databases as compared to existing algorithms in less amount of database scan. In the proposed algorithm, an optimized data structure is used i.e. Graph and Adjacency Matrix.

Keywords: association rules, data mining, dynamic item set counting, FP-growth, friendly algorithm, graph

Procedia PDF Downloads 391