Search results for: actionable pattern mining

Authors: Mekha Mathew, Varun P Gopi

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Wireless Capsule Endoscopy (WCE) has become a great device in Gastrointestinal (GI) tract diagnosis, which can examine the entire GI tract, especially the small intestine without invasiveness and sedation. Bleeding in the digestive tract is a symptom of a disease rather than a disease itself. Hence the detection of bleeding is important in diagnosing many diseases. In this paper we proposes a novel method for distinguishing bleeding regions from normal regions based on Contourlet transform and Local Binary Pattern (LBP). Experiments show that this method provides a high accuracy rate of 96.38% in CIE XYZ colour space for k-Nearest Neighbour (k-NN) classifier.

Keywords: Wireless Capsule Endoscopy, local binary pattern, k-NN classifier, contourlet transform

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Assem El-Ansary

Abstract:

This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach using the Healthcare Multi-Agent System. The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are: The Behavioral Pattern Analysis (BPA) modeling methodology. The development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases, Healthcare Multi-Agent System

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Authors: Soebowo Musa

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Mining industries are known to be very volatile due to their sensitive nature toward changes in the environment, particularly coal mining. Heavy equipment distributors and coal mining contractors are among heavily affected by such volatility. They are facing more uncertainty on the sustainability of the coal mining industry. Strategic alliances and organizational capabilities such as innovation capability have long been seen as ways to stay competitive with a focus more on the strategic alliances partner-to-partner in serving their customers. In today’s rapid change in the environment, a shift in consumer behaviors, and the human-centric business approach, this study looks at the strategic alliance partner-to-customer relationship in both the industrial organization and resource-based theories. This study was conducted based on 250 respondents from the strategic alliances partner-to-customer between heavy equipment distributors and coal mining contractors in Indonesia. This study finds strategic alliances have the highest association toward cost reduction, a proxy of operational efficiency followed by its association toward innovation capability. Further, strategic alliances and innovation capability have a positive relationship with customer loyalty, while innovation capability and customer loyalty have no significant relationships toward the firm’s competitive advantage. This study also indicates that cost reduction is not a condition to develop customer loyalty in the strategic alliance partner-to-customer relationship. It confirms strategic alliances are a strategy that creates a firm’s operational efficiency, innovation capability that develops customer loyalty, and competitive advantage.

Keywords: strategic alliance, innovation capability, cost reduction, customer loyalty, competitive advantage

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Authors: David Amoah Oduro

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In the ever-evolving terrain of international business, a profound revolution is underway, guided by the swift integration and advancement of disruptive technologies like digital analytics. In today's international business landscape, where competition is fierce, and decisions are data-driven, the essence of this paper lies in offering a tangible roadmap for practitioners. It is a guide that bridges the chasm between theory and actionable insights, helping businesses, investors, and entrepreneurs navigate the complexities of international expansion into sub-Saharan Africa. This practitioner paper distils essential insights, methodologies, and actionable recommendations for businesses seeking to leverage digital analytics in their pursuit of market entry and expansion across the African continent. What sets this paper apart is its unwavering focus on a region ripe with potential: sub-Saharan Africa. The adoption and adaptation of digital analytics are not mere luxuries but essential strategic tools for evaluating countries and entering markets within this dynamic region. With the spotlight firmly fixed on sub-Saharan Africa, the aim is to provide a compelling resource to guide practitioners in their quest to unearth the vast opportunities hidden within sub-Saharan Africa's digital desert. The paper illuminates the pivotal role of digital analytics in providing a data-driven foundation for market entry decisions. It highlights the ability to uncover market trends, consumer behavior, and competitive landscapes. By understanding Africa's incredible diversity, the paper underscores the importance of tailoring market entry strategies to account for unique cultural, economic, and regulatory factors. For practitioners, this paper offers a set of actionable recommendations, including the creation of cross-functional teams, the integration of local expertise, and the cultivation of long-term partnerships to ensure sustainable market entry success. It advocates for a commitment to continuous learning and flexibility in adapting strategies as the African market evolves. This paper represents an invaluable resource for businesses, investors, and entrepreneurs who are keen on unlocking the potential of digital analytics for informed market entry in Africa. It serves as a guiding light, equipping practitioners with the essential tools and insights needed to thrive in this dynamic and diverse continent. With these key insights, methodologies, and recommendations, this paper is a roadmap to prosperous and sustainable market entry in Africa. It is vital for anyone looking to harness the transformational potential of digital analytics to create prosperous and sustainable ventures in a region brimming with promise. In the ever-advancing digital age, this practitioner paper becomes a lodestar, guiding businesses and visionaries toward success amidst the unique challenges and rewards of sub-Saharan Africa's international business landscape.

Keywords: global analytics, digital analytics, sub-Saharan Africa, data analytics

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Authors: Assem El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Intelligent Agent Reservation System (IARS). The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are developing the Behavioral Pattern Analysis (BPA) modeling methodology, and developing an interactive software tool (DECISION) which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, intelligent agent, reservation system, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: Carla Idely Palencia-Aguilar

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Mining in Colombia represents around 2% of the GDP (USD 8 billion in 2018), with main productions represented by coal, nickel, gold, silver, emeralds, iron, limestone, gypsum, among others. Sand and Gravel had been decreasing its participation of the GDP with a reduction of 33.2 million m3 in 2015, to 27.4 in 2016, 22.7 in 2017 and 15.8 in 2018, with a consumption of approximately 3 tons/inhabitant. However, with the new government policies it is expected to increase in the following years. Mining causes temporary environmental impacts, once restoration and rehabilitation takes place, social, environmental and economic benefits are higher than the initial state. A way to demonstrate how the mining interventions had contributed to improve the characteristics of the region after sand and gravel mining, the NDVI (Normalized Difference Vegetation Index) from MODIS and ASTER were employed. The histograms show not only increments of vegetation in the area (8 times higher), but also topographies similar to the ones before the intervention, according to the application for sustainable development selected: either agriculture, forestry, cattle raising, artificial wetlands or do nothing. The decision was based upon a Multicriteria analysis for optimal land use, with three main variables: geostatistics, evapotranspiration and groundwater characteristics. The use of remote sensing, meteorological stations, piezometers, sunphotometers, geoelectric analysis among others; provide the information required for the multicriteria decision. For cattle raising and agricultural applications (where various crops were implemented), conservation of products were tested by means of nanotechnology. The results showed a duration of 2 years with no chemicals added for preservation and concentration of vitamins of the tested products.

Keywords: ASTER, Geostatistics, MODIS, Multicriteria

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Authors: Fatma M. Benrabha

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The aim of the study was to determine the type and pattern of antibiotic susceptibility of the pathogenic microorganisms causing chronic suppurative otitis media (CSOM), which could lead to better therapeutic decisions and consequently avoidance of appearance of resistance to specific antibiotics. Most frequently isolated agents were Pseudomonas aeruginosa 28.5%; followed by Staphylococcus aureus 18.2%; proteus mirabilis 13.9%; Providencia stuartti 6.7%; Bacteroides melaninogenicus, Aspergillus sp., candida sp., 4.2% each; and other microorganisms were represented in 3-0.2%. Drug sensitivities pattern of Pseudomonas aeruginosa showed that ciprofloxacin was active against the majority of isolates (93.9%) followed by ceftazidime 86.2%, amikacin 76.2% and gentamicin 40.8%. However, Staphylococcus aureus isolates were resistant to penicillin 72.7%, erythromycin 28.6%, cephalothin 18.2%, cloxacillin 8.3% and ciprofloxacin was active against 96.2% of isolates. The resistance pattern of proteus mirabilis were 55.6% to ampicillin, 47.1% to carbencillin, 29.4% to cephalothin, 14.3% to gentamicin and 4.8% to amikacin while 100% were sensitive to ciprofloxacin. We conclude that ciprofloxacin is the best drug of choice in treatment of CSOM caused by the common microorganisms.

Keywords: otitis media, chronic suppurative otitis media (CSOM), microorganism, drug sensitivity

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering.

Keywords: clustering, unsupervised learning, algorithms, hierarchical

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Authors: Mohammad Reza Ameri, Ali Massumi, Mohammad Haghbin

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Non-linear static analysis, commonly referred to as pushover analysis, is a powerful tool for assessing the seismic response of structures. A suitable lateral load pattern for pushover analysis can bring the results of this simple, quick and low-cost analysis close to the realistic results of nonlinear dynamic analyses. In this research, four samples of 10- and 15 story (two- and four-bay) reinforced concrete frames were studied. The lateral load distribution patterns recommended in FEMA 273/356 guidelines were applied to the sample models in order to perform pushover analyses. The results were then compared to the results obtained from several nonlinear incremental dynamic analyses for a range of earthquakes. Finally, a lateral load distribution pattern was proposed for pushover analysis of medium-rise reinforced concrete buildings based on the results of nonlinear static and dynamic analyses.

Keywords: lateral load pattern, nonlinear static analysis, incremental dynamic analysis, medium-rise reinforced concrete frames, performance based design

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Authors: Sahar Khedr, Dina Sayed, Ayman Hanafy

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Arabic is one of the most ancient and critical languages in the world. It has over than 250 million Arabic native speakers and more than twenty countries having Arabic as one of its official languages. In the past decade, we have witnessed a rapid evolution in smart devices, social network and technology sector which led to the need to provide tools and libraries that properly tackle the Arabic language in different domains. Stemming is one of the most crucial linguistic fundamentals. It is used in many applications especially in information extraction and text mining fields. The motivation behind this work is to enhance the Arabic light stemmer to serve the data mining industry and leverage it in an open source community. The presented implementation works on enhancing the Arabic light stemmer by utilizing and enhancing an algorithm that provides an extension for a new set of rules and patterns accompanied by adjusted procedure. This study has proven a significant enhancement for better search accuracy with an average 10% improvement in comparison with previous works.

Keywords: Arabic data mining, Arabic Information extraction, Arabic Light stemmer, Arabic stemmer

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Authors: Angelina Anani, Ignacio Ortiz Flores, Haitao Li

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The use of underground mining methods have increased significantly over the past decades. This increase has also been spared on by several mines transitioning from surface to underground mining. However, determining the transition depth can be a challenging task, especially when coupled with production schedule optimization. Several researchers have simplified the problem by excluding operational features relevant to production schedule optimization. Our research objective is to investigate the extent to which operational features of transition mines accounted for affect the optimal production schedule. We also provide a framework for factors to consider in production schedule optimization for transition mines. An integrated mixed-integer linear programming (MILP) model is developed that maximizes the NPV as a function of production schedule and transition depth. A case study is performed to validate the model, with a comparative sensitivity analysis to obtain operational insights.

Keywords: underground mining, transition mines, mixed-integer linear programming, production schedule

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Authors: Paul W. Murray

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This research explores the relationship between leadership style and continuous improvement (CI) teams. CI teams have several features that are not always found in other types of teams, including multi-functional members, short time period for performance, positive and actionable results, and exposure to senior leadership. There is not only one best style of leadership for these teams. Instead, it is important to select the best leadership style for the situation. The leader must have the flexibility to change styles and the skill to use the chosen style effectively in order to ensure the team’s success.

Keywords: leadership style, lean manufacturing, teams, cross-functional

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Authors: Sabeen Alam, Mehboob Alam

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The research work entitled “Effect of Bacillus pumilus strains on heavy metal accumulation in lettuce grown on contaminated soil” focused on functional role of Bacillus pumilus strains inoculated with lettuce seed in mitigating heavy metal in chromite mining soil. In this experiment, factor A was three Bacillus pumilus strains (sequence C-2PMW-8, C-1 SSK-8 and C-1 PWK-7) while soil used for this experiment was collected from Prang Ghar mining site and lettuce seeds were grown in three levels of chromite mining soil (2.27, 4.65 and 7.14 %). For mining soil minimum days to germinate noted in lettuce grown on garden soil inoculated with sequence. Maximum germination percentage noted was for C-1 SSK-8 grown on garden soil, maximum lettuce height for sequence C-2 PWM-8, fresh leaf weight for C-1 PWK-7 inoculated lettuce, dry weight of lettuce leaf for lettuce inoculated with C-1 SSK-8 and C-1 PWK-7 strains, number of leaves per plant for lettuce inoculated with C-1 SSK-8, leaf area for C-2 PMW-8 inoculated lettuce, survival percentage for C-1 SSK-8 treated lettuce and chlorophyll content for C-2 PMW-8. Results related to heavy metals accumulation showed that minimum chromium was in lettuce and in soil for all three sequences, cadmium (Cd) in lettuce and in soil for all three sequences, manganese (Mn) in lettuce and in soil for three sequences, lead (Pb) in lettuce and in soil for three sequences. It can be concluded that chromite mining soil significantly reduced the growth and survival of lettuce, but when lettuce was inoculated with Bacillus.pumilus strains, it enhances growth and survival. Similarly, minimum heavy metal accumulation in plant and soil, regardless of type of Bacillus pumilus used, all three sequences has same mitigating effect on heavy metal in both soil and lettuce. All the three Bacillus pumilus strains ensured reduction in heavy metals content (Mn, Cd, Cr) in lettuce, below the maximum permissible limits of WHO 2011.

Keywords: bacillus pumilus, heavy metals, permissible limits, lettuce, chromite mining soil, mitigating effect

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Authors: Thalia Viveros-Uehara

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

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

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: Assem I. El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Multi-Agent Railway Control System (MARCS). The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are the Behavioral Pattern Analysis (BPA) modeling methodology, and the development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, multi-agent, railway control, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: Yingxu Wang

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The base 6 structure and properties of mirror primes are discovered in this work towards the proof of Goldbach Conjecture. This paper reveals a fundamental pattern on pairs of mirror primes adjacent to any even number nₑ > 2 with symmetrical distances on both sides determined by a methodology of Mirror Prime Decomposition (MPD). MPD leads to a formal proof of the Goldbach conjecture, which states that the conjecture holds because any pivot even number, nₑ > 2, is a sum of at least an adjacent pair of primes divided by 2. This work has not only revealed the analytic pattern of base 6 primes but also proven the infinitive validation of the Goldbach conjecture.

Keywords: number theory, primes, mirror primes, double recursive patterns, Goldbach conjecture, formal proof, mirror-prime decomposition, applications

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Authors: Nagat M. Saeed, Mabruka S. Elashheb, Fatma M. Ben Rabaha, Aisha M Edrah

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The aim of the study was to determine the type and pattern of antibiotic susceptibility of the pathogenic microorganisms causing chronic suppurative otitis media (CSOM), which could lead to better therapeutic decisions and consequently avoidance of appearance of resistance to specific antibiotics. Most frequently isolated agents were Pseudomonas aeruginosa 28.5%; followed by Staphylococcus aureus 18.2%; proteus mirabilis 13.9%; Providencia stuartti 6.7%; Bacteroides melaninogenicus, Aspergillus sp., candida sp., 4.2% each; and other microorganisms were represented in 3-0.2%. Drug sensitivities pattern of Pseudomonas aeruginosa showed that ciprofloxacin was active against the majority of isolates (93.9%) followed by ceftazidime 86.2%, amikacin 76.2% and gentamicin 40.8%. However, Staphylococcus aureus isolates were resistant to penicillin 72.7%, erythromycin 28.6%, cephalothin 18.2%, cloxacillin 8.3% and ciprofloxacin was active against 96.2% of isolates. The resistance pattern of proteus mirabilis was 55.6% to ampicillin, 47.1% to carbencillin, 29.4% to cephalothin, 14.3% to gentamicin and 4.8% to amikacin while 100% were sensitive to ciprofloxacin. We conclude that ciprofloxacin is the best drug of choice in the treatment of CSOM caused by the common microorganisms.

Keywords: otitis media, chronic suppurative otitis media (CSOM), microorganisms, drug sensitivity

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Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

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Authors: Sandra Carrillo Hoyos

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Mining activities in South America have significantly grown during the last decades, given the abundance of natural resources, the implemented governmental policies to incentivize foreign investment as well as the boom in international prices for metals and oil between 2002 and 2008. While this context allowed the region to occupy a leading position between the top producers of minerals around the world, it has also meant an increase in socio-environmental conflicts which have generated costs and negative impacts not only for the companies but especially for the governments and local communities.During the latest decade, the mining sector in Peru has faced with the social resistance of a large number of communities, which began organizing actions against the implementation of high investing projects. The dissatisfaction has derived in the prevalence of socio-environmental conflicts associated with mining activities, some of them never solved into an agreement. In order to prevent those socio-environmental conflicts and obtain the social license from local communities, most of the mining companies have developed diverse initiatives within the framework of policies and practices of corporate social responsibility (CSR). This paper has assessed the mining sector’s contribution toward the local development management along the last decade, as part of CSR strategies as well as the policies promoted by the Peruvian State. This assessment found that, in the beginning, these initiatives have been based on a philanthropic approach and were reacting to pressures from local stakeholders to maintain the consent to operate from the surrounding communities as well as to create, as a result, a harmonious atmosphere for operations. Due to the weak State presence, such practices have increased the expectations of communities related to the participation of mining companies in solving structural development problems, especially those related to primary needs, infrastructure, education, health, among others. In other words, this paper was focused on analyze in what extent these initiatives have promoted local empowerment for development planning and integrated management of natural resources from a territorial approach. From this perspective, the analysis demonstrates that, while the design and planning of social investment initiatives have improved due to the sector´s sustainability approach, many companies have developed actions beyond their competence during this process. In some cases, the referenced actions have generated dependency with communities, even though this relationship has not exempted the companies of conflict situations with unfortunate consequences. Furthermore, the social programs developed have not necessarily generated a significant impact in improving the quality of life of affected populations. In fact, it is possible to identify that those regions with high mining resources and investment are facing with a situation of poverty and high dependency on mining production. In spite of the revenues derived from mining industry, local governments have not been able to translate the royalties into sustainable development opportunities. For this reason, the proposed paper suggests some challenges for the mining sector contribution to local development based on the best practices and lessons learnt from a benchmarking for the leading mining companies.

Keywords: corporate social responsibility, local development, mining, socio-environmental conflict

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Authors: Penina Orenstein

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In today’s world, particularly with COVID-19 a constant worldwide threat, organizations need greater visibility over their supply chains more than ever before, in order to find areas for improvement and greater efficiency, reduce the chances of disruption and stay competitive. The concept of supply chain mapping is one where every process and route is mapped in detail between each vendor and supplier. The simplest method of mapping involves sourcing publicly available data including news and financial information concerning relationships between suppliers. An additional layer of information would be disclosed by large, direct suppliers about their production and logistics sites. While this method has the advantage of not requiring any input from suppliers, it also doesn’t allow for much transparency beyond the first supplier tier and may generate irrelevant data—noise—that must be filtered out to find the actionable data. The primary goal of this research is to build data maps of supply chains by focusing on a layered approach. Using these maps, the secondary goal is to address the question as to whether the supply chain is re-engineered to make improvements, for example, to lower the carbon footprint. Using a drill-down approach, the end result is a comprehensive map detailing the linkages between tier-one, tier-two, and tier-three suppliers super-imposed on a geographical map. The driving force behind this idea is to be able to trace individual parts to the exact site where they’re manufactured. In this way, companies can ensure sustainability practices from the production of raw materials through the finished goods. The approach allows companies to identify and anticipate vulnerabilities in their supply chain. It unlocks predictive analytics capabilities and enables them to act proactively. The research is particularly compelling because it unites network science theory with empirical data and presents the results in a visual, intuitive manner.

Keywords: data mining, supply chain, empirical research, data mapping

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Faizah Almalki, Simeon Gill, Steve G. Hayes, Lisa Taylor

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A core element of cultural identity is the traditional costumes that provide insight into the heritage that has been acquired over time. This heritage is apparent in the use of colour, the styles and the functions of the clothing and it also reflects the skills of those who created the items and the time taken to produce them. Modern flat pattern drafting methods for making garment patterns are simple in comparison to the relatively laborious traditional approaches that would require personal interaction with the wearer throughout the production process. The current study reflects on the main elements of the pattern cutting system and how this has evolved in Saudi Arabia to affect the design of the Sawan garment. Analysis of the traditional methods for constructing Sawan garments was undertaken through observation of the practice and the garments and consulting documented guidance. This provided a foundation through which to explore how modern technology can be applied to improve the process. In this research, modern methods are proposed for producing traditional Saudi garments more efficiently while retaining elements of the conventional style and design. The current study has documented the vital aspects of Sawan garment style. The result showed that the method had been used to take the body measurements and pattern making was elementary and offered simple geometric shape and the Sawan garment is composed of four pieces. Consequently, this research allows for classical pattern shapes to be embedded in garments now worn in Saudi Arabia and for the continuation of cultural heritage.

Keywords: traditional Sawan garment technique, modern pattern cutting technique, the shape of the garment and software, Lectra Modaris

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Harry Aginta

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Using Phillips curve framework, this paper seeks for new empirical evidence on the relationship between inflation and output in a major emerging economy. By exploiting sub-national data, the contribution of this paper is threefold. First, it resolves the issue of using on-target national inflation rates that potentially causes weakening inflation-output nexus. This is very relevant for Indonesia as its central bank has been adopting inflation targeting framework based on national consumer price index (CPI) inflation. Second, the study tests the relevance of mining sector in output gap estimation. The test for mining sector is important to control for the effects of mining regulation and nominal effects of coal prices on real economic activities. Third, the paper applies panel econometric method by incorporating regional variation that help to improve model estimation. The results from this paper confirm the strong presence of Phillips curve in Indonesia. Positive output gap that reflects excess demand condition gives rise to the inflation rates. In addition, the elasticity of output gap is higher if the mining sector is excluded from output gap estimation. In addition to inflation adaptation, the dynamics of exchange rate and international commodity price are also found to affect inflation significantly. The results are robust to the alternative measurement of output gap

Keywords: Phillips curve, inflation, Indonesia, panel data

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Authors: Honggang Qu, Yong Xu, Rongmei Liu, Zhenji Gao, Bin Wang

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Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: Assem El-Ansary

Abstract:

This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Multi-Agent TeleRobotic Security Control System (MTSCS). The event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are the Behavioral Pattern Analysis (BPA) modeling methodology, and the development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, multi-agent, TeleRobotics control, security, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

Procedia PDF Downloads 408

Authors: J. K. Alhassan, S. A. Lawal

Abstract:

This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

Procedia PDF Downloads 343

Authors: Alok S Chauhan, Sridhar S., Pradyumna R.

Abstract:

In the process of precision investment casting of turbine hollow blade/vane components, a part of the dimensional deviations observed in the castings can be attributed to the wax pattern. In the process of injection moulding of wax to produce patterns, heated wax shrinks in size during cooling in the die, leading to a reduction in the dimensions of the pattern. Also, flow and thermal induced residual stresses result in shrinkage & warpage of the component after removal from the die, further adding to the deviations. Injection moulding parameters such as wax temperature, flow rate, packing pressure, etc. affect the flow and thermal behavior of the component and hence are directly responsible for the dimensional deviations. There is a need to precisely determine and control these deviations in order to achieve stringent dimensional accuracies imposed on these castings by aerospace standards. Simulation based approaches provide a platform to predict these dimensional deviations without resorting to elaborate experimentation. In the present paper, Moldex3D simulation package has been utilized to analyze the effect of variations in injection temperature, packing pressure and cooling time on the shrinkage behavior of a stepped pattern. Two types of waxes with different rheological properties have been included in the study to gauge the effect of change in wax on the dimensional deviations. A full factorial design of experiments has been configured with these parameters and results of analysis of variance have been presented.

Keywords: wax patterns, investment casting, pattern die/mould, wax injection, Moldex3D simulation

Procedia PDF Downloads 341