Search results for: mining context

Authors: Mei-Yi Wu, Shang-Ming Huang

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The online shoppers nowadays often search the product information on the Internet using some keywords of products. To use this kind of information searching model, shoppers should have a preliminary understanding about their interesting products and choose the correct keywords. However, if the products are first contact (for example, the worn clothes or backpack of passengers which you do not have any idea about the brands), these products cannot be retrieved due to insufficient information. In this paper, we discuss and study the applications in E-commerce using image retrieval and text mining techniques. We design a reasonable E-commerce application system containing three layers in the architecture to provide users product information. The system can automatically search and retrieval similar images and corresponding web pages on Internet according to the target pictures which taken by users. Then text mining techniques are applied to extract important keywords from these retrieval web pages and search the prices on different online shopping stores with these keywords using a web crawler. Finally, the users can obtain the product information including photos and prices of their favorite products. The experiments shows the efficiency of proposed system.

Keywords: mobile image retrieval, text mining, product information service system, online marketing

Procedia PDF Downloads 331

Authors: Şakir Bingöl, Bayram İslamoğlu, Ebubekir Furkan Tepeli, Fatih Mehmet Karakule, Fatih Küçük, Merve Sena Arpacık, Mustafa Taha Kabar, Muhammet Metin Molak, Osman Emre Turan, Ömer Faruk Yesir, Sıla İnanır

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This study introduces an IoT-based real-time safety system for mining, addressing global safety challenges. The wearable device, seamlessly integrated into miners' jackets, employs LoRa technology for communication and offers real-time monitoring of vital health and environmental data. Unique features include an LCD panel for immediate information display and sound-based location tracking for emergency response. The methodology involves sensor integration, data transmission, and ethical testing. Validation confirms the system's effectiveness in diverse mining scenarios. The study calls for ongoing research to adapt the system to different mining contexts, emphasizing its potential to significantly enhance safety standards in the industry.

Keywords: mining safety, internet of things, wearable technology, LoRa, RFID tracking, real-time safety system, safety alerts, safety measures

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Authors: Shu-Lu Hsu, Fang-Yi Chu

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With the advances in information and communications technology, mobile context-aware applications have become powerful marketing tools. In Apple online store, there are numerous mobile applications (APPs) developed for destination tour. This study investigated the determinants of adoption of context-aware APPs for destination tour services. A model is proposed based on Technology Acceptance Model and privacy concern theory. The model was empirically tested based on a sample of 259 users of a tourism APP published by Kaohsiung Tourism Bureau, Taiwan. The results showed that the fitness of the model is well and, among all the factors, the perceived usefulness and perceived ease of use have the most significant influences on the intention to adopt context-aware destination APPs. Finally, contrary to the findings of previous literature, the effect of privacy concern on the adoption intention of context-aware APP is insignificant.

Keywords: mobile application, context-aware, privacy concern, TAM

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Authors: Rezky Rifaini, Raden Bagus Fajriya Hakim

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

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

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Authors: Jason West

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Low profitability in agriculture production along with increasing scrutiny over environmental effects is limiting food production at scale. In contrast, the mining sector offers access to resources including energy, water, transport and chemicals for food production at low marginal cost. Scalable agricultural production can benefit from the nexus of resources (water, energy, transport) offered by mining activity in remote locations. A decision support bioeconomic model for controlled environment vertical farms was used. Four submodels were used: crop structure, nutrient requirements, resource-crop integration, and economic. They escalate to a macro mathematical model. A demonstrable dynamic systems framework is needed to prove productive outcomes are feasible. We demonstrate a generalized bioeconomic macro model for controlled environment production systems in minesites using systems dynamics modeling methodology. Despite the complexity of bioeconomic modelling of resource-agricultural dynamic processes and interactions, the economic potential greater than general economic models would assume. Scalability of production as an input becomes a key success feature.

Keywords: crop production systems, mathematical model, mining, agriculture, dynamic systems

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Authors: Sarah Werner, Michael J. Pritchard

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From pilots to engineers, the skills development within the aerospace industry is exceptionally broad. Employers often struggle with finding the right mixture of qualified skills to fill their organizational demands. This effort to find qualified talent is further complicated by the industrial delineation between two key areas: aviation and aerospace. In a broad sense, the aerospace industry overlaps with the aviation industry. In turn, the aviation industry is a smaller sector segment within the context of the broader definition of the aerospace industry. Furthermore, it could be conceptually argued that -in practice- there is little distinction between these two sectors (i.e., aviation and aerospace). However, through our unstructured text analysis of over 6,000 job listings captured, our team found a clear delineation between aviation-related jobs and aerospace-related jobs. Using techniques in natural language processing, our research identifies an integrated workforce skill pattern that clearly breaks between these two sectors. While the aviation sector has largely maintained its need for pilots, mechanics, and associated support personnel, the staffing needs of the aerospace industry are being progressively driven by integrative engineering needs. Increasingly, this is leading many aerospace-based organizations towards the acquisition of 'system level' staffing requirements. This research helps to better align higher educational institutions with the current industrial staffing complexities within the broader aerospace sector.

Keywords: aerospace industry, job demand, text mining, workforce development

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Authors: Karla N. Penna, Eloise J. Hoffman, Tonya R. Carter

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This paper suggests that effective cultural landscape management plans in an Indigenous context should be undertaken using multidisciplinary approach taken into consideration context-related social and cultural aspects. In relation to working in Indigenous and mining contexts, we draw upon and contribute to International policies on human rights that promote the development of management plans on that are co-designed through genuine engagement processes. We suggest that the production of management plans that are built upon culturally relevant frameworks, lead to more inclusive economic development, a greater sense of trust, and shared managerial responsibilities. In this paper, three issues related to Indigenous engagement and cultural landscape management plans will be addressed: (1) the need for effective communication channels between proponents and Traditional Owners (Australian original Aboriginal peoples who inhabited specific regions), (2) the use of a culturally sensitive approach to engage local representatives in the decision making processes, and (3) how design of new management plans can help in establishing shared management.

Keywords: culture-centred approach, Holons’ hierarchy, inclusive economic development, indigenous engagement

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: E. Fathallahi Aghdam, V. Hosseini

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

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

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Authors: Ezzeldin M. T. Ali

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Arguably viewers hardly benefit from screen subtitles due to the inconsistency between scenarios and their subtitles. Research in this area will provide an understanding of the association between these scenarios and subtitles via context. It attempts to eliminate the inconsistency existing between contexts and screen subtitles providing insights into the problem. Specifically, the study aims at examining the extent to which the understanding of screen subtitles largely depends on the force of linguistic and situational contexts. This is because the context is assumed to have a powerful effect on the interpretation of the source text. Both descriptive and experimental methods were adopted for data collection. These included a test and paper-pencil-questionnaires where participants provided their impressions about the role of context in eliminating interpretation problems of screen subtitles. Participants developed a good background about screen subtitles watching films. Results showed that context forms a powerful element in understanding screen subtitles. Results also revealed that communicative translation fits well screen translation boosting the contextual meaning. The association of context and communicative translation makes subtitles globally more economical and intelligible. Context forms a central element for film language to be intelligible.

Keywords: communicative translation, context, scenario, powerful, intellgible

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Sajjad Akbar, Rabia Bashir

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With the growth of the number of users, the Internet usage has evolved. Due to its key design principle, there is an incredible expansion in its size. This tremendous growth of the Internet has brought new applications (mobile video and cloud computing) as well as new user’s requirements i.e. content distribution environment, mobility, ubiquity, security and trust etc. The users are more interested in contents rather than their communicating peer nodes. The current Internet architecture is a host-centric networking approach, which is not suitable for the specific type of applications. With the growing use of multiple interactive applications, the host centric approach is considered to be less efficient as it depends on the physical location, for this, Information Centric Networking (ICN) is considered as the potential future Internet architecture. It is an approach that introduces uniquely named data as a core Internet principle. It uses the receiver oriented approach rather than sender oriented. It introduces the naming base information system at the network layer. Although ICN is considered as future Internet architecture but there are lot of criticism on it which mainly concerns that how ICN will manage the most relevant content. For this Web Content Mining(WCM) approaches can help in appropriate data management of ICN. To address this issue, this paper contributes by (i) discussing multiple ICN approaches (ii) analyzing different Web Content Mining approaches (iii) creating a new Internet architecture by merging ICN and WCM to solve the data management issues of ICN. From ICN, Content-Centric Networking (CCN) is selected for the new architecture, whereas, Agent-based approach from Web Content Mining is selected to find most appropriate data.

Keywords: agent based web content mining, content centric networking, information centric networking

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Authors: Xiaohui Zhao, Shahan Mafuz

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Context-aware technologies provide system applications with the awareness of environmental conditions, customer behaviour, object movements, etc. Further, with such capability system applications can be smart to adapt intelligently their responses to the changing conditions. Concerning business operations, this promises businesses that their business processes can run more intelligently, adaptively and flexibly, and thereby either improve customer experience, enhance reliability of service delivery, or lower operational cost, to make the business more competitive and sustainable. Aiming at realizing such context-aware business process management, this paper firstly explores its potential benefit and then identifies some gaps between the current business process management support and the expected. In addition, some preliminary solutions are also discussed with context definition, rule-based process execution, run-time process evolution, etc. A framework is also presented to give a conceptual architecture of context-aware business process management system to guide system implementation.

Keywords: business process adaptation, business process evolution, business process modelling, and context awareness

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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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: 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: 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: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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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: Imran Dayan, Ashiqul Khan

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Internal corporate fraud, which is fraud carried out by internal stakeholders of a company, affects the well-being of the organisation just like its external counterpart. Even if such an act is carried out for the short-term benefit of a corporation, the act is ultimately harmful to the entity in the long run. Internal fraud is often carried out by relying upon aberrations from usual business processes. Business processes are the lifeblood of a company in modern managerial context. Such processes are developed and fine-tuned over time as a corporation grows through its life stages. Modern corporations have embraced technological innovations into their business processes, and Enterprise Resource Planning (ERP) systems being at the heart of such business processes is a testimony to that. Since ERP systems record a huge amount of data in their event logs, the logs are a treasure trove for anyone trying to detect any sort of fraudulent activities hidden within the day-to-day business operations and processes. This research utilises the ERP systems in place within corporations to assess the likelihood of prospective internal fraud through developing a framework for measuring the risks of fraud through Process Mining techniques and hence finds risky designs and loose ends within these business processes. This framework helps not only in identifying existing cases of fraud in the records of the event log, but also signals the overall riskiness of certain business processes, and hence draws attention for carrying out a redesign of such processes to reduce the chance of future internal fraud while improving internal control within the organisation. The research adds value by applying the concepts of Process Mining into the analysis of data from modern day applications of business process records, which is the ERP event logs, and develops a framework that should be useful to internal stakeholders for strengthening internal control as well as provide external auditors with a tool of use in case of suspicion. The research proves its usefulness through a few case studies conducted with respect to big corporations with complex business processes and an ERP in place.

Keywords: enterprise resource planning, fraud risk framework, internal corporate fraud, process mining

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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: 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: Clif P. Lewis, Ise-Lu Möller

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The COVID-19 pandemic has had a profound impact on employment. The ‘future of work’ is now the ‘present of work’. Changes in the social context within which organisations are embedded necessitated drastic changes in how we work. Through the leveraging of technology and changes in mindset, we have seen exciting innovations in the world of work. This global shift in the context of employment offers a unique opportunity to examine a key unresolved issue in the study of Burnout, namely contextual antecedents. This study answers the call for deeper empirical insight into the contexts within which Burnout occur. We explore the emergence of Burnout within a remote work context by using survey data that incorporates the latest global work trends into the Areas of Worklife framework.

Keywords: burnout, remote work, pandemic, wellness

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Authors: Esraa Moustafa, Gaetan Rey, Stephane Lavirotte, Jean-Yves Tigli

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With the development of Internet of Things and Web of Things, computing becomes more pervasive, invisible and present everywhere. In fact, in our environment, we are surrounded by multiple devices that deliver (web) services that meet the needs of the users. However, the mobility of these devices as the users has important repercussions that challenge software design of these applications because the variability of the environment cannot be anticipated at the design time. Thus, it will be interesting to dynamically discover the environment and adapt the application during its execution to the new contextual conditions. We, therefore, propose a model of a context-aware middleware that can address this issue through a monitoring service that is capable of reasoning and observation channels capable of calculating the context during the runtime. The monitoring service evaluates the pre-defined X-Query predicates in the context manager and uses Prolog to deduce the services needed to respond back. An independent Observation Channel for each different predicate is then dynamically generated by the monitoring service depending on the current state of the environment. Each channel sends its result directly to the context manager which consequently calculates the context based on all the predicates’ results while preserving the reactivity of the self-adaptive system.

Keywords: auto-adaptation, context-awareness, middleware, reasoning engine

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Authors: Shiva Subhedar, Abhishek Jain, Shivin Mittal

Abstract:

Icons are not merely decorative elements in enterprise applications but very often used because of their many advantages such as compactness, visual appeal, etc. Despite these potential advantages, icons often cause usability problems when they are designed without consideration for their many potential downsides. The aim of the current study was to examine the effect of articulatory distance – the distance between the physical appearance of an interface element and what it actually means. In other words, will the subject find the association of the function and its appearance on the interface natural or is the icon difficult for them to associate with its function. We have calculated response time and quality of identification by varying icon concreteness, the context of usage and subject experience in the enterprise context. The subjects were asked to associate icons (prepared for study purpose) with given function options in context and out of context mode. Response time and their selection were recorded for analysis.

Keywords: HCI, icons, icon concreteness, icon recognition

Procedia PDF Downloads 236

Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

Abstract:

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

Procedia PDF Downloads 310

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

Abstract:

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

Procedia PDF Downloads 69