Search results for: Arabic data mining

Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Ashraf M. Khalifa, Hwat Bing So, Greg Maddocks

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Changes to the mining environmental approvals process in Queensland have been rolled out under the MERFP Act (2018). This includes requirements for a Progressive Rehabilitation and Closure Plan (PRC Plan). Key considerations of the landform design report within the PRC Plan must include: (i) identification of materials available for landform rehabilitation, including their ability to achieve the required landform design outcomes, (ii) erosion assessments to determine landform heights, gradients, profiles, and material placement, (iii) slope profile design considering the interactions between soil erodibility, rainfall erosivity, landform height, gradient, and vegetation cover to identify acceptable erosion rates over a long-term average, (iv) an analysis of future stability based on the factors described above e.g., erosion and /or landform evolution modelling. ACARP funded an extensive and thorough erosion assessment program using rainfall simulators from 1998 to 2010. The ACARP program included laboratory assessment of 35 soil and spoil samples from 16 coal mines and samples from a gold mine in Queensland using 3 x 0.8 m laboratory rainfall simulator. The reliability of the laboratory rainfall simulator was verified through field measurements using larger flumes 20 x 5 meters and catchment scale measurements at three sites (3 different catchments, average area of 2.5 ha each). Soil cover systems are a primary component of a constructed mine landform. The primary functions of a soil cover system are to sustain vegetation and limit the infiltration of water and oxygen into underlying reactive mine waste. If the external surface of the landform erodes, the functions of the cover system cannot be maintained, and the cover system will most likely fail. Assessing a constructed landform’s potential ‘long-term’ erosion stability requires defensible erosion rate thresholds below which rehabilitation landform designs are considered acceptably erosion-resistant or ‘stable’. The process used to quantify erosion rates using rainfall simulators (flumes) to measure rill and inter-rill erosion on bulk samples under laboratory conditions or on in-situ material under field conditions will be explained.

Keywords: open-cut, mining, erosion, rainfall simulator

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

Procedia PDF Downloads 118

Authors: Partha Saha, Uttam Kumar Banerjee

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Maternal and Child Healthcare (MCH) has always been regarded as one of the important issues globally. Reduction of maternal and child mortality rates and increase of healthcare service coverage were declared as one of the targets in Millennium Development Goals till 2015 and thereafter as an important component of the Sustainable Development Goals. Over the last decade, worldwide MCH indicators have improved but could not match the expected levels. Progress of both maternal and child mortality rates have been monitored by several researchers. Each of the studies has stated that only less than 26% of low-income and middle income countries (LMICs) were on track to achieve targets as prescribed by MDG4. Average worldwide annual rate of reduction of under-five mortality rate and maternal mortality rate were 2.2% and 1.9% as on 2011 respectively whereas rates should be minimum 4.4% and 5.5% annually to achieve targets. In spite of having proven healthcare interventions for both mothers and children, those could not be scaled up to the required volume due to fragmented health systems, especially in the developing and under-developed countries. In this research, a knowledge discovery based interactive Decision Support System (DSS) has been developed on web platform which would assist healthcare policy makers to develop evidence-based policies. To achieve desirable results in MCH, efficient resource planning is very much required. In maximum LMICs, resources are big constraint. Knowledge, generated through this system, would help healthcare managers to develop strategic resource planning for combatting with issues like huge inequity and less coverage in MCH. This system would help healthcare managers to accomplish following four tasks. Those are a) comprehending region wise conditions of variables related with MCH, b) identifying relationships within variables, c) segmenting regions based on variables status, and d) finding out segment wise key influential variables which have major impact on healthcare indicators. Whole system development process has been divided into three phases. Those were i) identifying contemporary issues related with MCH services and policy making; ii) development of the system; and iii) verification and validation of the system. More than 90 variables under three categories, such as a) educational, social, and economic parameters; b) MCH interventions; and c) health system building blocks have been included into this web-based DSS and five separate modules have been developed under the system. First module has been designed for analysing current healthcare scenario. Second module would help healthcare managers to understand correlations among variables. Third module would reveal frequently-occurring incidents along with different MCH interventions. Fourth module would segment regions based on previously mentioned three categories and in fifth module, segment-wise key influential interventions will be identified. India has been considered as case study area in this research. Data of 601 districts of India has been used for inspecting effectiveness of those developed modules. This system has been developed by importing different statistical and data mining techniques on Web platform. Policy makers would be able to generate different scenarios from the system before drawing any inference, aided by its interactive capability.

Keywords: maternal and child heathcare, decision support systems, data mining techniques, low and middle income countries

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Authors: Abhishek Khupsare, Ajay Parmar, Ajay Agarwal, Swapnil Wanjari

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The extreme demand for aggregate leads to the exploitation of river-bed for fine aggregates, affecting the environment adversely. Therefore, a suitable alternative to natural river sand is essentially required. This study focuses on preventing environmental impact by developing polymer sand to replace natural river sand (NRS). Development of polymer sand by mixing high volume fly ash, bottom ash, cement, natural river sand, and locally purchased high solid content polycarboxylate ether-based superplasticizer (HS-PCE). All the physical and chemical properties of polymer sand (P-Sand) were observed and satisfied the requirement of the Indian Standard code. P-Sand yields good specific gravity of 2.31 and is classified as zone-I sand with a satisfactory friction angle (37˚) compared to natural river sand (NRS) and Geopolymer fly ash sand (GFS). Though the water absorption (6.83%) and pH (12.18) are slightly more than those of GFS and NRS, the alkali silica reaction and soundness are well within the permissible limit as per Indian Standards. The chemical analysis by X-Ray fluorescence showed the presence of high amounts of SiO2 and Al2O3 with magnitudes of 58.879% 325 and 26.77%, respectively. Finally, the compressive strength of M-25 grade concrete using P-sand and Geopolymer sand (GFS) was observed to be 87.51% and 83.82% with respect to natural river sand (NRS) after 28 days, respectively. The results of this study indicate that P-sand can be a good alternative to NRS for construction work as it not only reduces the environmental effect due to sand mining but also focuses on utilising fly ash and bottom ash.

Keywords: polymer sand, fly ash, bottom ash, HSPCE plasticizer, river sand mining

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Kenny Raharjo, Ramon Lawrence

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Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.

Keywords: game design, multi-arm bandit, design exploration and data mining, player metric optimization and analytics

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Alicja Kot-Niewiadomska

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Securing sustainable access to raw materials is vital for the growth of the European economy and for the goals laid down in Strategy Europe 2020. One of the most important sources of mineral raw materials are primary deposits. The efficient management of them, including extraction, will ensure competitiveness of the European economy. A critical element of this approach is mineral deposits safeguarding and the most important tool - spatial planning. The safeguarding of deposits should be understood as safeguarding of land access, and safeguarding of area against development, which may (potential) prevent the use of the deposit and the necessary mining activities. Many European Union countries successfully integrated their mineral policy and spatial policy, which has ensured the proper place of mineral deposits in their spatial planning systems. These, in turn, are widely recognized as the most important mineral deposit safeguarding tool, the essence of which is to ensure long-term access to its resources. The examples of Austria, Portugal, Slovakia, Czech Republic, Sweden, and the United Kingdom, discussed in the paper, are often mentioned as examples of good practices in this area. Although none of these countries managed to avoid cases of social and environmental conflicts related to mining activities, the solutions they implement certainly deserve special attention. And for many countries, including Poland, they can be a potential source of solutions aimed at improving the protection of mineral deposits.

Keywords: mineral deposits, land use planning, mineral deposit safeguarding, European practices

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Authors: Mostafa Firoozabadi, Alireza Foroughi Nematollahi

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Wet mills are one of the most important equipment in the mining industries and any defect occurrence in them can stop the production line and it can make some irrecoverable damages to the system. Electromotors are the significant parts of a mill and their monitoring is a necessary process to prevent unwanted defects. The purpose of this study is to investigate the Electromotor bearing defects, theoretically and practically, using the vibration analysis method. When a defect happens in a bearing, it can be transferred to the other parts of the equipment like inner ring, outer ring, balls, and the bearing cage. The electromotor defects source can be electrical or mechanical. Sometimes, the electrical and mechanical defect frequencies are modulated and the bearing defect detection becomes difficult. In this paper, to detect the electromotor bearing defects, the electrical and mechanical defect frequencies are extracted firstly. Then, by calculating the bearing defect frequencies, and the spectrum and time signal analysis, the bearing defects are detected. In addition, the obtained frequency determines that the bearing level in which the defect has happened and by comparing this level to the standards it determines the bearing remaining lifetime. Finally, the defect length is calculated by theoretical equations to demonstrate that there is no need to replace the bearing. The results of the proposed method, which has been implemented on the wet mills in the Golgohar mining and industrial company in Iran, show that this method is capable of detecting the electromotor bearing defects accurately and on time.

Keywords: bearing defect length, defect frequency, electromotor defects, vibration analysis

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Authors: S. Chandrasekaran, S. Nandita, M. Shivathmika, Srikrishnan Shivakumar

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The objective of the research work is to analyze the computational chemical-composition of carbohydrates in the context of healthcare informatics. The computation involves the representation of complex chemical molecular structure of carbohydrate using graph theory and in a deployable Chemical Markup Language (CML). The parallel molecular structure of the chemical molecules with or without other adulterants for the sake of business profit can be analyzed in terms of robustness and derivatization measures. The rural healthcare program should create awareness in malnutrition to reduce ill-effect of decomposition and help the consumers to know the level of such energy storage mixtures in a quantitative way. The earlier works were based on the empirical and wet data which can vary from time to time but cannot be made to reuse the results of mining. The work is carried out on the quantitative computational chemistry on carbohydrates to provide a safe and secure right to food act and its regulations.

Keywords: carbohydrates, chemical-composition, chemical markup, robustness, food safety

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Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

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Authors: Tatiana Ponomarenko, Oksana Marinina, Marina Nevskaya

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Modern corporate social responsibility (CSR) is a sphere of multilevel responsibility of a company toward society represented by various stakeholders. The relevance of CSR management grows due to the active development of socially responsible investing (principles for responsible investment) taking into account factors of environmental, social and corporate governance (ESG), growing attention of the investment community in general to the long-term stability of companies and the quality of control of nonfinancial risks. The modern approach to CSR strategic management is aimed at the creation of trustful relationships with stakeholders, on the basis of which a contribution to the sustainable development of companies, regions, and national economics is insured. However, the practical concepts of social responsibility in mining companies are different, which leads to various degrees of application of CSR. A number of companies implement CSR using a traditional (limited) understanding of responsibility toward employees and counteragents, the others understand CSR much wider and try to use leverages of efficient cooperation. As in large mining companies the scope of CSR measures is diverse and characterized by different indices, the study was aimed at evaluating CSR efficiency on the basis of a proprietary methodology and determining the level of development of CSR management in terms of anti-crisis, reactive and proactive development. The methodology of the research includes analysis of integrated global reporting initiative (GRI) reports of large mining companies; choice of most representative sectoral agents by a criterion of the regularity of issuance and publication of reports; calculation of indices of evaluation of CSR level of the selected companies in dynamics. The methodology of evaluation of CSR level is based on a rating score of changes in standard indices of GRI reports by economic, environmental, and social directions. Result. By the results of the analysis, companies of fuel and energy and metallurgic complexes, in overwhelming majority, reflecting three indices out of a wide range of possible indicators of SDGs (Sustainable Development Goals), were selected for the study. The evaluation of the scopes of CSR of the companies Gazprom, LUKOIL, Metalloinvest, Nornikel, Rosneft, Severstal, SIBUR, SUEK corresponds to the reactive type of development according to a scale of CSR strategic management, which is the average value out of the possible values. The chief drawback is that companies, in the process of analyzing global goals, often choose the goals which relate to their own activities, paying insufficient attention to the interests of the stakeholders inside the country. This fact evidences the necessity of searching for more effective mechanisms of CSR control. Acknowledgment: This article is prepared within grant support of the RFBR, project 19-510-44013 'Development of the concept of mineral resources value formation in the context of sustainable development in resource-oriented economies'.

Keywords: sustainable development, corporate social responsibility, development strategies, efficiency assessment

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Authors: Kacper Tomasz Kröber-Mulawa

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This paper will address the topic of data ownership from an economic perspective, and examples of economic limitations of data property rights will be provided, which have been identified using methods and approaches of economic analysis of law. To properly build a background for the economic focus, in the beginning a short perspective of data and data ownership in the EU’s legal system will be provided. It will include a short introduction to its political and social importance and highlight relevant viewpoints. This will stress the importance of a Single Market for data but also far-reaching regulations of data governance and privacy (including the distinction of personal and non-personal data, data held by public bodies and private businesses). The main discussion of this paper will build upon the briefly referred to legal basis as well as methods and approaches of economic analysis of law.

Keywords: antitrust, data, data ownership, digital economy, property rights

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Authors: Abdullah Alsaeed

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Virtualized computing and cloud computing infrastructures are no longer fuzz or marketing term. They are a core reality in today’s corporate Information Technology (IT) organizations. Hence, developing an effective and efficient methodologies for data backup and data recovery is required more than any time. The purpose of data backup and recovery techniques are to assist the organizations to strategize the business continuity and disaster recovery approaches. In order to accomplish this strategic objective, a variety of mechanism were proposed in the recent years. This research paper will explore and examine the latest techniques and solutions to provide data backup and restoration for the cloud computing platforms.

Keywords: data backup, data recovery, cloud computing, business continuity, disaster recovery, cost-effective, data encryption.

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

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Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

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Authors: Heba I. Bakry

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Academic writing in the tertiary education has always been a challenge to EFL learners. This proposed study aims at investigating the academic English language needs for PhD students and candidates studying humanities and social sciences at Cairo University. The research problem arises from the fact that most of them study English as a Foreign Language (EFL) or for specific purposes (ESP) in their undergraduate years. They are hardly familiarized with the different academic genres, despite the fact that they use academic resources written in English, and they are required to publish a paper internationally. Upon understanding the conventions and constraints of academic writing, postgraduates will have the opportunity to interact with the international academic spheres conveniently. There is, thus, a need to be acquainted with the generally accepted features of the academic genres, such as academic papers and their part-genres, such as writing abstracts, in addition to other occluded genres, such as personal statements and recommendation letters. The lack of practicing many of these genres is caused by the fact that there are clear differences between the rhetoric and conventions of the students' native language, i.e., Arabic, and the target language they are learning in the academic context, i.e., English. Moreover, apart from the general culture represented ethno-linguistically, the learners' 'small' culture represented in a national setting like Cairo University is more defining than their general cultural affiliations that are associated with their nationality, race, or religion, for instance. The main research question of this proposed study is: What is the effect of teaching a genre-based EAP course on the research writing competence of PhD candidates? To reach an answer to this question, the study will attempt to answer the following sub-questions: 1. What are the Egyptian PhD candidates' EAP perceived needs? 2. What are the requisite academic research skills for Egyptian scholars? The study intends to assess the students’ needs, as a step to design and evaluate an EAP course that is based on explaining and scrutinizing a variety of academic genres. Adopting a diagnostic approach, the needs assessment uses quantitative data collected through questionnaires, and qualitative data assembled from semi-structured interviews with the students and their teachers, in addition to non-participant observations of a convenience sample.

Keywords: course design, English for academic purposes, genre-based, needs assessment

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Authors: Elkington Sibusiso Mnguni

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The Mokolo-Crocodile Water Augmentation Project (MCWAP) is located in the Limpopo Province in the northern-western part of South Africa. Its purpose is to increase water supply by 30 million cubic meters per year to meet current and future demand for users, including power stations, mining houses, and the local municipality in the Lephalale area. This paper documents the planning and implementation aspects of the MCWAP infrastructure project. The study will add to the body of knowledge with respect to bulk water infrastructure development in water-scarce regions. The method used to gather and collate relevant data and information was the desktop study. The key finding was that the project was successfully completed in 2015 using conventional project management and construction methods. The project is currently being operated and maintained by the National Department of Water and Sanitation.

Keywords: construction, contract management, infrastructure project, project management

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: data mining, Korean linguistic feature, literary fiction, relationship extraction

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Authors: K. Hardy, A. Maurushat

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Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: big data, open data, productivity, data governance

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Authors: Mohamed Fredj, Abdallah Hafsaoui, Radouane Nakache

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The study of the stability of the mining works in rock masses fractured is the major concern of the operating engineer. For geotechnical works in mines and quarries, it there is not today's general methodology for analysis and the quantification of the risks relating to the dangers inherent in these concrete types (falling boulders, landslides, etc.). The reasons for this are uncertainty, which weighs on available data or lack of knowledge of the values of the parameters required for this analysis type. Stability calculations must be based on reliable knowledge of the distribution of discontinuities that dissect the Rocky massif and the resistance to shear of the intact rock and discontinuities. This study is aimed to study the stability of slope of mine (Kef Sennoun - Tebessa, Algeria). The problem is analyzed using a numerical model based on the finite elements (software Plaxis 3D).

Keywords: stability, discontinuities, finite elements, rock mass, open-pit mine

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Authors: Angelina A. Tzacheva, Jaishree Ranganathan

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Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: actionable pattern discovery, education, emotion, data mining

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Abrehet Kahsay Mehari

Abstract:

Aquatic plants provide the physical structure of wetlands and increase their habitat complexity and heterogeneity, and as such, have a profound influence on other biotas. In this study, we investigated how human disturbance activities influenced the species richness and community composition of aquatic plants in the wetlands of Lake Tana, Ethiopia. Twelve wetlands were selected: four lacustrine, four river mouths, and four riverine papyrus swamps. Data on aquatic plants, environmental variables, and human activities were collected during the dry and wet seasons of 2018. A linear mixed effect model and a distance-based Redundancy Analysis (db-RDA) were used to relate aquatic plant species richness and community composition, respectively, to human activities and environmental variables. A total of 113 aquatic plant species, belonging to 38 families, were identified across all wetlands during the dry and wet seasons. Emergent species had the maximum area covered at 73.45 % and attained the highest relative abundance, followed by amphibious and other forms. The mean taxonomic richness of aquatic plants was significantly lower in wetlands with high overall human disturbance scores compared to wetlands with low overall human disturbance scores. Moreover, taxonomic richness showed a negative correlation with livestock grazing, tree plantation, and sand mining. The community composition also varied across wetlands with varying levels of human disturbance and was primarily driven by turnover (i.e., replacement of species) rather than nestedness resultant(i.e., loss of species). Distance-based redundancy analysis revealed that livestock grazing, tree plantation, sand mining, waste dumping, and crop cultivation were significant predictors of variation in aquatic plant communities’ composition in the wetlands. Linear mixed effect models and distance-based redundancy analysis also revealed that water depth, turbidity, conductivity, pH, sediment depth, and temperature were important drivers of variations in aquatic plant species richness and community composition. Papyrus swamps had the highest species richness and supported different plant communities. Conservation efforts should therefore focus on these habitats and measures should be taken to restore the highly disturbed and species poor wetlands near the river mouths.

Keywords: species richness, community composition, aquatic plants, wetlands, Lake Tana, human disturbance activities

Procedia PDF Downloads 99

Authors: Rimmy Yadav, Anmol Preet Kaur

Abstract:

Big Data has demonstrated the vast potential in streamlining, deciding, spotting business drifts in different fields, for example, producing, fund, Information Technology. This paper gives a multi-disciplinary diagram of the research issues in enormous information and its procedures, instruments, and system identified with the privacy, data storage management, network and energy utilization, adaptation to non-critical failure and information representations. Other than this, result difficulties and openings accessible in this Big Data platform have made.

Keywords: big data, privacy, data management, network and energy consumption

Procedia PDF Downloads 288

Authors: Emad A. Mohammed

Abstract:

Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

Procedia PDF Downloads 128

Authors: sulmaz Mozaffari

Abstract:

Nostalgia is one of the hot-debated issues in critical psychology which has been translated as the yearning or gloom in Persian. It is defined as the regret of the sweet past and the contrast of the present with the past. The feeling of alienation and being remote from the home, remembering death, the regret of childhood and youth, separation of the beloved, remembering the glorious era of history, desire for the ancient times, and the hope for Utopia are considered as its components. Nazik Al-Malaika, a contemporary poet of Arabic literature, has depicted some shapes and dimensions of sympathy, regret and anguish in her poems. Utilizing a nostalgic approach to the past, this paper has reflected upon love, memories of childhood and youth and hope for Utopia "and also aimed at explaining each one's manifestations through a comparative perspective.

Keywords: Nazik al-malaika, poem, nostalgia, personal memory, collective memory

Procedia PDF Downloads 392