Search results for: incomplete count data

Authors: Sara Ibn El Ahrache, Tajje-eddine Rachidi, Hassan Badir, Abderrahmane Sbihi

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Recently, there has been increasing confidence for a favorable usage of big data drawn out from the huge amount of information deposited in a cloud computing system. Data kept on such systems can be retrieved through the network at the user’s convenience. However, the data that users send include private information, and therefore, information leakage from these data is now a major social problem. The usage of secret sharing schemes for cloud computing have lately been approved to be relevant in which users deal out their data to several servers. Notably, in a (k,n) threshold scheme, data security is assured if and only if all through the whole life of the secret the opponent cannot compromise more than k of the n servers. In fact, a number of secret sharing algorithms have been suggested to deal with these security issues. In this paper, we present a Mapreduce implementation of Shamir’s secret sharing scheme to increase its performance and to achieve optimal security for cloud data. Different tests were run and through it has been demonstrated the contributions of the proposed approach. These contributions are quite considerable in terms of both security and performance.

Keywords: cloud computing, data security, Mapreduce, Shamir's secret sharing

Procedia PDF Downloads 293

Authors: Kyle De Freitas, Margaret Bernard

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Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

Procedia PDF Downloads 315

Authors: Vikrum Malhotra, Manpreet Kaur, Ayesha Ghotto

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Background: Cardiac registries such as ACC Percutaneous Coronary Intervention Registry require high quality data to be abstracted, including data elements such as nuclear cardiology, diagnostic coronary angiography, and PCI. Introduction: The audit tool created is used by data abstractors to provide data audits and assess the accuracy and inter-rater reliability of abstraction performed by the abstractors for a health system. This audit tool solution has been developed across 13 registries, including ACC/NCDR registries, PCI, STS, Get with the Guidelines. Methodology: The data audit tool was used to audit internal registry abstraction for all data elements, including stress test performed, type of stress test, data of stress test, results of stress test, risk/extent of ischemia, diagnostic catheterization detail, and PCI data elements for ACC/NCDR PCI registries. This is being used across 20 hospital systems internally and providing abstraction and audit services for them. Results: The data audit tool had inter-rater reliability and accuracy greater than 95% data accuracy and IRR score for the PCI registry in 50 PCI registry cases in 2021. Conclusion: The tool is being used internally for surgical societies and across hospital systems. The audit tool enables the abstractor to be assessed by an external abstractor and includes all of the data dictionary fields for each registry.

Keywords: abstraction, cardiac registry, cardiovascular registry, registry, data

Procedia PDF Downloads 95

Authors: Seyed Esmail Seyedi Bariran, Laysheng Ewe, Amy Ling

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Since supplier selection appears as a vital decision, selecting supplier based on the best and most accurate ways has a lot of importance for enterprises. In this study, a new Artificial Intelligence approach is exerted to remove weaknesses of supplier selection. The paper has three parts. First part is choosing the appropriate criteria for assessing the suppliers’ performance. Next one is collecting the data set based on experts. Afterwards, the data set is divided into two parts, the training data set and the testing data set. By the training data set the best structure of GEP and ANN are selected and to evaluate the power of the mentioned methods the testing data set is used. The result obtained shows that the accuracy of GEP is more than ANN. Moreover, unlike ANN, a mathematical equation is presented by GEP for the supplier selection.

Keywords: supplier selection, automotive supply chains, ANN, GEP

Procedia PDF Downloads 616

Authors: Yasuyuki Takahashi, Maya Yamashita, Kyoko Saito

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In general, dynamic SPECT data acquisition needs a few minutes for one rotation. Thus, the time-activity curve (TAC) derived from the dynamic SPECT is relatively coarse. In order to effectively shorten the interval, between data points, we adopted a 180-degree interpolation method. This method is already used for reconstruction of the X-ray CT data. In this study, we applied this 180-degree interpolation method to SPECT and investigated its effectiveness.To briefly describe the 180-degree interpolation method: the 180-degree data in the second half of one rotation are combined with the 180-degree data in the first half of the next rotation to generate a 360-degree data set appropriate for the time halfway between the first and second rotations. In both a phantom and a patient study, the data points from the interpolated images fell in good agreement with the data points tracking the accumulation of 99mTc activity over time for appropriate region of interest. We conclude that data derived from interpolated images improves the apparent time resolution of dynamic SPECT.

Keywords: dynamic SPECT, time resolution, 180-degree interpolation method, 99mTc-GSA.

Procedia PDF Downloads 487

Authors: Inigo de Miguel Beriain

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Genetic data of deceased persons are of great interest for both biomedical research and clinical use. This is due to several reasons. On the one hand, many of our diseases have a genetic component; on the other hand, we share genes with a good part of our biological family. Therefore, it would be possible to improve our response considerably to these pathologies if we could use these data. Unfortunately, at the present moment, the status of data on the deceased is far from being satisfactorily resolved by the EU data protection regulation. Indeed, the General Data Protection Regulation has explicitly excluded these data from the category of personal data. This decision has given rise to a fragmented legal framework on this issue. Consequently, each EU member state offers very different solutions. For instance, Denmark considers the data as personal data of the deceased person for a set period of time while some others, such as Spain, do not consider this data as such, but have introduced some specifically focused regulations on this type of data and their access by relatives. This is an extremely dysfunctional scenario from multiple angles, not least of which is scientific cooperation at the EU level. This contribution attempts to outline a solution to this dilemma through an alternative proposal. Its main hypothesis is that, in reality, health data are, in a sense, a rara avis within data in general because they do not refer to one person but to several. Hence, it is possible to think that all of them can be considered data subjects (although not all of them can exercise the corresponding rights in the same way). When the person from whom the data were obtained dies, the data remain as personal data of his or her biological relatives. Hence, the general regime provided for in the GDPR may apply to them. As these are personal data, we could go back to thinking in terms of a general prohibition of data processing, with the exceptions provided for in Article 9.2 and on the legal bases included in Article 6. This may be complicated in practice, given that, since we are dealing with data that refer to several data subjects, it may be complex to refer to some of these bases, such as consent. Furthermore, there are theoretical arguments that may oppose this hypothesis. In this contribution, it is shown, however, that none of these objections is of sufficient substance to delegitimize the argument exposed. Therefore, the conclusion of this contribution is that we can indeed build a general framework on the processing of personal data of deceased persons in the context of the GDPR. This would constitute a considerable improvement over the current regulatory framework, although it is true that some clarifications will be necessary for its practical application.

Keywords: collective data conceptual issues, data from deceased people, genetic data protection issues, GDPR and deceased people

Procedia PDF Downloads 143

Authors: Abdullah I. Alkraiji, Thomas W. Jackson, Ian Murray

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The proliferation of health data standards today is somewhat overlapping and conflicting, resulting in market confusion and leading to increasing proprietary interests. The government role and support in standardization for health data are thought to be crucial in order to establish credible standards for the next decade, to maximize interoperability across the health sector, and to decrease the risks associated with the implementation of non-standard systems. The normative literature missed out the exploration of the different steps required to be undertaken by the government towards the development of national health data standards. Based on the lessons learned from a qualitative study investigating the different issues to the adoption of health data standards in the major tertiary hospitals in Saudi Arabia and the opinions and feedback from different experts in the areas of data exchange and standards and medical informatics in Saudi Arabia and UK, a list of steps required towards the development of national health data standards was constructed. Main steps are the existence of: a national formal reference for health data standards, an agreed national strategic direction for medical data exchange, a national medical information management plan and a national accreditation body, and more important is the change management at the national and organizational level. The outcome of this study can be used by academics and practitioners to develop the planning of health data standards, and in particular those in developing countries.

Keywords: interoperabilty, medical data exchange, health data standards, case study, Saudi Arabia

Procedia PDF Downloads 328

Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

Procedia PDF Downloads 420

Authors: Zaccheaus Olasupo Apotiola

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This study evaluates the level of pollution in underground water from Ogijo and Odo-nla areas in lkorodu, Lagos State. Water sample were collected around various industries and transported in ice packs to the laboratory. Temperature and pH was determined on site, physicochemical parameters and total plate were determined using standard methods, while heavy metal concentration was determined using Atomic Absorption spectrophotometry method. The temperature was observed at a range of 20-28 oC, the pH was observed at a range of 5.64 to 6.91 mol/l and were significantly different (P < 0.05) from one another. The chloride content was observed at a range 70.92 to 163.10 mg/l there was no significant difference (P > 0.05) between sample 40 GAJ and ISUP, but there was significant difference (P < 0.05) between other samples. The acidity value varied from 11.0 – 34.5 (mg/l), the samples had no alkalinity. The Total plate count was found at 20-125 cfu/ml. Asernic, Lead, Cadmium, and Mercury concentration ranged between 0.03 - 0.09, 0.04 - 0.11, 0.00 -0.00, and 0.00 – 0.00(mg/l) respectively. However there was significant difference (p < 0.05) between all samples except for sample 4OGA, 5OGAJ, and 3SUTN that were not significantly different (P > 0.05). The results revealed all samples are not safe for human consumption as the levels of Asernic and Lead are above the maximum value of (0.01 mg/l) recommended by NIS 554 and WHO.

Keywords: arsenic, cadmium, lead mercury, WHO

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

Procedia PDF Downloads 393

Authors: J. Vveinhardt, V. B. Fominiene, L. Jeseviciute-Ufartiene

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Relevance of Research: Qualitative research is still regarded as highly subjective and not sufficiently scientific in order to achieve objective research results. However, it is agreed that a qualitative study allows revealing the hidden motives of the research participants, creating new theories, and highlighting the field of problem. There is enough research done to reveal these qualitative research aspects. However, each research area has its own specificity, and sport is unique due to the image of its participants, who are understood as strong and invincible. Therefore, a sport participant might have personal issues to recognize himself as a victim in the context of bullying and harassment. Accordingly, researcher has a dilemma in general making to speak a victim in sport. Thus, ethical aspects of qualitative research become relevant. The plenty fields of sport make a problem determining the sample size of research. Thus, the corresponding problem of this research is which and why qualitative research strategies are the most suitable revealing the phenomenon of bullying and harassment in sport. Object of research is qualitative research strategy for bullying and harassment in sport. Purpose of the research is to analyze strategies of qualitative research selecting suitable one for bullying and harassment in sport. Methods of research were scientific research analyses of qualitative research application for bullying and harassment research. Research Results: Four mane strategies are applied in the qualitative research; inductive, deductive, retroductive, and abductive. Inductive and deductive strategies are commonly used researching bullying and harassment in sport. The inductive strategy is applied as quantitative research in order to reveal and describe the prevalence of bullying and harassment in sport. The deductive strategy is used through qualitative methods in order to explain the causes of bullying and harassment and to predict the actions of the participants of bullying and harassment in sport and the possible consequences of these actions. The most commonly used qualitative method for the research of bullying and harassment in sports is semi-structured interviews in speech and in written. However, these methods may restrict the openness of the participants in the study when recording on the dictator or collecting incomplete answers when the participant in the survey responds in writing because it is not possible to refine the answers. Qualitative researches are more prevalent in terms of technology-defined research data. For example, focus group research in a closed forum allows participants freely interact with each other because of the confidentiality of the selected participants in the study. The moderator can purposefully formulate and submit problem-solving questions to the participants. Hence, the application of intelligent technology through in-depth qualitative research can help discover new and specific information on bullying and harassment in sport. Acknowledgement: This research is funded by the European Social Fund according to the activity ‘Improvement of researchers’ qualification by implementing world-class R&D projects of Measure No. 09.3.3-LMT-K-712.

Keywords: bullying, focus group, harassment, narrative, sport, qualitative research

Procedia PDF Downloads 168

Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee

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In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.

Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences

Procedia PDF Downloads 731

Authors: Sohail Ahmad Umer

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Since last few decades a question is raising on the subject of the importance of women in different societies of the world particularly in the developing societies of Asia and Africa. Female population constitutes almost 50% of the total population of the world and is playing a significant role in the economy with male population. In Pakistan, a developing country of Asia with majority of Muslim population, working women role is more focused. Women of rural background who are working as voluntary workers and their working hours are neither recorded nor recognized. Agricultural statistics shows that the female participation rate is below 40% while other sources claim them below 20%. Here in present study, another effort has been made to compare the women role in two different provinces of Pakistan to analyze the participation of women in agricultural activities like sowing, picking, irrigating the fields, harvesting and threshing of crops, caring and feeding of the animals, collecting the firewood and etc,as without these activities the farming would be incomplete. One hundred villages in the district Khushab (Punjab) and one hundred villages in district D.I.Khan (KPK) were selected and 33% of the families of each village have been interviewed to study their input in agriculture work. Another important feature is the social stratification therefore the contribution by different variables like the ownership, tenancy, education and caste has also been studied.

Keywords: caste, social stratification, tenancy, voluntary workers

Procedia PDF Downloads 365

Authors: Anusha Konduru, Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai

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Mobile applications are increasing in a significant amount, each to address the requirements of many users. However, the quick developments and enhancements are resulting in many underlying defects. Android apps create and handle a large variety of 'instance' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of Android, an app can be paused, sent into the background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. However, it is difficult for the programmer to manually test this issue for all the activities. This results in the issue of data loss that the data entered by the user are not saved when there is any interruption. This issue can degrade user experience because the user needs to reenter the information each time there is an interruption. Automated testing to detect such data loss is important to improve the user experience. This research proposes a tool, DroidDL, a data loss detector for Android, which detects the instance data loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.

Keywords: Android, automated testing, activity, data loss

Procedia PDF Downloads 225

Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

Procedia PDF Downloads 403

Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

Procedia PDF Downloads 265

Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

Procedia PDF Downloads 418

Authors: Sonia Alberti

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The Regional Conference to Restructure Psychiatric Care in Latin America, convened by the Pan American Health Organization (PAHO) in 1990, oriented the Brazilian Federal Act in 2001 that stipulated the psychiatric reform which requires deinstitutionalization and community-based treatment. Since then, the 15 years’ experience of different working teams in mental health led an academic working group – supervisors from personal practices, professors and researchers – to discuss certain clinical issues, as well as supervisions, and to organize colloquia in different cities as a methodology. These colloquia count on the participation of different working teams from the cities in which they are held, with team members with different levels of educational degrees and prior experiences, in order to increase dialogue right where it does not always appear to be possible. The principal aim of these colloquia is to gain interlocution between practitioners and academics. Working with the theory of case constructions, this methodology revealed itself helpful in unfolding new solutions. The paper also observes that there is not always harmony between what the psychiatric reform demands and clinical ethics.

Keywords: mental health, supervision, clinical cases, Brazilian experience

Procedia PDF Downloads 258

Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

Procedia PDF Downloads 197

Authors: Nambiar Ritu, Ali Ban, Munawar Farida, Israell Imelda, T. Farouk Eman Rasheeda, Jangalgi Renuka, S. Boma Nellie

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Aim: To define the ultrasonographic endometrial thickness (USG ET) cutoff for evacuation of retained pieces of conception (ERPC). Background: Studies of conservative management of 1st trimester miscarriage have questioned the need for post miscarriage curettage. Therapeutic decision making with transvaginal scan post miscarriage endometrial thickness in patients clinically thought to be incomplete miscarriage is often not clear. Method: Retrospective analysis of all 1ST trimester ERPC at Al Rahba Hospital from June 2012 to July 2013 was done. Total of 164 patients underwent ERPC. All cases were reviewed for pre-operative USG ET and post ERPC histopathological examination. TVS was done to evaluate the maximum ET of the uterine cavity along the long axis of the uterus and features of retained products was noted. All cases without preoperative USG ET measurement were excluded from the study, therefore only 62 out of 164 cases were included in the study. The patients were divided into three groups: o Group A: have retained products within endometrial cavity. o Group B: endometrial thickness equal or more than 20 mm. o Group C: endometrial thickness equal or less than 19.9 mm. o Post ERPC product was sent for HPE and the results were compared. Transvaginal sonographic findings can be used as a deciding factor in the management of patients with 1st trimester miscarriage who need ERPC. Our proposed cutoff in clinically stable patients requiring ERPC is more than 20 mm.

Keywords: ERPC, histopathological examination, long axis of the uterus, USG ET

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

Procedia PDF Downloads 41

Authors: Ashenafi Beyene Fanta, Daniel Makina

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Economic theory predicts that finance promotes economic growth. Although the finance-growth link is among the most researched areas in financial economics, our understanding of the link between the two is still incomplete. This is caused by, among others, wrong econometric specifications, using weak proxies of financial development, and inability to address the endogeneity problem. Studies on the finance growth link in South Africa consistently report economic growth driving financial development. Early studies found that economic growth drives financial development in South Africa, and recent studies have confirmed this using different econometric models. However, the monetary aggregate (i.e. M2) utilized used in these studies is considered a weak proxy for financial development. Furthermore, the fact that the models employed do not address the endogeneity problem in the finance-growth link casts doubt on the validity of the conclusions. For this reason, the current study examines the finance growth link in South Africa using data for the period 1990 to 2011 by employing a generalized method of moments (GMM) technique that is capable of addressing endogeneity, simultaneity and omitted variable bias problems. Unlike previous cross country and country case studies that have also used the same technique, our contribution is that we account for the development of bond markets and non-bank financial institutions rather than being limited to stock market and banking sector development. We find that bond market development affects economic growth in South Africa, and no similar effect is observed for the bank and non-bank financial intermediaries and the stock market. Our findings show that examination of individual elements of the financial system is important in understanding the unique effect of each on growth. The observation that bond markets rather than private credit and stock market development promotes economic growth in South Africa induces an intriguing question as to what unique roles bond markets play that the intermediaries and equity markets are unable to play. Crucially, our results support observations in the literature that using appropriate measures of financial development is critical for policy advice. They also support the suggestion that individual elements of the financial system need to be studied separately to consider their unique roles in advancing economic growth. We believe that our understanding of the channels through which bond market contribute to growth would be a fertile ground for future research.

Keywords: bond market, finance, financial sector, growth

Procedia PDF Downloads 406

Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

Procedia PDF Downloads 381

Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

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Authors: Jaya Mathew

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Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 364

Authors: Sven Gehrke, Johannes Ruhland

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Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

Procedia PDF Downloads 85

Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

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This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

Procedia PDF Downloads 475

Authors: Tzan-Chain Lee

Abstract:

Seeds are non-leaves green tissues. Photosynthesis begins with light absorption by chlorophyll and then the energy transfer between two pigment-protein complexes (PPC). Most studies of photosynthesis and PPC expression were focused on leaves; however, during seeds’ development were rare. Developed seeds from beginning pod (stage R3) to dried seed (stage R8), and the dried seed after sowing for 1-4 day, were analyzed for their chlorophyll contents. Thornber and MARS gel systems analysis compositions of PPC. Chlorophyll fluorescence was used to detect maximal photosynthetic efficiency (Fv/Fm). During soybean and black soybean seeds development (stages R3-R6), Fv/Fm up to 0.8, and then down-regulated after full seed (stage R7). In dried seed (stage R8), the two plant seeds lost photosynthetic activity (Fv/Fm=0), but chlorophyll degradation only occurred in soybean after full seed. After seeds sowing for 4 days, chlorophyll drastically increased in soybean seeds, and Fv/Fm recovered to 0.8 in the two seeds. In PPC, the two soybean seeds contained all PPC during seeds development (stages R3-R6), including CPI, CPII, A1, AB1, AB2, and AB3. However, many proteins A1, AB1, AB2, and CPI were totally missing in the two dried seeds (stage R8). The deficiency of these proteins in dried seeds might be caused by the incomplete photosynthetic activity. After seeds germination and seedling exposed to light for 4 days, all PPC were recovered, suggesting that completed PPC took place in the two soybean seeds. This study showed the reversibility of photosynthetic activity and pigment-protein complexes during soybean and black soybean seeds development.

Keywords: light-harvesting complex, pigment–protein complexes, soybean cotyledon, grana development

Procedia PDF Downloads 136

Authors: Romeo Botes, Imelda Smit

Abstract:

In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

Procedia PDF Downloads 233