Search results for: panel data

Authors: Ali Rashash Alzahrani, Elizabeth Stojanovski

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Data on various aspects of education are collected at the institutional and government level regularly. In Australia, for example, students at various levels of schooling undertake examinations in numeracy and literacy as part of NAPLAN testing, enabling longitudinal assessment of such data as well as comparisons between schools and states within Australia. Another source of educational data collected internationally is via the PISA study which collects data from several countries when students are approximately 15 years of age and enables comparisons in the performance of science, mathematics and English between countries as well as ranking of countries based on performance in these standardised tests. As well as student and school outcomes based on the tests taken as part of the PISA study, there is a wealth of other data collected in the study including parental demographics data and data related to teaching strategies used by educators. Overall, an abundance of educational data is available which has the potential to be used to help improve educational attainment and teaching of content in order to improve learning outcomes. A multivariate assessment of such data enables multiple variables to be considered simultaneously and will be used in the present study to help develop profiles of students based on performance in mathematics using data obtained from the PISA study.

Keywords: cluster analysis, education, mathematics, profiles

Procedia PDF Downloads 122

Authors: Priyoth Kittiteerasack, Alana Steffen, Alicia K. Matthews

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Depression is a leading cause of the worldwide burden of disability and disease burden. Notably, lesbian, gay, bisexual, and transgender (LGBT) populations are more likely to be a high-risk group for depression compared to their heterosexual and cisgender counterparts. To date, little is known about the rates and predictors of depression among Thai LGBT populations. As such, the purpose of this study was to: 1) measure the prevalence of depression among a diverse sample of Thai LGBT adults and 2) determine the influence of minority stress variables (discrimination, victimization, internalized homophobia, and identity concealment), general stress (stress and loneliness), and coping strategies (problem-focused, avoidance, and seeking social support) on depression outcomes. This study was guided by the Minority Stress Model (MSM). The MSM posits that elevated rates of mental health problems among LGBT populations stem from increased exposures to social stigma due to their membership in a stigmatized minority group. Social stigma, including discrimination and violence, represents unique sources of stress for LGBT individuals and have a direct impact on mental health. This study was conducted as part of a larger descriptive study of mental health among Thai LGBT adults. Standardized measures consistent with the MSM were selected and translated into the Thai language by a panel of LGBT experts using the forward and backward translation technique. The psychometric properties of translated instruments were tested and acceptable (Cronbach’s alpha > .8 and Content Validity Index = 1). Study participants were recruited using convenience and snowball sampling methods. Self-administered survey data were collected via an online survey and via in-person data collection conducted at a leading Thai LGBT organization. Descriptive statistics and multivariate analyses using multiple linear regression models were conducted to analyze study data. The mean age of participants (n = 411) was 29.5 years (S.D. = 7.4). Participants were primarily male (90.5%), homosexual (79.3%), and cisgender (76.6%). The mean score for depression of study participant was 9.46 (SD = 8.43). Forty-three percent of LGBT participants reported clinically significant levels of depression as measured by the Beck Depression Inventory. In multivariate models, the combined influence of demographic, stress, coping, and minority stressors explained 47.2% of the variance in depression scores (F(16,367) = 20.48, p < .001). Minority stressors independently associated with depression included discrimination (β = .43, p < .01) victimization (β = 1.53, p < .05), and identity concealment (β = -.54, p < .05). In addition, stress (β = .81, p < .001), history of a chronic disease (β = 1.20, p < .05), and coping strategies (problem-focused coping β = -1.88, p < .01, seeking social support β = -1.12, p < .05, and avoidance coping β = 2.85, p < .001) predicted depression scores. The study outcomes emphasized that minority stressors uniquely contributed to depression levels among Thai LGBT participants over and above typical non-minority stressors. Study findings have important implications for nursing practice and the development of intervention research.

Keywords: depression, LGBT, minority stress, sexual and gender minority, Thailand

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Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

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Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

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Authors: Milad Alizadeh Galdiani, Jabbar Ali Zakeri

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Lateral movement of CWR ballasted track occurs in sharp curves because of the lack of adequate lateral resistance. Several strategies have been proposed and used for increase the lateral resistance of ballasted tracks, but still there are some problems in tracks with small radius curves. In this paper, a new method has been presented for increase the lateral resistance. This method is using the lateral supports as numerical and field studies. In this paper, the field and laboratory tests have been conducted by using the single tie pressure test (STPT) and track panel loading test (LTPT). Then, their results were compared with the numerical results. The results of numerical and field tests showed that the lateral stiffness of ballasted tracks significantly increased when there were lateral supports in ballasted tracks. Also, the track structure had a bilinear behavior.

Keywords: ballasted railway, Lateral resistance, railway buckling, field and numerical studies

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Agata Zakrzewska, Dominik Kopeć, Adrian Ochtyra

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The growing number of new cameras, sensors, and research methods allow for a broader application of thermal data in remote sensing vegetation studies. The aim of this research was to check whether it is possible to use thermal infrared data with a spectral range (3.6-4.9 μm) obtained during the day and the night to assess the health condition of selected species of deciduous trees in an urban environment. For this purpose, research was carried out in the city center of Warsaw (Poland) in 2020. During the airborne data acquisition, thermal data, laser scanning, and orthophoto map images were collected. Synchronously with airborne data, ground reference data were obtained for 617 studied species (Acer platanoides, Acer pseudoplatanus, Aesculus hippocastanum, Tilia cordata, and Tilia × euchlora) in different health condition states. The results were as follows: (i) healthy trees are cooler than trees in poor condition and dying both in the daytime and nighttime data; (ii) the difference in the canopy temperatures between healthy and dying trees was 1.06oC of mean value on the nighttime data and 3.28oC of mean value on the daytime data; (iii) condition classes significantly differentiate on both daytime and nighttime thermal data, but only on daytime data all condition classes differed statistically significantly from each other. In conclusion, the aerial thermal data can be considered as an alternative to hyperspectral data, a method of assessing the health condition of trees in an urban environment. Especially data obtained during the day, which can differentiate condition classes better than data obtained at night. The method based on thermal infrared and laser scanning data fusion could be a quick and efficient solution for identifying trees in poor health that should be visually checked in the field.

Keywords: middle wave infrared, thermal imagery, tree discoloration, urban trees

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Authors: Viriyavudh Sim, Woo Young Jung

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The failure of roof cladding mostly occurs due to the failing of the connection between claddings and purlins, which is the pull-out of the screw connecting the two parts when the pull-out load, i.e. typhoon, is higher than the resistance of the connection screw. As typhoon disasters in Korea are constantly on the rise, probability risk assessment (PRA) has become a vital tool to evaluate the performance of civil structures. In this study, we attempted to determine the fragility of roof cladding with the screw connection. Experimental study was performed to evaluate the pull-out resistance of screw joints between honeycomb panels and back frames. Subsequently, by means of Monte Carlo Simulation method, probability of failure for these types of roof cladding was determined. The results that the failure of roof cladding was depends on their location on the roof, for example, the edge most panel has the highest probability of failure.

Keywords: Monte Carlo Simulation, roof cladding, screw pull-out strength, wind fragility

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Authors: Remi Szauer

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Introductory, Background, and Importance of the Study: For many years, religiosity in Poland remained at a stable level of religious practice. When the symptoms of secularization and privatization processes appeared in Poland, it was not clearly felt but rather related to the decline in compulsory practices carried out in public, the growing distance of respondents to catholic ethic, and the lack of acceptance regarding the intervention of the Church in legislation and policy. The basic indicators observed over the years kept the picture: more religious women - less religious men. By carrying out own research in the field of religious and moral attitudes in 2019-2021, it was noticed that a reversal of the trend preserved over the years could be observed. The data showed that women under 40 are radically different in their responses than women older than them - especially those over 50: in terms of practices or ties with the Church and many more specific aspects. This became the basis for a careful examination of the responses in the under 40 age cohorts among women. This study is significant because it shows completely new perspectives of women's perception of religiosity and allows us to notice clearly the aspects of social changes mapped in the minds of the surveyed women. Research Methodology: The original survey was carried out using the quantitative method among 2,346 respondents in northern Poland, 1,349 of whom were women. The findings from these observations led to deepening the topic of beliefs of women under 40 compared to other age cohorts of women. Hence, studies were carried out on the general population of women in Poland, which constituted a comparative sample. These were panel studies. The selection of the sample among women was random, respecting the age amounts so that the two statistical groups could be compared. The designated research parameters included: declarations of religious faith, declarations of religious practice, bond with the Church, acceptance of Mariological dogmas, attitude towards the image of women in the Church, and acceptance of selected issues in Catholic ethics. Main Research Findings: Among women under 40, the decline in declarations not only concerning compulsory public practices but also private practices and declarations of religious faith is more pronounced. Not only is the range of indifferent religious attitudes increasing, but also attitudes directly declaring religious disbelief, for which there are important justifications. Women under 40 years of age strongly distance themselves from the institutions of the Church and from accepting Mariological dogmas. Moreover, they note that the image of a woman is marked by stereotyping, favoring the intensification of violence against women, as well as disregarding her potential and agency. Concluding Statement: By analyzing the answers of the female respondents and the data obtained in the research, it can be observed a reevaluation of women's beliefs, which opens the perspective of analyzing the role of religion and the Church in Poland as well as religious socialization.

Keywords: religiosity, morality, gender, feminism, social change

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Authors: Abdelazziz Aouiche, El Moundher Aouiche, Mouhamed Salah Soudani

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Renewable energy sources now significantly contribute to the replacement of traditional fossil fuel energy sources. One of the most potent types of renewable energy that has developed quickly in recent years is photovoltaic energy. We all know that solar energy, which is sustainable and non-depleting, is the best knowledge form of energy that we have at our disposal. Due to changing weather conditions, the primary drawback of conventional solar PV cells is their inability to track their maximum power point. In this study, we apply artificial neural networks (ANN) to automatically track and measure the maximum power point (MPP) of solar panels. In MATLAB, the complete system is simulated, and the results are adjusted for the external environment. The results are better performance than traditional MPPT methods and the results demonstrate the advantages of using neural networks in solar PV systems.

Keywords: modeling, photovoltaic panel, artificial neural networks, maximum power point tracking

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Authors: Carmen Maria Hernandez-Nicolas, Juan Francisco Martín-Ugedo, Antonio Mínguez-Vera

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This paper analyses the influence of gender of the mayors of Spanish local governments on different budget items using a sample of 8,243 town councils between 2002 and 2010 period and 64,361 observations. The system Generalized Method of Moments (GMM) technique was employed to examine this panel data. This powerful methodology allows controlling for the endogenity of the variables and the heterogeneity of the sample. Unlike previous works focused on the study of gender influence on firm decisions, the present work analyzes the influence of the gender of the major in the council’s decisions. Specifically, we examine the differences in financial liabilities, security, protection and social promotion expenses and income items relating to public management. In addition, the study focuses on the Spanish context, which is characterized by the presence of decentralization of public responsibility to a greater extent than in neighboring countries, feeding the debate on the operational efficiency of local government increased with an open debate on the importance of gender in public management. The results show that female mayors tend to have lower expenses in general without significant differences in incomes obtained for men and women majors. We also find that female majors incur fewer financial liabilities, one of the most important problems in the Spanish public sector. However, despite of cutting in the public sector, these councils have higher expenditure on security, protection and social promotion. According to these evidences, the presence of women in politics may serve to improve the councils’ economic situation and it is not only necessary for social justice but for economics efficiency. Besides, in councils with more inhabitants, women mayors are more common, but women who served for a very long time are less common.

Keywords: councils, gender, local budgets, public management, women mayors

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Authors: Z. Abdullah, A. R. Hamdan

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

Keywords: clustering, unsupervised learning, algorithms, hierarchical

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Authors: Malay K. Pramanik

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In recent years, climate change has become a major threat and has been widely documented in the geographic distribution of many plant species. However, the impacts of climate change on the distribution of ecologically vulnerable medicinal species remain largely unknown. The identification of a suitable habitat for a species under climate change scenario is a significant step towards the mitigation of biodiversity decline. The study, therefore, aims to predict the impact of current, and future climatic scenarios on the distribution of the threatened Garcinia indica across the northern Western Ghats using Maximum Entropy (MaxEnt) modelling. The future projections were made for the year 2050 and 2070 with all Representative Concentration Pathways (RCPs) scenario (2.6, 4.5, 6.0, and 8.5) using 56 species occurrence data, and 19 bioclimatic predictors from the BCC-CSM1.1 model of the Intergovernmental Panel for Climate Change’s (IPCC) 5th assessment. The bioclimatic variables were minimised to a smaller number of variables after a multicollinearity test, and their contributions were assessed using jackknife test. The AUC value of 0.956 ± 0.023 indicates that the model performs with excellent accuracy. The study identified that temperature seasonality (39.5 ± 3.1%), isothermality (19.2 ± 1.6%), and annual precipitation (12.7 ± 1.7%) would be the major influencing variables in the current and future distribution. The model predicted 10.5% (19318.7 sq. km) of the study area as moderately to very highly suitable, while 82.60% (151904 sq. km) of the study area was identified as ‘unsuitable’ or ‘very low suitable’. Our predictions of climate change impact on habitat suitability suggest that there will be a drastic reduction in the suitability by 5.29% and 5.69% under RCP 8.5 for 2050 and 2070, respectively. Finally, the results signify that the model might be an effective tool for biodiversity protection, ecosystem management, and species re-habitation planning under future climate change scenarios.

Keywords: Garcinia Indica, maximum entropy modelling, climate change, MaxEnt, Western Ghats, medicinal plants

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Authors: Syed Kashif Ali, Usman Javaid, Abdullah Chohan

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In large enterprises multiple departments use different sort of information systems and databases according to their needs. These systems are independent and heterogeneous in nature and sharing information/data between these systems is not an easy task. The usage of middleware technologies have made data sharing between systems very easy. However, monitoring the exchange of data/information for verification purposes between target and source systems is often complex or impossible for maintenance department due to security/access privileges on target and source systems. In this paper, we are intended to present our experience of an end to end data monitoring approach at middle ware level implemented in Oracle BPEL for data verification without any help of monitoring tool.

Keywords: service level agreement, SOA, BPEL, oracle fusion middleware, web service monitoring

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Authors: Bakr Ali Al-Gamrh, Ku Nor Izah B. Ku Ismail

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We investigate the relationship between corporate governance, leverage, risk, and firm performance. We use a firm level panel that spans the period 2008 to 2012 of all listed firms on Abu Dhabi Stock Exchange and Dubai Financial Market. After constructing an index of corporate governance strength, we find a negative effect of corporate governance on firm performance. We, however, discover that corporate governance strength indirectly improves the negative influence of leverage on firm performance in normal times. On the contrary, the results completely reversed when there is a black swan event. Corporate governance strength plays a significantly negative role in moderating the relationship between leverage and firm performance during the financial crisis. We also reveal that corporate governance strength increases firms’ risk and deteriorates performance during crisis. Results provide evidence that corporate governance indirectly plays a completely different role in different time periods.

Keywords: corporate governance, firm performance, risk, leverage, the UAE

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Authors: K. Umbleja, M. Ichino

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Symbolic data mining has been developed to analyze data in very large datasets. It is also useful in cases when entry specific details should remain hidden. Symbolic data mining is quickly gaining popularity as datasets in need of analyzing are becoming ever larger. One type of such symbolic data is a histogram, which enables to save huge amounts of information into a single variable with high-level of granularity. Other types of symbolic data can also be described in histograms, therefore making histogram a very important and general symbolic data type - a method developed for histograms - can also be applied to other types of symbolic data. Due to its complex structure, analyzing histograms is complicated. This paper proposes a method, which allows to compare two histogram-valued variables and therefore find a dissimilarity between two histograms. Proposed method uses the Ichino-Yaguchi dissimilarity measure for mixed feature-type data analysis as a base and develops a dissimilarity measure specifically for histogram data, which allows to compare histograms with different number of bins and bin widths (so called general histogram). Proposed dissimilarity measure is then used as a measure for clustering. Furthermore, linkage method based on weighted averages is proposed with the concept of cluster compactness to measure the quality of clustering. The method is then validated with application on real datasets. As a result, the proposed dissimilarity measure is found producing adequate and comparable results with general histograms without the loss of detail or need to transform the data.

Keywords: dissimilarity measure, hierarchical clustering, histograms, symbolic data analysis

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Authors: I. Algul, G. Akgun, H. Kurtaran

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Dynamic analysis of composite doubly curved panels with variable thickness subjected to different pulse types using Generalized Differential Quadrature method (GDQ) is presented in this study. Panels with variable thickness are used in the construction of aerospace and marine industry. Giving variable thickness to panels can allow the designer to get optimum structural efficiency. For this reason, estimating the response of variable thickness panels is very important to design more reliable structures under dynamic loads. Dynamic equations for composite panels with variable thickness are obtained using virtual work principle. Partial derivatives in the equation of motion are expressed with GDQ and Newmark average acceleration scheme is used for temporal discretization. Several examples are used to highlight the effectiveness of the proposed method. Results are compared with finite element method. Effects of taper ratios, boundary conditions and loading type on the response of composite panel are investigated.

Keywords: differential quadrature method, doubly curved panels, laminated composite materials, small displacement

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Authors: Shidokht Rashiddadash, Mojtaba Sadighi, Soheil Dariushi

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Since sandwich structures are used in many areas ranging from ships, trains, automobiles, aircrafts, bridge and building, connecting sandwich structures is necessary almost in all industries. So application of metallic joints between sandwich panels is increasing. Various joining methods are available such as mechanically fastened joints (riveting or bolting) or adhesively bonded joints and choosing one of them depends on the application. In this research, sandwich specimens were fabricated with two different types of metallic connections with dissimilar geometries. These specimens included beams and plates and were manufactured using glass-epoxy skins and aluminum honeycomb core. After construction of the specimens, bending and low velocity impact tests were executed on them and the behaviors of specimens were discussed. Numerical models were developed using LS-DYNA software and validated with test results. Finally, parametric studies were performed on the thicknesses and lengths of two connections by employing the numerical models.

Keywords: connection, honeycomb, low velocity impact, sandwich panel, static test

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Authors: Majid Mokhtarnia, Alireza Amini

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Mobile operators deal with increasing in the data traffic as a critical issue. As a result, a vital responsibility of the operators is to deal with such a trend in order to create added values. This paper addresses a bundling method in a Canvas business model in a WiFi Data Offloading (WDO) strategy by which some elements of the model may be affected. In the proposed method, it is supposed to sell a number of data packages for subscribers in which there are some packages with a free given volume of data-offloaded WiFi complimentary. The paper on hands analyses this method in the views of attractiveness and profitability. The results demonstrate that the quality of implementation of the WDO strongly affects the final result and helps the decision maker to make the best one.

Keywords: bundling, canvas business model, telecommunication, WiFi data offloading

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Authors: Łukasz Ambroziak

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Although the impact of the production fragmentation on trade flows has been examined many times since the 1990s, the research was not comprehensive because of the limitations in traditional trade statistics. Early 2010s the complex databases containing world input-output tables (or indicators calculated on their basis) has made available. It increased the possibilities of examining the production sharing in the world. The trade statistic in value-added terms enables us better to estimate trade changes resulted from the internationalisation and globalisation as well as benefits of the countries from international trade. In the literature, there are many research studies on this topic. Unfortunately, trade in value added of the Central and Eastern European Countries (CEECs) has been so far insufficiently studied. Thus, the aim of the paper is to present changes in value added trade of the CEECs (Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia) in the period of 1995-2011. The concept 'trade in value added' or 'value added trade' is defined as the value added of a country which is directly and indirectly embodied in final consumption of another country. The typical question would be: 'How much value added is created in a country due to final consumption in the other countries?' The data will be downloaded from the World Input-Output Database (WIOD). The structure of this paper is as follows. First, theoretical and methodological aspects related to the application of the input-output tables in the trade analysis will be studied. Second, a brief survey of the empirical literature on this topic will be presented. Third, changes in exports and imports in value added of the CEECs will be analysed. A special attention will be paid to the differences in bilateral trade balances using traditional trade statistics (in gross terms) on one side, and value added statistics on the other. Next, in order to identify factors influencing value added exports and value added imports of the CEECs the generalised gravity model, based on panel data, will be used. The dependent variables will be value added exports and imports. The independent variables will be, among others, the level of GDP of trading partners, the level of GDP per capita of trading partners, the differences in GDP per capita, the level of the FDI inward stock, the geographical distance, the existence (or non-existence) of common border, the membership (or not) in preferential trade agreements or in the EU. For comparison, an estimation will also be made based on exports and imports in gross terms. The initial research results show that the gravity model better explained determinants of trade in value added than gross trade (R2 in the former is higher). The independent variables had the same direction of impact both on value added exports/imports and gross exports/imports. Only value of coefficients differs. The most difference concerned geographical distance. It had smaller impact on trade in value added than gross trade.

Keywords: central and eastern European countries, gravity model, input-output tables, trade in value added

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Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung

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In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.

Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Christopher I. Ifeacho, Adeleke Omolade

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The paper examines the relationship between energy price dynamics and green growth in Sub Sahara African Countries. The quest for adopting green energy in order to improve green growth that can engender sustainability and stability has received more attention from researchers in recent times. This study uses a panel autoregressive distributed lag approach to investigate this relationship. Findings from the result showed that energy price dynamics and exchange rates have more short-run significant impacts on green growth in individual countries rather than the pooled result. Furthermore, the long-run result confirmed that inflation and capital have a significant long-run relationship with green growth. The causality test result revealed the existence of a bi-directional relationship between green growth and energy price dynamics. The study recommends caution in a currency devaluation and improvement in renewable energy production in the Sub Sahara Africa in order to achieve sustainable green growth.

Keywords: green growth, energy price dynamics, Sub Saharan Africa, relationship

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Ashima Anurag Sharma

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Optical fiber based networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This research paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparison between various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 522

Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Mahdy Elhusseiny, Richard Gearhart, Mariam Alyammahi

Abstract:

In this study we analyse the impact of a number of major macroeconomic variables on industry-specific excess rates of return. In later specifications, we include time and recession fixed effects, to potentially capture time-specific trends that may have been changing over our panel. We have a number of results that bear mentioning. Seasonal and temporal factors found to have very large role in sector-specific excess returns. Increases in M1(money supply) decreases bank, insurance, real estate, and telecommunications, while increases industrial and transportation excess returns. The results indicate that the market return increases every sector-specific rate of return. The 2007 to 2009 recession significantly reduced excess returns in the bank, real estate, and transportation sectors.

Keywords: macroeconomic factors, industry returns, fixed effects, temporal factors

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Authors: Abdulrahman M. Homadi

Abstract:

As a great source of renewable energy, solar energy is considered to be one of the most important in the world, since it will be one of solutions cover the energy shortage in the future. Photovoltaic (PV) is the most popular and widely used among solar energy technologies. However, PV efficiency is fairly low and remains somewhat expensive. High temperature has a negative effect on PV efficiency and cooling system for these panels is vital, especially in warm weather conditions. This paper presents the results of a simulation study carried out on silicon solar cells to assess the effects of elevation on enhancing the efficiency of solar panels. The study included four different terrains. The study also took into account the direction of the wind hitting the solar panels. To ensure the simulation mimics reality, six silicon solar panels are designed in two columns and three rows, facing to the south at an angle of 30 ^o. The elevations are assumed to change from 10 meters to 200 meters. The results show that maximum increase in efficiency occurs when the wind comes from the north, hitting the back of the panels.

Keywords: solar panels, elevation, wind direction, efficiency

Procedia PDF Downloads 291

Authors: Fei Gao, Rodolfo C. Raga Jr.

Abstract:

This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Chang Qin, Daham Mustafa, Abderrahmane Khiat, Pierre Bienert, Paulo Zanini

Abstract:

Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration.

Keywords: data interoperability in industry 4.0, digital integration, industrial dictionary, semantic modeling

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

Abstract:

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, confidentiality, availability of the users. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute theoretical presentations in high-level computational processes that are based on number theory and algebra that can easily be integrated and leveraged in the Cloud computing with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, homomorphic, homomorphic encryption scheme

Procedia PDF Downloads 373