Search results for: data acquisition

Authors: Andrea Catorci, Federico Maria Tardella, Alessandro Brica, Muhammad Umair

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Climate change models predict for the Mediterranean region a strong increase of intensity and frequency of drought events, with an expected effect on grassland biodiversity and functioning. The research aim was to understand how the grassland species modulate their resource acquisition and conservation strategies to short-term variation of the pattern of summer water supply. The study area is mountain meadows located in the ‘‘Montagna di Torricchio’’ (1130 m a.s.l.) a Nature Reserve in central Italy. In 2017 we started a manipulative experiment for 2 year (2017-2018), and we defined two treatments, one with increasing water (watering condition) and the other with less water (drought condition). Then, we investigated how species change their resource strategies at different amount of water availability by measuring the specific leaf area (SLA) and leaf area (LA). We used ANOVAs to test the effect of treatment over time on leaf area and specific leaf area, considering all the species together and also separately according to their growth form (forb, grass, legume). Our results showed that species may respond differently depending on their growth form and that using all the species together may cover more detailed variation. Overall, resource retaining strategies (lower SLA, LA) are resulted by increase of drought condition, while increase in water amount and number of watering events fosters acquisitive strategies (higher SLA, LA). However, this pattern is not constant for all growth form. Grass species are able to maintain their strategies to variation of the pattern of water availability. Forb and legume species on the other side have shown decreasing trend of SLA, LA values with increasing drought condition, a pattern more marked for the latter growth form. These variations suggest not only an increase of slow-growing strategies for both growth form, but also a decrease of their nutrient pastoral values since their leaves are supposed to become harder. Local farmers should consider the effect of climate change on grassland and adapt their management practices to guarantee the cattle welfare.

Keywords: function strategies, grasslands, climate change, sub Mediterranean region

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Authors: Sarah K. Basudan, Ragad I. Al Jazzar, Zeinah Sulaihim, Hanan M. Al-Kadri

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Background: Cesarean section has been increasing in recent years, with a wide range of etiologies contributing to this rise. This study aimed to assess the indications, outcomes, and complications in Riyadh, Saudi Arabia. Methods: A Retrospective Cohort study was conducted at King Abdulaziz medical city. The study includes two cohorts: G1 (2009) and G2 (2020) groups who met the inclusion criteria. The data was transferred to the SPSS (statistical package for social sciences) version 24 for analysis. The initial descriptive statistics were run for all variables, including numerical and categorical data. The numerical data were reported as median, and standard deviation and categorical data were reported as frequencies and percentages. Results: The data were collected from 399 women who were divided into two groups, G1(199) and G2(200). The mean age of all participants is 32+-6​; G1 and G2 had significant differences in age means with 30+-6 and 34+-5, respectively, with a p-value of <0.001, which indicates delayed fertility by four years. Moreover, a breech presentation was less likely to occur in G2 (OR 0.64, CI: 0.21-0.62. P<0.001). Nonetheless, maternal causes such as repeated C-sections and maternal medical conditions were more likely to happen in G2 (OR 1.5, CI: 1.04-2.38, p=0.03) and (OR 5.4, CI: 1.12-23.9, P=0.01), respectively. Furthermore, postpartum hemorrhage showed an increase of 12% in G2 (OR 5.4, CI: 2.2-13.4, p<0.001). G2 was more likely to be admitted to the neonatal intensive care unit (NICU) (OR 16, CI: 7.4-38.7) and to special care baby (SCB) (OR 7.2, CI: 3.9-13.1), both with a p-value<0.001 compared to regular nursery admission. Conclusion: There are multiple factors that are contributing to the increase in c section rate in a Saudi tertiary hospitals. The factors were suggested to be previous c-sections, abnormal fetal heart rate, malpresentation, and maternal or fetal medical conditions.

Keywords: cesarean sections, maternal indications, maternal complications, neonatal condition

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Authors: Safayat Ali Shaikh

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Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India.

Keywords: artificial neural network, large irrigation project, modified simplex algorithm, optimal cropping pattern

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Authors: Daniel Fundi Murithi

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Missing data is a real bane in many surveys. To overcome the problems caused by missing data, partial deletion, and single imputation methods, among others, have been proposed. However, problems such as discarding usable data and inaccuracy in reproducing known population parameters and standard errors are associated with them. For regression and stochastic imputation, it is assumed that there is a variable with complete cases to be used as a predictor in estimating missing values in the other variable, and the relationship between the two variables is linear, which might not be realistic in practice. In this project, we estimate population total in presence of missing values in two-phase sampling. Instead of regression or stochastic models, non-parametric model based regression model is used in imputing missing values. Empirical study showed that nonparametric model-based regression imputation is better in reproducing variance of population total estimate obtained when there were no missing values compared to mean, median, regression, and stochastic imputation methods. Although regression and stochastic imputation were better than nonparametric model-based imputation in reproducing population total estimates obtained when there were no missing values in one of the sample sizes considered, nonparametric model-based imputation may be used when the relationship between outcome and predictor variables is not linear.

Keywords: finite population total, missing data, model-based imputation, two-phase sampling

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Authors: Abigail Gragg

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The goal of this research study was to adjudicate if implementing Howard Gardner’s multiple levels of intelligence would enhance student achievement levels in an Algebra 1 College Preparatory class. This was conducted within every class by incorporating one level of the eight levels of intelligence into small group work in stations. Every class was conducted utilizing small-group instruction. Achievement levels were measured through various forms of collected data that expressed student understandings in class through formative assessments versus student understandings on summative assessments. The data samples included: assessments (i.e. summative and formative assessments), observable data, video recordings, a daily log book, student surveys, and checklists kept during the observation periods. Formative assessments were analyzed during each class period to measure in-class understanding. Summative assessments were dissected per question per accuracy to review the effects of each intelligence implemented. The data was collated into a coding workbook for further analysis to conclude the resulting themes of the research. These themes include 1) there was no correlation to multiple levels of intelligence enhancing student achievement, 2) bodily-kinesthetic intelligence showed to be the intelligence that had the most improvement on test questions and 3) out of all of the bits of intelligence, interpersonal intelligence enhanced student understanding in class.

Keywords: stations, small group instruction, multiple levels of intelligence, Mathematics, Algebra 1, student achievement, secondary school, instructional Pedagogies

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Authors: Saru Arora, Anurag Sharma, Harsukhpreet Singh

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To meet the growing need of high data rate and bandwidth, various efforts has been made nowadays for the efficient communication systems. Optical Code Division Multiple Access over Free space optics communication system seems an effective role for providing transmission at high data rate with low bit error rate and low amount of multiple access interference. This paper demonstrates the OCDMA over FSO communication system up to the range of 7000 m at a data rate of 5 Gbps. Initially, the 8 user OCDMA-FSO system is simulated and pseudo orthogonal codes are used for encoding. Also, the simulative analysis of various performance parameters like power and core effective area that are having an effect on the Bit error rate (BER) of the system is carried out. The simulative analysis reveals that the length of the transmission is limited by the multi-access interference (MAI) effect which arises when the number of users increases in the system.

Keywords: FSO, PSO, bit error rate (BER), opti system simulation, multiple access interference (MAI), q-factor

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Authors: Asim Majeed, Anwar Ul Haq, Ayesha Asim, Mike Lloyd-Williams, Arshad Jamal, Usman Butt

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This study aims to investigate the impact of data leak of M&S customers on digital communities. Modern businesses are using digital communities as an important public relations tool for marketing purposes. This form of communication helps companies to build better relationship with their customers which also act as another source of information. The communication between the customers and the organizations is not regulated so users may post positive and negative comments. There are new platforms being developed on a daily basis and it is very crucial for the businesses to not only get themselves familiar with those but also know how to reach their existing and perspective consumers. The driving force of marketing and communication in modern businesses is the digital communities and these are continuously increasing and developing. This phenomenon is changing the way marketing is conducted. The current research has discussed the implications on M&S business performance since the data was exploited on digital communities; users contacted M&S and raised the security concerns. M&S closed down its website for few hours to try to resolve the issue. The next day M&S made a public apology about this incidence. This information was proliferated on various digital communities and it has impacted negatively on M&S brand name, sales and customers. The content analysis approach is being used to collect qualitative data from 100 digital bloggers including social media communities such as Facebook and Twitter. The results and finding provide useful new insights into the nature and form of security concerns of digital users. Findings have theoretical and practical implications. This research will showcase a large corporation utilizing various digital community platforms and can serve as a model for future organizations.

Keywords: Digital, communities, performance, dissemination, implications, data, exploitation

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

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Authors: Bernardo Cedano, Ahmed H. Eltom, Bob Hay, Jim Glass, Raga Ahmed

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The deployment of smart grid devices such as smart meters and smart switches (SS) supported by a reliable and fast communications system makes automated distribution possible, and thus, provides great benefits to electric power consumers and providers alike. However, more research is needed before the full utility of smart switch data is realized. This paper presents new automated switching techniques using SS within the electric power grid. A concise background of the SS is provided, and operational examples are shown. Organization and presentation of data obtained from SS are shown in the context of the future goal of total automation of the distribution network. The description of application techniques, the examples of success with SS, and the vision outlined in this paper serve to motivate future research pertinent to disturbance analysis automation.

Keywords: disturbance automation, electric power grid, smart grid, smart switches

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Authors: ِAbdallah A. A. Shaheen

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Gaza Strip faces economic and political issues such as conflict, siege and urbanization; all these have led to an increase in the air pollution over Gaza Strip. In this study, Particulate matter 10 (PM10) concentration over Gaza Strip has been estimated by Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) data, based on a multispectral algorithm. Simultaneously, in-situ measurements for the corresponding particulate are acquired for selected time period. Landsat and ground data for eleven years are used to develop the algorithm while four years data (2002, 2006, 2010 and 2014) have been used to validate the results of algorithm. The developed algorithm gives highest regression, R coefficient value i.e. 0.86; RMSE value as 9.71 µg/m³; P values as 0. Average validation of algorithm show that calculated PM10 strongly correlates with measured PM10, indicating high efficiency of algorithm for the mapping of PM10 concentration during the years 2000 to 2014. Overall results show increase in minimum, maximum and average yearly PM10 concentrations, also presents similar trend over urban area. The rate of urbanization has been evaluated by supervised classification of the Landsat image. Urban sprawl from year 2000 to 2014 results in a high concentration of PM10 in the study area.

Keywords: PM10, landsat, atmospheric reflectance, Gaza strip, urbanization

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Authors: Hesham A. Khalek, Shafik S. Khoury, Remon F. Aziz, Mohamed A. Hakam

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Slipforming operation’s linearity is a source of planning complications, and operation is usually subjected to bottlenecks at any point, so careful planning is required in order to achieve success. On the other hand, Discrete-event simulation concepts can be applied to simulate and analyze construction operations and to efficiently support construction scheduling. Nevertheless, preparation of input data for construction simulation is very challenging, time-consuming and human prone-error source. Therefore, to enhance the benefits of using DES in construction scheduling, this study proposes an integrated module to establish a framework for automating the generation of time schedules and decision support for Slipform construction projects, particularly through the project feasibility study phase by using data exchange between project data stored in an Intermediate database, DES and Scheduling software. Using the stored information, proposed system creates construction tasks attribute [e.g. activities durations, material quantities and resources amount], then DES uses all the given information to create a proposal for the construction schedule automatically. This research is considered a demonstration of a flexible Slipform project modeling, rapid scenario-based planning and schedule generation approach that may be of interest to both practitioners and researchers.

Keywords: discrete-event simulation, modeling, construction planning, data exchange, scheduling generation, EZstrobe

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Authors: Melvin Mothoa

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Small Micro and Medium Enterprises are very significant for the development of their market economies. They are the main creators of the new working places, and they present a vital core of the market economy in countries across the globe. Access to finance is identified as crucial for small, micro, and medium-sized enterprises for their growth and innovation. This paper is conceived to propose a perception-based SMME framework to aid in access to financial support. Furthermore, the study will address issues that impede SMMEs in South Africa from obtaining finance from financial institutions. The framework will be tested against data collected from 200 Small Micro & Medium Enterprises in the Gauteng province of South Africa. The study adopts a quantitative method, and the delivery of self-administered questionnaires to SMMEs will be the primary data collection tool. Structural equation modeling will be used to further analyse the data collected.

Keywords: finance, small business, growth, development

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Authors: Olushola S. Ayanda, Olalekan S. Fatoki, Folahan A. Adekola, Bhekumusa J. Ximba, Cecilia O. Akintayo

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In the present study, the kinetics, equilibrium and thermodynamics of the adsorption of triphenyltin (TPT) from TPT-contaminated water onto nanoSiO2/fly ash/activated carbon composite was investigated in batch adsorption system. Equilibrium adsorption data were analyzed using Langmuir, Freundlich, Temkin and Dubinin–Radushkevich (D-R) isotherm models. Pseudo first- and second-order, Elovich and fractional power models were applied to test the kinetic data and in order to understand the mechanism of adsorption, thermodynamic parameters such as ΔG°, ΔSo and ΔH° were also calculated. The results showed a very good compliance with pseudo second-order equation while the Freundlich and D-R models fit the experiment data. Approximately 99.999 % TPT was removed from the initial concentration of 100 mg/L TPT at 80oC, contact time of 60 min, pH 8 and a stirring speed of 200 rpm. Thus, nanoSiO2/fly ash/activated carbon composite could be used as effective adsorbent for the removal of TPT from contaminated water and wastewater.

Keywords: isotherm, kinetics, nanoSiO₂/fly ash/activated carbon composite, tributyltin

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Authors: Nisful Laila, Fatin Fadhilah Hasib, Puji Sucia Sukmaningrum, Achsania Hendratmi

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The purpose of this study is to determine and comparing the level of efficiency of Islamic Banks in Indonesia and United Kingdom during eurozone sovereign debt crisis. This study using a quantitative non-parametric approach with Data Envelopment Analysis (DEA) VRS assumption, and a statistical tool Mann-Whitney U-Test. The samples are 11 Islamic Banks in Indonesia and 4 Islamic Banks in England. This research used mediating approach. Input variable consists of total deposit, asset, and the cost of labour. Output variable consists of financing and profit/loss. This study shows that the efficiency of Islamic Bank in Indonesia and United Kingdom are varied and fluctuated during the observation period. There is no significant different the efficiency performance of Islamic Banks in Indonesia and United Kingdom.

Keywords: data envelopment analysis, efficiency, eurozone crisis, islamic bank

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Authors: Chi-Lun Liu

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Requirements modeling and analysis are important in successful information systems' maintenance. Unified Modeling Language (UML) class diagrams are useful standards for modeling information systems. To our best knowledge, there is a lack of a systems development methodology described by the organism metaphor. The core concept of this metaphor is adaptation. Using the knowledge representation and reasoning approach and ontologies to adopt new requirements are emergent in recent years. This paper proposes an organic methodology which is based on constructivism theory. This methodology is a knowledge representation and reasoning approach to analyze new requirements in the class diagrams maintenance. The process and rules in the proposed methodology automatically analyze inconsistencies in the class diagram. In the big data era, developing an automatic tool based on the proposed methodology to analyze large amounts of class diagram data is an important research topic in the future.

Keywords: knowledge representation, reasoning, ontology, class diagram, software engineering

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Authors: Ali A. Alshahrani

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This study aims to identify Saudi English as a Foreign Language (EFL) students' perspectives towards using the English language in their studies. The study explores students' self-confident and its association with students' actual performance in English courses in their different academic programs. A multimodal methodology was used to fulfill the research purpose and answer the research questions. A 25-item survey questionnaire and final examination grades were used to collect data. Two hundred forty-one students agreed to participate in the study. They completed the questionnaire and agreed to release their final grades to be a part of the collected data. The data were coded and analyzed by SPSS software. The findings indicated a significant difference in students' performance in English courses between participants' academic programs on the one hand. Students' self-confidence in their English language skills, on the other hand, was not significantly different between participants' academic programs. Data analysis also revealed no correlational relationship between students' self-confidence level and their language skills and their performance. The study raises more questions about other vital factors such as course instructors' views of the materials, faculty members of the target department, family belief in the usefulness of the program, potential employers. These views and beliefs shape the student's preparation process and, therefore, should be explored further.

Keywords: English language intensive program, language proficiency, performance, self-confidence

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Authors: Iveta Řepková

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The aim of this paper is to estimate the efficiency of the Slovak commercial banks employing the Data Envelopment Analysis (DEA) window analysis approach during the period 2003-2012. The research is based on unbalanced panel data of the Slovak commercial banks. Undesirable output was included into analysis of banking efficiency. It was found that most efficient banks were Postovabanka, UniCredit Bank and Istrobanka in CCR model and the most efficient banks were Slovenskasporitelna, Istrobanka and UniCredit Bank in BCC model. On contrary, the lowest efficient banks were found Privatbanka and CitiBank. We found that the largest banks in the Slovak banking market were lower efficient than medium-size and small banks. Results of the paper is that during the period 2003-2008 the average efficiency was increasing and then during the period 2010-2011 the average efficiency decreased as a result of financial crisis.

Keywords: data envelopment analysis, efficiency, Slovak banking sector, window analysis

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: semantic links, data mining, linked data, SKOS

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Authors: Supattra Kanchanopast

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The purpose of this research was to identify factors that influenced the trust of e-commerce within retail businesses. In order to achieve the objectives of this research, the researcher collected data from random e-commerce users in Bangkok. The data was comprised of the results of 382 questionnaires. The data was analyzed by using descriptive statistics, which included frequency, percentages, and the standard deviation of pertinent factors. Multiple regression analysis was also used. The findings of this research revealed that the majority of the respondents were female, 25-40 years old, and graduated a bachelor degree. The respondents mostly worked in private sectors and had monthly income between 15,000-25,000 baht. The findings also indicate that information quality factors, website design factors, service quality factor, security factor and advertising factors as significant factors effecting customer trust of e-commerce in online retail. The hypotheses testing revealed that these factors in e-commerce had an effect on customer’s trust in the same direction with high level.

Keywords: e-commerce, online retail, Retail business, trust, website

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Joseph K. A. Johnson

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Hypertension affects 20 percent of the people within the ages 55 upward in Ghana. Of these, almost one-third are unaware of their condition. Also at the age of 55, more men turned to have hypertension than women. After that age, the condition becomes more prevalent with women. Hypertension is significantly more common in African Americans of both sexes than the racial or ethnic groups. This study was conducted to determine the causes of high blood pressure in Ashanti Region, Ghana. The study employed One Hundred and Seventy (170) respondents. The sample population for the study was all the available respondents at the time of the data collection. The research was conducted using primary data where convenience sampling was used to locate the respondents. A set of questionnaire were used to gather the data for the study. The gathered data was analysed using principal component analysis. The study revealed that, personal description, lifestyle behavior and risk awareness as some of the causes of high blood pressure in Ashanti Region. The study therefore recommend that people must be advice to see to their personal characteristics that may contribute to high blood pressure such as controlling of their temper and how to react perfectly to stressful situations. They must be educated on the factors that may increase the level of their blood pressure such as the essence of seeing a medical doctor before taking in any drug. People must also be made known by the public health officers to those lifestyles behaviour such as smoking and drinking of alcohol which are major contributors of high blood pressure.

Keywords: high blood pressure, principal component analysis, hypertension, public health

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Authors: Renz Paolo B. Ramos, Jake S. Espina

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The Local Government Academy of the Philippines explains that Local legislation is both a power and a process by which it enacts ordinances and resolutions that have the force and effect of law while engaging with a range of stakeholders for their implementation. Legislative effectiveness is crucial for the development of any given area. This study's objective is to evaluate the legislative performance of the 10th Sangguniang of Kawayan, a legislative body in a fifth-class municipality in the Province of Biliran, during the height of the COVID-19 pandemic (2019-2021) with a focus on legislation, accountability, and participation, institution-building, and intergovernmental relations. The aim of the study was that a mixed-methods strategy was used to gather data. The Local Legislative Performance Appraisal Form (LLPAF) was completed, while Focus Interviews for Local Government Unit (LGU) personnel, a survey questionnaire for constituents, and ethnographic diary-writing were conducted. Convenience Sampling was utilized for LGU workers, whereas Simple Random Sampling was used to identify the number of constituents participating. Interviews were analyzed using thematic analysis, while frequency data analysis was employed to describe and evaluate the nature and connection of the data to the underlying population. From this data, the researchers draw opportunities and challenges met by the local legislature during the height of the pandemic.

Keywords: local legislation, local governance, legislative effectiveness, legislative analysis

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Ekaterina Tikhonova

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This research is aimed to evaluate the effectiveness of T&D (Training and Development) on the example of two T&D programs for the Executive TOP Management run in 2012, 2015-2016 in Komos Group. This study is commissioned to research the effectiveness of two similar corporate T&D programs (within one company) in two periods of time (2012, 2015-2016) through evaluating the programs’ effectiveness using the four-level Kirkpatrick’s model of evaluating T&D programs and calculating ROI as an instrument for T&D program measuring by Phillips’ formula. The research investigates the correlation of two figures: the ROI calculated and the rating percentage scale per the ROI implementation (Wagle’s scale). The study includes an assessment of feedback 360 (Kirkpatrick's model) and Phillips’ ROI Methodology that provides a step-by-step process for collecting data, summarizing and processing the collected information. The data is collected from the company accounting data, the HR budgets, MCFO and the company annual reports for the research periods. All analyzed data and reports are organized and presented in forms of tables, charts, and graphs. The paper also gives a brief description of some constrains of the research considered. After ROI calculation, the study reveals that ROI ranges between the average implementation (65% to 75%) by Wagle’s scale that can be considered as a positive outcome. The paper also gives some recommendations how to use ROI in practice and describes main benefits of ROI implementation.

Keywords: ROI, organizational performance, efficacy of T&D program, employee performance

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Authors: Lukáš Klein, Karel Žídek

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Hyperspectral imaging counts among the most frequently used multidimensional sensing methods. While there are many approaches to capturing a hyperspectral data cube, optical compression is emerging as a valuable tool to reduce the setup complexity and the amount of data storage needed. Hyperspectral compressive imagers have been created in the past; however, they have primarily focused on relatively narrow sections of the electromagnetic spectrum. A broader spectral study of samples can provide helpful information, especially for applications involving the harmonic generation and advanced material characterizations. We demonstrate a broadband hyperspectral microscope based on the single-pixel camera principle. Captured spatially encoded data are processed to reconstruct a hyperspectral cube in a combined visible and near-infrared spectrum (from 400 to 2500 nm). Hyperspectral cubes can be reconstructed with a spectral resolution of up to 3 nm and spatial resolution of up to 7 µm (subject to diffraction) with a high compressive ratio.

Keywords: compressive imaging, hyperspectral imaging, near-infrared spectrum, single-pixel camera, visible spectrum

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

Abstract:

Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: Xinyuan Y. Song, Kai Kang

Abstract:

Joint models for analyzing longitudinal and survival data are widely used to investigate the relationship between a failure time process and time-variant predictors. A common assumption in conventional joint models in the survival analysis literature is that all predictors are observable. However, this assumption may not always be supported because unobservable traits, namely, latent variables, which are indirectly observable and should be measured through multiple observed variables, are commonly encountered in the medical, behavioral, and financial research settings. In this study, a joint modeling approach to deal with this feature is proposed. The proposed model comprises three parts. The first part is a dynamic factor analysis model for characterizing latent variables through multiple observed indicators over time. The second part is a random coefficient trajectory model for describing the individual trajectories of latent variables. The third part is a proportional hazard model for examining the effects of time-invariant predictors and the longitudinal trajectories of time-variant latent risk factors on hazards of interest. A Bayesian approach coupled with a Markov chain Monte Carlo algorithm to perform statistical inference. An application of the proposed joint model to a study on the Alzheimer's disease neuroimaging Initiative is presented.

Keywords: Bayesian analysis, joint model, longitudinal data, time-to-event data

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Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

Abstract:

This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

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Authors: Brian Romansky

Abstract:

There are many practical, commercially available robots and semi-autonomous systems that are currently available for use in a wide variety of construction tasks. Adoption of these technologies has the potential to reduce the time and cost to deliver a project, reduce variability and risk in delivery time, increase quality, and improve safety on the job site. These benefits come with a cost for equipment rental or contract fees, access to specialists to configure the system, and time needed for set-up and support of the machines while in use. Calculation of the net ROI (Return on Investment) requires detailed information about the geometry of the site, the volume of work to be done, the overall project schedule, as well as data on the capabilities and past performance of available robotic systems. Assembling the required data and comparing the ROI for several options is complex and tedious. Many project managers will only consider the use of a robot in targeted applications where the benefits are obvious, resulting in low levels of adoption of automation in the construction industry. This work demonstrates how data already resident in many BIM (Building Information Model) projects can be used to automate ROI estimation for a sample set of commercially available construction robots. Calculations account for set-up and operating time along with scheduling support tasks required while the automated technology is in use. Configuration parameters allow for prioritization of time, cost, or safety as the primary benefit of the technology. A path toward integration and use of automatic ROI calculation with a database of available robots in a BIM platform is described.

Keywords: automation, BIM, robot, ROI.

Procedia PDF Downloads 87