Search results for: exploratory data analysis

Authors: Hasan Vural

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Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: Danièle Peto

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A qualitative research based on twenty-eight semi-structured interviews of students in Social Work, in Brussels (Belgium), showed specific results for the Arab and Muslim students: second and third generations immigrants build their identity on the basis of a mix of differentiation with and recognition of their parents' culture of origin. Building a bridge between Modernity and Tradition, they claim active citizenship; at the same time they show and live by values and religious believes which reinforce the link to their parents’ origins. But they present those values and believes as their own rational choices among other choices, all available and rich for our multicultural society. The way they speak of themselves is highly modern. But, they still have to build a third way to find a place for themselves in society: one allowing them to live their religion as a partially public matter (when the Occidental society leaves no such place for religion) while ensuring, at the same time, the development of independent critical thought. On this basis, other semi-structured interviews are being laid with Social workers working with families from diverse ethnic backgrounds. They will verify the reality of those identity and cultural bricolages when those young adults of second and third generations build their own family. In between the theoretical models of traditional family and modern family, shall we find a new model, hybrid and more or less stable, combining some aspects of the former and the latter? The exploratory research phase focuses on three aspects of building a family life in this context : the way those generations play, discursively or not, in between their parents and the society in which they grew up; the importance of intercultural dialogue in this process of building; and testing the hypothesis that some families, in our society, show a special way of courting Modernity.

Keywords: family models, identity bricolages, intercultural, modernity and tradition

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Authors: Mehmet Akif Aslan, Mehmet Simsek, Eyup Sensoy

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Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics.

Keywords: big data, logistics, operational efficiency, risk management

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Naohiro Nakamura, Takuya Kinoshita, Hiroshi Fukuyama

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The 2011 off The Pacific Coast of Tohoku Earthquake caused considerable damage to wide areas of eastern Japan. A large number of earthquake observation records were obtained at various places. To design more earthquake-resistant buildings and improve earthquake disaster prevention, it is necessary to utilize these data to analyze and evaluate the behavior of a building during an earthquake. This paper presents an earthquake response simulation analysis (hereafter a seismic response analysis) that was conducted using data recorded during the main earthquake (hereafter the main shock) as well as the earthquakes before and after it. The data were obtained at a high-rise steel-reinforced concrete (SRC) building in the bay area of Tokyo. We first give an overview of the building, along with the characteristics of the earthquake motion and the building during the main shock. The data indicate that there was a change in the natural period before and after the earthquake. Next, we present the results of our seismic response analysis. First, the analysis model and conditions are shown, and then, the analysis result is compared with the observational records. Using the analysis result, we then study the effect of soil-structure interaction on the response of the building. By identifying the characteristics of the building during the earthquake (i.e., the 1st natural period and the 1st damping ratio) by the Auto-Regressive eXogenous (ARX) model, we compare the analysis result with the observational records so as to evaluate the accuracy of the response analysis. In this study, a lumped-mass system SR model was used to conduct a seismic response analysis using observational data as input waves. The main results of this study are as follows: 1) The observational records of the 3/11 main shock put it between a level 1 and level 2 earthquake. The result of the ground response analysis showed that the maximum shear strain in the ground was about 0.1% and that the possibility of liquefaction occurring was low. 2) During the 3/11 main shock, the observed wave showed that the eigenperiod of the building became longer; this behavior could be generally reproduced in the response analysis. This prolonged eigenperiod was due to the nonlinearity of the superstructure, and the effect of the nonlinearity of the ground seems to have been small. 3) As for the 4/11 aftershock, a continuous analysis in which the subject seismic wave was input after the 3/11 main shock was input was conducted. The analyzed values generally corresponded well with the observed values. This means that the effect of the nonlinearity of the main shock was retained by the building. It is important to consider this when conducting the response evaluation. 4) The first period and the damping ratio during a vibration were evaluated by an ARX model. Our results show that the response analysis model in this study is generally good at estimating a change in the response of the building during a vibration.

Keywords: ARX model, response analysis, SRC building, the 2011 off the Pacific Coast of Tohoku Earthquake

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Authors: Rahman Ali, Muhammad Sajjad, Farkhund Iqbal, Muhammad Sadiq Hassan Zada, Mohammed Hussain

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Emission of Carbon Dioxide (CO₂) has adversely affected the environment. One of the major sources of CO₂ emission is transportation. In the last few decades, the increase in mobility of people using vehicles has enormously increased the emission of CO₂ in the environment. To reduce CO₂ emission, sustainable transportation system is required in which smart parking is one of the important measures that need to be established. To contribute to the issue of reducing the amount of CO₂ emission, this research proposes a smart parking system. A cloud-based solution is provided to the drivers which automatically searches and recommends the most preferred parking slots. To determine preferences of the parking areas, this methodology exploits a number of unique parking features which ultimately results in the selection of a parking that leads to minimum level of CO₂ emission from the current position of the vehicle. To realize the methodology, a scenario-based implementation is considered. During the implementation, a mobile application with GPS signals, vehicles with a number of vehicle features and a list of parking areas with parking features are used by sorting, multi-level filtering, exploratory data analysis (EDA, Analytical Hierarchy Process (AHP)) and weighted sum model (WSM) to rank the parking areas and recommend the drivers with top-k most preferred parking areas. In the EDA process, “2020testcar-2020-03-03”, a freely available dataset is used to estimate CO₂ emission of a particular vehicle. To evaluate the system, results of the proposed system are compared with the conventional approach, which reveal that the proposed methodology supersedes the conventional one in reducing the emission of CO₂ into the atmosphere.

Keywords: car parking, Co2, Co2 reduction, IoT, merge sort, number plate recognition, smart car parking

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Authors: Olivier Delage, Hassan Bencherif, Alain Bourdier

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Signal decomposition approaches represent an important step in time series analysis, providing useful knowledge and insight into the data and underlying dynamics characteristics while also facilitating tasks such as noise removal and feature extraction. As most of observational time series are nonlinear and nonstationary, resulting of several physical processes interaction at different time scales, experimental time series have fluctuations at all time scales and requires the development of specific signal decomposition techniques. Most commonly used techniques are data driven, enabling to obtain well-behaved signal components without making any prior-assumptions on input data. Among the most popular time series decomposition techniques, most cited in the literature, are the empirical mode decomposition and its variants, the empirical wavelet transform and singular spectrum analysis. With increasing popularity and utility of these methods in wide ranging applications, it is imperative to gain a good understanding and insight into the operation of these algorithms. In this work, we describe all of the techniques mentioned above as well as their ability to denoise signals, to capture trends, to identify components corresponding to the physical processes involved in the evolution of the observed system and deduce the dimensionality of the underlying dynamics. Results obtained with all of these methods on experimental total ozone columns and rainfall time series will be discussed and compared

Keywords: denoising, empirical mode decomposition, singular spectrum analysis, time series, underlying dynamics, wavelet analysis

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Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

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Authors: Juliana A. Knocikova

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Methods of nonlinear signal analysis are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Considering the dynamical systems, entropy is usually understood as a rate of information production. Changes in temporal dynamics of physiological data are indicating evolving of system in time, thus a level of new signal pattern generation. During last decades, many algorithms were introduced to assess some patterns of physiological responses to external stimulus. However, the reflex responses are usually characterized by short periods of time. This characteristic represents a great limitation for usual methods of nonlinear analysis. To solve the problems of short recordings, parameter of approximate entropy has been introduced as a measure of system complexity. Low value of this parameter is reflecting regularity and predictability in analyzed time series. On the other side, increasing of this parameter means unpredictability and a random behavior, hence a higher system complexity. Reduced neurophysiological data complexity has been observed repeatedly when analyzing electroneurogram and electromyogram activities during defence reflex responses. Quantitative phrenic neurogram changes are also obvious during severe hypoxia, as well as during airway reflex episodes. Concluding, the approximate entropy parameter serves as a convenient tool for analysis of reflex behavior characterized by short lasting time series.

Keywords: approximate entropy, neurophysiological data, nonlinear dynamics, reflex

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Authors: T. Penkova, A. Korobko, V. Nicheporchuk, L. Nozhenkova, A. Metus

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This paper presents an approach of on-line control of the state of technosphere and environment objects based on the integration of Data Warehouse, OLAP and Expert systems technologies. It looks at the structure and content of data warehouse that provides consolidation and storage of monitoring data. There is a description of OLAP-models that provide a multidimensional analysis of monitoring data and dynamic analysis of principal parameters of controlled objects. The authors suggest some criteria of emergency risk assessment using expert knowledge about danger levels. It is demonstrated now some of the proposed solutions could be adopted in territorial decision making support systems. Operational control allows authorities to detect threat, prevent natural and anthropogenic emergencies and ensure a comprehensive safety of territory.

Keywords: decision making support systems, emergency risk assessment, natural and anthropogenic safety, on-line control, territory

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Authors: Sarthak Mohapatra, Ajith Babu, Shyam Prasad Ghosh

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The community of gamers has been at the crux of stigmatization and marginalization by the larger society, resulting in dignity erosion. India presents a unique context where e-sports have recently seen large-scale investments, a massive userbase, and appreciable demand for gaming as a career option. Yet the apprehension towards gaming is salient among parents and non-gamers who engage in the de-dignification of gamers, by advocating the discourse of violence promotion via video games. Even the government is relentless in banning games due to data privacy issues. Thus, the current study explores the experiences of gamers and how they navigate these de-dignifying circumstances. The study follows an exploratory qualitative approach where in-depth interviews are used as data collection tools guided by a semi-structured questionnaire. A total of 25 individuals were interviewed comprising casual gamers, professional gamers, and individuals who are indirectly impacted by gaming including parents, relatives, and friends of gamers. Thematic analysis via three-level coding is used to arrive at broad themes (categories) and their sub-themes. The results indicate that the de-dignification of gamers results from attaching stereotypes of introversion, aggression, low intelligence, and low aspirations to them. It is interesting to note that the intensity of de-dignification varies and is more salient in violent shooting games which are perceived to require low cognitive resources to master. The moral disengagement of gamers while playing violent video games becomes the basis for de-dignification. Findings reveal that circumventing de-dignification required gamers to engage in several tactics that included playing behind closed doors, consciously hiding the gamer identity, rationalizing behavior by idolizing professionals, bragging about achievements within the game, and so on. Theoretically, it contributes to dignity and social identity literature by focusing on stereotyping and stigmatization. From a policy perspective, improving legitimacy toward gaming is expected to improve the social standing of gamers and professionals. For practitioners, it is important that proper channels of promotion and communication are used to educate the non-gamers so that the stereotypes blur away.

Keywords: dignity, social identity, stereotyping, video games

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Authors: Nazirah Hassan, Andrew Kendrick

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Punishment inside the penal institution has always been practiced in order to maintain discipline and keep order. Nonetheless, criminologists have long debated that the enforcement of discipline by punishing inmates is often ineffective and has a detrimental impact on inmates’ conduct. This paper uses data from a sample of 289 incarcerated young offenders to investigate the prevalence of institutional misconduct. It explores punitive cultural practices inside institutions and how this culture affects the inmates’ conduct during confinement. The project focused on male and female young offenders aged 12 to 21 years old, in eight juvenile justice institutions. The research collected quantitative and qualitative data using a mixed-method approach. All participants completed the Direct and Indirect Prisoner behavior Checklist-Scaled Version Revised (DIPC-SCALED-R). In addition, exploratory interviews were carried out with sixteen inmates and eight institutional staff. Results of the questionnaire survey show that almost half of the inmates reported a higher level of involvement in perpetration. It demonstrates a remarkable convergence of direct, rather than indirect, perpetration. Also, inmates reported a higher level of tobacco used and behavior associated with negative attitudes towards staff and institutional rules. In addition to this, the qualitative data suggests that the punitive culture encourages the onset of misconduct by increasing the stressful and oppressive conditions within the institution. In general, physical exercise and locking up inmates are two forms of punishment that were ubiquitous throughout the institutions. Interestingly, physical exercise is not only enforced by institutional staff but also inmates. These findings are discussed in terms of existing literature and their practical implications are considered.

Keywords: institutional punishment, incarcerated young offenders, punitive culture, institutional misconduct

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Alam Ali, Ashok Kumar Pathak

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Path analysis is a statistical technique used to evaluate the direct and indirect effects of variables in path models. One or more structural regression equations are used to estimate a series of parameters in path models to find the better fit of data. However, sometimes the assumptions of classical regression models, such as ordinary least squares (OLS), are violated by the nature of the data, resulting in insignificant direct and indirect effects of exogenous variables. This article aims to explore the effectiveness of a copula-based regression approach as an alternative to classical regression, specifically when variables are linked through an elliptical copula.

Keywords: path analysis, copula-based regression models, direct and indirect effects, k-fold cross validation technique

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Authors: Kevin Potoczny, Katsuichiro Goda

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The St. Lawrence lowlands extend from Ottawa to Quebec City and are known for large deposits of sensitive Leda clay. Leda clay deposits are responsible for many large landslides, such as the 1993 Lemieux and 2010 St. Jude (4 fatalities) landslides. Due to the large extent and sensitivity of Leda clay, regional hazard analysis for landslides is an important tool in risk management. A 2018 regional study by Farzam et al. on the susceptibility of Leda clay slopes to landslide hazard uses 1 arc second topographical data. A qualitative method known as Hazus is used to estimate susceptibility by checking for various criteria in a location and determine a susceptibility rating on a scale of 0 (no susceptibility) to 10 (very high susceptibility). These criteria are slope angle, geological group, soil wetness, and distance from waterbodies. Given the flat nature of St. Lawrence lowlands, the current assessment fails to capture local slopes, such as the St. Jude site. Additionally, the data did not allow one to analyze failure planes accurately. This study majorly improves the analysis performed by Farzam et al. in two aspects. First, regional assessment with high resolution data allows for identification of local locations that may have been previously identified as low susceptibility. This then provides the opportunity to conduct a more refined analysis on the failure plane of the slope. Slopes derived from 1 arc second data are relatively gentle (0-10 degrees) across the region; however, the 1- and 2-meter resolution 2022 HRDEM provided by NRCAN shows that short, steep slopes are present. At a regional level, 1 arc second data can underestimate the susceptibility of short, steep slopes, which can be dangerous as Leda clay landslides behave retrogressively and travel upwards into flatter terrain. At the location of the St. Jude landslide, slope differences are significant. 1 arc second data shows a maximum slope of 12.80 degrees and a mean slope of 4.72 degrees, while the HRDEM data shows a maximum slope of 56.67 degrees and a mean slope of 10.72 degrees. This equates to a difference of three susceptibility levels when the soil is dry and one susceptibility level when wet. The use of GIS software is used to create a regional susceptibility map across the St. Lawrence lowlands at 1- and 2-meter resolutions. Failure planes are necessary to differentiate between small and large landslides, which have so far been ignored in regional analysis. Leda clay failures can only retrogress as far as their failure planes, so the regional analysis must be able to transition smoothly into a more robust local analysis. It is expected that slopes within the region, once previously assessed at low susceptibility scores, contain local areas of high susceptibility. The goal is to create opportunities for local failure plane analysis to be undertaken, which has not been possible before. Due to the low resolution of previous regional analyses, any slope near a waterbody could be considered hazardous. However, high-resolution regional analysis would allow for more precise determination of hazard sites.

Keywords: hazus, high-resolution DEM, leda clay, regional analysis, susceptibility

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Authors: Hasna Mekkaoui Alaoui, Mariem Mekkaoui Alaoui

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In a context marked by a growing and pressing demand for online training within Moroccan universities, massive open online courses (Moocs) are undergoing constant evolution, amplified by the widespread use of digital technology and accentuated by the Coronavirus pandemic. However, despite their growing popularity and expansion, these courses are still lacking in terms of tools, enabling teachers and researchers to carry out a fine-grained analysis of the learning processes taking place within them. What's more, the circulation and sharing of knowledge within these environments is becoming increasingly important. The crucial aspect of traceability emerges here, as MOOCs record and generate traces from the most minute to the most visible. This leads us to consider traceability as a valuable approach in the field of educational research, where the trace is envisaged as a research tool in its own right. In this exploratory research project, we are looking at aspects of community knowledge sharing based on traces observed in the "Communication des organisations" Mooc. Focusing in particular on the mediating trace and its impact in identifying knowledge circulation processes in this learning space, we have mobilized the traces of video capsules as an index of knowledge circulation in the Mooc device. Our study uses a methodological approach based on thematic analysis, and although the results show that learners reproduce knowledge from different video vignettes in almost identical ways, they do not limit themselves to the knowledge provided to them. This research offers concrete perspectives for improving the dynamics of online devices, with a potentially positive impact on the quality of online university teaching.

Keywords: circulation, index, digital environments, mediation., trace

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Authors: Dwi Wijiastutik, Tarjo, Yuni Rimawati

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The purpose of this empirical study is to analyse how to the market reaction and the conservative degree changes on the adoption of International Financial Reporting Standart (IFRS) through Jakarta Islamic Index. The study also has given others additional analysis on the profitability, capital structure and size company toward IFRS adoption. The data collection methods used in this study reveals as secondary data and deep analysis to the company’s annual report and daily price stock at yahoo finance. We analyse 40 companies listed on Jakarta Islamic Index from 2010 to 2014. The result of the study concluded that IFRS has given a different on the depth analysis to the two of variance analysis: Moderated Regression Analysis and Wilcoxon Signed Rank to test developed hypotheses. Our result on the regression analysis shows that market response and conservatism principle is not significantly after IFRS Adoption in Jakarta Islamic Index. Furthermore, in addition, analysis on profitability, capital structure, and company size show that significantly after IFRS adoption. The findings of our study help investor by showing the impact of IFRS for making decided investment.

Keywords: IFRS, earnings response coefficient, conservatism principle

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Authors: J. R. Wang, W. Y. Cheng, J. S. Yeh, S. W. Chen, Y. M. Ferng, J. H. Yang, W. S. Hsu, C. Shih

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To understand the risk for dry storage concrete casks in the cask loading, transfer, and storage phase, the purpose of this research is to establish the probabilistic risk assessment (PRA) analysis methodology for dry storage concrete casks by using SAPHIRE 8 code. This analysis methodology is used to perform the study of Taiwan nuclear power plants (NPPs) dry storage system. The process of research has three steps. First, the data of the concrete casks and Taiwan NPPs are collected. Second, the PRA analysis methodology is developed by using SAPHIRE 8. Third, the PRA analysis is performed by using this methodology. According to the analysis results, the maximum risk is the multipurpose canister (MPC) drop case.

Keywords: PRA, dry storage, concrete cask, SAPHIRE

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Authors: T. Vandeyar

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The purpose of this study is to explore how Government policy on ICT influences teaching and learning in South African schools. An instrumental case study using backward mapping principles as a strategy of inquiry was used. Utilizing a social constructivist lens and guided by a theoretical framework of a sociocultural approach to policy analysis, this exploratory qualitative research study set out to investigate how teachers appropriate government policy on ICT in South African schools. Three major findings emanated from this study. First, although teachers were ignorant of the national e-education policy their professionalism and agency were key in formulating and implementing an e-education policy in practice. Second, teachers repositioned themselves not as recipients or reactors of the e-education policy but as social and cultural actors of policy appropriation and formulation. Third, the lack of systemic support to teachers catalyzed improved school and teacher collaborations, teachers became drivers of ICT integration through collaboration, innovation, institutional practice and institutional leadership.

Keywords: ICT, teachers as change agents, practice as policy, teacher's beliefs, teacher's attitudes

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Authors: Lamidi Lawal Aduozava

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Masquerade tradition is an indigenous culture of the Anetuno an extraction of the Ebira referred to as Ebira chao. This paper seeks to make aesthetic analysis of the masquerades in terms of their costumes and socio-cultural significance. To this end, the study examined and documented the functions and roles of Anipo and Idowo masquerades in terms of therapeutic, economic, prophetic and divination, entertainment, and funeral functions to the owner community(Eziobe group of families) in Igarra, Edo State of Nigeria, West Africa. For the purpose of data collection, focus group discussion, participatory, visual and observatory methods of data collection were used. All the data collected were aesthetically, descriptively and historically analyzed.

Keywords: Aesthetics, , Costume, , Masquerades, , Significance.

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Authors: Kassahun Tilahun

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Most of the studies available on adult psychological well-being have been centered on Western countries. However, psychological well-being does not have the same meaning across the world. The Euro-American and African conceptions and experiences of psychological well-being differ systematically. As a result, questions like, how do people living in developing African countries, like Ethiopia, report their psychological well-being; what would the context-specific prominent determinants of their psychological well-being be, needs a definitive answer. This study was, therefore, aimed at developing a new theory that would address these socio-cultural issues of psychological well-being. Consequently, data were obtained through interview and open ended questionnaire. A total of 438 adults, working in governmental and non-governmental organizations situated in Addis Ababa, participated in the study. Appropriate qualitative method of data analysis, i.e. thematic content analysis, was employed for analyzing the data. The thematic analysis involves a type of abductive analysis, driven both by theoretical interest and the nature of the data. Reliability and credibility issues were addressed appropriately. The finding identified five major categories of themes, which are viewed as essential in determining the conceptions and experiences of psychological well-being of Ethiopian adults. These were; socio-cultural harmony, social cohesion, security, competence and accomplishment, and the self. Detailed discussion on the rational for including these themes was made and appropriate positive psychology interventions were proposed. Researchers are also encouraged to expand this qualitative research and in turn develop a suitable instrument taping the psychological well-being of adults with different sociocultural orientations.

Keywords: sociocultural, psychological, well-being Ethiopia, adults

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Authors: A. Satty, H. Mwambi

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Much clinical trials data-based research are characterized by the unavoidable problem of dropout as a result of missing or erroneous values. This paper aims to review some of the various techniques to address the dropout problems in longitudinal clinical trials. The fundamental concepts of the patterns and mechanisms of dropout are discussed. This study presents five general techniques for handling dropout: (1) Deletion methods; (2) Imputation-based methods; (3) Data augmentation methods; (4) Likelihood-based methods; and (5) MNAR-based methods. Under each technique, several methods that are commonly used to deal with dropout are presented, including a review of the existing literature in which we examine the effectiveness of these methods in the analysis of incomplete data. Two application examples are presented to study the potential strengths or weaknesses of some of the methods under certain dropout mechanisms as well as to assess the sensitivity of the modelling assumptions.

Keywords: incomplete longitudinal clinical trials, missing at random (MAR), imputation, weighting methods, sensitivity analysis

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Authors: Murat Yazici

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

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

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Authors: Azazhu Wassie

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This study tried to evaluate the maximum design flood for appurtenance structure design using the given climatological and hydrological data analysis on the referenced study area. The maximum design flood is determined by using flood frequency analysis. Using this method, the peak discharge is 32,583.67 m3/s, but the data is transferred because the dam site is not on the gauged station. Then the peak discharge becomes 38,115 m3/s. The study was conducted in June 2023. This dam is built across a river to create a reservoir on its upstream side for impounding water. The water stored in the reservoir is used for various purposes, such as irrigation, hydropower, navigation, fishing, etc. The total average volume of annual runoff is estimated to be 115.1 billion m3. The total potential of the land for irrigation development can go beyond 3 million ha.

Keywords: dam design, flow duration curve, peak flood, rainfall, reservoir capacity, risk and reliability

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Authors: Richard Byrne, Emma Mulliner

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Smart cities are a vision for the future that is increasingly becoming a reality. While a key concept of the smart city is the ability to capture, communicate, and process data that has long been produced through day-to-day activities of the city, much of the assessment models in place neglect this fact to focus on ‘smartness’ concepts. Although it is true technology often provides the opportunity to capture and communicate data in more effective ways, there are also human processes involved that are just as important. The growing importance with regards to the use and ownership of data in society can be seen by all with companies such as Facebook and Google increasingly coming under the microscope, however, why is the same scrutiny not applied to cities? The research area is therefore of great importance to the future of our cities here and now, while the findings will be of just as great importance to our children in the future. This research aims to understand the influence data is having on organisations operating throughout the smart cities sector and employs a mixed-method research approach in order to best answer the following question: Would a data-based evaluation model for smart cities be more appropriate than a smart-based model in assessing the development of the smart city? A fully comprehensive literature review concluded that there was a requirement for a data-driven assessment model for smart cities. This was followed by a documentary analysis to understand the root source of data integration to the smart city. A content analysis of city data platforms enquired as to the alternative approaches employed by cities throughout the UK and draws on best practice from New York to compare and contrast. Grounded in theory, the research findings to this point formulated a qualitative analysis framework comprised of: the changing environment influenced by data, the value of data in the smart city, the data ecosystem of the smart city and organisational response to the data orientated environment. The framework was applied to analyse primary data collected through the form of interviews with both public and private organisations operating throughout the smart cities sector. The work to date represents the first stage of data collection that will be built upon by a quantitative research investigation into the feasibility of data network effects in the smart city. An analysis into the benefits of data interoperability supporting services to the smart city in the areas of health and transport will conclude the research to achieve the aim of inductively forming a framework that can be applied to future smart city policy. To conclude, the research recognises the influence of technological perspectives in the development of smart cities to date and highlights this as a challenge to introduce theory applied with a planning dimension. The primary researcher has utilised their experience working in the public sector throughout the investigation to reflect upon what is perceived as a gap in practice of where we are today, to where we need to be tomorrow.

Keywords: data, planning, policy development, smart cities

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

Abstract:

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

Procedia PDF Downloads 357

Authors: Elahe Sarfi, Esmat Noroozi, Farhad Hosseinzadeh Lotfi

Abstract:

One of the important issues in Data Envelopment Analysis is the ranking of Decision Making Units. In this paper, a method for ranking DMUs is presented through which the weights related to efficient units should be chosen in a way that the other units preserve a certain percentage of their efficiency with the mentioned weights. To this end, a model is presented for ranking DMUs on the base of their superefficiency by considering the mentioned restrictions related to weights. This percentage can be determined by decision Maker. If the specific percentage is unsuitable, we can find a suitable and feasible one for ranking DMUs accordingly. Furthermore, the presented model is capable of ranking all of the efficient units including nonextreme efficient ones. Finally, the presented models are utilized for two sets of data and related results are reported.

Keywords: data envelopment analysis, efficiency, ranking, weight

Procedia PDF Downloads 460

Authors: Adel Assiri, Ahmed Emam, Hmood Al-Dossari

Abstract:

Sentiment analysis (SA) has received growing attention in Arabic language research. However, few studies have yet to directly apply SA to Arabic due to lack of a publicly available dataset for this language. This paper partially bridges this gap due to its focus on one of the Arabic dialects which is the Saudi dialect. This paper presents annotated data set of 4700 for Saudi dialect sentiment analysis with (K= 0.807). Our next work is to extend this corpus and creation a large-scale lexicon for Saudi dialect from the corpus.

Keywords: Arabic, sentiment analysis, Twitter, annotation

Procedia PDF Downloads 638

Authors: Youngji Yoo, Seung Hwan Park, Daewoong An, Sung-Shick Kim, Jun-Geol Baek

Abstract:

The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.

Keywords: semiconductor, wafer bin map, feature extraction, spatial point patterns, contour map

Procedia PDF Downloads 388

Authors: Ruangdech Sirikit

Abstract:

The purposes of this research were to study the variables affecting the quality assessment of mathematics subject in Thailand by using value-added analysis on TIMSS 2011. The data used in this research is the secondary data from the 2011 Trends in International Mathematics and Science Study (TIMSS), collected from 6,124 students in 172 schools from Thailand, studying only mathematics subjects. The data were based on 14 assessment tests of knowledge in mathematics. There were 3 steps of data analysis: 1) To analyze descriptive statistics 2) To estimate competency of students from the assessment of their mathematics proficiency by using MULTILOG program; 3) analyze value added in the model of quality assessment using Value-Added Model with Hierarchical Linear Modeling (HLM) and 2 levels of analysis. The research results were as follows: 1. Student level variables that had significant effects on the competency of students at .01 levels were Parental care, Resources at home, Enjoyment of learning mathematics and Extrinsic motivation in learning mathematics. Variable that had significant effects on the competency of students at .05 levels were Education of parents and self-confident in learning mathematics. 2. School level variable that had significant effects on competency of students at .01 levels was Extra large school. Variable that had significant effects on competency of students at .05 levels was medium school.

Keywords: quality assessment, value-added model, TIMSS, mathematics, Thailand

Procedia PDF Downloads 286