Search results for: data reliability

Authors: Suphaphon Udomluck, Pannathorn Chachvarat

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Information Communication Technology (ICT) using management is essential for effective decision making in organization. The Concerns Based Adoption Model (CBAM) was employed as the conceptual framework. The purposes of the study were to assess the situation of Information Communication Technology (ICT) using management in College of Nursing under the Praboromarajchanok Institute. The samples were multi – stage sampling of 10 colleges of nursing that participated include directors, vice directors, head of learning groups, teachers, system administrator and responsible for ICT. The total participants were 280; the instrument used were questionnaires that include 4 parts, general information, Information Communication Technology (ICT) using management, the Stage of concern Questionnaires (SoC), and the Levels of Use (LoU) ICT Questionnaires respectively. Reliability coefficients were tested; alpha coefficients were 0.967for Information Communication Technology (ICT) using management, 0.884 for SoC and 0.945 for LoU. The data were analyzed by frequency, percentage, mean, standard deviation, Pearson Product Moment Correlation and Multiple Regression. They were founded as follows: The high level overall score of Information Communication Technology (ICT) using management and issue were administration, hardware, software, and people. The overall score of the Stage of concern (SoC)ICTis at high level and the overall score of the Levels of Use (LoU) ICTis at moderate. The Information Communication Technology (ICT) using management had the positive relationship with the Stage of concern (SoC)ICTand the Levels of Use (LoU) ICT(p < .01). The results of Multiple Regression revealed that administration hardwear, software and people ware could predict SoC of ICT (18.5%) and LoU of ICT (20.8%).The factors that were significantly influenced by SoCs were people ware. The factors that were significantly influenced by LoU of ICT were administration hardware and people ware.

Keywords: information communication technology (ICT), management, the concerns-based adoption model (CBAM), stage of concern(SoC), the levels of use(LoU)

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Authors: Jalel Khelil

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In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Mehran Nasiri, Ardeshir Poornemat

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The aim of this study was to investigate the current and ideal situations of the process of talent identification in Iranian football from the point of view of Iranian instructors of the Asian Football Confederation (AFC). This research was a descriptive-analytical study; in data collection phase a questionnaire was used, whose face validity was confirmed by experts of Physical Education and Sports Science. The reliability of questionnaire was estimated through the use of Cronbach's alpha method (0.91). This study involved 122 participants of Iranian instructors of the AFC who were selected based on stratified random sampling method. Descriptive statistics were used to describe the variables and inferential statistics (Chi-square) were used to test the hypotheses of the study at significant level (p ≤ 0.05). The results of Chi-square test related to the point of view of Iranian instructors of the AFC showed that the grass-roots scientific method was the best way to identify football players (0.001), less than 10 years old were the best ages for talent identification (0.001), the Football Federation was revealed to be the most important organization in talent identification (0.002), clubs were shown to be the most important institution in developing talents (0.001), trained scouts of Football Federation were demonstrated to be the best and most appropriate group for talent identification (0.001), and being referred by the football academy coaches was shown to be the best way to attract talented football players in Iran (0.001). It was also found that there was a huge difference between the current and ideal situation of the process of talent identification in Iranian football from the point of view of Iranian instructors of the AFC. Hence, it is recommended that the policy makers of talent identification for Iranian football provide a comprehensive, clear and systematic model of talent identification and development processes for the clubs and football teams, so that the talent identification process helps to nurture football talents more efficiently.

Keywords: current situation, talent finding, ideal situation, instructors (AFC)

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Authors: Zafer Sakka, Husain Al-Khaiat

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Due to social and economic considerations, owners all over the world desire to keep and use existing structures, including aging ones. However, these structures, especially those that are dear, need accurate condition assessment, and proper safety evaluation. More than half of the budget spent on construction activities in developed countries is related to the repair and maintenance of these reinforced concrete (R/C) structures. Also, periodical evaluation and assessment of relatively old concrete structures are vital and imperative. If the evaluation and assessment of structural components of a particular aging R/C structure reveal that repairs are essential for these components, these repairs should not be delayed. Delaying the repairs has the potential of losing serviceability of the whole structure and/or causing total failure and collapse of the structure. In addition, if repairs are delayed, the cost of maintenance will skyrocket as well. It can also be concluded from the above that the assessment of existing needs to receive more consideration and thought from the structural engineering societies and professionals. Ten major existing structures in Kuwait city that were constructed in the 1970s were assessed for structural reliability and integrity. Numerous concrete samples were extracted from the structural systems of the investigated buildings. This paper presents the results of the compressive strength tests that were conducted on the extracted cores. The results are compared for the buildings’ columns and beams elements and compared with the design strengths. The collected data were statistically analyzed. The average compressive strengths of the concrete cores that were extracted from the ten buildings had a large variation. The lowest average compressive strength for one of the buildings was 158 kg/cm². This building was deemed unsafe and economically unfeasible to be repaired; accordingly, it was demolished. The other buildings had an average compressive strengths fall in the range 215-317 kg/cm². Poor construction practices were the main cause for the strengths. Although most of the drawings and information for these buildings were lost during the invasion of Kuwait in 1990, however, information gathered indicated that the design strengths of the beams and columns for most of these buildings were in the range of 280-400 kg/cm². Following the study, measures were taken to rehabilitate the buildings for safety. The mean compressive strength for all cores taken from beams and columns of the ten buildings was 256.7 kg/cm². The values range was 139 to 394 kg/cm². For columns, the mean was 250.4 kg/cm², and the values ranged from 137 to 394 kg/cm². However, the mean compressive strength for the beams was higher than that of columns. It was 285.9 kg/cm², and the range was 181 to 383 kg/cm². In addition to the concrete cores that were extracted from the ten buildings, the 28-day compressive strengths of more than 24,660 concrete cubes were collected from a major ready-mixed concrete supplier in Kuwait. The data represented four different grades of ready-mix concrete (250, 300, 350, and 400 kg/cm²) manufactured between the year 2003 and 2018. The average concrete compressive strength for the different concrete grades (250, 300, 350 and 400 kg/cm²) was found to be 318, 382, 453 and 504 kg/cm², respectively, and the coefficients of variations were found to be 0.138, 0.140, 0.157 and 0.131, respectively.

Keywords: concrete compressive strength, concrete structures, existing building, statistical analysis.

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Authors: Nilüfer Kuru, Berrin Akman

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In this study, science process skills of children with ages varying between 48-months and 66-months are analyzed. Science process skills of children are investigated in terms of factors including gender of children, attendance of children to the previous educational institution and duration of their attendance, educational background of their parents, ages of children and teachers, professional experience of teachers, educational background, and department of graduation of teachers, type of pre-school education institution of teachers and children. Sample of research consists of 250 children aged between 48-months and 66-months who attend state and private kindergartens under the Ministry of National Education, nursery classes of elementary schools and kindergartens of establishments in central districts of Ankara and 50 teachers who serve in these children’s classes. Science Observation Form, reached from the website of Alaska Department of Education & Early, are analyzed in terms of language, content, construct validity, and reliability by the researchers. Additionally, Personal Information Form is also developed by the researchers. Data obtained in the study are analyzed with SPSS 16.0 package program to obtain percentage and frequency, Kruskal Wallis H-test, and Mann- Whitney U test, which are one of the non-parametric tests, are used. Within the context of this study it has been seen that independent variables of age, type of school attending and status of attendance to pre-school education, educational background of children’s father are meaningful expositive in gaining science process skills for children. It has been seen that period of service of teachers, duration of attendance to pre-school education for children, gender of children and educational background of children’s mother are not meaningful expositive in gaining science process skills for children.

Keywords: preschool, science process skills, early childhood education, science

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Authors: Felix Oromena Egara, Moeketsi Mosia

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The study aimed to evaluate the impact of blended learning on mathematics retention among secondary school students. Conducted in the Isoko North Local Government Area of Delta State, Nigeria, the research involved 1,235 senior class one (SS 1) students. Employing a non-equivalent control group pre-test-post-test quasi-experimental design, a sample of 70 students was selected from two secondary schools with ICT facilities through purposive sampling. Random allocation of students into experimental and control groups was achieved through balloting within each selected school. The investigation included three assessment points: pre-Mathematics Achievement Test (MAT), post-MAT, and post-post-MAT (retention), administered systematically by the researchers. Data collection utilized the established MAT instrument, which demonstrated a high reliability score of 0.86. Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS) version 28, with mean and standard deviation addressing study questions and analysis of covariance scrutinizing hypotheses at a significance level of .05. Results revealed significantly greater improvements in mathematics retention scores among students exposed to blended learning compared to those instructed through conventional methods. Moreover, noticeable differences in mean retention scores were observed, with male students in the blended learning group exhibiting notably higher performance. Based on these findings, recommendations were made, advocating for mathematics educators to integrate blended learning, particularly in geometry teaching, to enhance students’ retention of mathematical concepts.

Keywords: blended learning, flipped classroom model, secondary school students, station rotation model

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: Mohammadreza Mojtahedi

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Rapid urbanization presents multifaceted challenges, including environmental degradation and public health concerns. As the inevitability of urban sprawl continues, it becomes essential to devise strategies to alleviate its pressures on natural ecosystems and elevate socio-economic benchmarks within cities. This research investigates urban agriculture's economic contributions, emphasizing its pivotal role in food provisioning and export potential. Adopting a descriptive-analytical approach, field survey data was primarily collected via questionnaires. The tool's validity was affirmed by expert opinions, and its reliability secured by achieving a Cronbach's alpha score over 0.70 from 30 preliminary questionnaires. The research encompasses Neishabour's populace of 264,375, extracting a sample size of 384 via Cochran's formula. Findings reveal the significance of urban agriculture in food supply and its potential for exports, underlined by a p-value < 0.05. Neishabour's urban farming can augment the export of organic commodities, fruits, vegetables, ornamental plants, and foster product branding. Moreover, it supports the provision of fresh produce, bolstering dietary quality. Urban agriculture further impacts urban development metrics—enhancing environmental quality, job opportunities, income levels, and aesthetics, while promoting rainwater utilization. Popular cultivations include peaches, Damask roses, and poultry, tailored to available spaces. Structural equation modeling indicates urban agriculture's overarching influence, accounting for a 56% variance, predominantly in food sufficiency and export proficiency.

Keywords: urban agriculture, food supply, export potential, urban development, environmental health, structural equation modeling

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: Tanapat Chongkamunkong

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The objective of this paper is to present a practical use of Geographic Information System (GIS) to the public health from spatial correlation between multiple factors and dengue fever outbreak. Meteorological factors, demographic factors and environmental factors are compiled using GIS techniques along with the Global Satellite Mapping Remote Sensing (RS) data. We use monthly dengue fever cases, population density, precipitation, Digital Elevation Model (DEM) data. The scope cover study area under climate change of the El Niño–Southern Oscillation (ENSO) indicated by sea surface temperature (SST) and study area in 12 provinces of Thailand as remote sensing (RS) data from January 2007 to December 2014.

Keywords: dengue fever, sea surface temperature, Geographic Information System (GIS), remote sensing

Procedia PDF Downloads 182

Authors: Zülfiye Bıkmaz, Aytolan Yıldırım

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This study was planned as a descriptive regressive study in order to determine the relationship between the competency levels of working nurses, the levels of competency expected by nursing students, the critical thinking disposition of nurses, their perceived autonomy levels, and certain socio demographic characteristics. It is also a methodological study with regard to the intercultural adaptation of the Nursing Competence Scale (NCS) in both working and student samples. The sample of the study group of nurses at a university hospital for at least 6 months working properly and consists of 443 people filled out questionnaires. The student group, consisting of 543 individuals from the 4 public university nursing 3rd and 4th grade students. Data collection tools consisted of a questionnaire prepared in order to define the socio demographic, economic, and personal characteristics of the participants, the ‘Nursing Competency Scale’, the ‘Autonomy Subscale of the Sociotropy – Autonomy Scale’, and the ‘California Critical Thinking Disposition Inventory’. In data evaluation, descriptive statistics, nonparametric tests, Rasch analysis and correlation and regression tests were used. The language validity of the ‘NCS’ was performed by translation and back translation, and the context validity of the scale was performed with expert views. The scale, which was formed into its final structure, was applied in a pilot application from a group consisting of graduate and student nurses. The time constancy of the test was obtained by analysis testing retesting method. In order to reduce the time problems with the two half reliability method was used. The Cronbach Alfa coefficient of the scale was found to be 0.980 for the nurse group and 0.986 for the student group. Statistically meaningful relationships between competence and critical thinking and variables such as age, gender, marital status, family structure, having had critical thinking training, education level, class of the students, service worked in, employment style and position, and employment duration were found. Statistically meaningful relationships between autonomy and certain variables of the student group such as year, employment status, decision making style regarding self, total duration of employment, employment style, and education status were found. As a result, it was determined that the NCS which was adapted interculturally was a valid and reliable measurement tool and was found to be associated with autonomy and critical thinking.

Keywords: nurse, nursing student, competence, autonomy, critical thinking, Rasch analysis

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Authors: B. O. Diyaolu

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The stadium transcends a field of play to cultural heritage of a club especially when there is security of life and property and a conducive environment with exciting media facilities, CCTV and adequate field of play. Football fans love watching their clubs’ matches especially when nothing discourages their presence in the stadium. This study investigated the influence of security on fans’ attendance during Nigeria Professional Football League matches. Descriptive survey research design was used and the population consists of all Nigeria Professional Football League fans. Simple random sampling technique was used to pick a state from the six geo-political zones. 600 respondents comprising male and female fans were sampled from the ten selected vendors’ stands in each selected state. A structured questionnaire on Security and Fan attendance scale (SFAS) was used. The instrument consists of two sections. Section A seeks information on demographic data of the respondents, while section B was used to elicit information on security and fans’ attendance. The modified instrument which consists of 20 items has a reliability coefficient of 0.73. The hypothesis was tested at 0.05 significance level. The completed questionnaire was collated, coded and analyzed using descriptive statistics of frequency counts and percentage and inferential statistics of chi-square (X²). Findings of this study revealed that adequate security significantly influences fan attendance during Nigeria Professional Football League matches. There is no sport that can develop if the facilities in use are inadequate. Improving the condition of the stadium in Nigeria is paramount to the development of the Nigeria Professional Football League. All stakeholders in the organization of the League must put into consideration the need to improve the standard of the stadium as it will help to increase the attendance of fans during matches. Only the standard ones should be used during matches.

Keywords: adequate security, fans attendance, football fans, football stadium, Nigeria professional football league

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Pan Hongxia, Wang Zhenhua

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In this paper, the EMD decomposition in the process of endpoint effect adopted data based on AR model to predict the continuation and window function method of combining the two effective inhibition. Proven by simulation of the simulation signal obtained the ideal effect, then, apply this method to the gearbox test data is also achieved good effect in the process, for the analysis of the subsequent data processing to improve the calculation accuracy. In the end, under various working conditions for the gearbox fault diagnosis laid a good foundation.

Keywords: gearbox, fault diagnosis, ar model, end effect

Procedia PDF Downloads 358

Authors: Amir Reza Heydari, Yaser Jenab

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Coronary artery disease, a common cardiovascular disease, is attributed to the accumulation of cholesterol-based plaques in the coronary arteries, leading to atherosclerosis. This disease is associated with risk factors such as smoking, hypertension, diabetes, and elevated cholesterol levels, contributing to severe clinical consequences, including acute coronary syndromes and myocardial infarction. Treatment approaches such as from lifestyle interventions to surgical procedures like percutaneous coronary intervention and coronary artery bypass surgery. These interventions often employ stents, including bare-metal stents (BMS), drug-eluting stents (DES), and bioresorbable vascular scaffolds (BVS), each with its advantages and limitations. Computational tools have emerged as critical in optimizing stent designs and assessing their performance. The aim of this study is to provide an overview of the computational methods of studies based on the finite element (FE) method in the field of coronary stenting and discuss the potential for development and clinical application of stent devices. Additionally, the importance of assessing the ability of computational models is emphasized to represent real-world phenomena, supported by recent guidelines from the American Society of Mechanical Engineers (ASME). Validation processes proposed include comparing model performance with in vivo, ex-vivo, or in vitro data, alongside uncertainty quantification and sensitivity analysis. These methods can enhance the credibility and reliability of in silico simulations, ultimately aiding in the assessment of coronary stent designs in various clinical contexts.

Keywords: atherosclerosis, materials, restenosis, review, validation

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Authors: Maria Jędrzejczak, Patryk Pieniążek

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The European legal reality is on the eve of significant change. In European Union law, there is talk of a “fourth industrial revolution”, which is driven by massive data resources linked to powerful algorithms and powerful computing capacity. The above is closely linked to technological developments in the area of artificial intelligence, which has prompted an analysis covering both the legal environment as well as the economic and social impact, also from an ethical perspective. The discussion on the regulation of artificial intelligence is one of the most serious yet widely held at both European Union and Member State level. The literature expects legal solutions to guarantee security for fundamental rights, including privacy, in artificial intelligence systems. There is no doubt that personal data have been increasingly processed in recent years. It would be impossible for artificial intelligence to function without processing large amounts of data (both personal and non-personal). The main driving force behind the current development of artificial intelligence is advances in computing, but also the increasing availability of data. High-quality data are crucial to the effectiveness of many artificial intelligence systems, particularly when using techniques involving model training. The use of computers and artificial intelligence technology allows for an increase in the speed and efficiency of the actions taken, but also creates security risks for the data processed of an unprecedented magnitude. The proposed regulation in the field of artificial intelligence requires analysis in terms of its impact on the regulation on personal data protection. It is necessary to determine what the mutual relationship between these regulations is and what areas are particularly important in the personal data protection regulation for processing personal data in artificial intelligence systems. The adopted axis of considerations is a preliminary assessment of two issues: 1) what principles of data protection should be applied in particular during processing personal data in artificial intelligence systems, 2) what regulation on liability for personal data breaches is in such systems. The need to change the regulations regarding the rights and obligations of data subjects and entities processing personal data cannot be excluded. It is possible that changes will be required in the provisions regarding the assignment of liability for a breach of personal data protection processed in artificial intelligence systems. The research process in this case concerns the identification of areas in the field of personal data protection that are particularly important (and may require re-regulation) due to the introduction of the proposed legal regulation regarding artificial intelligence. The main question that the authors want to answer is how the European Union regulation against data protection breaches in artificial intelligence systems is shaping up. The answer to this question will include examples to illustrate the practical implications of these legal regulations.

Keywords: data protection law, personal data, AI law, personal data breach

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Handie Pramana Putra, Ani Dijah Rahajoe

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The proliferation of smart devices and advancements in mobile communication technologies have permeated various facets of life with the widespread influence of e-commerce. Detecting abnormal transactions holds paramount significance in this realm due to the potential for substantial financial losses. Moreover, the fusion of data flow and control flow assumes a critical role in the exploration of process modeling and data analysis, contributing significantly to the accuracy and security of business processes. This paper introduces an alternative approach to identify abnormal transactions through a model that integrates both data and control flows. Referred to as the Extended Data Petri net (DPNE), our model encapsulates the entire process, encompassing user login to the e-commerce platform and concluding with the payment stage, including the mobile transaction process. We scrutinize the model's structure, formulate an algorithm for detecting anomalies in pertinent data, and elucidate the rationale and efficacy of the comprehensive system model. A case study validates the responsive performance of each system component, demonstrating the system's adeptness in evaluating every activity within mobile transactions. Ultimately, the results of anomaly detection are derived through a thorough and comprehensive analysis.

Keywords: database, data analysis, DPNE, extended data flow, e-commerce

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Authors: Amal Mohammed Alqahatni

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This paper is a non-empirical analysis of existing literature on digital leadership competency, data-driven organizations, and dealing with AI technology (big data). This paper will provide insights into the importance of developing the leader’s analytical skills and style to be more effective for high-quality decision-making in a data-driven organization and achieve creativity during the organization's transformation to be digitalized. Despite the enormous potential that big data has, there are not enough experts in the field. Many organizations faced an issue with leadership style, which was considered an obstacle to organizational improvement. It investigates the obstacles to leadership style in this context and the challenges leaders face in coaching and development. The leader's lack of analytical skill with AI technology, such as big data tools, was noticed, as was the lack of understanding of the value of that data, resulting in poor communication with others, especially in meetings when the decision should be made. By acknowledging the different dynamics of work competency and organizational structure and culture, organizations can make the necessary adjustments to best support their leaders. This paper reviews prior research studies and applies what is known to assist with current obstacles. This paper addresses how analytical leadership will assist in overcoming challenges in a data-driven organization's work environment.

Keywords: digital leadership, big data, leadership style, digital leadership challenge

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Authors: Chaitanya Varma, Arpan Mehar

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The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

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Authors: Jisong Ryu, Woosik Lee, Yonggu Jang

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The paper discusses the process of establishing a reliable test bed for verifying the usability of Ground Penetrating Radar (GPR) exploration equipment based on an integrated underground spatial map in Korea. The aim of this study is to construct a test bed consisting of metal and non-metal pipelines to verify the performance of GPR equipment and improve the accuracy of the underground spatial integrated map. The study involved the design and construction of a test bed for metal and non-metal pipe detecting tests. The test bed was built in the SOC Demonstration Research Center (Yeoncheon) of the Korea Institute of Civil Engineering and Building Technology, burying metal and non-metal pipelines up to a depth of 5m. The test bed was designed in both vehicle-type and cart-type GPR-mounted equipment. The study collected data through the construction of the test bed and conducting metal and non-metal pipe detecting tests. The study analyzed the reliability of GPR detecting results by comparing them with the basic drawings, such as the underground space integrated map. The study contributes to the improvement of GPR equipment performance evaluation and the accuracy of the underground spatial integrated map, which is essential for urban planning and construction. The study addressed the question of how to verify the usability of GPR exploration equipment based on an integrated underground spatial map and improve its performance. The study found that the test bed is reliable for verifying the performance of GPR exploration equipment and accurately detecting metal and non-metal pipelines using an integrated underground spatial map. The study concludes that the establishment of a test bed for verifying the usability of GPR exploration equipment based on an integrated underground spatial map is essential. The proposed Korean-style test bed can be used for the evaluation of GPR equipment performance and support the construction of a national non-metal pipeline exploration equipment performance evaluation center in Korea.

Keywords: Korea-style GPR testbed, GPR, metal pipe detecting, non-metal pipe detecting

Procedia PDF Downloads 87

Authors: Lior Rake, Shmulik Pinkert

Abstract:

Methane hydrate has been found in significant quantities in soils offshore within continental margins and in permafrost within arctic regions where low temperature and high pressure are present. The mechanical parameters for geotechnical engineering are commonly evaluated in geomechanical laboratories adapted to simulate the environmental conditions of methane hydrate-bearing sediments (MHBS). Due to the complexity and high cost of natural MHBS sampling, most laboratory investigations are conducted on artificially formed samples. MHBS artificial samples can be formed using different hydrate formation methods in the laboratory, where methane gas and water are supplied into the soil pore space under the methane hydrate phase conditions. The most commonly used formation method is the excess gas method which is considered a relatively simple, time-saving, and repeatable testing method. However, there are several differences in the procedures and techniques used to produce the hydrate using the excess gas method. As a result of the difference between the test facilities and the experimental approaches that were carried out in previous studies, different measurement criteria and analyses were proposed for MHBS geomechanics. The lack of uniformity among the various experimental investigations may adversely impact the reliability of integrating different data sets for unified mechanical model development. In this work, we address some fundamental aspects relevant to reliable MHBS geomechanical investigations, such as hydrate homogeneity in the sample, the hydrate formation duration criterion, the hydrate-saturation evaluation method, and the effect of temperature measurement accuracy. Finally, a set of recommendations for repeatable and reliable MHBS formation will be suggested for future standardization of MHBS geomechanical investigation.

Keywords: experimental study, laboratory investigation, excess gas, hydrate formation, standardization, methane hydrate-bearing sediment

Procedia PDF Downloads 46

Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

Abstract:

Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing

Procedia PDF Downloads 652

Authors: Ade Dwi Utami

Abstract:

Pedagogic competence is the basic competence of teachers to perform their tasks as educators. The ability to assess has become one of the demands in teachers pedagogic competence. Teachers ability to assess is related to curriculum instructions and applications. This research is aimed at obtaining data concerning teachers ability to assess that comprises of understanding assessment, determining assessment type, tools and procedure, conducting assessment process, and using assessment result information. It uses mixed method of explanatory technique in which qualitative data is used to verify the quantitative data obtained through a survey. The technique of quantitative data collection is by test whereas the qualitative data collection is by observation, interview and documentation. Then, the analyzed data is processed through a proportion study technique to be categorized into high, medium and low. The result of the research shows that teachers ability to assess can be grouped into 3 namely, 2% of high, 4% of medium and 94% of low. The data shows that teachers ability to assess is still relatively low. Teachers are lack of knowledge and comprehension in assessment application. The statement is verified by the qualitative data showing that teachers did not state which aspect was assessed in learning, record children’s behavior, and use the data result as a consideration to design a program. Teachers have assessment documents yet they only serve as means of completing teachers administration for the certification program. Thus, assessment documents were not used with the basis of acquired knowledge. The condition should become a consideration of the education institution of educators and the government to improve teachers pedagogic competence, including the ability to assess.

Keywords: assessment, early childhood education, pedagogic competence, teachers

Procedia PDF Downloads 240

Authors: G. Balakrishnan

Abstract:

Stereo Correspondence plays a major role in estimation of distance of an object from the stereo camera pair for various applications. In this paper, a stereo correspondence algorithm based on block-matching technique is presented. Initially, an energy matrix is calculated for every disparity obtained using modified Sum of Absolute Difference (SAD). Higher energy matrix errors are removed by using threshold value in order to reduce the mismatch errors. A smoothening filter is applied to eliminate unreliable disparity estimate across the object boundaries. The purpose is to improve the reliability of calculation of disparity map. The experimental results obtained shows that the final depth map produce better results and can be used to all the applications using stereo cameras.

Keywords: stereo matching, filters, energy matrix, disparity

Procedia PDF Downloads 206

Authors: Y. N. Phang, E. F. Loh

Abstract:

Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling over-dispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling over-dispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling over-dispersed medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling over-dispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian, and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling over-dispersed medical count data when ZIP and ZINB are inadequate.

Keywords: zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit

Procedia PDF Downloads 526

Authors: Uzma Bashir, Jamaludin Md. Ali

Abstract:

This study is concerned with the visualization of monotone data using a piece-wise C1 rational trigonometric interpolating scheme. Four positive shape parameters are incorporated in the structure of rational trigonometric spline. Conditions on two of these parameters are derived to attain the monotonicity of monotone data and other two are left-free. Figures are used widely to exhibit that the proposed scheme produces graphically smooth monotone curves.

Keywords: trigonometric splines, monotone data, shape preserving, C1 monotone interpolant

Procedia PDF Downloads 259