Search results for: R data science

Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

Abstract:

This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24 percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: behavior, data mining technique, a priori algorithm, knowledge discovery

Procedia PDF Downloads 404

Authors: Shilpa Balan

Abstract:

Radio Frequency Identification, also popularly known as RFID is used to automatically identify and track tags attached to items. This study focuses on the application of RFID in healthcare. The adoption of RFID in healthcare is a crucial technology to patient safety and inventory management. Data from RFID tags are used to identify the locations of patients and inventory in real time. Medical errors are thought to be a prominent cause of loss of life and injury. The major advantage of RFID application in healthcare industry is the reduction of medical errors. The healthcare industry has generated huge amounts of data. By discovering patterns and trends within the data, big data analytics can help improve patient care and lower healthcare costs. The number of increasing research publications leading to innovations in RFID applications shows the importance of this technology. This study explores the current state of research of RFID in healthcare using a text mining approach. No study has been performed yet on examining the current state of RFID research in healthcare using a data mining approach. In this study, related articles were collected on RFID from healthcare journal and news articles. Articles collected were from the year 2000 to 2015. Significant keywords on the topic of focus are identified and analyzed using open source data analytics software such as Rapid Miner. These analytical tools help extract pertinent information from massive volumes of data. It is seen that the main benefits of adopting RFID technology in healthcare include tracking medicines and equipment, upholding patient safety, and security improvement. The real-time tracking features of RFID allows for enhanced supply chain management. By productively using big data, healthcare organizations can gain significant benefits. Big data analytics in healthcare enables improved decisions by extracting insights from large volumes of data.

Keywords: RFID, data mining, data analysis, healthcare

Procedia PDF Downloads 233

Authors: Adel M. Qatawneh

Abstract:

This paper aimed to determine the importance of knowledge innovation for external audit on anti-corruption in the entire Jordanian bank companies are listed in Amman Stock Exchange (ASE). The study importance arises from the need to recognize the Knowledge innovation for external audit and anti-corruption as the development in the world of business, the variables that will be affected by external audit innovation are: reliability of financial data, relevantly of financial data, consistency of the financial data, Full disclosure of financial data and protecting the rights of investors to achieve the objectives of the study a questionnaire was designed and distributed to the society of the Jordanian bank are listed in Amman Stock Exchange. The data analysis found out that the banks in Jordan have a positive importance of Knowledge innovation for external audit on anti-corruption. They agree on the benefit of Knowledge innovation for external audit on anti-corruption. The statistical analysis showed that Knowledge innovation for external audit had a positive impact on the anti-corruption and that external audit has a significantly statistical relationship with anti-corruption, reliability of financial data, consistency of the financial data, a full disclosure of financial data and protecting the rights of investors.

Keywords: knowledge innovation, external audit, anti-corruption, Amman Stock Exchange

Procedia PDF Downloads 465

Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

Abstract:

Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

Procedia PDF Downloads 123

Authors: Roseminu Varghese, Laveena Anitha Barboza, Jyothi Chakrabarty, Ravishankar

Abstract:

Depression among post-stroke survivors is prevalent, but it is unidentified. The purpose of this review was to determine the pooled prevalence of depression among post-stroke survivors in the South Asian region from all published health sciences research articles. The review also aimed to analyze the disparities in the prevalence of depression among the post-stroke survivors from different study locations. Data search to identify the relevant research articles published from 2005 to 2016 was done by using mesh terms and keywords in Web of Science, PubMed Medline, CINAHL, Scopus, J gate, IndMED databases. The final analysis comprised of 9 studies, including a population of 1,520 men and women. Meta-analysis was performed in STATA version 13.0. The overall pooled post-stroke depression prevalence was 0.46, 95% (CI), (0.3- 0.62). The prevalence rate in this systematic review is evident of depression among post-stroke survivors in the South Asian Region. Identifying the prevalence of post-stroke depression at an early stage is important to improve outcomes of the rehabilitative process of stroke survivors and for its early intervention.

Keywords: depression, post stroke survivors, prevalence, systematic review

Procedia PDF Downloads 158

Authors: Siti Azrina B. A. Aziz, Siti Hafizah A. Hamid

Abstract:

New approaches to analyze and visualize data stream in real-time basis is important in making a prompt decision by the decision maker. Financial market trading and surveillance, large-scale emergency response and crowd control are some example scenarios that require real-time analytic and data visualization. This situation has led to the development of techniques and tools that support humans in analyzing the source data. With the emergence of Big Data and social media, new techniques and tools are required in order to process the streaming data. Today, ranges of tools which implement some of these functionalities are available. In this paper, we present chronological evolution evaluation of technologies for supporting of real-time analytic and visualization of the data stream. Based on the past research papers published from 2002 to 2014, we gathered the general information, main techniques, challenges and open issues. The techniques for streaming text visualization are identified based on Text Visualization Browser in chronological order. This paper aims to review the evolution of streaming text visualization techniques and tools, as well as to discuss the problems and challenges for each of identified tools.

Keywords: information visualization, visual analytics, text mining, visual text analytics tools, big data visualization

Procedia PDF Downloads 399

Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

Abstract:

Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

Procedia PDF Downloads 147

Authors: Bekkem Chakradhar Reddy, Y. Soni Priya, Mathivanan G., L. K. Joshila Grace, N. Srinivasan, Asha P.

Abstract:

Currently, there are a lot of problems related to marking attendance in schools, offices, or other places. Organizations tasked with collecting daily attendance data have numerous concerns. There are different ways to mark attendance. The most commonly used method is collecting data manually by calling each student. It is a longer process and problematic. Now, there are a lot of new technologies that help to mark attendance automatically. It reduces work and records the data. We have proposed to implement attendance marking using the latest technologies. We have implemented a system based on face identification and analyzing faces. The project is developed by gathering faces and analyzing data, using deep learning algorithms to recognize faces effectively. The data is recorded and forwarded to the host through mail. The project was implemented in Python and Python libraries used are CV2, Face Recognition, and Smtplib.

Keywords: python, deep learning, face recognition, CV2, smtplib, Dlib.

Procedia PDF Downloads 58

Authors: Nizar Karim Uribe-Orihuela, Fernando Brambila-Paz, Ivette Caldelas, Rodrigo Montufar-Chaveznava

Abstract:

In this paper, we present the advances corresponding to the implementation of a strategy to visualize geographical data using a Software Development Kit (SDK) for video games. We use multispectral images from Landsat 7 platform and Laser Imaging Detection and Ranging (LIDAR) data from The National Institute of Geography and Statistics of Mexican (INEGI). We select a place of interest to visualize from Landsat platform and make some processing to the image (rotations, atmospheric correction and enhancement). The resulting image will be our gray scale color-map to fusion with the LIDAR data, which was selected using the same coordinates than in Landsat. The LIDAR data is translated to 8-bit raw data. Both images are fused in a software developed using Unity (an SDK employed for video games). The resulting image is then displayed and can be explored moving around. The idea is the software could be used for students of geology and geophysics at the Engineering School of the National University of Mexico. They will download the software and images corresponding to a geological place of interest to a smartphone and could virtually visit and explore the site with a virtual reality visor such as Google cardboard.

Keywords: virtual reality, interactive technologies, geographical data visualization, video games technologies, educational material

Procedia PDF Downloads 246

Authors: Chad Brown

Abstract:

This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

Procedia PDF Downloads 41

Authors: Getinet Gezahegn Gebre

Abstract:

The study examined the impact of gender differences on Crop productivity in Decha woreda of southwest Kafa zone, located 140 Km from Jimma Town and 460 km southwest of Addis Ababa, between Bonga town and Omo River. The specific objectives were to assess the extent to which the agricultural production system is gender oriented, to examine access and control over productive resources, and to estimate men’s and women’s productivity in agriculture. Cross-sectional data collected from a total of 140 respondents were used in this study, whereby 65 were female-headed and 75 were male-headed households. The data were analyzed by using Statistical Package for Social Science (SPSS). Descriptive statistics such as frequency, mean, percentage, t-test and chi-square were used to summarize and compare the information between the two groups. Moreover, Cobb-Douglas(CD) production function was used to estimate the productivity difference in agriculture between male and female-headed households. Results of the study showed that male-headed households (MHH) own more productive resources such as land, livestock, labor and other agricultural inputs as compared to female-headed households (FHH). Moreover, the estimate of CD production function shows that livestock, herbicide use, land size and male labor were statistically significant for MHH, while livestock, land size, herbicides use and female labor were significant variables for FHH. The crop productivity difference between MHH and FHH was about 68.83% in the study area. However, if FHH had equal access to the inputs as MHH, the gross value of the output would be higher by 23.58% for FHH. This might suggest that FHH would be more productive than MHH if they had equal access to inputs as MHH. Based on the results obtained, the following policy implication can be drawn: accessing FHH to inputs that increase the productivity of agriculture, such as herbicides, livestock and male labor; increasing the productivity of land; and introducing technologies that reduce the time and energy of women, especially for enset processing.

Keywords: gender difference, crop productivity, GDP, efficiency

Procedia PDF Downloads 74

Authors: Vangelis Papadimitropoulos

Abstract:

The paper is part of the research project “Techno-Social Innovation in the Collaborative Economy'', funded by the Hellenic Foundation of Research and Innovation for the years 2022-2024. The research project examines the normative and empirical conditions of grassroots technologically driven innovation, potentially enabling the transition towards a commons-oriented post-capitalist economy. The project carries out a conceptually led and empirically grounded multi-case study of the digital commons, open-source technologies, platform cooperatives, open cooperatives and Distributed Autonomous Organizations (DAOs) on the Blockchain. The methodological scope of research is interdisciplinary inasmuch as it comprises political theory, economics, sustainability science and computer science, among others. The research draws specifically on Michel Bauwens and Vasilis Kostakis' model of open cooperativism between the commons, ethical market entities and a partner state. Bauwens and Kostakis advocate for a commons-based counter-hegemonic post-capitalist transition beyond and against neoliberalism. The research further employs Laclau and Mouffe's discourse theory of hegemony to introduce a post-hegemonic conceptualization of the model of open cooperativism. Thus, the paper aims to outline the theoretical contribution of the research project to contemporary political theory debates on post-capitalism and the collaborative economy.

Keywords: open cooperativism, techno-social innovation, post-hegemony, post-capitalism

Procedia PDF Downloads 66

Authors: Y. K. Park, B. K. Kim, J. W. Lee, S. J. Lee

Abstract:

To assess the risk of the underground structures and surrounding ground, we collect basic data by the engineering method of measurement, exploration and surveys and, derive the risk through proper analysis and each assessment for urban railroad underground structures and surrounding ground including station inflow. Basic data are obtained by the fiber-optic sensors, MEMS sensors, water quantity/quality sensors, tunnel scanner, ground penetrating radar, light weight deflectometer, and are evaluated if they are more than the proper value or not. Based on these data, we analyze the risk level of urban railroad underground structures and surrounding ground. And we develop the risk management system to manage efficiently these data and to support a convenient interface environment at input/output of data.

Keywords: urban railroad, underground structures, ground subsidence, station inflow, risk

Procedia PDF Downloads 336

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

Procedia PDF Downloads 260

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

Abstract:

Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud Ccomputing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 477

Authors: S. Curtin, A. Trace

Abstract:

Introduction: As patients can often be ambivalent about or resistant to any change in their smoking behavior the traditional ‘5 A’ model may be limited as it assumes that patients are ready and motivated to change. However, there is a stage model that is helpful to give guidance for dental students: the Transtheoretical Model (TTM). This model allows students to understand the tasks and goals for the pre-contemplation stage. The TTM was introduced in early stages as a core component of a smoking cessation programme that was integrated into a Behavioral Science programme as applied to dentistry. The aim of the present study is to evaluate and illustrate the students’ current level of knowledge from the questions the students generated in order to engage patients in the tasks and goals of the pre-contemplation stage. Method: N=47 responses of fifth-year undergraduate dental students. These responses were the data set for this study and related to their knowledge base of appropriate questions for a dentist to ask at the pre-contemplation stage of change. A deductive -descriptive analysis was conducted on the data. The goals and tasks of the pre-contemplation stage of the TTM provided a template for this deductive analysis. Results: 51% of students generated relevant, open, exploratory questions for the pre-contemplation stage, whilst 100% of students generated closed questions. With regard to those questions appropriate for the pre-contemplation stage, 19% were open and exploratory, while 66% were closed questions. A deductive analysis of the open exploratory questions revealed that 53% of the questions addressed increased concern about the current pattern of behavior, 38% of the questions concerned increased awareness of a need for change and only 8% of the questions dealt with the envisioning of the possibility of change. Conclusion: All students formulated relevant questions for the pre-contemplation stage, and half of the students generated the open, exploratory questions that increased patients’ awareness of the need to change. More training is required to facilitate a shift in the formulation from closed to open questioning, especially given that, traditionally, smoking cessation was modeled on the ‘5 As’, and that the general training for dentists supports an advisory and directive approach.

Keywords: behaviour change, pre-contemplation stage, trans-theoretical model, undergraduate dentistry students

Procedia PDF Downloads 413

Authors: Chiahsu Yang, Peiling Wu, Ted Ho

Abstract:

From emphasizing economic development to stressing public happiness, the international community mainly hopes to be able to understand whether the quality of life for the public is becoming better. The Better Life Index (BLI) constructed by OECD uses living conditions and quality of life as starting points to cover 11 areas of life and to convey the state of the general public’s well-being. In light of the BLI framework, the Directorate General of Budget, Accounting and Statistics (DGBAS) of the Executive Yuan instituted the Gross National Happiness Index to understand the needs of the general public and to measure the progress of the aforementioned conditions in residents across the island. Whereas living conditions consist of income and wealth, jobs and earnings, and housing conditions, health status, work and life balance, education and skills, social connections, civic engagement and governance, environmental quality, personal security. The ICT area consists of health care, living environment, ICT-enabled communication, transportation, government, education, pleasure, purchasing, job & employment. In the wake of further science and technology development, rapid formation of information societies, and closer integration between lifestyles and information societies, the public’s well-being within information societies has indeed become a noteworthy topic. the Board of Science and Technology of the Executive Yuan use the OECD’s BLI as a reference in the establishment of the Taiwan-specific ICT-Enabled Better Life Index. Using this index, the government plans to examine whether the public’s quality of life is improving as well as measure the public’s satisfaction with current digital quality of life. This understanding will enable the government to gauge the degree of influence and impact that each dimension of digital services has on digital life happiness while also serving as an important reference for promoting digital service development. The content of the ICT Enabled Better Life Index. Information and communications technology (ICT) has been affecting people’s living styles, and further impact people’s quality of life (QoL). Even studies have shown that ICT access and usage have both positive and negative impact on life satisfaction and well-beings, many governments continue to invest in e-government programs to initiate their path to information society. This research is the few attempts to link the e-government benchmark to the subjective well-being perception, and further address the gap between user’s perception and existing hard data assessment, then propose a model to trace measurement results back to the original public policy in order for policy makers to justify their future proposals.

Keywords: information and communications technology, quality of life, satisfaction, well-being

Procedia PDF Downloads 355

Authors: Wang Yichen, Haruka Yamashita

Abstract:

In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

Procedia PDF Downloads 133

Authors: Muhamamd Shahid Farooq, Maazan Zafar, Rizawana Akhtar

Abstract:

E-learning technologies are the blessings of advancements in science and technology. These facilitate the learners to get information at any place and any time by improving their self-confidence, self-efficacy and effectiveness in teaching learning process. E-learning provides an individualized learning experience for learners and remove barriers faced by students during new and creative ways of gaining information. It provides a wide range of facilities to enable the teachers and students for effective and purposeful learning. This study was conducted to explore the attitudes of university students and teachers towards e-learning working in a metropolitan university of Pakistan. The personal, institutional and technological characteristics of the teachers and students of higher education institution effect the adoption of e-learning. For this descriptive study 449 students and 35 university teachers were surveyed by using a Likert scale type questionnaire consisting of 52 statements relating to six factors "perceived usefulness, intention to adopt e-learning, ease of e-learning use, availability resources, e-learning stressors, and pressure to use e-learning". Data were analyzed by making comparisons on the basis of different demographic factors. The findings of the study show that both type of respondents have positive attitude towards e-learning. However, the male and female respondents differ in their opinion for e-learning implementation.

Keywords: e-learning, ICT, e-sources of learning, questionnaire

Procedia PDF Downloads 527

Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

Abstract:

The demand for cloud storage is increasing because users want continuous access their data. Cloud Storage revolutionized the way how users access their data. A lot of cloud storage service providers are available as DropBox, G Drive, and providing limited free storage and for extra storage; users have to pay money, which will act as a burden on users. To avoid the issue of limited free storage, the concept of Multi Cloud Storage introduced. In this paper, we will discuss the limitations of existing Multi Cloud Storage systems for mobile devices.

Keywords: cloud storage, data privacy, data security, multi cloud storage, mobile devices

Procedia PDF Downloads 699

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

Abstract:

Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

Procedia PDF Downloads 52

Authors: Wuttigrai Ngamsirijit

Abstract:

Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.    

Keywords: decision making, human capital analytics, talent management, talent value chain

Procedia PDF Downloads 187

Authors: Ouafa Amira, Jiangshe Zhang

Abstract:

Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

Procedia PDF Downloads 259

Authors: Umair Shoukat Ali, Kamil Zafar, Rashna Hoshang Sukhia, Mubassar Fida, Aqeel Ahmed

Abstract:

Objective: This systematic review and meta-analysis aimed to summarize the effectiveness of bonded and removable retainers (Hawley and Essix retainer) in terms of improvement in occlusal settling (occlusal contact points/areas) after orthodontic treatment. Search Method: We searched the Cochrane Library, CINAHL Plus, PubMed, Web of Science, Orthodontic journals, and Google scholar for eligible studies. We included randomized control trials (RCT) along with Cohort studies. Studies that reported occlusal contacts/areas during retention with fixed bonded and removable retainers were included. To assess the quality of the RCTs Cochrane risk of bias tool was utilized, whereas Newcastle-Ottawa Scale was used for assessing the quality of cohort studies. Data analysis: The data analysis was limited to reporting mean values of occlusal contact points/areas with different retention methods. By utilizing the RevMan software V.5.3, a meta-analysis was performed for all the studies with the quantitative data. For the computation of the summary effect, a random effect model was utilized in case of high heterogeneity. I2 statistics were utilized to assess the heterogeneity among the selected studies. Results: We included 6 articles in our systematic review after scrutinizing 219 articles and eliminating them based on duplication, titles, and objectives. We found significant differences between fixed and removable retainers in terms of occlusal settling within the included studies. Bonded retainer (BR) allowed faster and better posterior tooth settling as compared to Hawley retainer (HR). However, HR showed good occlusal settling in the anterior dental arch. Essix retainer showed a decrease in occlusal contact during the retention phase. Meta-analysis showed no statistically significant difference between BR and removable retainers. Conclusions: HR allowed better overall occlusal settling as compared to other retainers in comparison. However, BR allowed faster settling in the posterior teeth region. Overall, there are insufficient high-quality RCTs to provide additional evidence, and further high-quality RCTs research is needed.

Keywords: orthodontic retainers, occlusal contact, Hawley, fixed, vacuum-formed

Procedia PDF Downloads 125

Authors: Balarabe Bilyaminu Ismail, Osman Cavus, Fügen Durlu Özkaya

Abstract:

This study is aimed at determining the level of awareness of chefs and students of food science and technology on food safety in general and allergens in particular. To get appropriate data, a questionnaire comprising of 19 questions covering many food safety issues and allergens in foods were used to collect information for the study through face to face interviews. Interviews were conducted for 284 people in Nigeria and Turkey. Sixty-eight percent of respondents from Turkey; 31.3% were students and 68.7% were chefs. Thirty-one percent of respondents from Nigeria include 33.7% students and 66.3% chefs. The result of the study indicated that most of the findings of scientific studies on food safety issues have not been directly applied by the people working in the food sector. Additionally, the knowledge level of the gastronomy and culinary arts students on food safety and allergens are significantly higher than the restaurant chefs that prepare the food and serve it to the public. The study, therefore, concluded that proper training of food business operators is critical to ensuring the safety of foods and control of allergens.

Keywords: allergens, food safety, questionnaire survey, training

Procedia PDF Downloads 362

Authors: Wookyong Kwon, Sangmoon Lee

Abstract:

In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: model predictive control, sampled-data control, linear parameter varying systems, LPV

Procedia PDF Downloads 309

Authors: Nasser A. Al-Azri

Abstract:

The effectiveness of passive cooling techniques is assessed based on bioclimatic charts that require the typical meteorological year (TMY) for a specified location for their development. However, TMYs are not always available; mainly due to the scarcity of records of solar radiation which is an essential component used in developing common TMYs intended for general uses. Since solar radiation is not required in the development of the bioclimatic chart, this work suggests developing TMYs based solely on the relevant parameters. This approach improves the accuracy of the developed TMY since only the relevant parameters are considered and it also makes the development of the TMY more accessible since solar radiation data are not used. The presented paper will also discuss the development of the TMY from the raw data available at the NOAA-NCDC archive of world weather data and the construction of the bioclimatic charts for some randomly selected locations around the world.

Keywords: bioclimatic charts, passive cooling, TMY, weather data

Procedia PDF Downloads 240

Authors: Tuğçe Yavaş Akış

Abstract:

In recent years, due to an increased use of radioactive materials, radioactive sources are constantly being transported via air, road and ocean ways for medical, industrial, research etc. purposes throughout the world. The quantity of radioactive materials transported all around the world varies from negligible quantities in shipments of consumer products to very large quantities in shipments of irradiated nuclear fuel. Radioactive materials have been less attractive for social science researchers in literature. In this study, it is aimed to discover desired flow of radioactive materials from logistics service quality (LSQ) perspective. In doing so, case study approach will be employed by using secondary data collected from one of the world’s leading transportation companies’ customer care system reports. Movement of radioactive cargoes containing IR-192 and logistics process will be analyzed with the help of logistics service quality dimensions. Based on the case study that will be conducted, interaction between dimensions, the importance of each dimension in desired flow, and their relevance with desired flow of radioactive materials will be explained. This study will bring out the desired flow of radioactive materials transportation and be a guide for all other companies, employees and researchers.

Keywords: logistics service quality, LSQ dimension , radioactive material, transportation

Procedia PDF Downloads 239

Authors: D. Nam Kim, D. Jin Kim, Jeonghwan Jeon

Abstract:

Defense Defensive Modeling and Simulation (M&S) is a system which enables impracticable training for reducing constraints of time, space and financial resources. The necessity of defensive M&S has been increasing not only for education and training but also virtual fight. Soldiers who are using defensive M&S for education and training will obtain empirical knowledge and know-how. However, the obtained knowledge of individual soldiers have not been managed and utilized yet since the nature of military organizations: confidentiality and frequent change of members. Therefore, this study aims to develop a management system for the experience of defensive M&S based on data mining approach. Since individual empirical knowledge gained through using the defensive M&S is both quantitative and qualitative data, data mining approach is appropriate for dealing with individual empirical knowledge. This research is expected to be helpful for soldiers and military policy makers.

Keywords: data mining, defensive m&s, management system, knowledge management

Procedia PDF Downloads 255

Authors: Hyun-Woo Cho

Abstract:

The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

Procedia PDF Downloads 236