Search results for: minimum data set

Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

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Authors: Mohammad H. Yarmohammadian, Reza Safdari, Mahmoud Keyvanara, Nahid Tavakoli

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One of the greatest challenges in disaster and emergencies is patient tracking. The concept of tracking has different denotations. One of the meanings refers to tracking patients’ physical locations and the other meaning refers to tracking patients ‘medical needs during emergency services. The main goal of patient tracking is to provide patient safety during disaster and emergencies and manage the flow of patient and information in different locations. In most of cases, there are not sufficient and accurate data regarding the number of injuries, medical conditions and their accommodation and transference. The objective of the present study is to survey on patient tracking issue in natural disaster and emergencies. Methods: This was a narrative study in which the population was E-Journals and the electronic database such as PubMed, Proquest, Science direct, Elsevier, etc. Data was gathered by Extraction Form. All data were analyzed via content analysis. Results: In many countries there is no appropriate and rapid method for tracking patients and transferring victims after the occurrence of incidents. The absence of reliable data of patients’ transference and accommodation, even in the initial hours and days after the occurrence of disasters, and coordination for appropriate resource allocation, have faced challenges for evaluating needs and services challenges. Currently, most of emergency services are based on paper systems, while these systems do not act appropriately in great disasters and incidents and this issue causes information loss. Conclusion: Patient tracking system should update the location of patients or evacuees and information related to their states. Patients’ information should be accessible for authorized users to continue their treatment, accommodation and transference. Also it should include timely information of patients’ location as soon as they arrive somewhere and leave therein such a way that health care professionals can be able to provide patients’ proper medical treatment.

Keywords: patient tracking, challenges, disaster, emergency

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Authors: Neda Zamani

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The present study aimed to investigate the effectiveness of training courses and their limitations using the CIPP model. The investigations were done on Isfahan Refinery as a case study. From a purpose point of view, the present paper is included among applied research and from a data gathering point of view, it is included among descriptive research of the field type survey. The population of the study included participants in training courses, their supervisors and experts of the training department. Probability-proportional-to-size (PPS) was used as the sampling method. The sample size for participants in training courses included 195 individuals, 30 supervisors and 11 individuals from the training experts’ group. To collect data, a questionnaire designed by the researcher and a semi-structured interview was used. The content validity of the data was confirmed by training management experts and the reliability was calculated through 0.92 Cronbach’s alpha. To analyze the data in descriptive statistics aspect (tables, frequency, frequency percentage and mean) were applied, and inferential statistics (Mann Whitney and Wilcoxon tests, Kruskal-Wallis test to determine the significance of the opinion of the groups) have been applied. Results of the study indicated that all groups, i.e., participants, supervisors and training experts, absolutely believe in the importance of training courses; however, participants in training courses regard content, teacher, atmosphere and facilities, training process, managing process and product as to be in a relatively appropriate level. The supervisors also regard output to be at a relatively appropriate level, but training experts regard content, teacher and managing processes as to be in an appropriate and higher than average level.

Keywords: training courses, limitations of training effectiveness, CIPP model, Isfahan oil refinery company

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Authors: Kumar Manoj, Ramesh Kumar, Kumari Arti, Kumar Prashant

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Routing protocols have a central role in any mobile ad hoc network (MANET). There are many routing protocols that exhibit different performance levels in different scenarios. In this paper we compare AODV, DSDV, DSR and ZRP routing protocol in mobile ad hoc networks to determine the best operational conditions for each protocol. We analyses these routing protocols by extensive simulations in OPNET simulator and show that how pause time and the number of nodes affect their performance. In this study, performance is measured in terms of control traffic received, control traffic sent, data traffic received, data traffic sent, throughput, retransmission attempts.

Keywords: MANET, AODV, DSDV, DSR, ZRP

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Fouzia Amireche-Ziar, Ilham Mokbel, Jacques Jose

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The vapour pressures of the three binary mixtures: 1, 2- dichloroethane + 1,3-dioxolane, + 1,4-dioxane or + tetrahydropyrane, are carried out at ten temperatures ranging from 273 to 353.15 K. An accurate static device was employed for these measurements. The VLE data were reduced using the Redlich-Kister equation by taking into consideration the vapour pressure non-ideality in terms of the second molar virial coefficient. The experimental data were compared to the results predicted with the DISQUAC and Dortmund UNIFAC group contribution models for the total pressures P and the excess molar Gibbs energies GE.

Keywords: disquac model, dortmund UNIFAC model, excess molar Gibbs energies GE, VLE

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Authors: Mathieu Bourdeau, Philippe Basset, Julien Waeytens, Elyes Nefzaoui

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As residential buildings account for a third of the overall energy consumption and greenhouse gas emissions in Europe, building energy modeling is an essential tool to reach energy efficiency goals. In the energy modeling process, calibration is a mandatory step to obtain accurate and reliable energy simulations. Nevertheless, the comparison between simulation results and the actual building energy behavior often highlights a significant performance gap. The literature discusses different origins of energy performance gaps, from building design to building operation. Then, building operation description in energy models, especially energy usages and users’ behavior, plays an important role in the reliability of simulations but is also the most accessible target for post-occupancy energy management and optimization. Therefore, the present study aims to discuss results on the calibration ofresidential building energy models using real operation data. Data are collected through a sensor network of more than 180 sensors and advanced energy meters deployed in three collective residential buildings undergoing major retrofit actions. The sensor network is implemented at building scale and in an eight-apartment sample. Data are collected for over one year and half and coverbuilding energy behavior – thermal and electricity, indoor environment, inhabitants’ comfort, occupancy, occupants behavior and energy uses, and local weather. Building energy simulations are performed using a physics-based building energy modeling software (Pleaides software), where the buildings’features are implemented according to the buildingsthermal regulation code compliance study and the retrofit project technical files. Sensitivity analyses are performed to highlight the most energy-driving building features regarding each end-use. These features are then compared with the collected post-occupancy data. Energy-driving features are progressively replaced with field data for a step-by-step calibration of the energy model. Results of this study provide an analysis of energy performance gap on an existing residential case study under deep retrofit actions. It highlights the impact of the different building features on the energy behavior and the performance gap in this context, such as temperature setpoints, indoor occupancy, the building envelopeproperties but also domestic hot water usage or heat gains from electric appliances. The benefits of inputting field data from an extensive instrumentation campaign instead of standardized scenarios are also described. Finally, the exhaustive instrumentation solution provides useful insights on the needs, advantages, and shortcomings of the implemented sensor network for its replicability on a larger scale and for different use cases.

Keywords: calibration, building energy modeling, performance gap, sensor network

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Authors: Zhidong Zhang

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This study established a mixed method model in assessing statistics learning with Bayesian network models. There are three variants in exploratory sequential designs. There are three linked steps in one of the designs: qualitative data collection and analysis, quantitative measure, instrument, intervention, and quantitative data collection analysis. The study used a scoring model of analysis of variance (ANOVA) as a content domain. The research study is to examine students’ learning in both semantic and performance aspects at fine grain level. The ANOVA score model, y = α+ βx1 + γx1+ ε, as a cognitive task to collect data during the student learning process. When the learning processes were decomposed into multiple steps in both semantic and performance aspects, a hierarchical Bayesian network was established. This is a theory-driven process. The hierarchical structure was gained based on qualitative cognitive analysis. The data from students’ ANOVA score model learning was used to give evidence to the hierarchical Bayesian network model from the evidential variables. Finally, the assessment results of students’ ANOVA score model learning were reported. Briefly, this was a mixed method research design applied to statistics learning assessment. The mixed methods designs expanded more possibilities for researchers to establish advanced quantitative models initially with a theory-driven qualitative mode.

Keywords: exploratory sequential design, ANOVA score model, Bayesian network model, mixed methods research design, cognitive analysis

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Authors: Alvaro Armenta-Ramade, Arturo Serrano-Santoyo, Veronica Rojas-Mendizabal, Roberto Conte-Galvan

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The availability and affordability of commercial off-the-shell products have brought a major impetus in the development of university projects related to the design, construction and launching of small satellites on a global scale. Universities in emerging economies as well as in least developed countries have been able to develop prototypes of small satellites (cubesats and cansats) with limited budgets. The experience gained in the development of small satellites gives rise to capacity building for designing more complex aerospace systems. This trend has significantly increased the pace and number of aerospace university projects around the world. In the case of Mexico, projects funded by different agencies have been very effective in accelerating the capacity building and technology transfer initiatives in the aerospace ecosystem. However, many of this initiatives have centered their efforts in technology development matters with minimum or no considerations of key regulatory issues related to frequency assignment, management and licensing, as well as launching requirements and measures of mitigation of space debris. These regulatory concerns are fundamental to accomplish successful missions that take into account the complete value chain of an aerospace project. The purpose of this paper is to develop a regulatory framework to support the efforts of educational institutions working on the development of small satellites in Mexico. We base our framework on recommendations from the International Telecommunications Union (ITU), the United Nations Office for Outer Space Affairs (UNOOSA) and other major actors of the Mexican regulatory ecosystem. In order to develop an integrated and cohesive framework, we draw on complexity science to identify the agents, their role and interactions. Our goal is to create a guiding instrument available both in print and online that can also be used in other regions of the world

Keywords: capacity building, complexity science, cubesats, space regulations, small satellites

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Authors: Panwasn Mahalawalert

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The purposes of this research were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2013 (SWUSAT). SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was analyzed in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of DIF and DTF analysis for all 10 tests in year 2013. Gender was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF is between 6.67% - 60%. There are 5 tests that most of items favors female group and 2 tests that most of items favors male group. There are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small level.

Keywords: aptitude test, differential item functioning, differential test functioning, educational measurement

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Authors: J. F. Reinoso-Gordo, F. J. Ariza-López, A. Mozas-Calvache, J. L. García-Balboa, S. Eddargani

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The DEMs quality may depend on several factors such as data source, capture method, processing type used to derive them, or the cell size of the DEM. The two most important capture methods to produce regional-sized DEMs are photogrammetry and LiDAR; DEMs covering entire countries have been obtained with these methods. The quality of these DEMs has traditionally been evaluated by the national cartographic agencies through punctual sampling that focused on its vertical component. For this type of evaluation there are standards such as NMAS and ASPRS Positional Accuracy Standards for Digital Geospatial Data. However, it seems more appropriate to carry out this evaluation by means of a method that takes into account the superficial nature of the DEM and, therefore, its sampling is superficial and not punctual. This work is part of the Research Project "Functional Quality of Digital Elevation Models in Engineering" where it is necessary to control the quality of a DEM whose data source is an experimental LiDAR flight with a density of 14 points per square meter to which we call Point Cloud Product (PCpro). In the present work it is described the capture data on the ground and the postprocessing tasks until getting the point cloud that will be used as reference (PCref) to evaluate the PCpro quality. Each PCref consists of a patch 50x50 m size coming from a registration of 4 different scan stations. The area studied was the Spanish region of Navarra that covers an area of 10,391 km2; 30 patches homogeneously distributed were necessary to sample the entire surface. The patches have been captured using a Leica BLK360 terrestrial laser scanner mounted on a pole that reached heights of up to 7 meters; the position of the scanner was inverted so that the characteristic shadow circle does not exist when the scanner is in direct position. To ensure that the accuracy of the PCref is greater than that of the PCpro, the georeferencing of the PCref has been carried out with real-time GNSS, and its accuracy positioning was better than 4 cm; this accuracy is much better than the altimetric mean square error estimated for the PCpro (<15 cm); The kind of DEM of interest is the corresponding to the bare earth, so that it was necessary to apply a filter to eliminate vegetation and auxiliary elements such as poles, tripods, etc. After the postprocessing tasks the PCref is ready to be compared with the PCpro using different techniques: cloud to cloud or after a resampling process DEM to DEM.

Keywords: data quality, DEM, LiDAR, terrestrial laser scanner, accuracy

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Authors: Yemisrach Mihertu Amsale

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The purpose of this study was to compare burnout and coping strategies between HIV counselors in face to face and online counseling settings in Addis Ababa. The study was mixed approach design that was quantitative and qualitative. For the quantitative data the participants involved in this study included 64 face to face and 47 online HIV counselors in both counseling settings. In addition, 23 participants were involved to offer qualitative data from both counseling settings. For the purpose of gathering the quantitative data, the instruments, namely, demographic questionnaire, Maslach Burnout Inventory and the COPE questionnaire, were used to gather quantitative data. Qualitative data was also gathered in the FGD Guide and Interview Guide. Thus, this study revealed that HIV counselors in online counseling settings scored high on emotional exhaustion, depersonalization and low in personal accomplishment dimensions of burnout as compared to HIV counselors in face to face setting and the difference was statistically significant in emotional exhaustion and personal accomplishment, but there was no a significant difference on depersonalization dimension of burnout between the two groups. In addition, the present study revealed a statistically significant difference on problem focused coping strategy between the two groups and yet for on the emotion focused coping strategy the difference was not statistically significant. Statistically negative correlation was observed between some demographic variables such as age with emotional exhaustion and depersonalization dimensions of burnout; years of experiences and personal accomplishment dimension of burnout. A statistically positive correlation was also observed between average number of clients served per day and emotional exhaustion. Sex was having a statistically positive correlation with coping strategy. Lastly, a significant positive correlation was also observed in the emotional exhaustion dimension of the burnout and the emotional focused coping strategy. Generally, this study has shown that HIV counselors suffer from moderate to high level of burnout. Based on the findings, conclusions were made and recommendations were forwarded.

Keywords: counseling, burnout management, psychological, behavioral sciences

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Authors: Jamuna Loganath

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Education is the key to the development of any individual’s personality. Today’s students will be tomorrow’s citizens of the global society. The education of the student is the edifice on which his/her future will be built. Schools therefore should provide an all-round development of students so as to foster a healthy society. The behaviors and the attitude of the students in school play an essential role for the success of the education process. Frequent reports of misbehaviors such as clowning, harassing classmates, verbal insults are becoming common in schools today. If this issue is left unattended, it may develop a negative attitude and increase the delinquent behavior. So, the need of the hour is to find a solution to this problem. To solve this issue, it is important to monitor the students’ behaviors in school and give necessary feedback and mentor them to develop a positive attitude and help them to become a successful grownup. Nevertheless, measuring students’ behavior and attitude is extremely challenging. None of the present technology has proven to be effective in this measurement process because actions, reactions, interactions, response of the students are rarely used in the course of the data due to complexity. The purpose of this proposal is to recommend an effective supervising system after carrying out a feasibility study by measuring the behavior of the Students. This can be achieved by equipping schools with CCTV cameras. These CCTV cameras installed in various schools of the world capture the facial expressions and interactions of the students inside and outside their classroom. The real time raw videos captured from the CCTV can be uploaded to the cloud with the help of a network. The video feeds get scooped into various nodes in the same rack or on the different racks in the same cluster in Hadoop HDFS. The video feeds are converted into small frames and analyzed using various Pattern recognition algorithms and MapReduce algorithm. Then, the video frames are compared with the bench marking database (good behavior). When misbehavior is detected, an alert message can be sent to the counseling department which helps them in mentoring the students. This will help in improving the effectiveness of the education process. As Video feeds come from multiple geographical areas (schools from different parts of the world), BIG DATA helps in real time analysis as it analyzes computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It also analyzes data that can’t be analyzed by traditional software applications such as RDBMS, OODBMS. It has also proven successful in handling human reactions with ease. Therefore, BIG DATA could certainly play a vital role in handling this issue. Thus, effectiveness of the education process can be enhanced with the help of video analytics using the latest BIG DATA technology.

Keywords: big data, cloud, CCTV, education process

Procedia PDF Downloads 240

Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Ismail Seçer, Kerime Meryem Dereli̇oğlu

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This study is conducted to determine the effects of group counseling program on secondary school 9th grade students’ assertiveness skills. The study group was formed of 100 students who have received education in Erzurum Kültür Elementary School in 2015-2016 education years. RAE-Rathus Assertiveness Schedule developed by Voltan Acar was applied on this group to gather data. 40 students who got lower grades from the inventory were divided randomly into experimental and control groups. Each group is formed of 20 students. Group counseling program was carried out on the experimental group to improve the students’ assertiveness skills for 8 weeks. Single-way and two-way analysis of covariance (ANCOVA) were used in the analysis of the data. The data was analyzed by using the SPSS 19.00. The results of the study show that assertiveness skills of the students who participate in the group counseling program increased meaningfully compared to the control group and pre-experiment. Besides, it was determined that the change observed in the experimental group occurred separately from the age and socio-economic level variables, and it was determined with the monitoring test applied after four months that this affect was continued. According to this result, it can be said that the applied group counseling program is an effective means to improve the assertiveness skills of secondary school students.

Keywords: high school, assertiveness, assertiveness inventory, assertiveness education

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Authors: Jihad S. Daba, J. P. Dubois, Yvette Antar

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With the exponential growth of cellular users, a new generation of cellular networks is needed to enhance the required peak data rates. The co-channel interference between neighboring base stations inhibits peak data rate increase. To overcome this interference, multi-cell cooperation known as coordinated multipoint transmission is proposed. Such a solution makes use of multiple-input-multiple-output (MIMO) systems under two different structures: Micro- and macro-diversity. In this paper, we study the capacity and bit error rate in cellular networks using MIMO technology. We analyse both micro- and macro-diversity schemes and develop a hybrid model that switches between macro- and micro-diversity in the case of hard handoff based on a cut-off range of signal-to-noise ratio values. We conclude that our hybrid switched micro-macro MIMO system outperforms classical MIMO systems at the cost of increased hardware and software complexity.

Keywords: cooperative multipoint transmission, ergodic capacity, hard handoff, macro-diversity, micro-diversity, multiple-input-multiple output systems, orthogonal frequency division multiplexing

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Seval Kizildag, Ozge Can Aran

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The effective intervention of counselors’ in conflict between spouses may be effective in increasing the quality of marital relationship. At this point, it is necessary for counselors to consider their own value systems at first and then reflect this correctly to the counseling process. For this reason, it is primarily important to determine the needs of counselors. Starting from this point of view, in this study, it is aimed to reveal the perspective of counselor candidates about the universal values in marriage relation. The study group of the survey was formed by sampling, which is one of the prospective sampling methods. As a criterion being a candidate for counseling area and having knowledge of the concepts of the Marriage and Family Counseling course is based, because, that candidate students have a comprehensive knowledge of the field and that students have mastered the concepts of marriage and family counseling will strengthen the findings of this study. For this reason, 61 counselor candidates, 32 (52%) female and 29 (48%) male counselor candidates, who were about to graduate from a university in south-east Turkey and who took a Marriage and Family Counseling course, voluntarily participated in the study. The average age of counselor candidates’ is 23. At the same time, 70 % of the parents of these candidates brought about their marriage through arranged marriage, 13% through flirting, 8% by relative marriage, 7% through friend circles and 2% by custom. The data were collected through Demographic Information Form and a form titled ‘Universal Values Form in Marriage’ which consists of six questions prepared by researchers. After the data were transferred to the computer, necessary statistical evaluations were made on the data. The qualitative data analysis was used on the data which was obtained in the study. The universal values which include six basic values covering trustworthiness, respect, responsibility, fairness, caring, citizenship, determined under the name as ‘six pillar of character’ are used as base and frequency values of the data were calculated trough content analysis. According to the findings of the study, while the value which most students find the most important value in marriage relation is being reliable, the value which they find the least important is to have citizenship consciousness. Also in this study, it is found out that counselor candidates associate the value of being trustworthiness ‘loyalty’ with (33%) as the highest in terms of frequency, the value of being respect ‘No violence’ with (23%), the value of responsibility ‘in the context of gender roles and spouses doing their owns’ with (35%) the value of being fairness ‘impartiality’ with (25%), the value of being caring ‘ being helpful’ with (25%) and finally as to the value of citizenship ‘love of country’ with (14%) and’ respect for the laws ‘ with (14%). It is believed that these results of the study will contribute to the arrangements for the development of counseling skills for counselor candidates regarding value in marriage and family counseling curricula.

Keywords: caring, citizenship, counselor candidate, fairness, marriage relationship, respect, responsibility, trustworthiness, value system

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Raluca Ana Maria Viziteu, Anna Prudnikova

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Industrial control systems (ICS) have received much attention in recent years due to the convergence of information technology (IT) and operational technology (OT) that has increased the interdependence of safety and security issues to be considered. These issues require ICS-tailored solutions. That led to the need to creation of a methodology for supporting ICS device manufacturers and system integrators in carrying out threat modeling of embedded ICS devices in a way that guarantees the quality of the identified threats and minimizes subjectivity in the threat identification process. To research, the possibility of creating such a methodology, a set of existing standards, regulations, papers, and publications related to threat modeling in the ICS sector and other sectors was reviewed to identify various existing methodologies and methods used in threat modeling. Furthermore, the most popular ones were tested in an exploratory phase on a specific PLC device. The outcome of this exploratory phase has been used as a basis for defining specific characteristics of ICS embedded devices and their deployment scenarios, identifying the factors that introduce subjectivity in the threat modeling process of such devices, and defining metrics for evaluating the minimum quality requirements of identified threats associated to the deployment of the devices in existing infrastructures. Furthermore, the threat modeling methodology was created based on the previous steps' results. The usability of the methodology was evaluated through a set of standardized threat modeling requirements and a standardized comparison method for threat modeling methodologies. The outcomes of these verification methods confirm that the methodology is effective. The full paper includes the outcome of research on different threat modeling methodologies that can be used in OT, their comparison, and the results of implementing each of them in practice on a PLC device. This research is further used to build a threat modeling methodology tailored to OT environments; a detailed description is included. Moreover, the paper includes results of the evaluation of created methodology based on a set of parameters specifically created to rate threat modeling methodologies.

Keywords: device manufacturers, embedded devices, industrial control systems, threat modeling

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Authors: Mehry Daraei

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The goal of this study was to survey the components and affective factors on organizational commitment of Islamic Azad university Khoramabad Baranch’s faculty members. The research method was correlation by causal modeling and data were gathered by questionnaire. Statistical society consisted of 147 faculty members in Islamic Azad University Khoramabad Branch and sample size was determined as 106 persons by Morgan’s sample table that were selected by class sampling. Correlation test, T-single group test and path analysis test were used for analysis of data. Data were analyzed by Lisrel software. The results showed that organizational corporate was the most effective element on organizational commitment and organizational corporate, experience work and organizational justice were only in direct relation with organizational commitment. Also, job security had direct and indirect effect on OC. Job security had effect on OC by gender. Gender variable had direct and indirect effect on OC. Gender had effect on OC by organizational corporate. Job opportunities out of university also had direct and indirect effect on OC, which means job opportunities had indirect effect on OC by organizational corporate.

Keywords: organization, commitment, job security, Islamic Azad University

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

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Authors: Muhammad Faizan Rehman Qureshi, Ahmed Al-Kaisy

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Accurate estimation of traffic loads is critical for pavement and bridge design, among other transportation applications. Given the disproportional impact of heavier axle loads on pavement and bridge structures, truck and heavy vehicle traffic is expected to be a major determinant of traffic load estimation. Further, heavy vehicle traffic is also a major input in transportation planning and economic studies. The traditional method for estimating heavy vehicle traffic primarily relies on AADT estimation using Monthly Day of the Week (MDOW) adjustment factors as well as the percent heavy vehicles observed using statewide data collection programs. The MDOW factors are developed using daily and seasonal (or monthly) variation patterns for total traffic, consisting predominantly of passenger cars and other smaller vehicles. Therefore, while using these factors may yield reasonable estimates for total traffic (AADT), such estimates may involve a great deal of approximation when applied to heavy vehicle traffic. This research aims at assessing the approximation involved in estimating heavy vehicle traffic using MDOW adjustment factors for total traffic (conventional approach) along with three other methods of using MDOW adjustment factors for total trucks (class 5-13), combination-unit trucks (class 8-13), as well as adjustment factors for each vehicle class separately. Results clearly indicate that the conventional method was outperformed by the other three methods by a large margin. Further, using the most detailed and data intensive method (class-specific adjustment factors) does not necessarily yield a more accurate estimation of heavy vehicle traffic.

Keywords: traffic loads, heavy vehicles, truck traffic, adjustment factors, traffic data collection

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Authors: Soukat Kumar Das

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Rapid industrialization and increase in population has resulted in the scarcity of suitable ground conditions. It has driven the need of ground improvement by means of reinforcement with geosynthetics with the minimum possible settlement and with maximum possible safety. Prestressing the geosynthetics offers an economical yet safe method of gaining the goal. Commercially available software PLAXIS 3D has made the analysis of prestressed geosynthetics simpler with much practical simulations of the ground. Attempts have been made so far to analyse the effect of prestressing geosynthetics and the effect of interference of footing on Unreinforced (UR), Geogrid Reinforced (GR) and Prestressed Geogrid Reinforced (PGR) soil on the load bearing capacity and the settlement characteristics of prestressed geogrid reinforced soil using the numerical analysis by using the software PLAXIS 3D. The results of the numerical analysis have been validated and compared with those given in the referred paper. The results have been found to be in very good agreement with those of the actual field values with very small variation. The GR soil has been found to be improve the bearing pressure 240 % whereas the PGR soil improves it by almost 500 % for 1mm settlement. In fact, the PGR soil has enhanced the bearing pressure of the GR soil by almost 200 %. The settlement reduction has also been found to be very significant as for 100 kPa bearing pressure the settlement reduction of the PGR soil has been found to be about 88 % with respect to UR soil and it reduced to up to 67 % with respect to GR soil. The prestressing force has resulted in enhanced reinforcement mechanism, resulting in the increased bearing pressure. The deformation at the geogrid layer has been found to be 13.62 mm for GR soil whereas it decreased down to mere 3.5 mm for PGR soil which certainly ensures the effect of prestressing on the geogrid layer. The parameter Improvement factor or conventionally known as Bearing Capacity Ratio for different settlements and which depicts the improvement of the PGR with respect to UR and GR soil and the improvement of GR soil with respect to UR soil has been found to vary in the range of 1.66-2.40 in the present analysis for GR soil and was found to be vary between 3.58 and 5.12 for PGR soil with respect to UR soil. The effect of prestressing was also observed in case of two interfering square footings. The centre to centre distance between the two footings (SFD) was taken to be B, 1.5B, 2B, 2.5B and 3B where B is the width of the footing. It was found that for UR soil the improvement of the bearing pressure was up to 1.5B after which it remained almost same. But for GR soil the zone of influence rose up to 2B and for PGR it further went up to 2.5B. So the zone of interference for PGR soil has increased by 67% than Unreinforced (UR) soil and almost 25 % with respect to GR soil.

Keywords: bearing, geogrid, prestressed, reinforced

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

Procedia PDF Downloads 101

Authors: K. Myška, L. Pilařová

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The article deals with possibilities of using cheap mobile devices with the combination of free or open source software tools as an alternative to professional hardware and software equipment. Especially in social work, it is important to find cheap yet functional solution that can compete with complex but expensive solutions for digitizing paper materials. Our research was focused on the analysis of cheap and affordable solutions for digitizing the most frequently used paper materials that are being commonly used by terrain workers in social work. We used comparative analysis as a research method. Social workers need to process data from paper forms quite often. It is still more affordable, time and cost-effective to use paper forms to get feedback in many cases. Collecting data from paper quizzes and questionnaires can be done with the help of professional scanners and software. These technologies are very powerful and have advanced options for digitizing and processing digitized data, but are also very expensive. According to results of our study, the combination of open source software and mobile phone or cheap scanner can be considered as a cost-effective alternative to professional equipment.

Keywords: digitalization, e-learning, mobile devices, questionnaire

Procedia PDF Downloads 152

Authors: Dinesha Siriwardhane, Indralal De Silva, Sampath Amaratunge

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Objective of this study is to explore the recent trends, patterns and the structural changes in the labour migration from Sri Lanka to Middle East countries and to discuss the possible impacts of those changes on the remittance flow. Study uses secondary data published by Sri Lanka Bureau of Foreign Employment and Central Bank. Thematic analysis of the secondary data revealed that the migration for labour has increased rapidly during past decades. Parallel with that the gender and the skill composition of the migration flow has been changing. Similarly, the destinations for male migration have changed over the period. These show positive implications on the international remittance receipts to the country.

Keywords: migration, middle east, Sri Lanka, social sciences

Procedia PDF Downloads 399

Authors: Ying Chern Yeoh, Mark J. Forshaw

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Although hypnosis has been widely endorsed in Europe since 1950s, it was still viewed as a typically new therapy in Asia. There are very little findings regarding hypnosis in Asian countries, especially in culturally diverse countries such as Malaysia. The knowledge of the beneficial effects of hypnosis was not widespread to the public, however knowledge of the negative effects was frequently being highlighted. Therefore, the acceptance of hypnosis as a new effective health treatment can be a challenge in Malaysia. Recognising Malaysian’s perception, belief and attitude towards hypnosis could increase the public awareness of hypnosis, which in turn will alter their misconception and increase acceptance of hypnosis as an effective therapy. Eight individuals (N = 8) from the general public with different background, ethnicity (Malays, Chinese and Indians) and religion (Islamic, Buddhism, Hinduism, Taoism, Christianity, free-thinker) and two local experienced practitioners with minimum of five years experiences (N = 2) were being interviewed to determine their views, beliefs and level of acceptance towards hypnosis. Semi-structured interviews were conducted, transcribed with pseudonyms and analyzed by using Interpretative Phenomenological Analysis. The three emergent themes were illustrated under the captions of ‘traditional vs mainstream’, ‘myths vs truth’, and ‘dissemination and public awareness’. The finding suggested that individual knowledge and personal experience primarily influenced people’s level of acceptance towards hypnosis as a beneficial health treatment, rather than the diversity of cultural and religious background. Subsequent findings regarding hypnosis and the effort of promoting it will provide the society an opportunity to increase public education and health awareness. Several associations had started to advance its development by organizing conferences and setting up therapeutic centers. However, health promotion on hypnosis is yet to be conducted to raise public awareness of its beneficial effects. By requesting for hypnosis to be included as a subject in medical education and psychology curriculum and formatting it under Ministry of Health’s legislation body might enhance the knowledge of hypnosis for Malaysian as one of the health intervention in the future.

Keywords: awareness, hypnosis, intervention, Malaysian, promotion

Procedia PDF Downloads 158

Authors: Froogh Shooshtaryzadeh, Pramod Pandey

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The present study investigates place and manner harmony in typically developing (TD) children and children with phonological disorder (PD) who are acquiring Farsi as their first language. Five TD and five PD children are examined regarding their place and manner harmony patterns. Data is collected through a Picture-Naming Task using 132 pictures of different items designed to elicit the production of 132 different words. The examination of the data has indicated some similarities and differences in harmony patterns in PD and TD children. Moreover, the results of this study on the place and manner harmony have illustrated some differences with the results of the preceding studies on languages other than Farsi. The results of this study are discussed and compared with results from other studies. Optimality Theory is employed to explain some of the findings of this study.

Keywords: place harmony, manner harmony, phonological development, Farsi

Procedia PDF Downloads 313