Search results for: support vector machine learning.

Authors: R. Abdulrahman, A. Eardley, A. Soliman

Abstract:

The proliferation in the development of mobile learning (m-learning) has played a vital role in the rapidly growing electronic learning market. This relatively new technology can help to encourage the development of in learning and to aid knowledge transfer a number of areas, by familiarizing students with innovative information and communications technologies (ICT). M-learning plays a substantial role in the deployment of learning methods for nursing students by using the Internet and portable devices to access learning resources ‘anytime and anywhere’. However, acceptance of m-learning by students is critical to the successful use of m-learning systems. Thus, there is a need to study the factors that influence student’s intention to use m-learning. This paper addresses this issue. It outlines the outcomes of a study that evaluates the unified theory of acceptance and use of technology (UTAUT) model as applied to the subject of user acceptance in relation to m-learning activity in nurse education. The model integrates the significant components across eight prominent user acceptance models. Therefore, a standard measure is introduced with core determinants of user behavioural intention. The research model extends the UTAUT in the context of m-learning acceptance by modifying and adding individual innovativeness (II) and quality of service (QoS) to the original structure of UTAUT. The paper goes on to add the factors of previous experience (of using mobile devices in similar applications) and the nursing students’ readiness (to use the technology) to influence their behavioural intentions to use m-learning. This study uses a technique called ‘convenience sampling’ which involves student volunteers as participants in order to collect numerical data. A quantitative method of data collection was selected and involves an online survey using a questionnaire form. This form contains 33 questions to measure the six constructs, using a 5-point Likert scale. A total of 42 respondents participated, all from the Nursing Institute at the Armed Forces Hospital in Saudi Arabia. The gathered data were then tested using a research model that employs the structural equation modelling (SEM), including confirmatory factor analysis (CFA). The results of the CFA show that the UTAUT model has the ability to predict student behavioural intention and to adapt m-learning activity to the specific learning activities. It also demonstrates satisfactory, dependable and valid scales of the model constructs. This suggests further analysis to confirm the model as a valuable instrument in order to evaluate the user acceptance of m-learning activity.

Keywords: Mobile learning, nursing institute, unified theory of acceptance and use of technology model.

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Authors: A. Elbyed

Abstract:

Nowadays, ontologies are used for achieving a common understanding within a user community and for sharing domain knowledge. However, the de-centralized nature of the web makes indeed inevitable that small communities will use their own ontologies to describe their data and to index their own resources. Certainly, accessing to resources from various ontologies created independently is an important challenge for answering end user queries. Ontology mapping is thus required for combining ontologies. However, mapping complete ontologies at run time is a computationally expensive task. This paper proposes a system in which mappings between concepts may be generated dynamically as the concepts are encountered during user queries. In this way, the interaction itself defines the context in which small and relevant portions of ontologies are mapped. We illustrate application of the proposed system in the context of Technology Enhanced Learning (TEL) where learners need to access to learning resources covering specific concepts.

Keywords: Resources query, ontologies, ontology mapping, similarity measures, semantic web, e-learning.

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

Abstract:

Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: Feature selection, mass spectrometry, biomarker discovery, cancer.

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Authors: Jingwen Shan

Abstract:

In order to explore the key factors affecting the robot program learning intention of school students, this study takes the technology acceptance model as the theoretical basis and invites 167 students from Jiading District of Shanghai as the research subjects. In the robot course, the model of school students on their learning behavior is constructed. By verifying the causal path relationship between variables, it is concluded that teachers can enhance students’ perceptual usefulness to robotics courses by enhancing subjective norms, entertainment perception, and reducing technical anxiety, such as focusing on the gradual progress of programming and analyzing learner characteristics. Students can improve perceived ease of use by enhancing self-efficacy. At the same time, robot hardware designers can optimize in terms of entertainment and interactivity, which will directly or indirectly increase the learning intention of the robot course. By changing these factors, the learning behavior of primary and secondary school students can be more sustainable.

Keywords: TAM, learning behavior intentions, robot courses, primary and secondary school students.

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Authors: Neilal A. Isaac, Madison Edwards, Kirthana Sugunathevan, Mohan Kumar

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The COVID-19 pandemic challenged the education system and forced medical schools to transition to online learning. With this transition, one of the major concerns for students and educators was to ensure that Physical Examination (PE) skills were still being mastered. Thus, the formative peer assessment model was designed to enhance the learning of PE skills during the COVID-19 pandemic in the online learning landscape. Year 1 and year 2 students enrolled in clinical skills courses at the University of Medicine and Health Sciences, St. Kitts were asked to record themselves demonstrating PE skills with a healthy patient volunteer after every skills class. Each student was assigned to exchange feedback with one peer in the course. At the end of the first two semesters of this learning activity, a cross-sectional survey was conducted for the two cohorts of year-1 and year-2 students. The year-1 cohorts most frequently rated the peer assessment exercise as 4 on a 5-point Likert scale, with a mean score of 3.317 [2.759, 3.875]. The year-2 cohorts most frequently rated the peer assessment exercise as 4 on a 5-point Likert scale, with a mean score of 3.597 [2.978, 4.180]. Students indicated that guidance from faculty, flexible deadlines, and detailed and timely feedback from peers were areas for improvement in this process.

Keywords: COVID-19 pandemic, distant learning, online medical education, peer assessment, physical examination.

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Authors: Elena Ezhova, Vadim Mottl, Olga Krasotkina

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The problem of estimating time-varying regression is inevitably concerned with the necessity to choose the appropriate level of model volatility - ranging from the full stationarity of instant regression models to their absolute independence of each other. In the stationary case the number of regression coefficients to be estimated equals that of regressors, whereas the absence of any smoothness assumptions augments the dimension of the unknown vector by the factor of the time-series length. The Akaike Information Criterion is a commonly adopted means of adjusting a model to the given data set within a succession of nested parametric model classes, but its crucial restriction is that the classes are rigidly defined by the growing integer-valued dimension of the unknown vector. To make the Kullback information maximization principle underlying the classical AIC applicable to the problem of time-varying regression estimation, we extend it onto a wider class of data models in which the dimension of the parameter is fixed, but the freedom of its values is softly constrained by a family of continuously nested a priori probability distributions.

Keywords: Time varying regression, time-volatility of regression coefficients, Akaike Information Criterion (AIC), Kullback information maximization principle.

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Authors: Priyadharshini Ahrumugam

Abstract:

Cooperative groups through much research have been recognized to churn remarkable achievements instead of solitary or individualistic efforts. Based on Johnson and Johnson’s model of cooperative learning, the five key components of cooperation are positive interdependence, face-to-face promotive interaction, individual accountability, social skills, and group processing. In 2011, the Malaysian Ministry of Higher Education (MOHE) introduced the Holistic Student Development policy with the aim to develop morally sound individuals equipped with lifelong learning skills. The Community Service project was included in the improvement initiative. The purpose of this study is to assess the relationship of team-based learning in facilitating particularly students’ positive interdependence and face-to-face promotive interaction. The research methods involve in-depth interviews with the team leaders and selected team members, and a content analysis of the undergraduate students’ reflective journals. A significant positive relationship was found between students’ progressive outlook towards teamwork and the highlighted two components. The key findings show that students have gained in their individual learning and work results through teamwork and interaction with other students. The inclusion of Community Service as a MOHE subject resonates with cooperative learning methods that enhances supportive relationships and develops students’ social skills together with their professional skills.

Keywords: Community service, cooperative learning, positive interdependence, teamwork.

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Authors: Guojian Cheng, Tianshi Liu, Jiaxin Han, Zheng Wang

Abstract:

The competitive learning is an adaptive process in which the neurons in a neural network gradually become sensitive to different input pattern clusters. The basic idea behind the Kohonen-s Self-Organizing Feature Maps (SOFM) is competitive learning. SOFM can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappings are topology preserving, feature mappings and probability distribution approximation of input patterns. To overcome some limitations of SOFM, e.g., a fixed number of neural units and a topology of fixed dimensionality, Growing Self-Organizing Neural Network (GSONN) can be used. GSONN can change its topological structure during learning. It grows by learning and shrinks by forgetting. To speed up the training and convergence, a new variant of GSONN, twin growing cell structures (TGCS) is presented here. This paper first gives an introduction to competitive learning, SOFM and its variants. Then, we discuss some GSONN with fixed dimensionality, which include growing cell structures, its variants and the author-s model: TGCS. It is ended with some testing results comparison and conclusions.

Keywords: Artificial neural networks, Competitive learning, Growing cell structures, Self-organizing feature maps.

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Authors: Jung–Min Yang

Abstract:

In this paper, fault recovery for parallel interconnected asynchronous sequential machines is studied. An adversarial input can infiltrate into one of two submachines comprising parallel composition of the considered asynchronous sequential machine, causing an unauthorized state transition. The control objective is to elucidate the condition for the existence of a corrective controller that makes the closed-loop system immune against any occurrence of adversarial inputs. In particular, an efficient existence condition is presented that does not need the complete modeling of the interconnected asynchronous sequential machine.

Keywords: Asynchronous sequential machines, parallel composition, corrective control, fault tolerance.

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Authors: Yoshitaka Fujiwara, Jun-ichirou Fukushima, Yasunari Maeda

Abstract:

Keys to high-quality face-to-face education are ensuring flexibility in the way lectures are given, and providing care and responsiveness to learners. This paper describes a face-to-face education support system that is designed to raise the satisfaction of learners and reduce the workload on instructors. This system consists of a lecture adaptation assistance part, which assists instructors in adapting teaching content and strategy, and a Q&A assistance part, which provides learners with answers to their questions. The core component of the former part is a “learning achievement map", which is composed of a Bayesian network (BN). From learners- performance in exercises on relevant past lectures, the lecture adaptation assistance part obtains information required to adapt appropriately the presentation of the next lecture. The core component of the Q&A assistance part is a case base, which accumulates cases consisting of questions expected from learners and answers to them. The Q&A assistance part is a case-based search system equipped with a search index which performs probabilistic inference. A prototype face-to-face education support system has been built, which is intended for the teaching of Java programming, and this approach was evaluated using this system. The expected degree of understanding of each learner for a future lecture was derived from his or her performance in exercises on past lectures, and this expected degree of understanding was used to select one of three adaptation levels. A model for determining the adaptation level most suitable for the individual learner has been identified. An experimental case base was built to examine the search performance of the Q&A assistance part, and it was found that the rate of successfully finding an appropriate case was 56%.

Keywords: Bayesian network, face-to-face education, lecture adaptation, Q&A assistance.

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Authors: Nalinee Sophatsathit

Abstract:

This research focuses on the use of a recommender system in decision support by means of a used car dealer case study in Bangkok Metropolitan. The goal is to develop an effective used car purchasing system for dealers based on the above premise. The underlying principle rests on content-based recommendation from a set of usability surveys. A prototype was developed to conduct buyers- survey selected from 5 experts and 95 general public. The responses were analyzed to determine the mean and standard deviation of buyers- preference. The results revealed that both groups were in favor of using the proposed system to assist their buying decision. This indicates that the proposed system is meritorious to used car dealers.

Keywords: Recommender Systems, Decision Support, Content- Based Recommendation, used car dealer.

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Authors: D. Del Corso, A. Tartaglia, E. Tresso, M. Cambiolo, L. Forno, G. Morrone

Abstract:

A key requirement for e-learning materials is reusability and interoperability, that is the possibility to use at least part of the contents in different courses, and to deliver them trough different platforms. These features make possible to limit the cost of new packages, but require the development of material according to proper specifications. SCORM (Sharable Content Object Reference Model) is a set of guidelines suitable for this purpose. A specific adaptation project has been started to make possible to reuse existing materials. The paper describes the main characteristics of SCORM specification, and the procedure used to modify the existing material.

Keywords: SCORM, e-learning, standard, educational effectiveness, assessment, methodology, open access.

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Authors: Vassilios Moussas, Dimos N. Pantazis, Panagiotis Stratakis

Abstract:

The aim of this paper is to present the optimization methodology developed in the frame of a Coastal Transport Information System. The system will be used for the effective design of coastal transportation lines and incorporates subsystems that implement models, tools and techniques that may support the design of improved networks. The role of the optimization and decision subsystem is to provide the user with better and optimal scenarios that will best fulfill any constrains, goals or requirements posed. The complexity of the problem and the large number of parameters and objectives involved led to the adoption of an evolutionary method (Genetic Algorithms). The problem model and the subsystem structure are presented in detail, and, its support for simulation is also discussed.

Keywords: Coastal transport, modeling, optimization.

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Authors: D. Almazo, M. Toledo, C. Rodríguez

Abstract:

This work presents a methodology for the design and manufacture of propellers oriented to the experimental verification of theoretical results based on the combined model. The design process begins by using algorithms in Matlab which output data contain the coordinates of the points that define the blade airfoils, in this case the NACA 6512 airfoil was used. The modeling for the propeller blade was made in NX7, through the imported files in Matlab and with the help of surfaces. Later, the hub and the clamps were also modeled. Finally, NX 7 also made possible to create post-processed files to the required machine. It is possible to find the block of numbers with G & M codes about the type of driver on the machine. The file extension is .ptp. These files made possible to manufacture the blade, and the hub of the propeller.

Keywords: Airfoil, CAM, manufacturing, mathematical algorithm, numeric control, propeller design, simulation.

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Authors: Maria Visitacion N. Gumabay, Byron Joseph A. Hallar, Annjeannette Alain D. Galang

Abstract:

This study was intended to provide a more efficient and convenient way for mobile users to enhance their knowledge about ASEAN countries. The researchers evaluated the utility of the developed crossword puzzle application and assessed the general usability of its user interface for its intended purpose and audience of users. The descriptive qualitative research method for the research design and the Mobile-D methodology was employed for the development of the software application output. With a generally favorable reception from its users, the researchers concluded that the iSEA Mobile Based Learning Application can be considered ready for general deployment and use. It was also concluded that additional studies can also be done to make a more complete assessment of the knowledge gained by its users before and after using the application.

Keywords: Mobile learning, e-learning, crossword, ASEAN, iSEA.

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Authors: Manpreet Singh, Parvinder Singh, Sumitter Bir Singh

Abstract:

The emergence of information technology has resulted in an ever-increasing demand to use computers for the efficient management and dissemination of information. Keeping in view the strong need of farmers to collect important and updated information for interactive, flexible and quick decision-making, a model of Decision Support System for Farm Management is developed. The paper discusses the use of Internet technology for the farmers to take decisions. A model is developed for the farmers to access online interactive and flexible information for their farm management. The workflow of the model is presented highlighting the information transfer between different modules.

Keywords: Decision Support System, dissemination.

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Authors: A. El Shahat, A. Keyhani, H. El Shewy

Abstract:

High Speed PM Generators driven by micro-turbines are widely used in Smart Grid System. So, this paper proposes comparative study among six classical, optimized and genetic analytical design cases for 400 kW output power at tip speed 200 m/s. These six design trials of High Speed Permanent Magnet Synchronous Generators (HSPMSGs) are: Classical Sizing; Unconstrained optimization for total losses and its minimization; Constrained optimized total mass with bounded constraints are introduced in the problem formulation. Then a genetic algorithm is formulated for obtaining maximum efficiency and minimizing machine size. In the second genetic problem formulation, we attempt to obtain minimum mass, the machine sizing that is constrained by the non-linear constraint function of machine losses. Finally, an optimum torque per ampere genetic sizing is predicted. All results are simulated with MATLAB, Optimization Toolbox and its Genetic Algorithm. Finally, six analytical design examples comparisons are introduced with study of machines waveforms, THD and rotor losses.

Keywords: High Speed, Micro - Turbines, Optimization, PM Generators, Smart Grid, MATLAB.

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Authors: Ahmed M. Hemeida, Wael A. Farag, Osama A. Mahgoub

Abstract:

This paper focuses on developing an integrated reliable and sophisticated model for ultra large wind turbines And to study the performance and analysis of vector control on large wind turbines. With the advance of power electronics technology, direct driven multi-pole radial flux PMSG (Permanent Magnet Synchronous Generator) has proven to be a good choice for wind turbines manufacturers. To study the wind energy conversion systems, it is important to develop a wind turbine simulator that is able to produce realistic and validated conditions that occur in real ultra MW wind turbines. Three different packages are used to simulate this model, namely, Turbsim, FAST and Simulink. Turbsim is a Full field wind simulator developed by National Renewable Energy Laboratory (NREL). The wind turbine mechanical parts are modeled by FAST (Fatigue, Aerodynamics, Structures and Turbulence) code which is also developed by NREL. Simulink is used to model the PMSG, full scale back to back IGBT converters, and the grid.

Keywords: FAST, Permanent Magnet Synchronous Generator(PMSG), TurbSim, Vector Control and Pitch Control

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

Abstract:

Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Classification, Bayesian network; structure learning, K2 algorithm, expert knowledge, surface water analysis.

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Authors: Maha Jazouli

Abstract:

Suicide is one of the leading causes of death among prisoners, both in Canada and internationally. In recent years, rates of attempts of suicide and self-harm suicide have increased, with hangings being the most frequently used method. The objective of this article is to propose a method to automatically detect suicidal behaviors in real time. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Tests show that the proposed system gives satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: Suicide detection, Kinect Azure, RGB-D camera, SVM, gesture recognition.

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Authors: Ali Reza Mehrabian, Caro Lucas

Abstract:

In this paper a new control strategy based on Brain Emotional Learning (BEL) model has been introduced. A modified BEL model has been proposed to increase the degree of freedom, controlling capability, reliability and robustness, which can be implemented in real engineering systems. The performance of the proposed BEL controller has been illustrated by applying it on different nonlinear uncertain systems, showing very good adaptability and robustness, while maintaining stability.

Keywords: Learning control systems, emotional decision making, nonlinear systems, adaptive control.

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Authors: Youngjae Jin, Daeshik Kim

Abstract:

This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in VerilogHDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: Auto-encoder, Behavior model simulation, Digital hardware design, Pre-route simulation, Unsupervised feature learning.

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Authors: Chalermwut Wijit

Abstract:

This research is conducted in order to 1) study the result of applying “Active Learning” in general education subject to develop creativity 2) explore problems and obstacles in applying Active Learning in general education subject to improve the creativity in 1780 undergraduate students who registered this subject in the first semester 2013. The research is implemented by allocating the students into several groups of 10 -15 students and assigning them to design the activities for society under the four main conditions including 1) require no financial resources 2) practical 3) can be attended by every student 4) must be accomplished within 2 weeks. The researcher evaluated the creativity prior and after the study. Ultimately, the problems and obstacles from creating activity are evaluated from the open-ended questions in the questionnaires. The study result states that overall average scores on students’ ability increased significantly in terms of creativity, analytical ability and the synthesis, the complexity of working plan and team working. It can be inferred from the outcome that active learning is one of the most efficient methods in developing creativity in general education.

Keywords: Creative Thinking, Active Learning, General Education.

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Authors: N. Ahmed, A.J. Day, J.L. Victory L. Zeall, B. Young

Abstract:

The manufacture of large-scale precision aerospace components using CNC requires a highly effective maintenance strategy to ensure that the required accuracy can be achieved over many hours of production. This paper reviews a strategy for a maintenance management system based on Failure Mode Avoidance, which uses advanced techniques and technologies to underpin a predictive maintenance strategy. It is shown how condition monitoring (CM) is important to predict potential failures in high precision machining facilities and achieve intelligent and integrated maintenance management. There are two distinct ways in which CM can be applied. One is to monitor key process parameters and observe trends which may indicate a gradual deterioration of accuracy in the product. The other is the use of CM techniques to monitor high status machine parameters enables trends to be observed which can be corrected before machine failure and downtime occurs. It is concluded that the key to developing a flexible and intelligent maintenance framework in any precision manufacturing operation is the ability to evaluate reliably and routinely machine tool condition using condition monitoring techniques within a framework of Failure Mode Avoidance.

Keywords: Maintenance, Condition Monitoring, CNC, Machining, Accuracy, Capability, Key Process Parameters, Critical Parameters

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Authors: Ye Wei, Sitalakshmi Venkatraman, Fahri Benli, Fiona Wahr

Abstract:

The present digital world poses many challenges to various stakeholders in the education sector. In particular, lecturers of higher education (HE) are faced with the problem of ensuring that students are able to achieve the required learning outcomes despite rapid changes taking place worldwide. Different strategies are adopted to retain student engagement and commitment in classrooms to address the differences in learning habits, preferences and styles of the digital generation of students recently. Further, with the onset of coronavirus disease (COVID-19) pandemic, online classroom has become the most suitable alternate mode of teaching environment to cope with lockdown restrictions. These changes have compounded the problems in the learning engagement and short attention span of HE students. New Agile methodologies that have been successfully employed to manage projects in different fields are gaining prominence in the education domain. In this paper, we present the application of Scrum as an agile methodology to enhance student learning and engagement in an online smart classroom environment. We demonstrate the use of our proposed approach using a case study to teach key topics in information technology that require students to gain technical and business-related data analytics skills.

Keywords: Agile methodology, Scrum, online learning, smart classroom environment, student engagement, active learning.

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Authors: Z.Farhadpour, Mohammad.R.Meybodi

Abstract:

A number of routing algorithms based on learning automata technique have been proposed for communication networks. How ever, there has been little work on the effects of variation of graph scarcity on the performance of these algorithms. In this paper, a comprehensive study is launched to investigate the performance of LASPA, the first learning automata based solution to the dynamic shortest path routing, across different graph structures with varying scarcities. The sensitivity of three main performance parameters of the algorithm, being average number of processed nodes, scanned edges and average time per update, to variation in graph scarcity is reported. Simulation results indicate that the LASPA algorithm can adapt well to the scarcity variation in graph structure and gives much better outputs than the existing dynamic and fixed algorithms in terms of performance criteria.

Keywords: Learning automata, routing, algorithm, sparse graph

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Authors: Amal A. Abdel-Sattar

Abstract:

The curriculum of architecture department in Prince Sultan University includes ‘Building Surveying’ course which is usually a part of civil engineering courses. As a fundamental requirement of the course, it requires a strong background in mathematics and physics, which are not usually preferred subjects to the architecture students and many of them are not giving the required and necessary attention to these courses during their preparation year before commencing their architectural study. This paper introduces the concept and the methodology of the student-centered learning approach in the course of building surveying for architects. One of the major outcomes is the improvement in the students’ involvement in the course and how this will cover and strength their analytical weak points and improve their mathematical skills. The study is conducted through three semesters with a total number of 99 students. The effectiveness of the student-centered learning approach is studied using the student survey at the end of each semester and teacher observations. This survey showed great acceptance of the students for these methods. Also, the teachers observed a great improvement in the students’ mathematical abilities and how keener they became in attending the classes which were clearly reflected on the low absence record.

Keywords: Architecture, building surveying, student-centered learning, teaching, and learning.

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Authors: Hiroaki Sumi, Kiichiro Hayashi

Abstract:

Now, the use of renewable energy power generation has been advanced. In this paper, we compared the usable amount of resource for photovoltaic power generation which was estimated using the NEDO formula and the expansion support policy of photovoltaic power generation which was researched using Internet in the municipality level in Hokkaido and Aichi Prefecture, Japan. This paper will contribute to grasp the current situation especially about the policy. As a result, there were municipalities which seemed to be no consideration of fitting the amount of resources. We think it would need to consider the suitability between the resources and policies.

Keywords: Photovoltaic power generation, expansion support policy, amount of resources, Japan.

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Authors: Nikhil Byanna, Abdolghani Ebrahimi, Diego Klabjan

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In this paper we objectively measure the performance of an individual offensive lineman in the NFL. The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensive lineman’s performance, we develop a framework to objectively analyze the overall performance of an individual offensive lineman and determine specific linemen who are overvalued or undervalued relative to their salary. We identify eight players across the 2013-2014 and 2014-2015 NFL seasons that are considered to be overvalued or undervalued and corroborate the results with existing metrics that are based on subjective evaluation. To the best of our knowledge, the techniques set forth in this work have not been utilized in previous works to evaluate the performance of NFL players at any position, including offensive linemen.

Keywords: offensive lineman, player performance, NFL, machine learning

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

Abstract:

This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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