Search results for: learning using labeled and unlabelled data

Authors: Kalinga Ellen A., Bagile Burchard B.

Abstract:

Tanzania Secondary Schools e-Learning (TanSSe-L) system is a customized learning management system (LMS) developed to enable ICT support in teaching and learning functions. Methodologies involved in the development of TanSSe-L system are Object oriented system analysis and design with UML to create and model TanSSe-L system database structure in the form of a design class diagram, Model Driven Architecture (MDA) to provide a well defined process in TanSSe-L system development, where MDA conceptual layers were integrated with system development life cycle and customization of open source learning management system which was used during implementation stage to create a timely functional TanSSe-L system. Before customization, a base for customization was prepared. This was the manual transformation from TanSSe-L system platform independent models (PIM) to TanSSe-L system specific PIM. This paper presents how Moodle open source LMS was analyzed and prepared to be the TanSSe-L system specific PIM as applied by MDA.

Keywords: Customization, e-Learning, MDA Transformation, Moodle, Secondary Schools, Tanzania.

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Authors: Malathi Balakrishnan, Shabeshan Rengasamy, Mohd Salleh Aman

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The study investigated the effects of Teaching Games for Understanding approach on students ‘cognitive learning outcome. The study was a quasi-experimental non-equivalent pretest-posttest control group design whereby 10 year old primary school students (n=72) were randomly assigned to an experimental and a control group. The experimental group students were exposed with TGfU approach and the control group with the Traditional Skill approach of handball game. Game Performance Assessment Instrument (GPAI) was used to measure students' tactical understanding and decision making in 3 versus 3 handball game situations. Analysis of covariance (ANCOVA) was used to analyze the data. The results reveal that there was a significant difference between the TGfU approach group and the traditional skill approach group students on post test score (F (1, 69) = 248.83, p < .05). The findings of this study suggested the importance of TGfU approach to improve primary students’ tactical understanding and decision making in handball game.

Keywords: Constructivism, learning outcome, tactical understanding, and Teaching Game for Understanding (TGfU)

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Authors: F.Katlane, M.S.Naceur, M.A.Loghmari

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Automatic methods of detecting changes through satellite imaging are the object of growing interest, especially beca²use of numerous applications linked to analysis of the Earth’s surface or the environment (monitoring vegetation, updating maps, risk management, etc...). This work implemented spatial analysis techniques by using images with different spatial and spectral resolutions on different dates. The work was based on the principle of control charts in order to set the upper and lower limits beyond which a change would be noted. Later, the a contrario approach was used. This was done by testing different thresholds for which the difference calculated between two pixels was significant. Finally, labeled images were considered, giving a particularly low difference which meant that the number of “false changes” could be estimated according to a given limit.

Keywords: multi-scale, a contrario approach, significantthresholds, change detection.

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Authors: Liang Wang, Wei Zhang

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Nowadays sustainable development has increasingly become an important research topic of engineering education all over the world. Engineering Education for Sustainable Development (EESD) highlighted the importance of addressing sustainable development in engineering practice. However, whether and how the professional engineering learning and experience affect those perceptions is an interesting research topic especially in Chinese context. Our study fills this gap by investigating perceptions bias of EESD among first-grade engineering students, fourth-grade engineering students and experienced engineers using a triple-dimensional model. Our goal is to find the effect of engineering learning and experience on sustainable development and make these learning and experiences more accessible for students and engineers in school and workplace context. The data (n = 138) came from a Likert questionnaire based on the triple-dimensional model of EESD adopted from literature reviews and the data contain 48 first-grade students, 56 fourth-grade students and 34 engineers with rich working experience from Environmental Engineering, Energy Engineering, Chemical Engineering and Civil Engineering in or graduated from Zhejiang University, China. One-way ANOVA analysis was used to find the difference in different dimensions among the three groups. The statistical results show that both engineering students and engineers have a well understanding of sustainable development in ecology dimension of EESD while there are significant differences among three groups as to the socio-economy and value rationality dimensions of EESD. The findings provide empirical evidence that both engineering learning and professional engineering experience are helpful to cultivate the cognition and perception of sustainable development in engineering education. The results of this work indicate that more practical content should be added to students’ engineering education while more theoretical content should be added to engineers’ training in order to promote the engineering students’ and engineers’ perceptions of sustainable development. In addition, as to the design of engineering courses and professional practice system for sustainable development, we should not only pay attention to the ecological aspects, but also emphasize the coordination of ecological, socio-economic and human-centered sustainable development (e.g., engineer's ethical responsibility).

Keywords: Engineering education, sustainable development, experienced engineers, engineering students.

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Authors: N. Ismail, O. Thammajinda, U. Thongpanya

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Games-based learning (GBL) has become increasingly important in teaching and learning. This paper explains the first two phases (analysis and design) of a GBL development project, ending up with a prototype design based on students’ and teachers’ perceptions. The two phases are part of a full cycle GBL project aiming to help secondary school students in Thailand in their study of Comprehensive Sex Education (CSE). In the course of the study, we invited 1,152 students to complete questionnaires and interviewed 12 secondary school teachers in focus groups. This paper found that GBL can serve students in their learning about CSE, enabling them to gain understanding of their sexuality, develop skills, including critical thinking skills and interact with others (peers, teachers, etc.) in a safe environment. The objectives of this paper are to outline the development of GBL variables from the research question(s) into the developers’ flow chart, to be responsive to the GBL beneficiaries’ preferences and expectations, and to help in answering the research questions. This paper details the steps applied to generate GBL variables that can feed into a game flow chart to develop a GBL prototype. In our approach, we detailed two models: (1) Game Elements Model (GEM) and (2) Game Object Model (GOM). There are three outcomes of this research – first, to achieve the objectives and benefits of GBL in learning, game design has to start with the research question(s) and the challenges to be resolved as research outcomes. Second, aligning the educational aims with engaging GBL end users (students) within the data collection phase to inform the game prototype with the game variables is essential to address the answer/solution to the research question(s). Third, for efficient GBL to bridge the gap between pedagogy and technology and in order to answer the research questions via technology (i.e. GBL) and to minimise the isolation between the pedagogists “P” and technologist “T”, several meetings and discussions need to take place within the team.

Keywords: Games-based learning, design, engagement, pedagogy, preferences, prototype, variables.

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Authors: M. Govindarajan, R. M.Chandrasekaran

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Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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Authors: Yakin Hajlaoui, Richard Labib, Jean-Franc¸ois Plante, Michel Gamache

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This study presents the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs’ processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW’s ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. We employ gradient descent and backpropagation to train ML-IDW. The performance of the proposed model is compared against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. Our results highlight the efficacy of ML-IDW, particularly in handling complex spatial dataset, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: Deep Learning, Multi-Layer Neural Networks, Gradient Descent, Spatial Interpolation, Inverse Distance Weighting.

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Authors: E. S. Gower, M. O. J. Hawksford

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An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm-s robustness to outliers.

Keywords: expectation-maximization, Pitman estimator, sparsedecomposition

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Authors: Joachim Kimmerle, Johannes Moskaliuk, Ulrike Cress

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The development of Internet technology in recent years has led to a more active role of users in creating Web content. This has significant effects both on individual learning and collaborative knowledge building. This paper will present an integrative framework model to describe and explain learning and knowledge building with shared digital artifacts on the basis of Luhmann-s systems theory and Piaget-s model of equilibration. In this model, knowledge progress is based on cognitive conflicts resulting from incongruities between an individual-s prior knowledge and the information which is contained in a digital artifact. Empirical support for the model will be provided by 1) applying it descriptively to texts from Wikipedia, 2) examining knowledge-building processes using a social network analysis, and 3) presenting a survey of a series of experimental laboratory studies.

Keywords: Individual learning, collaborative knowledge building, systems theory, equilibration.

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Authors: A. R. Ommani, M. Chizari

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The purpose of the study reported here was designing Information Dissemination System (IDS) based E-learning in agricultural of Iran. A questionnaire was developed to designing Information Dissemination System. The questionnaire was distributed to 96 extension agents who work for Management of Extension and Farming System of Khuzestan province of Iran. Data collected were analyzed using the Statistical Package for the Social Sciences (SPSS). Appropriate statistical procedures for description (frequencies, percent, means, and standard deviations) were used. In this study there was a significant relationship between the age , IT skill and knowledge, years of extension work, the extend of information seeking motivation, level of job satisfaction and level of education with use of information technology by extension agent. According to extension agents five factors were ranked respectively as five top essential items to designing Information Dissemination System (IDS) based E-learning in agricultural of Iran. These factors include: 1) Establish communication between farmers, coordinators (extension agents), agricultural experts, research centers, and community by information technology. 2) The communication between all should be mutual. 3) The information must be based farmers need. 4) Internet used as a facility to transfer the advanced agricultural information to the farming community. 5) Farmers can be illiterate and speak a local and they are not expected to use the system directly. Knowledge produced by the agricultural scientist must be transformed in to computer understandable presentation. To designing Information Dissemination System, electronic communication, in the agricultural society and rural areas must be developed. This communication must be mutual between all factors.

Keywords: E-learning, information dissemination system, information technology.

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: Deregulated energy market, forecasting, machine learning, system marginal price, energy efficiency and quality.

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Authors: Ali Reza Mehrabian, Caro Lucas

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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: Boyan Bontchev, Dessislava Vassileva

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Internet computer games turn to be more and more attractive within the context of technology enhanced learning. Educational games as quizzes and quests have gained significant success in appealing and motivating learners to study in a different way and provoke steadily increasing interest in new methods of application. Board games are specific group of games where figures are manipulated in competitive play mode with race conditions on a surface according predefined rules. The article represents a new, formalized model of traditional quizzes, puzzles and quests shown as multimedia board games which facilitates the construction process of such games. Authors provide different examples of quizzes and their models in order to demonstrate the model is quite general and does support not only quizzes, mazes and quests but also any set of teaching activities. The execution process of such models is explained and, as well, how they can be useful for creation and delivery of adaptive e-learning courseware.

Keywords: Quiz, board game, e-learning, adaptive.

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Authors: Chalermwut Wijit

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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: Z.Farhadpour, Mohammad.R.Meybodi

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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: Pilar Rey-del-Castillo, Jesús Cardeñosa

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There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

Keywords: Classifier, imputation techniques, fuzzy systems, fuzzy min-max neural networks.

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Authors: S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias

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Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.

Keywords: regression algorithms, supervised machine learning.

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Authors: Hamed.Vezvaei, Sepideh.Ordibeheshti, Mehdi.Ardjmand

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Gasoline Octane Number is the standard measure of the anti-knock properties of a motor in platforming processes, that is one of the important unit operations for oil refineries and can be determined with online measurement or use CFR (Cooperative Fuel Research) engines. Online measurements of the Octane number can be done using direct octane number analyzers, that it is too expensive, so we have to find feasible analyzer, like ANFIS estimators. ANFIS is the systems that neural network incorporated in fuzzy systems, using data automatically by learning algorithms of NNs. ANFIS constructs an input-output mapping based both on human knowledge and on generated input-output data pairs. In this research, 31 industrial data sets are used (21 data for training and the rest of the data used for generalization). Results show that, according to this simulation, hybrid method training algorithm in ANFIS has good agreements between industrial data and simulated results.

Keywords: Adaptive Neuro-Fuzzy Inference Systems, GasolineOctane Number, Soft-sensor, Catalytic Naphtha Reforming

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Authors: Amal A. Abdel-Sattar

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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: Seema Chopra, R. Mitra, Vijay Kumar

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A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Keywords: Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.

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Authors: Richard W. Longman

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Iterative learning control aims to achieve zero tracking error of a specific command. This is accomplished by iteratively adjusting the command given to a feedback control system, based on the tracking error observed in the previous iteration. One would like the iterations to converge to zero tracking error in spite of any error present in the model used to design the learning law. First, this need for stability robustness is discussed, and then the need for robustness of the property that the transients are well behaved. Methods of producing the needed robustness to parameter variations and to singular perturbations are presented. Then a method involving reverse time runs is given that lets the world behavior produce the ILC gains in such a way as to eliminate the need for a mathematical model. Since the real world is producing the gains, there is no issue of model error. Provided the world behaves linearly, the approach gives an ILC law with both stability robustness and good transient robustness, without the need to generate a model.

Keywords: Iterative learning control, stability robustness, monotonic convergence.

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Authors: Indiramma M., K. R. Anandakumar

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Trust management and Reputation models are becoming integral part of Internet based applications such as CSCW, E-commerce and Grid Computing. Also the trust dimension is a significant social structure and key to social relations within a collaborative community. Collaborative Decision Making (CDM) is a difficult task in the context of distributed environment (information across different geographical locations) and multidisciplinary decisions are involved such as Virtual Organization (VO). To aid team decision making in VO, Decision Support System and social network analysis approaches are integrated. In such situations social learning helps an organization in terms of relationship, team formation, partner selection etc. In this paper we focus on trust learning. Trust learning is an important activity in terms of information exchange, negotiation, collaboration and trust assessment for cooperation among virtual team members. In this paper we have proposed a reinforcement learning which enhances the trust decision making capability of interacting agents during collaboration in problem solving activity. Trust computational model with learning that we present is adapted for best alternate selection of new project in the organization. We verify our model in a multi-agent simulation where the agents in the community learn to identify trustworthy members, inconsistent behavior and conflicting behavior of agents.

Keywords: Collaborative Decision making, Trust, Multi Agent System (MAS), Bayesian Network, Reinforcement Learning.

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Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

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This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: Designing, education management, information systems, matrix technology, process affinity.

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Authors: Sara M. Ismael, Ali H. Al-Badi

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The rapid development and growth of technology has changed the method of obtaining information for educators and learners. Technology has created a new world of collaboration and communication among people. Incorporating new technology into the teaching process can enhance learning outcomes. Billions of individuals across the world are now connected together, and are cooperating and contributing their knowledge and intelligence. Time is no longer wasted in waiting until the teacher is ready to share information as learners can go online and get it immediatelt.

The objectives of this paper are to understand the reasons why changes in teaching and learning methods are necessary, to find ways of improving them, and to investigate the challenges that present themselves in the adoption of new ICT tools in higher education institutes.

 To achieve these objectives two primary research methods were used: questionnaires, which were distributed among students at higher educational institutes and multiple interviews with faculty members (teachers) from different colleges and universities, which were conducted to find out why teaching and learning methodology should change.

The findings show that both learners and educators agree that educational technology plays a significant role in enhancing instructors’ teaching style and students’ overall learning experience; however, time constraints, privacy issues, and not being provided with enough up-to-date technology do create some challenges.

Keywords: E-books, educational technology, educators, e-learning, learners, social media, Web 2.0, LMS.

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Authors: B. Nassar, W. Hussein, M. Mokhtar

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The critical concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission, but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the problem above coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions, and the results show that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: Space telemetry monitoring, multivariate analysis, PCA algorithm, space operations.

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: Time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition.

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Authors: Ani Grubišić, Slavomir Stankov, Branko Žitko

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This paper describes a concept of stereotype student model in adaptive knowledge acquisition e-learning system. Defined knowledge stereotypes are based on student's proficiency level and on Bloom's knowledge taxonomy. The teacher module is responsible for the whole adaptivity process: the automatic generation of courseware elements, their dynamic selection and sorting, as well as their adaptive presentation using templates for statements and questions. The adaptation of courseware is realized according to student-s knowledge stereotype.

Keywords: Adaptive e-learning systems, adaptive courseware, stereotypes, Bloom's knowledge taxonomy.

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Authors: Weidong Xia, Malgorzata Kolotylo, Xuan Tan

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This study examines the key factors that influence general practitioners’ learning and transfer of specialized arthritis knowledge and self-care techniques to patients during normal patient visits. Drawing on the theory of planed behavior and using matched survey data collected from general practitioners before and after training sessions provided by specialized orthopedic physicians, the study suggests that the general practitioner’s intention to use and transfer learned knowledge was influenced mainly by intrinsic motivation, organizational learning culture and absorptive capacity, but was not influenced by extrinsic motivation. The results provide both theoretical and practical implications.

Keywords: Empirical study, healthcare knowledge management, patient self-care, physician knowledge transfer.

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Authors: Evisa Mitrou, Nicholas Tsitsianis, Supriya Shinde

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In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affect the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients.

Keywords: BDA-clients, BDA-vendors, big data analytics, financial performance.

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

Abstract:

The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: Convolutional neural networks, deep learning, foot recognition, knee rehabilitation.

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