Search results for: machine learning techniques

Authors: Migbar Assefa

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The performance of a machine tool is eventually assessed by its ability to produce a component of the required geometry in minimum time and at small operating cost. It is customary to base the structural design of any machine tool primarily upon the requirements of static rigidity and minimum natural frequency of vibration. The operating properties of machines like cutting speed, feed and depth of cut as well as the size of the work piece also have to be kept in mind by a machine tool structural designer. This paper presents a novel approach to the design of machine tool column for static and dynamic rigidity requirement. Model evaluation is done effectively through use of General Finite Element Analysis software ANSYS. Studies on machine tool column are used to illustrate finite element based concept evaluation technique. This paper also presents results obtained from the computations of thin walled box type columns that are subjected to torsional and bending loads in case of static analysis and also results from modal analysis. The columns analyzed are square and rectangle based tapered open column, column with cover plate, horizontal partitions and with apertures. For the analysis purpose a total of 70 columns were analyzed for bending, torsional and modal analysis. In this study it is observed that the orientation and aspect ratio of apertures have no significant effect on the static and dynamic rigidity of the machine tool structure.

Keywords: Finite Element Modeling, Modal Analysis, Machine tool structure, Static Analysis.

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Authors: Sameerchand Pudaruth, Avinash Mantaye

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E-learning refers to the specific kind of learning experienced within the domain of educational technology, which can be used in or out of the classroom. In this paper, we give an overview of an e-learning platform 'An Innovative Interactive and Online English Platform for Upper Primary Students' is an interactive web-based application which will serve as an aid to the primary school students in Mauritius. The objectives of this platform are to offer quality learning resources for the English subject at our primary level of education, encourage self-learning and hence promote e-learning. The platform developed consists of several interesting features, for example, the English Verb Conjugation tool, Negative Form tool, Interrogative Form tool and Close Test Generator. Thus, this learning platform will be useful at a time where our country is looking for an alternative to private tuition and also, looking forward to increase the pass rate.

Keywords: educational technology, e-learning, Mauritius.

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Authors: Jui P. Hung, Yuan L. Lai, Hui T. You

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Chatter vibration has been a troublesome problem for a machine tool toward the high precision and high speed machining. Essentially, the machining performance is determined by the dynamic characteristics of the machine tool structure and dynamics of cutting process, which can further be identified in terms of the stability lobe diagram. Therefore, realization on the machine tool dynamic behavior can help to enhance the cutting stability. To assess the dynamic characteristics and machining stability of a vertical milling system under the influence of a linear guide, this study developed a finite element model integrated the modeling of linear components with the implementation of contact stiffness at the rolling interface. Both the finite element simulations and experimental measurements reveal that the linear guide with different preload greatly affects the vibration behavior and milling stability of the vertical column spindle head system, which also clearly indicate that the predictions of the machining stability agree well with the cutting tests. It is believed that the proposed model can be successfully applied to evaluate the dynamics performance of machine tool systems of various configurations.

Keywords: Machining stability, Vertical milling machine, Linearguide, Contact stiffness.

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Authors: M. V. G. Silva, J. Gazzola, I. M. Dal Fabbro, A. C. L. Lino

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Computer aided design accounts with the support of parametric software in the design of machine components as well as of any other pieces of interest. The complexities of the element under study sometimes offer certain difficulties to computer design, or ever might generate mistakes in the final body conception. Reverse engineering techniques are based on the transformation of already conceived body images into a matrix of points which can be visualized by the design software. The literature exhibits several techniques to obtain machine components dimensional fields, as contact instrument (MMC), calipers and optical methods as laser scanner, holograms as well as moiré methods. The objective of this research work was to analyze the moiré technique as instrument of reverse engineering, applied to bodies of nom complex geometry as simple solid figures, creating matrices of points. These matrices were forwarded to a parametric software named SolidWorks to generate the virtual object. Volume data obtained by mechanical means, i.e., by caliper, the volume obtained through the moiré method and the volume generated by the SolidWorks software were compared and found to be in close agreement. This research work suggests the application of phase shifting moiré methods as instrument of reverse engineering, serving also to support farm machinery element designs.

Keywords: Reverse engineering, Moiré technique, three dimensional image generation.

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Authors: Asif Ekbal, Sivaji Bandyopadhyay

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Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes 1, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) 2. In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages.

Keywords: Named Entity (NE), Named Entity Recognition (NER), Support Vector Machine (SVM), Bengali, Hindi.

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: Academic environment model, decision trees, FSASEC, K-nearest neighbor, machine learning, popularity index, support vector machine.

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Authors: Hassan Haleh, Zohreh Nasiri, Parisa Farahpour

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During recent years, the traditional learning approaches have undergone fundamental changes due to the emergence of new technologies such as multimedia, hypermedia and telecommunication. E-learning is a modern world phenomenon that has come into existence in the information age and in a knowledgebased society. E-learning has developed significantly within a short period of time. Thus it is of a great significant to secure information, allow a confident access and prevent unauthorized accesses. Making use of individuals- physiologic or behavioral (biometric) properties is a confident method to make the information secure. Among the biometrics, fingerprint is more acceptable and most countries use it as an efficient methods of identification. This article provides a new method to compare the fingerprint comparison by pattern recognition and image processing techniques. To verify fingerprint, the shortest distance method is used together with perceptronic multilayer neural network functioning based on minutiae. This method is highly accurate in the extraction of minutiae and it accelerates comparisons due to elimination of false minutiae and is more reliable compared with methods that merely use directional images.

Keywords: Fingerprint, minutiae, extraction of properties, multilayer neural network

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Authors: Soe Sandar Aung, Han Phyo Wai, Nyein Nyein Soe

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The induction hardening machines are utilized in the industries which modify machine parts and tools needed to achieve high ware resistance. This paper describes the model of induction heating process design of inverter circuit and the results of induction surface hardening of heating coil. In the design of heating coil, the shape and the turn numbers of the coil are very important design factors because they decide the overall operating performance of induction heater including resonant frequency, Q factor, efficiency and power factor. The performance will be tested by experiments in some cases high frequency induction hardening machine.

Keywords: Induction Heating, Resonant Circuit, InverterCircuit, Coil Design, Induction Hardening Machine.

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD, as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches, such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: Autism spectrum disorder, clustering, optimization, unsupervised machine learning.

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Authors: Mohammad Mohammad

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With the exponential progress of technological development comes a strong sense that events are moving too quickly for our schools and that teachers may be losing control of them in the process. This paper examines the impact of e-learning and e-teaching in universities, from both the student and teacher perspective. In particular, it is shown that e-teachers should focus not only on the technical capacities and functions of IT materials and activities, but must attempt to more fully understand how their e-learners perceive the learning environment. From the e-learner perspective, this paper indicates that simply having IT tools available does not automatically translate into all students becoming effective learners. More evidence-based evaluative research is needed to allow e-learning and e-teaching to reach full potential.

Keywords: e-learning, e-teaching, distance learning, education

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Authors: Pimploi Tirastittam, Sawanath Treesathon, Amornrath Ongkawat

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Learning Management System (LMS) is the system which uses to manage the learning in order to grouping the content and learning activity between the lecturer and learner including online examination and evaluation. Nowadays, it is the borderless learning era so the learning activities can be accessed from everywhere in the world and also anytime via the information technology and media. The learner can easily access to the knowledge so the different in time and distance is not a constraint for learning anymore. The learning pattern which was used in this research is the integration of the in-class learning and online learning via internet and will be able to monitor the progress by the Learning management system which will create the fast response and accessible learning process via the social media. In order to increase the capability and freedom of the learner, the system can show the current and history of the learning document, video conference and also has the chat room for the learner and lecturer to interact to each other. So the objectives of the “The Design and Applied of Learning Management System via Social Media on Internet: Case Study of Operating System for Business Subject” are to expand the opportunity of learning and to increase the efficiency of learning as well as increase the communication channel between lecturer and student. The data of this research was collect from 30 users of the system which are students who enroll in the subject. And the result of the research is in the “Very Good” which is conformed to the hypothesis.

Keywords: Learning Management System, Social Media.

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Authors: Belkacem Chikhaoui, Helene Pigot

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Cognitive models allow predicting some aspects of utility and usability of human machine interfaces (HMI), and simulating the interaction with these interfaces. The action of predicting is based on a task analysis, which investigates what a user is required to do in terms of actions and cognitive processes to achieve a task. Task analysis facilitates the understanding of the system-s functionalities. Cognitive models are part of the analytical approaches, that do not associate the users during the development process of the interface. This article presents a study about the evaluation of a human machine interaction with a contextual assistant-s interface using ACTR and GOMS cognitive models. The present work shows how these techniques may be applied in the evaluation of HMI, design and research by emphasizing firstly the task analysis and secondly the time execution of the task. In order to validate and support our results, an experimental study of user performance is conducted at the DOMUS laboratory, during the interaction with the contextual assistant-s interface. The results of our models show that the GOMS and ACT-R models give good and excellent predictions respectively of users performance at the task level, as well as the object level. Therefore, the simulated results are very close to the results obtained in the experimental study.

Keywords: HMI, interface evaluation, Analytical evaluation, cognitivemodeling, user modeling, user performance.

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: Chemical reaction optimization, expectation maximization, initial, objective function clustering.

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Authors: Chutchada Nusai, Yoshimi Suzuki, Haruaki Yamazaki

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Selecting the word translation from a set of target language words, one that conveys the correct sense of source word and makes more fluent target language output, is one of core problems in machine translation. In this paper we compare the 3 methods of estimating word translation probabilities for selecting the translation word in Thai – English Machine Translation. The 3 methods are (1) Method based on frequency of word translation, (2) Method based on collocation of word translation, and (3) Method based on Expectation Maximization (EM) algorithm. For evaluation we used Thai – English parallel sentences generated by NECTEC. The method based on EM algorithm is the best method in comparison to the other methods and gives the satisfying results.

Keywords: Machine translation, EM algorithm.

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Authors: Babak Mohajeri, Sen Bao, Timo Nyberg

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Customer experience is increasingly the differentiator between successful companies and those who struggle. Currently, customer experiences become more dynamic; and they advance with each interaction between the company and a customer. Every customer conversation and any effort to evolve these conversations would be beneficial and should ultimately result in a positive customer experience. The aim of this paper is to analyze the evolving customer experience management landscape and the relevant challenges and opportunities. A case study on the “paper machine” companies is chosen. Hence, this paper analyzes the challenges and opportunities in customer experience management of paper machine companies for the case of “road to steel”. Road to steel shows the journey of steel from raw material to end product (i.e. paper machine in this paper). ALPHA (Steel company) and BETA (paper machine company), are chosen and their efforts to evolve the customer experiences are investigated. Semi-structured interviews are conducted with experts in those companies to identify the challenges and opportunities of the evolving customer experience management from their point of view. The findings of this paper contribute to the theory and business practices in the realm of the evolving customer experience management landscape.

Keywords: Customer experience management, paper machine risk analysis, value chain management.

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Authors: Nasser Alalwan, Ahmed Alzahrani, Mohamed Sarrab

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The technology usages of high speed Internet leads to establish and start new era of online education. With the advancement of the information technology and communication systems new opportunities have been created. This leads universities to have various online education channels to meet the demand of different learners- needs. One of these channels is M-learning, which can be used to improve the online education environment. With using such mobile technology in learning both students and instructors can easily access educational courses anytime from anywhere. The paper first presents literature about mobile learning and to what extent this approach can be utilized to enhance the overall learning system. It provides a comparison between mobile learning and traditional elearning showing the wide array of benefits of the new generation of technology. The possible challenges and potential advantages of Mlearning in the online education system are also discussed.

Keywords: Mobile learning, M-learning, eLearning, Educational system.

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Authors: Chris Landorf, Stephen Ward

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This paper addresses a virtual environment approach to work integrated learning for students in construction-related disciplines. The virtual approach provides a safe and pedagogically rigorous environment where students can apply theoretical knowledge in a simulated real-world context. The paper describes the development of a 4-dimensional digital construction environment and associated learning activities funded by the Australian Office for Learning and Teaching. The environment was trialled with over 1,300 students and evaluated through questionnaires, observational studies and coursework analysis. Results demonstrate a positive impact on students’ technical learning and collaboration skills, but there is need for further research in relation to critical thinking skills and work-readiness.

Keywords: Architectural education, construction industry, digital learning environments, immersive learning.

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Authors: H. Benjamin Fredrick David, R. Balasubramanian, A. Anbarasa Pandian

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Image retrieval is the most interesting technique which is being used today in our digital world. CBIR, commonly expanded as Content Based Image Retrieval is an image processing technique which identifies the relevant images and retrieves them based on the patterns that are extracted from the digital images. In this paper, two research works have been presented using CBIR. The first work provides an automated and interactive approach to the analysis of CBIR techniques. CBIR works on the principle of supervised machine learning which involves feature selection followed by training and testing phase applied on a classifier in order to perform prediction. By using feature extraction, the image transforms such as Contourlet, Ridgelet and Shearlet could be utilized to retrieve the texture features from the images. The features extracted are used to train and build a classifier using the classification algorithms such as Naïve Bayes, K-Nearest Neighbour and Multi-class Support Vector Machine. Further the testing phase involves prediction which predicts the new input image using the trained classifier and label them from one of the four classes namely 1- Normal brain, 2- Benign tumour, 3- Malignant tumour and 4- Severe tumour. The second research work includes developing a tool which is used for tumour stage identification using the best feature extraction and classifier identified from the first work. Finally, the tool will be used to predict tumour stage and provide suggestions based on the stage of tumour identified by the system. This paper presents these two approaches which is a contribution to the medical field for giving better retrieval performance and for tumour stages identification.

Keywords: Brain tumour detection, content based image retrieval, classification of tumours, image retrieval.

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Authors: Ehi E. Aimiuwu

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As of 2017, many online learning professionals, institutions, and journals are still wondering how instructors can keep student engaged in the online learning environment to facilitate active learning effectively. The purpose of this qualitative single-case and narrative research is to explore whether online professors understand their role as mentors and facilitators of students’ academic success by keeping students engaged in active learning based on personalized experience in the field. Data collection tools that were used in the study included an NVivo 12 Plus qualitative software, an interview protocol, a digital audiotape, an observation sheet, and a transcription. Seven online professors in the United States from LinkedIn and residencies were interviewed for this study. Eleven online teaching techniques from previous research were used as the study framework. Data analysis process, member checking, and key themes were used to achieve saturation. About 85.7% of professors agreed on rubric as the preferred online grading technique. About 57.1% agreed on professors logging in daily, students logging in about 2-5 times weekly, knowing students to increase accountability, email as preferred communication tool, and computer access for adequate online learning. About 42.9% agreed on syllabus for clear class expectations, participation to show what has been learned, and energizing students for creativity.

Keywords: Class facilitation, class management, online teaching, online education, pedagogy.

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Authors: Kamarul Hawari Ghazali, Mohd. Marzuki Mustafa, Aini Hussain

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Machine vision is an application of computer vision to automate conventional work in industry, manufacturing or any other field. Nowadays, people in agriculture industry have embarked into research on implementation of engineering technology in their farming activities. One of the precision farming activities that involve machine vision system is automatic weeding strategy. Automatic weeding strategy in oil palm plantation could minimize the volume of herbicides that is sprayed to the fields. This paper discusses an automatic weeding strategy in oil palm plantation using machine vision system for the detection and differential spraying of weeds. The implementation of vision system involved the used of image processing technique to analyze weed images in order to recognized and distinguished its types. Image filtering technique has been used to process the images as well as a feature extraction method to classify the type of weed images. As a result, the image processing technique contributes a promising result of classification to be implemented in machine vision system for automated weeding strategy.

Keywords: Machine vision, Automatic Weeding Strategy, filter, feature extraction

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Authors: O. Siriporn, S. Benjawan

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This paper represents four unsupervised clustering algorithms namely sIB, RandomFlatClustering, FarthestFirst, and FilteredClusterer that previously works have not been used for network traffic classification. The methodology, the result, the products of the cluster and evaluation of these algorithms with efficiency of each algorithm from accuracy are shown. Otherwise, the efficiency of these algorithms considering form the time that it use to generate the cluster quickly and correctly. Our work study and test the best algorithm by using classify traffic anomaly in network traffic with different attribute that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and compare it to the previously used (K-Means). Our research will be use to develop anomaly detection system to more efficiency and more require in the future.

Keywords: Unsupervised, clustering, anomaly, machine learning.

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Authors: Lee Bih Ni, Emily Doreen Lee, Wee Hui Yean

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This paper was prepared to see the perceptions of online lectures regarding collaborative learning, in terms of how lecturers view online collaborative learning in the higher learning institution. The purpose of this study was conducted to determine the perceptions of online lectures about collaborative learning, especially how lecturers see online collaborative learning in the university. Adult learning education enhance collaborative learning culture with the target of involving learners in the learning process to make teaching and learning more effective and open at the university. This will finally make students learning that will assist each other. It is also to cut down the pressure of loneliness and isolation might felt among adult learners. Their ways in collaborative online was also determined. In this paper, researchers collect data using questionnaires instruments. The collected data were analyzed and interpreted. By analyzing the data, researchers report the results according the proof taken from the respondents. Results from the study, it is not only dependent on the lecturer but also a student to shape a good collaborative learning practice. Rational concepts and pattern to achieve these targets be clear right from the beginning and may be good seen by a number of proposals submitted and include how the higher learning institution has trained with ongoing lectures online. Advantages of online collaborative learning show that lecturers should be trained effectively. Studies have seen that the lecturer aware of online collaborative learning. This positive attitude will encourage the higher learning institution to continue to give the knowledge and skills required.

Keywords: Collaborative Online Learning, Lecturers’ Training.

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Authors: Simon Thiel, Stavros Dalakakis, Dieter Roller

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This paper outlines the development of a learning retrieval agent. Task of this agent is to extract knowledge of the Active Semantic Network in respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user-s intention. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.

Keywords: Reinforcement learning, learning retrieval agent, search in semantic networks.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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Authors: H. M. El-Zomor, M. Hany

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Recycling of aluminum beverage cans is an important issue due to its economic and environmental effect. One of the significant factors in aluminum cans recycling process is the transportation cost from the landfill space. An automatic compression baler (ACB) machine has been designed and built to densify the aluminum beverage cans. It has been constructed using numerous fabricated components. Two types of control methodology have been introduced in this ACB machine to achieve its goal. The first is a semi-automatic system, and the second is a mechatronic system by using a Programmable Logic Control (PLC). The effect of single and double pre-compression for the beverage cans have been evaluated by using the PLC control. Comparisons have been performed between the two types of control methodologies by operating this ACB machine in different working conditions. The double pre-compression in PLC control proves that there is an enhancement in the ACB performance by 133% greater than the direct compression in the semi-automatic control. In addition, the percentage of the reduction ratio in volume reaches 77%, and the compaction ratio reaches about four times of the initial volume.

Keywords: Aluminum can recycling, Fully automatic machine, Hydraulic system control, Multi-compression.

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Authors: Chaiwat Tantarangsee

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The purposes of this study are 1) to identify learning styles of university students in Bangkok, and 2) to study the frequency of the relevant instructional context of the identified learning styles. Learning Styles employed in this study are those of Honey and Mumford, which include 1) Reflectors, 2) Theorists, 3) Pragmatists, and 4) Activists. The population comprises 1383 students and 5 lecturers. Research tools are 2 questionnaires – one used for identifying students- learning styles, and the other used for identifying the frequency of the relevant instructional context of the identified learning styles. The research findings reveal that 32.30 percent - are Activists, while 28.10 percent are Theorists, 20.10 are Reflectors, and 19.50 are Pragmatists. In terms of the relevant instructional context of the identified 4 learning styles, it is found that the frequency level of the instructional context is totally in high level. Moreover, 2 lists of the context being conducted most frequently are 'Lead'in activity to review background knowledge,- and 'Information retrieval report.' And these two activities serve the learning styles of theorists and activists. It is, therefore, suggested that more instructional context supporting the activists, the majority of the population, learning best by doing, as well as emotional learning situation should be added.

Keywords: Instructional Context, Learning Styles, Learning Style Preference, and Learning Style Questionnaire.

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Authors: Aldan A. Sapargaliyev, Yerbol A. Sapargaliyev

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Since the invention, the electric machine (EM) can be defined as oEM – one-vector electric machine, as it works due to one-vector inductive coupling with use of one-vector electromagnet. The disadvantages of oEM are large size and limited efficiency at low and medium power applications. This paper describes multi-vector electric machine (mEM) based on multi-vector inductive coupling, which is characterized by the increased surface area of ​​the inductive coupling per EM volume, with a reduced share of inefficient and energy-consuming part of the winding, in comparison with oEM’s. Particularly, it is considered, calculated and compared the performance of three different electrical motors and their power at the same volumes and rotor frequencies. It is also presented the result of calculation of correlation between power density and volume for oEM and mEM. The method of multi-vector inductive coupling enables mEM to possess 1.5-4.0 greater density of power per volume and significantly higher efficiency, in comparison with today’s oEM, especially in low and medium power applications. mEM has distinct advantages, when used in transport vehicles such as electric cars and aircrafts.

Keywords: Electric machine, electric motor, electromagnet, efficiency of electric motor.

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Authors: Ayodeji Adesina, Derek Molloy

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Traditional higher-education classrooms allow lecturers to observe students- behaviours and responses to a particular pedagogy during learning in a way that can influence changes to the pedagogical approach. Within current e-learning systems it is difficult to perform continuous analysis of the cohort-s behavioural tendency, making real-time pedagogical decisions difficult. This paper presents a Virtual Learning Process Environment (VLPE) based on the Business Process Management (BPM) conceptual framework. Within the VLPE, course designers can model various education pedagogies in the form of learning process workflows using an intuitive flow diagram interface. These diagrams are used to visually track the learning progresses of a cohort of students. This helps assess the effectiveness of the chosen pedagogy, providing the information required to improve course design. A case scenario of a cohort of students is presented and quantitative statistical analysis of their learning process performance is gathered and displayed in realtime using dashboards.

Keywords: Business process management, cohort analytics, learning processes, virtual learning environment.

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Authors: Masimuddin Mohd Khaled

Abstract:

This paper subsidises to the discussion of inter-organisational learning. This study has a main aim which is to examine the inter-organisational learning from a supply chain perspective. The integration and importance of supply chain with inter-organisational learning till date is discussed. The steps that are involved in the consideration of inter-organisational learning are looked throughout with emphasis done to supply chain management. The paper studies the impact of absorptive capacity, the supply chain orientation and design as well as discusses on fostering the inter-organisational learning.

Keywords: Absorptive Capacity, Inter-organisational Learning, Supply Chain Management, supply chain orientation.

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Authors: Anne M. Sørebø, Øystein Sørebø

Abstract:

The present study was designed to test the influence of confirmed expectations, perceived usefulness and perceived competence on e-learning satisfaction among university teachers. A questionnaire was completed by 125 university teachers from 12 different universities in Norway. We found that 51% of the variance in university teachers- satisfaction with e-learning could be explained by the three proposed antecedents. Perceived usefulness seems to be the most important predictor of teachers- satisfaction with e-learning.

Keywords: E-learning, User satisfaction, Teachers, IS success.

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