Search results for: machine learning tools and techniques

Authors: Jay N. Vyas, Sachin Daxini

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Numerical simulation techniques are widely used now in product development and testing instead of expensive, time-consuming and sometimes dangerous laboratory experiments. Numerous numerical methods are available for performing simulation of physical problems of different engineering fields. Grid based methods, like Finite Element Method, are extensively used in performing various kinds of static, dynamic, structural and non-structural analysis during product development phase. Drawbacks of grid based methods in terms of discontinuous secondary field variable, dealing fracture mechanics and large deformation problems led to development of a relatively a new class of numerical simulation techniques in last few years, which are popular as Meshless methods or Meshfree Methods. Meshless Methods are expected to be more adaptive and flexible than Finite Element Method because domain descretization in Meshless Method requires only nodes. Present paper introduces Meshless Methods and differentiates it with Finite Element Method in terms of following aspects: Shape functions used, role of weight function, techniques to impose essential boundary conditions, integration techniques for discrete system equations, convergence rate, accuracy of solution and computational effort. Capabilities, benefits and limitations of Meshless Methods are discussed and concluded at the end of paper.

Keywords: numerical simulation, Grid-based methods, Finite Element Method, Meshless Methods

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Authors: Restituto I. Rodelas

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The higher education has adopted the outcomes-based education from K-12. In this approach, the teacher uses any teaching and learning strategies that enable the students to achieve the learning outcomes. The students may be required to exert more effort and figure things out on their own. Hence, outcomes-based students are assumed to be more responsible and more capable of applying the knowledge learned. Another approach that the higher education in the Philippines is starting to adopt from other countries is blended learning. This combination of classroom and fully online instruction and learning is expected to be more effective. Participating in the online sessions, however, is entirely up to the students. Thus, the effect of blended learning on the performance of students in Statistics may be mediated by outcomes-based education. If there is a significant positive mediating effect, then blended learning can be optimized by integrating outcomes-based education. In this study, the sample will consist of four blended learning Statistics classes at Jose Rizal University in the second semester of AY 2015–2016. Two of these classes will be assigned randomly to the experimental group that will be handled using outcomes-based education. The two classes in the control group will be handled using the traditional lecture approach. Prior to the discussion of the first topic, a pre-test will be administered. The same test will be given as posttest after the last topic is covered. In order to establish equality of the groups’ initial knowledge, single factor ANOVA of the pretest scores will be performed. Single factor ANOVA of the posttest-pretest score differences will also be conducted to compare the performance of the experimental and control groups. When a significant difference is obtained in any of these ANOVAs, post hoc analysis will be done using Tukey's honestly significant difference test (HSD). Mediating effect will be evaluated using correlation and regression analyses. The groups’ initial knowledge are equal when the result of pretest scores ANOVA is not significant. If the result of score differences ANOVA is significant and the post hoc test indicates that the classes in the experimental group have significantly different scores from those in the control group, then outcomes-based education has a positive effect. Let blended learning be the independent variable (IV), outcomes-based education be the mediating variable (MV), and score difference be the dependent variable (DV). There is mediating effect when the following requirements are satisfied: significant correlation of IV to DV, significant correlation of IV to MV, significant relationship of MV to DV when both IV and MV are predictors in a regression model, and the absolute value of the coefficient of IV as sole predictor is larger than that when both IV and MV are predictors. With a positive mediating effect of outcomes-base education on the effect of blended learning on student performance, it will be recommended to integrate outcomes-based education into blended learning. This will yield the best learning results.

Keywords: outcome-based teaching, blended learning, face-to-face, student-centered

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Authors: Richard Mababu Mukiur

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Leadership is an important drive that plays a key role in the success and development of organizations, particularly in the current context of digital transformation, highly competitivity and globalization. Leaders are persons that hold a dominant and privileged position within an organization, field, or sector of activities and are able to manage, motivate and exercise a high degree of influence over other in order to achieve the institutional goals. They achieve commitment and engagement of others to embrace change, and to make good decisions. Leadership studies in higher education institutions have examined how effective leaders hold their organizations, and also to find approaches which fit best in the organizations context for its better management, transformation and improvement. Moreover, recent studies have highlighted the impact of leadership styles on organizational performance and innovation capacities, since some styles give better results than others. Effective leadership is part of learning process that take place through day-to-day tasks, responsibilities, and experiences that influence the organizational performance, innovation and engagement of employees. The adoption of appropriate leadership styles can improve organization results and encourage learning process, team skills and performance, and employees' motivation and engagement. In the case of case of Information Technology sector, leadership styles are particularly crucial since this sector is leading relevant changes and transformations in the knowledge society. In this context, the main objective of this study is to analyze managers leadership styles with their relation to organizational performance and innovation that may be mediated by learning organization process and demographic variables. Therefore, it was hypothesized that the transformational and transactional leadership will be the main style adopted in Information Technology sector and will influence organizational performance and innovation capacity. A sample of 540 participants from Information technology sector has been determined in order to achieve the objective of this study. The Multifactor Leadership Questionnaire was administered as the principal instrument, Scale of innovation and Learning Organization Questionnaire. Correlations and multiple regression analysis have been used as the main techniques of data analysis. The findings indicate that leadership styles have a relevant impact on organizational performance and innovation capacity. The transformational and transactional leadership are predominant styles in Information technology sector. The effective leadership style tend to be characterized by the capacity of generating and sharing knowledge that improve organization performance and innovation capacity. Managers are adopting and adapting their leadership styles that respond to the new organizational, social and cultural challenges and realities of contemporary society. Managers who encourage innovation, foster learning process, share experience are useful to the organization since they contribute to its development and transformation. Learning process capacity and demographic variables (age, gender, and job tenure) mediate the relationship between leadership styles, innovation capacity and organizational performance. The transformational and transactional leadership tend to enhance the organizational performance due to their significant impact on team-building, employees' engagement and satisfaction. Some practical implications and future lines of research have been proposed.

Keywords: leadership styles, tranformational leadership, organisational performance, organisational innovation

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Authors: Leila Goosen, Dalize van Heerden

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The performance outcomes of first year Computer Science and Information Technology students across the world are of great concern, whether they are being taught in a face-to-face environment or via distance education. In the face-to-face environment, it is, however, somewhat easier to teach and support students than it is in a distance education environment. The face-to-face academic can more easily gauge the level of understanding and participation of students and implement interventions to address issues, which may arise. With the inroads that Web 2.0 and Web 3.0 technologies are making, the world of online teaching and learning are rapidly expanding, bringing about technologies, which allows for similar interactions between online academics and their students as available to their face-to-face counter parts. At the University of South Africa (UNISA), the Learning Management System (LMS) is called myUNISA and it is deployed on a SAKAI platform. In this paper, we will take a look at some of the myUNISA technologies implemented in the teaching of a first year programming course, how they are implemented and, in some cases, we will indicate how this affects the performance outcomes of students.

Keywords: computer science, Distance Education Technologies, Learning Management System, face-to-face environment

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Authors: Tigist Girma Kidane, Yalew Dessalegn Asfaw

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Abstract: The current investigation aims to measure the longitudinal and transversal three-point bending tests of bamboo fibres polypropylene composites (BFPPCs) for the application of the automobile industry. Research has not been done on the properties of Ethiopian bamboo fibres for the utilization of composite development. The samples of bamboo plants have been harvested in 3–groups of age, 2–harvesting seasons, and 3–regions of bamboo species. Roll milling machine used for the extraction of bamboo fibres which has been developed by the authors. Chemical constituents measured using gravimetric methods. Unidirectional bamboo fibres prepreg has been produced using PP and hot press machine, then BFPPCs were produced using 6 layers of prepregs at automatic hot press machine. Age, harvesting month, and bamboo species have a statistically significant effect on the longitudinal and transverse flexural strength (FS), modulus of elasticity (MOE), and failure strain at α = 0.05 as evaluated by one-way ANOVA. 2–yrs old of BFPPCs have the highest FS and MOE, whereas November has the highest value of flexural properties. The highest to the lowest FS and MOE of BFPPCs has measured in Injibara, Mekaneselam, and Kombolcha, respectively. The transverse 3-point bending test has a lower FS and MOE compared to the longitudinal direction. The chemical constituents of Injibara, Mekaneselam, and Kombolcha have the highest to the lowest, respectively. 2-years old of bamboo fibres has the highest chemical constituent. The chemical constituents improved the flexural properties. Bamboo fibres in Ethiopia can be relevant for composite development, which has been applied in the area of requiring higher flexural properties.

Keywords: age, bamboo species, flexural properties, harvesting season, polypropylene

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Authors: Madiha Khan, Sidrah Ramzan, Seemab Khan, Shahzad Hassan, Kamran Saeed

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Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information.

Keywords: Graphviz representation, semantic relatedness, similarity measurement, WordNet similarity

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Authors: Frederik Calsius, Mirela Popa, Alexia Briassouli

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In the field of machine learning, it is common practice to use benchmark datasets to prove the working of a method. The domain of action recognition in videos often uses datasets like Kinet-ics, Something-Something, UCF-101 and HMDB-51 to report results. Considering the properties of the datasets, there are no datasets that focus solely on very short clips (2 to 3 seconds), and on highly-similar fine-grained actions within one specific domain. This paper researches how current state-of-the-art action recognition methods perform on a dataset that consists of highly similar, fine-grained actions. To do so, a dataset of skateboarding tricks was created. The performed analysis highlights both benefits and limitations of state-of-the-art methods, while proposing future research directions in the activity recognition domain. The conducted research shows that the best results are obtained by fusing RGB data with OpenPose data for the Temporal Shift Module.

Keywords: activity recognition, fused deep representations, fine-grained dataset, temporal modeling

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Robin Lok Wang Ma

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The purpose of this paper is to investigate how engineering students’ motivation and interests are maintained in their journeys. In recent years, different pedagogies of teaching, including entrepreneurship, experiential and lifelong learning, as well as dream builder, etc., have been widely used for education purposes. University advocates hands-on practice, learning by experiencing and experimenting throughout different courses. Students are not limited to gaining knowledge via traditional lectures, laboratory demonstrations, tutorials, and so on. The capability to identify both complex problems and their corresponding solutions in daily life are one of the criteria/skill sets required for graduates to obtain their careers at professional organizations and companies. A project-based course, namely Mechatronic Design and Prototyping, was developed for students to design and build a physical prototype for solving existing problems in their daily lives, thereby encouraging them as an entrepreneur to explore further possibilities to commercialize their designed prototypes and launch them to the market. Feedbacks from students show that they are keen to propose their own ideas freely with guidance from the instructor instead of using either suggested or assigned topics. Proposed ideas of the prototypes reflect that if students’ interests are maintained, they acquire the knowledge and skills they need, including essential communication, logical thinking, and, more importantly, problem solving for their lifelong learning journey.

Keywords: problem solving, lifelong learning, entrepreneurship, engineering

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Authors: Natasha Petrovska, Mirjana Pavlovic, Maria M. Larrondo-Petrie

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Neural stem cells (NSCs) are multi-potent, self-renewing cells that generate new neurons. Three subtypes of NSCs can be separated regarding the stages of NSC lineage: quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs), but their gene expression signatures are not utterly understood yet. Single-cell examinations have started to elucidate the complex structure of NSC populations. Nevertheless, there is a lack of thorough molecular interpretation of the NSC lineage heterogeneity and an increasing need for tools to analyze and improve the efficiency and correctness of single-cell sequencing data. Feature selection and ordering can identify and classify the gene expression signatures of these subtypes and can discover novel subpopulations during the NSCs activation and differentiation processes. The aim here is to review the implementation of the feature selection technique on NSC subtypes and the classification techniques that have been used for the identification of gene expression signatures.

Keywords: feature selection, feature similarity, neural stem cells, genes, feature selection methods

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Authors: Leila Najeh Bel'Kiry

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Artificial intelligence (AI) brought about many language robots, with ChatGPT being the most sophisticated thanks to its human-like linguistic capabilities. This aspect raises the idea of using ChatGPT in learning foreign languages. Starting from the premise that positions ChatGPT as a mediator between the language and the leaner, functioning as a “ghost teacher" offering a peaceful and secure learning space, this study aims to explore the attitudes of Tunisian students of English towards ChatGPT as a “Foreign Language Teacher” . Forty-five students, in their third year of fundamental English at Tunisian universities and high institutes, completed a Likert scale questionnaire consisting of thirty-two items and covering various aspects of language (phonology, morphology, syntax, semantics, and pragmatics). A scale ranging from 'Strongly Disagree,' 'Disagree,' 'Undecided,' 'Agree,' to 'Strongly Agree.' is used to assess the attitudes of the participants towards the integration of ChaGPTin learning a foreign language. Results indicate generally positive attitudes towards the reliance on ChatGPT in learning foreign languages, particularly some compounds of language like syntax, phonology, and morphology. However, learners show insecurity towards ChatGPT when it comes to pragmatics and semantics, where the artificial model may fail when dealing with deeper contextual and nuanced language levels.

Keywords: artificial language model, attitudes, foreign language learning, ChatGPT, linguistic capabilities, Tunisian English language learners

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Authors: B. Chandra

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In Association Rule Mining, much attention has been paid for developing algorithms for large (frequent/closed/maximal) itemsets but very little attention has been paid to improve the performance of rule generation algorithms. Rule generation is an important part of Association Rule Mining. In this paper, a novel approach named NARG (Association Rule using Antecedent Support) has been proposed for rule generation that uses memory resident data structure named FCET (Frequent Closed Enumeration Tree) to find frequent/closed itemsets. In addition, the computational speed of NARG is enhanced by giving importance to the rules that have lower antecedent support. Comparative performance evaluation of NARG with fast association rule mining algorithm for rule generation has been done on synthetic datasets and real life datasets (taken from UCI Machine Learning Repository). Performance analysis shows that NARG is computationally faster in comparison to the existing algorithms for rule generation.

Keywords: knowledge discovery, association rule mining, antecedent support, rule generation

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Authors: Sayed Hadi Sadeghi

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This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.

Keywords: support services, e-Network practice, Australian universities, United States universities

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Authors: Ihuarulam A. Ikenna

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In the past few decades, the field of chemistry education has grown tremendously and researches indicated that after traditional chemistry instruction students often lacked deep conceptual understanding and failed to integrate their ideas into coherent conceptual framework. For several concepts in chemistry, students at all levels have demonstrated difficulty in changing their initial perceptions. Their perceptions are most often wrong and do not agree with correct scientific concepts. This study explored the effectiveness of intervention discussion sections for a college general chemistry course designed to apply research on students preconceptions, knowledge integration and student explanation. Three interventions discussions lasting three hours on bond energy and spontaneity were done tested and intervention (treatment) students’ performances were compared with that of control group which did not use the experimental pedagogy. Results indicated that this instruction which was capable of identifying students' misconceptions, initial conceptions and integrating those ideas into class discussion led to enhanced conceptual understanding and better achievement for the experimental group.

Keywords: remedying, students’ misconceptions, learning, intervention discussion, learning model

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Authors: Mamochana, A. Ramatea, Fumane, P. Khanare

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Enabling the learning environment is a significant and adaptive technique necessary to navigate learners’ educational challenges. However, research has indicated that quality provision of education in the environments that are enabling, especially to learners with visual impairments (LVIs, hereafter) in rural schools, remain an ongoing challenge globally. Hence, LVIs often have a lower level of academic performance as compared to their peers. To balance this gap and fulfill learners'fundamentalhuman rights¬ of receiving an equal quality education, appropriate measures and structures that make enabling learning environment a better place to learn must be better understood. This paper, therefore, intends to find possible means that rural schools of Lesotho can employ to make the learning environment for LVIs enabling. The present study aims to determine suitable assets that can be drawn to make the learning environment for LVIs enabling. The study is also informed by the transformative paradigm and situated within a qualitative research approach. Data were generated through focus group discussions with twelve teachers who were purposefully selected from two rural primary schools in Lesotho. The generated data were then analyzed thematically using Braun and Clarke's six-phase framework. The findings of the study indicated that participating teachers do have an understanding that rural schools boast of assets (existing and hidden) that have a positive influence in responding to the special educational needs of LVIs. However, the participants also admitted that although their schools boast of assets, they still experience limited knowledge about the use of the existing assets and thus, realized a need for improved collaboration, involvement of the existing assets, and enhancement of academic resources to make LVIs’ learning environment enabling. The findings of this study highlight the significance of the effective use of assets. Additionally, coincides with literature that shows recognizing and tapping into the existing assets enable learning for LVIs. In conclusion, the participants in the current study indicated that for LVIs’ learning environment to be enabling, there has to be sufficient use of the existing assets. The researchers, therefore, recommend that the appropriate use of assets is good, but may not be sufficient if the existing assets are not adequately managed. Hence,VILs experience a vicious cycle of vulnerability. It was thus, recommended that adequate use of assets and teachers' engagement as active assets should always be considered to make the learning environment a better place for LVIs to learan in the future

Keywords: assets, enabling learning environment, rural schools, learners with visual impairments

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Adnan Z. Mkhelif

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Grammatical collocations (GCs) are word combinations containing a preposition or a grammatical structure, such as an infinitive (e.g. smile at, interested in, easy to learn, etc.). Such collocations tend to be difficult for Iraqi EFL university students (IUS) to master. To help address this problem, it is important to identify the factors causing it. This study aims at investigating the effects of L2 proficiency, frequency of GCs and their transparency on IUSs’ productive knowledge of GCs. The study involves 112 undergraduate participants with different proficiency levels, learning English in formal contexts in Iraq. The data collection instruments include (but not limited to) a productive knowledge test (designed by the researcher using the British National Corpus (BNC)), as well as the grammar part of the Oxford Placement Test (OPT). The study findings have shown that all the above-mentioned factors have significant effects on IUSs’ productive knowledge of GCs. In addition to establishing evidence of which factors of L2 learning might be relevant to learning GCs, it is hoped that the findings of the present study will contribute to more effective methods of teaching that can better address and help overcome the problems IUSs encounter in learning GCs. The study is thus hoped to have significant theoretical and pedagogical implications for researchers, syllabus designers as well as teachers of English as a foreign/second language.

Keywords: corpus linguistics, frequency, grammatical collocations, L2 vocabulary learning, productive knowledge, proficiency, transparency

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Authors: Rao Muhammad Afzal Khan

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Nano technology is emerging as a useful technology in nearly all areas of Science and Technology. Its role in medical imaging is attracting the researchers towards existing and new imaging modalities and techniques. This presentation gives an overview of the development of the work done throughout the world. Furthermore, it lays an idea into the scope of the future use of this technology for diagnosing different diseases. A comparative analysis has also been discussed with an emphasis to detect diseases, in general, and cancer, in particular.

Keywords: medical imaging, cancer detection, diagnosis, nano-imaging, nanotechnology

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Authors: David Rafetseder, Walter Bauer, Florian Poltschak, Wolfgang Amrhein

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A flat double-stator linear induction machine (DSLIM) with a short squirrel-cage mover is designed for high thrust force at moderate speed < 5m/s. The performance and motor parameters are determined on the basis of a 2D time-transient simulation with the finite element (FE) software Maxwell 2015. Design guidelines and transformation rules for space vector theory of the LIM are presented. Resulting thrust calculated by flux and current vectors is compared with the FE results showing good coherence and reduced noise. The parameters of the equivalent circuit model are obtained.

Keywords: equivalent circuit model, finite element model, linear induction motor, space vector theory

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Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

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Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

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This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

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Authors: I. Tiahi

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The aim of the current paper is to explore educational practices in contemporary Algeria. Researches explain such practices bear traditional approach and the overlooks modern teaching methods such as mobile learning. That is why the research output of examining student engagement in respect of mobile learning was obtained from the following objectives: (1) To evaluate the current practice of English language teaching within Algerian higher education institutions, (2) To explore how social constructivism theory and m-learning help students’ engagement in the classroom and (3) To explore the feasibility and acceptability of m-learning amongst institutional leaders. The methodology underpins a case study and action research. For the case study, the researcher engaged with 6 teachers, 4 institutional leaders, and 30 students subjected for semi-structured interviews and classroom observations to explore the current teaching methods for English as a foreign language. For the action research, the researcher applied an intervention course to investigate the possibility and implications for future implementation of mobile learning in higher education institutions. The results were deployed using thematic analysis. The research outcome showed that the disengagement of students in English language learning has many aspects. As seen from the interviews from the teachers, the researcher found that they do not have enough resources except for using ppt for some teacher. According to them, the teaching method they are using is mostly communicative and competency-based approach. Teachers informed that students are disengaged because they have psychological barriers. In classroom setting, the students are conscious about social approval from the peer, and thus if they are to face negative reinforcement which would damage their image, it is seen as a preventive mechanism to be scared of committing mistakes. This was also very reflective in this finding. A lot of other arguments can be given for this claim; however, in Algerian setting, it is usual practice where teachers do not provide positive reinforcement which is open up students for possible learning. Thus, in order to overcome such a psychological barrier, proper measures can be taken. On a conclusive remark, it is evident that teachers, students, and institutional leaders provided positive feedback for using mobile learning. It is not only motivating but also engaging in learning processes. Apps such as Kahoot, Padlet and Slido were well received and thus can be taken further to examine its higher impact in Algerian context. Thus, in the future, it will be important to implement m-learning effectively in higher education to transform the current traditional practices into modern, innovative and active learning. Persuasion for this change for stakeholder may be challenging; however, its long-term benefits can be reflective from the current research paper.

Keywords: Algerian context, mobile learning, social constructivism, student engagement

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Authors: Hyun-Woo Cho

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The monitoring of industrial production quality has to be implemented to alarm early warning for unusual operating conditions. Furthermore, identification of their assignable causes is necessary for a quality control purpose. For such tasks many multivariate statistical techniques have been applied and shown to be quite effective tools. This work presents a process data-based monitoring scheme for production processes. For more reliable results some additional steps of noise filtering and preprocessing are considered. It may lead to enhanced performance by eliminating unwanted variation of the data. The performance evaluation is executed using data sets from test processes. The proposed method is shown to provide reliable quality control results, and thus is more effective in quality monitoring in the example. For practical implementation of the method, an on-line data system must be available to gather historical and on-line data. Recently large amounts of data are collected on-line in most processes and implementation of the current scheme is feasible and does not give additional burdens to users.

Keywords: detection, filtering, monitoring, process data

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

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Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

Procedia PDF Downloads 139

Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

Procedia PDF Downloads 38

Authors: Marina Fuertes

Abstract:

Introduction: In everyday life experiences, children are solicited to cooperate with others. Often they perform cooperative tasks with their parents (e.g., setting the table for dinner) or in school. These tasks are very significant since children may learn to turn taking in interactions, to participate as well to accept others participation, to trust, to respect, to negotiate, to self-regulate their emotions, etc. Indeed, cooperative tasks contribute to children social, motor, cognitive and linguistic development. Therefore, it is important to study what learning, social and affective experiences are provided to children during these tasks. In this study, we included parents and preschool educators. Parents and educators are both significant: educative, interactive and affective figures. Rarely parents and educators behavior have been compared in studies about cooperative tasks. Parents and educators have different but complementary styles of interaction and communication. Aims: Therefore, this study aims to compare parents and educators' (of both genders) interactive behavior (cooperativity, empathy, ability to challenge the child, reciprocity, elaboration) during a play/individualized situation involving a cooperative task. Moreover, to compare parents and educators' behavior with girls and boys. Method: A quasi-experimental study with 45 dyads educators-children and 45 dyads with parents and their children. In this study, participated children between 3 and 5 years old and with age appropriate development. Adults and children were videotaped using a variety of materials (e.g., pencils, wood, wool) and tools (e.g., scissors, hammer) to produce together something of their choice during 20-minutes. Each dyad (one adult and one child) was observed and videotaped independently. Adults and children agreed and consented to participate. Experimental conditions were suitable, pleasant and age appropriated. Results: Findings indicate that parents and teachers offer different learning experiences. Teachers were more likely to challenged children to explore new concepts and to accept children ideas. In turn, parents gave more support to children actions and were more likely to use their own example to teach children. Multiple regression analysis indicates that parent versus educator status predicts their behavior. Gender of both children and adults affected the results. Adults acted differently with girls and boys (e.g., adults worked more cooperatively with girls than boys). Male participants supported more girls participation rather than boys while female adults allowed boys to make more decisions than girls. Discussion: Taking our results and past studies, we learn that different qualitative interactions and learning experiences are offered by parents, educators according to parents and children gender. Thus, the same child needs to learn different cooperative strategies according to their interactive patterns and specific context. Yet, cooperative play and individualized activities with children generate learning opportunities and benefits children participation and involvement.

Keywords: early childhood education, parenting, gender, cooperative tasks, adult-child interaction

Procedia PDF Downloads 320

Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

Procedia PDF Downloads 359

Authors: Seulki Lee, Seoung Bum Kim

Abstract:

Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

Procedia PDF Downloads 296

Authors: Su-Hyun Jung, Chang-Bin Ha, Hyoung-Kyu Song

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Recently, increasing the quality of experience (QoE) is an important issue. Since performance degradation at cell edge extremely reduces the QoE, several techniques are defined at LTE/LTE-A standard to remove inter-cell interference (ICI). However, the conventional techniques have disadvantage because there is a trade-off between resource allocation and reliable communication. The proposed scheme reduces the ICI more efficiently by using channel state information (CSI) smartly. It is shown that the proposed scheme can reduce the ICI with less resources.

Keywords: adaptive beamforming, CoMP, LTE-A, ICI reduction

Procedia PDF Downloads 463