Search results for: Learning by modeling

Authors: Vânia Graça, Paula Quadros-Flores, Altina Ramos

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The use of Digital Technologies in teaching and learning processes is currently a reality, namely in initial teacher training. This study aims at knowing the digital reality of students in initial teacher training in order to improve training in the educational use of ICT and to promote digital technology integration strategies in an educational context. It is part of the IFITIC Project "Innovate with ICT in Initial Teacher Training to Promote Methodological Renewal in Pre-school Education and in the 1st and 2nd Basic Education Cycle" which involves the School of Education, Polytechnic of Porto and Institute of Education, University of Minho. The Project aims at rethinking educational practice with ICT in the initial training of future teachers in order to promote methodological innovation in Pre-school Education and in the 1st and 2nd Cycles of Basic Education. A qualitative methodology was used, in which a questionnaire survey was applied to teachers in initial training. For data analysis, the techniques of content analysis with the support of NVivo software were used. The results point to the following aspects: a) future teachers recognize that they have more technical knowledge about ICT than pedagogical knowledge. This result makes sense if we consider the objective of Basic Education, so that the gaps can be filled in the Master's Course by students who wish to follow the teaching; b) the respondents are aware that the integration of digital resources contributes positively to students' learning and to the life of children and young people, which also promotes preparation in life; c) to be a teacher in the digital age there is a need for the development of digital literacy, lifelong learning and the adoption of new ways of teaching how to learn. Thus, this study aims to contribute to a reflection on the teaching profession in the digital age.

Keywords: Digital technologies, initial teacher training, pedagogical use of ICT, skills.

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Authors: Theerawat Songyot, Tham Manjing, Bunyarit Uyyanonvara, Chanjira Sinthanayothin

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Corner detection and optical flow are common techniques for feature-based video stabilization. However, these algorithms are computationally expensive and should be performed at a reasonable rate. This paper presents an algorithm for discarding irrelevant feature points and maintaining them for future use so as to improve the computational cost. The algorithm starts by initializing a maintained set. The feature points in the maintained set are examined against its accuracy for modeling. Corner detection is required only when the feature points are insufficiently accurate for future modeling. Then, optical flows are computed from the maintained feature points toward the consecutive frame. After that, a motion model is estimated based on the simplified affine motion model and least square method, with outliers belonging to moving objects presented. Studentized residuals are used to eliminate such outliers. The model estimation and elimination processes repeat until no more outliers are identified. Finally, the entire algorithm repeats along the video sequence with the points remaining from the previous iteration used as the maintained set. As a practical application, an efficient video stabilization can be achieved by exploiting the computed motion models. Our study shows that the number of times corner detection needs to perform is greatly reduced, thus significantly improving the computational cost. Moreover, optical flow vectors are computed for only the maintained feature points, not for outliers, thus also reducing the computational cost. In addition, the feature points after reduction can sufficiently be used for background objects tracking as demonstrated in the simple video stabilizer based on our proposed algorithm.

Keywords: background object tracking, feature point reduction, low cost tracking, video stabilization.

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Authors: Wanida Suwunniponth

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The objectives of this research paper were to study the influencing factors that contributed to the success of electronic commerce (e-commerce) and to study the approach to enhance the standard of e-commerce for small and medium enterprises (SME). The research paper focused the study on only sole proprietorship SMEs in Bangkok, Thailand. The factors contributed to the success of SME included business management, learning in the organization, business collaboration, and the quality of website. A quantitative and qualitative mixed research methodology was used. In terms of quantitative method, a questionnaire was used to collect data from 251 sole proprietorships. The System Equation Model (SEM) was utilized as the tool for data analysis. In terms of qualitative method, an in-depth interview, a dialogue with experts in the field of ecommerce for SMEs, and content analysis were used. By using the adjusted causal relationship structure model, it was revealed that the factors affecting the success of e-commerce for SMEs were found to be congruent with the empirical data. The hypothesis testing indicated that business management influenced the learning in the organization, the learning in the organization influenced business collaboration and the quality of the website, and these factors, in turn, influenced the success of SMEs. Moreover, the approach to enhance the standard of SMEs revealed that the majority of respondents wanted to enhance the standard of SMEs to a high level in the category of safety of e-commerce system, basic structure of e-commerce, development of staff potentials, assistance of budget and tax reduction, and law improvement regarding the e-commerce respectively.

Keywords: Electronic Commerce, Influencing Factors, Small and Medium Enterprises.

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Authors: Kristina Davoian, Wolfram-M. Lippe

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Evolutionary Programming (EP) represents a methodology of Evolutionary Algorithms (EA) in which mutation is considered as a main reproduction operator. This paper presents a novel EP approach for Artificial Neural Networks (ANN) learning. The proposed strategy consists of two components: the self-adaptive, which contains phenotype information and the dynamic, which is described by genotype. Self-adaptation is achieved by the addition of a value, called the network weight, which depends on a total number of hidden layers and an average number of neurons in hidden layers. The dynamic component changes its value depending on the fitness of a chromosome, exposed to mutation. Thus, the mutation step size is controlled by two components, encapsulated in the algorithm, which adjust it according to the characteristics of a predefined ANN architecture and the fitness of a particular chromosome. The comparative analysis of the proposed approach and the classical EP (Gaussian mutation) showed, that that the significant acceleration of the evolution process is achieved by using both phenotype and genotype information in the mutation strategy.

Keywords: Artificial Neural Networks (ANN), Learning Theory, Evolutionary Programming (EP), Mutation, Self-Adaptation.

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Authors: M. Daoudi, A. Belghachi, L. Varani

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In this paper, we analyze the problem of quasiballistic electron transport in ultra small of mercury -cadmiumtelluride (Hg0.8Cd0.2Te -MCT) n+-n- n+ devices from hydrodynamic point view. From our study, we note that, when the size of the active layer is low than 0.1μm and for low bias application( ( ≥ 9mV), the quasi-ballistic transport has an important effect.

Keywords: Hg0.8Cd0.2Te semiconductor, Hydrodynamicmode, Quasi-ballistic transport, Submicron diode

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Authors: T. Aboumahboub, R. Brecha, H. B. Shrestha, U. F. Hutfilter, A. Geiges, W. Hare, M. Schaeffer, L. Welder, M. Gidden

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The proposed stringent mitigation targets require an immediate start for a drastic transformation of the whole energy system. The current Australian energy system is mainly centralized and fossil fuel-based in most states with coal and gas-fired plants dominating the total produced electricity over the recent past. On the other hand, the country is characterized by a huge, untapped renewable potential, where wind and solar energy could play a key role in the decarbonization of the Australia’s future energy system. However, integrating high shares of such variable renewable energy sources (VRES) challenges the power system considerably due to their temporal fluctuations and geographical dispersion. This raises the concerns about flexibility gap in the system to ensure the security of supply with increasing shares of such intermittent sources. One main flexibility dimension to facilitate system integration of high shares of VRES is to increase the cross-sectoral integration through coupling of electricity to other energy sectors alongside the decarbonization of the power sector and reinforcement of the transmission grid. This paper applies a multi-sectoral energy system optimization model for Australia. We investigate the cost-optimal configuration of a renewable-based Australian energy system and its transformation pathway in line with the ambitious range of proposed climate change mitigation targets. We particularly analyse the implications of linking the electricity and transport sectors in a prospective, highly renewable Australian energy system.

Keywords: Decarbonization, energy system modeling, sector coupling, variable renewable energies.

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Authors: Mohammed El Raey, Moustafa Osman Mohammed

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The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s system. Naturally exchanged patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s system. The Probabilistic Risk Assessment (PRA) technique is utilized to assess the safety of an industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. The plant safety parameters are identified for engineering topology as employed in assessment safety of industrial ecology. In particular, the most severe accidental release of hazardous gaseous is postulated, analyzed and assessment in industrial region. The IAEA-safety assessment procedure is used to account the duration and rate of discharge of liquid chlorine. The ecological model of plume dispersion width and concentration of chlorine gas in the downwind direction is determined using Gaussian Plume Model in urban and rural areas and presented with SURFER®. The prediction of accident consequences is traced in risk contour concentration lines. The local greenhouse effect is predicted with relevant conclusions. The spatial-ecological model is predicted for multiple factors distribution schemes of multi-criteria analysis. The input–output analysis is explored from the spillover effect, and we conducted Monte Carlo simulations for sensitivity analysis. Their unique structure is balanced within “equilibrium patterns”, such as the composite index for biosphere with collective structure of many distributed feedback flows. These dynamic structures are related to have their physical and chemical properties and enable a gradual and prolonged incremental pattern. While this spatial model structure argues from ecology, resource savings, static load design, financial and other pragmatic reasons, the outcomes are not decisive in an artistic/architectural perspective. The hypothesis is deployed to unify analytic and analogical spatial structure in development urban environments using optimization loads as an example of integrated industrial structure where the process is based on engineering topology of systems ecology.

Keywords: Spatial-ecological modeling, spatial structure orientation impact, composite structure, industrial ecology.

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Authors: T. C. Manjunath, B. Bandyopadhyay

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Active vibration control is an important problem in structures. The objective of active vibration control is to reduce the vibrations of a system by automatic modification of the system-s structural response. In this paper, the modeling and design of a fast output sampling feedback controller for a smart flexible beam system embedded with shear sensors and actuators for SISO system using Timoshenko beam theory is proposed. FEM theory, Timoshenko beam theory and the state space techniques are used to model the aluminum cantilever beam. For the SISO case, the beam is divided into 5 finite elements and the control actuator is placed at finite element position 1, whereas the sensor is varied from position 2 to 5, i.e., from the nearby fixed end to the free end. Controllers are designed using FOS method and the performance of the designed FOS controller is evaluated for vibration control for 4 SISO models of the same plant. The effect of placing the sensor at different locations on the beam is observed and the performance of the controller is evaluated for vibration control. Some of the limitations of the Euler-Bernoulli theory such as the neglection of shear and axial displacement are being considered here, thus giving rise to an accurate beam model. Embedded shear sensors and actuators have been considered in this paper instead of the surface mounted sensors and actuators for vibration suppression because of lot of advantages. In controlling the vibration modes, the first three dominant modes of vibration of the system are considered.

Keywords: Smart structure, Timoshenko beam theory, Fast output sampling feedback control, Finite Element Method, State space model, SISO, Vibration control, LMI

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Authors: Phalaunnaphat Siriwongs

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The classroom of the 21st century is an ever changing forum for new and innovative thoughts and ideas. With increasing technology and opportunity, students have rapid access to information that only decades ago would have taken weeks to obtain. Unfortunately, new techniques and technology is not a cure for the fundamental problems that have plagued the classroom ever since education was established. Class size has been an issue long debated in academia. While it is difficult to pin point an exact number, it is clear that in this case more does not mean better. By looking into the success and pitfalls of classroom size the true advantages of smaller classes will become clear. Previously, one class was comprised of 50 students. Being seventeen and eighteen-year-old students, sometimes it was quite difficult for them to stay focused. To help them understand and gain much knowledge, a researcher introduced “The Theory of Multiple Intelligence” and this, in fact, enabled students to learn according to their own learning preferences no matter how they were being taught. In this lesson, the researcher designed a cycle of learning activities involving all intelligences so that everyone had equal opportunities to learn.

Keywords: Multiple Intelligences, role play, performance assessment, formative assessment.

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Authors: Souad Demigha

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The paper aims to specify and build a system, a learning support in radiology-senology (breast radiology) dedicated to help assist junior radiologists-senologists in their radiologysenology- related activity based on experience of expert radiologistssenologists. This system is named SAFRS (i.e. system supporting the training of radiologists-senologists). It is based on the exploitation of radiologic-senologic images (primarily mammograms but also echographic images or MRI) and their related clinical files. The aim of such a system is to help breast cancer screening in education. In order to acquire this expert radiologist-senologist knowledge, we have used the CBR (case-based reasoning) approach. The SAFRS system will promote the evolution of teaching in radiology-senology by offering the “junior radiologist" trainees an advanced pedagogical product. It will permit a strengthening of knowledge together with a very elaborate presentation of results. At last, the know-how will derive from all these factors.

Keywords: Learning support, radiology-senology, training, education, CBR, accumulated experience.

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Authors: A. Pashayev, R. Sadiqov, C. Ardil, F. Ildiz , H. Karabork

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The new technology of fuzzy neural networks for identification of parameters for mathematical models of geofields is proposed and checked. The effectiveness of that soft computing technology is demonstrated, especially in the early stage of modeling, when the information is uncertain and limited.

Keywords: Identification, interpolation methods, neuro-fuzzy networks, geofield.

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Authors: Daehwan Kim, Jaemin Kim, Seongwon Cho, Yongsuk Jang, Sun-Tae Chung, Boo-Gyoun Kim

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AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feature parameter vector fitting the inner facial feature points of the face and later localize the feature points of the whole face using the first information. The proposed progressive AAM-based face alignment algorithm utilizes the fact that the feature points of the inner part of the face are less variant and less affected by the background surrounding the face than those of the outer part (like the chin contour). The proposed algorithm consists of two stages: modeling and relation derivation stage and fitting stage. Modeling and relation derivation stage first needs to construct two AAM models: the inner face AAM model and the whole face AAM model and then derive relation matrix between the inner face AAM parameter vector and the whole face AAM model parameter vector. In the fitting stage, the proposed algorithm aligns face progressively through two phases. In the first phase, the proposed algorithm will find the feature parameter vector fitting the inner facial AAM model into a new input face image, and then in the second phase it localizes the whole facial feature points of the new input face image based on the whole face AAM model using the initial parameter vector estimated from using the inner feature parameter vector obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment algorithm is more robust with respect to pose, illumination, and face background than the conventional basic AAM-based face alignment algorithm.

Keywords: Face Alignment, AAM, facial feature detection, model matching.

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Authors: M. N. Zelenin, V. S. Mikhailov, R. P. Zhivotovsky

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It is known that residual welding deformations give negative effect to processability and operational quality of welded structures, complicating their assembly and reducing strength. Therefore, selection of optimal technology, ensuring minimum welding deformations, is one of the main goals in developing a technology for manufacturing of welded structures. Through years, JSC SSTC has been developing a theory for estimation of welding deformations and practical activities for reducing and compensating such deformations during welding process. During long time a methodology was used, based on analytic dependence. This methodology allowed defining volumetric changes of metal due to welding heating and subsequent cooling. However, dependences for definition of structures deformations, arising as a result of volumetric changes of metal in the weld area, allowed performing calculations only for simple structures, such as units, flat sections and sections with small curvature. In case of complex 3D structures, estimations on the base of analytic dependences gave significant errors. To eliminate this shortage, it was suggested to use finite elements method for resolving of deformation problem. Here, one shall first calculate volumes of longitudinal and transversal shortenings of welding joints using method of analytic dependences and further, with obtained shortenings, calculate forces, which action is equivalent to the action of active welding stresses. Further, a finiteelements model of the structure is developed and equivalent forces are added to this model. Having results of calculations, an optimal sequence of assembly and welding is selected and special measures to reduce and compensate welding deformations are developed and taken.

Keywords: Finite elements method, modeling, expected welding deformations, welding, assembling.

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Authors: B. Golchin, N. Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: T.C. Manjunath, B. Bandyopadhyay

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This paper features the modeling and design of a Robust Decentralized Fast Output Sampling (RDFOS) Feedback control technique for the active vibration control of a smart flexible multimodel Euler-Bernoulli cantilever beams for a multivariable (MIMO) case by retaining the first 6 vibratory modes. The beam structure is modeled in state space form using the concept of piezoelectric theory, the Euler-Bernoulli beam theory and the Finite Element Method (FEM) technique by dividing the beam into 4 finite elements and placing the piezoelectric sensor / actuator at two finite element locations (positions 2 and 4) as collocated pairs, i.e., as surface mounted sensor / actuator, thus giving rise to a multivariable model of the smart structure plant with two inputs and two outputs. Five such multivariable models are obtained by varying the dimensions (aspect ratios) of the aluminium beam. Using model order reduction technique, the reduced order model of the higher order system is obtained based on dominant Eigen value retention and the Davison technique. RDFOS feedback controllers are designed for the above 5 multivariable-multimodel plant. The closed loop responses with the RDFOS feedback gain and the magnitudes of the control input are obtained and the performance of the proposed multimodel smart structure system is evaluated for vibration control.

Keywords: Smart structure, Euler-Bernoulli beam theory, Fastoutput sampling feedback control, Finite Element Method, Statespace model, Vibration control, LMI, Model order Reduction.

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Authors: Mary Jane B. Suarez

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Speech anxiety is innumerable in any traditional communication classes especially for ESL students. The speech anxiety intensifies when communication skills assessments have taken its toll in an online mode of learning due to the perils of the COVID-19 virus. Teachers and students have experienced vast ambiguity on how to realize a still effective way to teach and learn various speaking skills amidst the pandemic. This mixed method study determined the factors that affected the public speaking skills of students in online performances, delineated the assessment gaps in assessing speaking skills in an online setup, and recommended ways to address students’ speech anxieties. Using convergent parallel design, quantitative data were gathered by examining the desired learning competencies of the English course including a review of the teacher’s class record to analyze how students’ performances reflected a significantly high level of anxiety in online speech delivery. Focus group discussion was also conducted for qualitative data describing students’ public speaking anxiety and assessment gaps. Results showed a significantly high level of students’ speech anxiety affected by time constraints, use of technology, lack of audience response, being conscious of making mistakes, and the use of English as a second language. The study presented recommendations to redesign curricular assessments of English teachers and to have a robust diagnosis of students’ speaking anxiety to better cater to the needs of learners in attempt to bridge any gaps in cultivating public speaking skills of students as educational institutions segue from the pandemic to the post-pandemic milieu.

Keywords: Blended learning, communication skills assessment, online speech delivery, public speaking anxiety, speech anxiety.

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Authors: Hamidullah Sokout, Samiullah Paracha, Abdul Rashid Ahmadi

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Passing the entrance exam to a university is a major step in one's life. University entrance exam commonly known as Kankor is the nationwide entrance exam in Afghanistan. This examination is prerequisite for all public and private higher education institutions at undergraduate level. It is usually taken by students who are graduated from high schools. In this paper, we reflect the major educational school graduates issues and propose ICT-based test preparation environment, known as ‘Online Kankor Exam Prep System’ to give students the tools to help them pass the university entrance exam on the first try. The system is based on Intelligent Tutoring System (ITS), which introduced an essential package of educational technology for learners that features: (I) exam-focused questions and content; (ii) self-assessment environment; and (iii) test preparation strategies in order to help students to acquire the necessary skills in their carrier and keep them up-to-date with instruction.

Keywords: Web-based test prep systems, Learner-centered design, E-Learning, Intelligent tutoring system.

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Authors: Amal S. Al-Badi, Ali H. Al-Badi

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Many organizations are opting to adopt Open Source Software (OSS) as it is the current trend to rely on each other rather than on companies (Software vendors). It is a clear shift from organizations to individuals, the concept being to rely on collective participation rather than companies/vendors.

The main objectives of this research are 1) to identify the current level of OSS usage in Sultan Qaboos University; 2) to identify the potential benefits of using OSS in educational institutes; 3) to identify the OSS applications that are most likely to be used within an educational institute; 4) to identify the existing and potential barriers to the successful adoption of OSS in education.

To achieve these objectives a two-stage research method was conducted. First a rigorous literature review of previously published material was performed (interpretive/descriptive approach), and then a set of interviews were conducted with the IT professionals at Sultan Qaboos University in Oman in order to explore the extent and nature of their usage of OSS.

Keywords: Open source software; social software, e-learning 2.0, Web 2.0, connectivism, personal learning environment (PLE), OpenID, OpenSocial and OpenCourseWare.

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: Amal M. Alrayes, Hayat M. Ali

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Since the beginning of distance education with the rapid evolution of technology, the social network plays a vital role in the educational process to enforce the interaction been the learners and teachers. There are many Web 2.0 technologies, services and tools designed for educational purposes. This research aims to investigate instructors’ acceptance towards web-based learning systems in higher educational institutes in Kingdom of Bahrain. Questionnaire is used to investigate the instructors’ usage of Web 2.0 and the factors affecting their acceptance. The results confirm that instructors had high accessibility to such technologies. However, patterns of use were complex. Whilst most expressed interest in using online technologies to support learning activities, learners seemed cautious about other values associated with web-based system, such as the shared construction of knowledge in a public format. The research concludes that there are main factors that affect instructors’ adoption which are security, performance expectation, perceived benefits, subjective norm, and perceived usefulness.

Keywords: Web 2.0, Higher education, Acceptance, Students’ perception.

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Authors: Indra Budi, Rizal Fathoni Aji, Agus Widodo

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Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar validation dataset. The dataset used in the experiment is time series derived from research report in Garuda, which is an online sites belongs to the Ministry of Education in Indonesia, over the past 20 years. The experimental result demonstrates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, we can forecast emerging research topics for the next few years.

Keywords: Combination, emerging topics, ensemble, forecasting, machine learning, prediction, research topics, similarity measure, time series.

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: Electrocardiogram, dictionary learning, sparse coding, classification.

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Authors: Bharatendra Rai

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Sequences of words in text data have long-term dependencies and are known to suffer from vanishing gradient problem when developing deep learning models. Although recurrent networks such as long short-term memory networks help overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine advantages of long short-term memory networks and convolutional neural networks, can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting of a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning. 

Keywords: Convolutional recurrent networks, hyperparameter tuning, long short-term memory networks, Tukey honest significant differences

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Authors: Elena A. Railean

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Although scientific methods have been the subject of a large number of papers, the term ‘scientific methods in educational management’ is still not well defined. In this paper, it is adopted the metasystems perspective to define the mentioned term and distinguish them from methods used in time of the scientific management and knowledge management paradigms. In our opinion, scientific methods in educational management rely on global phenomena, events, and processes and their influence on the educational organization. Currently, scientific methods in educational management are integrated with the phenomenon of globalization, cognitivisation, and openness, etc. of educational systems and with global events like the COVID-19 pandemic. Concrete scientific methods are nested in a hierarchy of more and more abstract models of educational management, which form the context of the global impact on education, in general, and learning outcomes, in particular. However, scientific methods can be assigned to a specific mission, strategy, or tactics of educational management of the concrete organization, either by the global management, local development of school organization, or/and development of the life-long successful learner. By accepting this assignment, the scientific method becomes a personal goal of each individual with the educational organization or the option to develop the educational organization at the global standards. In our opinion, in educational management, the scientific methods need to confine the scope to the deep analysis of concrete tasks of the educational system (i.e., teaching, learning, assessment, development), which result in concrete strategies of organizational development. More important are seeking the ways for dynamic equilibrium between the strategy and tactic of the planetary tasks in the field of global education, which result in a need for ecological methods of learning and communication. In sum, distinction between local and global scientific methods is dependent on the subjective conception of the task assignment, measurement, and appraisal. Finally, we conclude that scientific methods are not holistic scientific methods, but the strategy and tactics implemented in the global context by an effective educational/academic manager.

Keywords: Educational management, scientific management, educational leadership, scientific method in educational management.

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Authors: Suttipong Boonphadung

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Practicum placements are an critical factor for student teachers on Education Programs. How can student teachers become professionals? This study was to investigate problems, weakness and obstacles of practicum placements and develop guidelines for partnership in the practicum placements. In response to this issue, a partnership concept was implemented for developing student teachers into professionals. Data were collected through questionnaires on attitude toward problems, weaknesses, and obstacles of practicum placements of student teachers in Rajabhat universities and included focus group interviews. The research revealed that learning management, classroom management, curriculum, assessment and evaluation, classroom action research, and teacher demeanor are the important factors affecting the professional development of Education Program student teachers. Learning management plan and classroom management concerning instructional design, teaching technique, instructional media, and student behavior management are another important aspects influencing the professional development for student teachers.

Keywords: Developing student teacher, Partnership concepts, Professional teachers.

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Authors: Jacopo Baboni Schilingi

Abstract:

We here describe the theoretical and philosophical understanding of a long term use and development of algorithmic computer-based tools applied to music composition. The findings of our research lead us to interrogate some specific processes and systems of communication engaged in the discovery of specific cultural artworks: artistic creation in the sono-musical domain. Our hypothesis is that the patterns of auditory learning cannot be only understood in terms of social transmission but would gain to be questioned in the way they rely on various ranges of acoustic stimuli modes of consciousness and how the different types of memories engaged in the percept-action expressive systems of our cultural communities also relies on these shadowy conscious entities we named “Reduced Descriptive Structures”.

Keywords: Algorithmic sonic computation, corrected and self-correcting learning patterns in acoustic perception, morphological derivations in sensorial patterns, social unconscious modes of communication.

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Authors: Pelin Yildiz

Abstract:

The term interactive education indicates the meaning related with multidisciplinary aspects of distance education following contemporary means around a common basis with different functional requirements. The aim of this paper is to reflect the new techniques in education with the new methods and inventions. These methods are better supplied by interactivity. The integration of interactive facilities in the discipline of education with distance learning is not a new concept but in addition the usage of these methods on design issue is newly being adapted to design education. In this paper the general approach of this method and after the analysis of different samples, the advantages and disadvantages of these approaches are being identified. The method of this paper is to evaluate the related samples and then analyzing the main hypothesis. The main focus is to mention the formation processes of this education. Technological developments in education should be filtered around the necessities of the design education and the structure of the system could then be formed or renewed. The conclusion indicates that interactive methods of education in design issue is a meaning capturing not only technical and computational intelligence aspects but also aesthetical and artistic approaches coming together around the same purpose.

Keywords: Interactive education, distance learning, designeducation, computational intelligence.

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Authors: C. N. Vanitha, M. Usha

Abstract:

In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Abeer Aljohani

Abstract:

The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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Authors: Yasin Tadayonrad, Alassane Ballé Ndiaye

Abstract:

Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading/unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is the loading/unloading capacity in each source/destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods (FMCG) industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on Python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: Supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming.

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