Search results for: supervised machine learning algorithm

Authors: Wiem Zemzem, Moncef Tagina

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In this work, we are addressing the problem of an autonomous mobile robot navigating in a large, unknown and dynamic environment using reinforcement learning abilities. This problem is principally related to the exploration/exploitation dilemma, especially the need to find a solution letting the robot detect the environmental change and also learn in order to adapt to the new environmental form without ignoring knowledge already acquired. Firstly, a new action selection strategy, called ε-greedy-MPA (the ε-greedy policy favoring the most promising actions) is proposed. Unlike existing exploration/exploitation policies (EEPs) such as ε-greedy and Boltzmann, the new EEP doesn’t only rely on the information of the actual state but also uses those of the eventual next states. Secondly, as the environment is large, an exploration favoring least recently visited states is added to the proposed EEP in order to accelerate learning. Finally, various simulations with ball-catching problem have been conducted to evaluate the ε-greedy-MPA policy. The results of simulated experiments show that combining this policy with the Qlearning method is more effective and efficient compared with the ε-greedy policy in stationary environments and the utility-based reinforcement learning approach in non stationary environments.

Keywords: autonomous mobile robot, exploration/ exploitation policy, large, dynamic environment, reinforcement learning

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Authors: Yi Ding

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With the rapid development of educational technologies, higher education institutions are facing the challenge of creating an inclusive learning environment for students from diverse backgrounds. Academic libraries, the hubs of research, instruction, and innovation at higher educational institutions, are facing the same challenge. While academic librarians worldwide have been working hard to provide services for emerging information technology such as information literacy education, online learning support, and scholarly communication advocacy, the problem of digital exclusion remains a difficult one at higher education institutions. Information services provided by academic libraries can result in the digital exclusion of students from diverse backgrounds, such as students with various digital readiness levels, students with disabilities, as well as English-as-a-Second-Language learners. This research study shows how academic librarians can design digital learning objects that are cognizant of differences in learner traits and student profiles through the lens of design thinking. By demonstrating how the design process of digital learning objects can take into consideration users’ needs, experiences, and engagement with different technologies, this research study explains design principles of accessibility, connectivity, and scalability in creating inclusive digital learning objects as shown in various case studies. Equipped with the mindset and techniques to be mindful of diverse student learning traits and profiles when designing information services, academic libraries can improve the digital inclusion and ultimately student success at higher education institutions.

Keywords: academic librarians, digital inclusion, information services, digital learning objects, student success

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: knowledge representation, pervasive computing, agent technology, ECA rules

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Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: Şirin Küçük, Ayşe Kök, İsmail Şahin

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Mobile devices are very popular and useful tools for assisting people in daily life. With the advancement of mobile technologies, the issue of mobile learning has been widely investigated in education. Many researches consider that it is important to integrate pedagogical and technical strengths of mobile technology into learning environments. For this reason, the purpose of this research is to examine the effect of mobile technology use in education with meta-analysis method. Meta-analysis is a statistical technique which combines the findings of independent studies in a specific subject. In this respect, the articles will be examined by searching the databases for researches which are conducted between 2005 and 2014. It is expected that the results of this research will contribute to future research related to mobile technology use in education.

Keywords: mobile learning, meta-analysis, mobile technology, education

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Authors: Andrew Nowlan

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During the Coronavirus pandemic, collaborative online international learning (COIL) emerged as an alternative to overseas sojourns. However, now that face-to-face classes have resumed and students are studying abroad, the rationale for doing COIL is not always clear amongst educators and students. Also, the logistics of COIL become increasingly complicated when participants involved in a potential collaboration have different second language (L2) learning goals. In this paper, the researcher will report on a study involving two bilingual, cross-cultural COIL courses between students at a university in Japan and those studying in North America, from April to December, 2022. The students in Japan were enrolled in an intercultural communication class in their L2 of English, while the students in Canada and the United States were studying intermediate Japanese as their L2. Based on a qualitative survey and journaling data received from 31 students in Japan, and employing a transcendental phenomenological research design, the researcher will highlight the students’ essence of experience during COIL. Essentially, students benefited from the experience through improved communicative competences and increased knowledge of the target culture, even when the L2 learning goals between institutions differed. Students also reported that the COIL experience was effective in preparation for actual study abroad, as opposed to a replacement for it, which challenges the existing literature. Both educators and administrators will be exposed to the perceptions of Japanese university students towards COIL, which could be generalized to other higher education contexts, including those in Southeast Asia. Readers will also be exposed to ideas for developing more effective pre-departure study abroad programs and domestic intercultural curriculum through COIL, even when L2 learning goals may differ between participants.

Keywords: collaborative online international learning, study abroad, phenomenology, EdTech, intercultural communication

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Authors: Khaled Shehata, Hanady Hussien, Sara Yehia

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Securing the data stored on E-passport is a very important issue. RSA encryption algorithm is suitable for such application with low data size. In this paper the design and implementation of 1024 bit-key RSA encryption and decryption module on an FPGA is presented. The module is verified through comparing the result with that obtained from MATLAB tools. The design runs at a frequency of 36.3 MHz on Virtex-5 Xilinx FPGA. The key size is designed to be 1024-bit to achieve high security for the passport information. The whole design is achieved through VHDL design entry which makes it a portable design and can be directed to any hardware platform.

Keywords: RSA, VHDL, FPGA, modular multiplication, modular exponential

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Authors: Seyed Nima Tayarani Bathaie

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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

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Authors: Catherine Omole

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Examination malpractice includes acts that temper with collecting accurate results during the conduct of an examination, thereby giving undue advantage to a student over his colleagues. Even though examination malpractice has been a lingering problem, examinations may not be easy to do away with completely as it is an important feedback tool in the learning process with several other functions e.g for the purpose of selection, placement, certification and promotion. Examination malpractice has created a lot of problems such as a relying on a weak work force based on false assessment results. The question is why is this problem still persisting, despite measures that have been taken to curb this ugly trend over the years? This opinion paper has identified modifications that could help relieve the student of the examination stress and thus increase the student’s effort towards effective learning and discourage examination malpractice in the long run.

Keywords: assessment, examination malpractice, learning, science classroom

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Authors: Omaima N. Ahmad AL-Allaf

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Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100.

Keywords: image watermarking, genetic algorithm, particle swarm optimization, discrete wavelet transform

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Authors: Maria Janet, Anita David, P. Vijayasamundeeswarimaria

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Introduction: The main purpose of this study is to compare the effectiveness of web-based and blended learning on Paediatric Basic Life Support on competency among undergraduate nursing students in selected nursing colleges in Chennai. Materials and methods: A descriptive pre-test and post-test study design were used for this study. Samples of 100 Fourth year B.Sc., nursing students at Sri Ramachandra Faculty of Nursing SRIHER, Chennai, 100 Fourth year B.Sc., nursing students at Apollo College of Nursing, Chennai, were selected by purposive sampling technique. The instrument used for data collection was Knowledge Questionnaire on Paediatric Basic Life Support (PBLS). It consists of 29 questions on the general expansion of Basic Life Support and Cardiopulmonary Resuscitation, Prerequisites of Basic Life Support, and Knowledge on Paediatric Basic Life Support in which each question has four multiple choices answers, each right answer carrying one mark and no negative scoring. This questionnaire was formed with reference to AHA 2020 (American Heart Association) revised guidelines. Results: After the post-test, in the web-based learning group, 58.8% of the students had an inadequate level of objective performance score, while 41.1% of them had an adequate level of objective performance score. In the blended learning group, 26.5% of the students had an inadequate level of an objective performance score, and 73.4% of the students had an adequate level of an objective performance score. There was an association between the post-test level of knowledge and the demographic variables of undergraduate nursing students undergoing blended learning. The age was significant at a p-value of 0.01, and the performance of BLS before was significant at a p-value of 0.05. The results show that there was a significant positive correlation between knowledge and objective performance score of undergraduate nursing students undergoing web-based learning on paediatric basic life support.

Keywords: basic life support, paediatric basic life support, web-based learning, blended learning

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Authors: Gertrude Nkechi Okenwa

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Higher institutions, especially the universities that are saddled with the responsibilities of teaching, learning, research, publications and social services for the production of graduates that are worthy in learning and character, and the creation of up-to-date knowledge and innovations for the total socio-economic and even political development of a given nation. Therefore, the purpose of the study was to identify the teaching, learning techniques used in the Enugu State University of Science and Technology to ensure or ascertain students’ perception on these techniques. To guide the study, survey research method was used. The population for the study was made up of second and final year students which summed up to one hundred and twenty-six students in the faculty of education. Stratified random sampling technique was adopted. A sample size of sixty (60) students was drawn for the study. The instrument used for data collection was questionnaire. To analyze the data, mean and standard deviation were used to answers the research questions. The findings revealed that direct instruction and construction techniques are used in the university. On the whole, it was observed that the students perceived constructivist techniques to be more useful and effective than direct instruction technique. Based on the findings recommendations were made to include diversification of teaching techniques among others.

Keywords: Strategies, Teaching and Learning, Constructive Technique, Direct Instructional Technique

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Authors: Sobhy Fathy A. Hashesh

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The study was carried out in the Elementary Classes of Andalus Private Schools, girls section using control and experimental groups formed by Random Assignment Strategy. The study aimed at investigating the effect of Al-Andalus Fused Curriculum (AFC) model of learning and the effect of separate subjects’ approach on the development of students’ conceptual learning and skills acquiring. The society of the study composed of Al-Andalus Private Schools, elementary school students, Girls Section (N=240), while the sample of the study composed of two randomly assigned groups (N=28) with one experimental group and one control group. The study followed the quantitative and qualitative approaches in collecting and analyzing data to investigate the study hypotheses. Results of the study revealed that there were significant statistical differences between students’ conceptual learning and skills acquiring for the favor of the experimental group. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: AFC, STEAM, lego education, Al-Andalus fused curriculum, mechatronics

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Authors: Yi-Hsiang Pan, Chen-Hui Huang, Ching-Hsiang Chen, Wei-Ting Hsu

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The purposes of the study were to explore the students' learning effect of physical education curriculum between merging Teaching Personal and Social Responsibility (TPSR) with sport education model and TPSR with traditional teaching model, which these learning effects included sport self-efficacy, sport enthusiastic, group cohesion, responsibility and game performance. The participants include 3 high school physical education teachers and 6 physical education classes, 133 participants with experience group 75 students and control group 58 students, and each teacher taught an experimental group and a control group for 16 weeks. The research methods used questionnaire investigation, interview, focus group meeting. The research instruments included personal and social responsibility questionnaire, sport enthusiastic scale, group cohesion scale, sport self-efficacy scale and game performance assessment instrument. Multivariate Analysis of covariance and Repeated measure ANOVA were used to test difference of students' learning effects between merging TPSR with sport education model and TPSR with traditional teaching model. The findings of research were: 1) The sport education model with TPSR could improve students' learning effects, including sport self-efficacy, game performance, sport enthusiastic, group cohesion and responsibility. 2) The traditional teaching model with TPSR could improve students' learning effect, including sport self-efficacy, responsibility and game performance. 3) the sport education model with TPSR could improve more learning effects than traditional teaching model with TPSR, including sport self-efficacy, sport enthusiastic,responsibility and game performance. 4) Based on qualitative data about learning experience of teachers and students, sport education model with TPSR significant improve learning motivation, group interaction and game sense. The conclusions indicated sport education model with TPSR could improve more learning effects in physical education curriculum. On other hand, the curricular projects of hybrid TPSR-Sport Education model and TPSR-Traditional Teaching model are both good curricular projects of moral character education, which may be applied in school physical education.

Keywords: character education, sport season, game performance, sport competence

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Authors: Daniel A. Walzer

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As the options for course delivery and development expand, plenty of misconceptions still exist concerning e-learning and online course delivery. Classroom instructors often discuss pedagogy, methodologies, and best practices regarding teaching from a singular, traditional in-class perspective. As more professors integrate online, blended, and hybrid courses into their dossier, a clearly defined rubric for gauging online course delivery is essential. The transition from a traditional learning structure towards an updated distance-based format requires careful planning, evaluation, and revision. This paper examines how new media stimulates reflective practice and guided inquiry to improve pedagogy, engage interdisciplinary collaboration, and supply rich qualitative data for future research projects in media arts disciplines.

Keywords: action research, inquiry, new media, reflection

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Authors: Darren Zou

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Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

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Authors: Anam Gopi

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The undesired torque and flux ripple may occur in conventional direct torque control (DTC) induction motor drive. DTC can improve the system performance at low speeds by continuously tuning the regulator by adjusting the Kp, Ki values. In this Teaching Learning Based Optimization (TLBO) is proposed to adjust the parameters (Kp, Ki) of the speed controller in order to minimize torque ripple, flux ripple, and stator current distortion. The TLBO based PI controller has resulted is maintaining a constant speed of the motor irrespective of the load torque fluctuations.

Keywords: teaching learning based optimization, direct torque control, PI controller

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

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As we enter the new era of Artificial Intelligence (AI) and Cloud Computing, we mostly rely on the Machine and Natural Language Processing capabilities of AI, and Energy Efficient Hardware and Software Devices in almost every industry sector. In these industry sectors, much emphasis is on developing new and innovative methods for producing and conserving energy and sustaining the depletion of natural resources. The core pillars of sustainability are economic, environmental, and social, which is also informally referred to as the 3 P's (People, Planet and Profits). The 3 P's play a vital role in creating a core Sustainability Model in the Enterprise. Natural resources are continually being depleted, so there is more focus and growing demand for renewable energy. With this growing demand, there is also a growing concern in many industries on how to reduce carbon emissions and conserve natural resources while adopting sustainability in corporate business models and policies. In our paper, we would like to discuss the driving forces such as Climate changes, Natural Disasters, Pandemic, Disruptive Technologies, Corporate Policies, Scaled Business Models and Emerging social media and AI platforms that influence the 3 main pillars of Sustainability (3P’s). Through this paper, we would like to bring an overall perspective on enterprise strategies and the primary focus on bringing cultural shifts in adapting energy-efficient operational models. Overall, many industries across the globe are incorporating core sustainability principles such as reducing energy costs, reducing greenhouse gas (GHG) emissions, reducing waste and increasing recycling, adopting advanced monitoring and metering infrastructure, reducing server footprint and compute resources (Shared IT services, Cloud computing, and Application Modernization) with the vision for a sustainable environment.

Keywords: climate change, pandemic, disruptive technology, government policies, business model, machine learning and natural language processing, AI, social media platform, cloud computing, advanced monitoring, metering infrastructure

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Authors: Agis Andriani

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To create an effective teaching condition, the teacher requests the students as the instruction to guide the them interactively in the learning activities in the classroom. This study involves 160 Indonesian students who study English in the university, as participants in the discourse completion test, and ten of them are interviewed. The result shows that when the students response the teacher’s request, it realizes assertives, directives, commisives, expressives, and declaratives. These indicate that the students are active, motivated, and responsive in the learning process, although in the certain condition these responses are to prevent their faces from the shyness of their silence in interaction. Therefore, it needs the teacher’s creativity to give the conducive atmosphere in order to support the students’ participation in learning English.

Keywords: discourse completion test, effective teaching, request, teacher’s creativity

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Authors: Jun Su Park, Byung Kwan Oh, Jin Woo Hwang, Yousok Kim, Hyo Seon Park

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For the structural safety, the maximum stress calculated from the stress distribution of a structure is widely used. The stress distribution can be estimated by deformed shape of the structure obtained from measurement. Although the estimation of stress is strongly affected by the location and number of sensing points, most studies have conducted the stress estimation without reasonable basis on sensing plan such as the location and number of sensors. In this paper, an optimal sensing technique for estimating the stress distribution is proposed. This technique proposes the optimal location and number of sensing points for a 2-D frame structure while minimizing the error of stress distribution between analytical model and estimation by cubic smoothing splines using genetic algorithm. To verify the proposed method, the optimal sensor measurement technique is applied to simulation tests on 2-D steel frame structure. The simulation tests are performed under various loading scenarios. Through those tests, the optimal sensing plan for the structure is suggested and verified.

Keywords: genetic algorithm, optimal sensing, optimizing sensor placements, steel frame structure

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Authors: Tesfaye Mengistu

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This thesis aims to investigate the effectiveness of Reinforcement Learning (RL) for Autonomous Energy Management solutions. The study explores the potential of Model Free RL approaches, such as Monte Carlo RL and Q-learning, to improve energy management by autonomously adjusting energy management strategies to maximize efficiency. The research investigates the implementation of RL algorithms for optimizing energy consumption in a single-agent environment. The focus is on developing a framework for the implementation of RL algorithms, highlighting the importance of RL for enabling autonomous systems to adapt quickly to changing conditions and make decisions based on previous experiences. Moreover, the paper proposes RL as a novel energy management solution to address nations' CO2 emission goals. Reinforcement learning algorithms are well-suited to solving problems with sequential decision-making patterns and can provide accurate and immediate outputs to ease the planning and decision-making process. This research provides insights into the challenges and opportunities of using RL for energy management solutions and recommends further studies to explore its full potential. In conclusion, this study provides valuable insights into how RL can be used to improve the efficiency of energy management systems and supports the use of RL as a promising approach for developing autonomous energy management solutions in residential buildings.

Keywords: artificial intelligence, reinforcement learning, monte carlo, energy management, CO2 emission

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Authors: Akram Hashemi

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This study provided an empirical evidence for learners of elementary level of language proficiency to investigate the potential role of contextualization in vocabulary learning. Prior to the main study, pilot study was performed to determine the reliability and validity of the researcher-made pretest and posttest. After manifesting the homogeneity of the participants, the participants (n = 90) were randomly assigned into three equal groups, i.e., two experimental groups and a control group. They were pretested by a vocabulary test, in order to test participants' pre-knowledge of vocabulary. Then, vocabulary instruction was provided through three methods of visual instruction, the use of context and the use of conventional techniques. At the end of the study, all participants took the same posttest in order to assess their vocabulary gain. The results of independent sample t-test indicated that there is a significant difference between learning vocabulary visually and learning vocabulary contextually. The results of paired sample t-test showed that different teaching strategies have significantly different impacts on learners’ vocabulary gains. Also, the contextual strategy was significantly more effective than visual strategy in improving students’ performance in vocabulary test.

Keywords: vocabulary instruction, explicit instruction, implicit instruction, strategy

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Authors: Murad Almadani, Khalil Abu-Hantash, Xinyu Wang, Herbert Jelinek, Kinda Khalaf

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Gait analysis is used by healthcare professionals as a tool to gain a better understanding of the movement impairment and track progress. In most circumstances, monitoring patients in their real-life environments with low-cost equipment such as cameras and wearable sensors is more important. Inertial sensors, on the other hand, cannot provide enough information on angular dynamics. This research offers a method for tracking 2D joint coordinates using cutting-edge vision algorithms and a single RGB camera. We provide an end-to-end comprehensive deep learning pipeline for marker-less gait parameter estimation, which, to our knowledge, has never been done before. To make our pipeline function in real-time for real-world applications, we leverage the AlphaPose human posture prediction model and a deep learning transformer. We tested our approach on the well-known GPJATK dataset, which produces promising results.

Keywords: gait analysis, human pose estimation, deep learning, real time gait estimation, AlphaPose, transformer

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Authors: Abdelhadi Adel, Kadri Ouahab

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This paper proposes a hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.

Keywords: multi-agent systems, emergence, genetic algorithm, makespan, systematic maintenance, scheduling, hybrid flow shop scheduling

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Authors: Malathi Balakrishnan, Cheng Lee Ooi, Chander Vengadasalam

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The purpose of this study is to explore a case study of trainee teachers’ learning experience on innovating traditional games during the traditional game carnival. It explores issues arising from multiple case studies of trainee teachers learning experiences in innovating traditional games. A qualitative methodology was adopted through observations, semi-structured interviews and reflective journals’ content analysis of trainee teachers’ learning experiences creating and implementing innovative traditional games. Twelve groups of 36 trainee teachers who registered for Sports and Physical Education Management Course were the participants for this research during the traditional game carnival. Semi structured interviews were administrated after the trainee teachers learning experiences in creating innovative traditional games. Reflective journals were collected after carnival day and the content analyzed. Inductive data analysis was used to evaluate various data sources. All the collected data were then evaluated through the Nvivo data analysis process. Inductive reasoning was interpreted based on the Self Determination Theory (SDT). The findings showed that the trainee teachers had positive game participation experiences, game knowledge about traditional games and positive motivation to innovate the game. The data also revealed the influence of themes like cultural significance and creativity. It can be concluded from the findings that the organized game carnival, as a requirement of course work by the Institute of Teacher Training Malaysia, was able to enhance teacher trainers’ innovative thinking skills. The SDT, as a multidimensional approach to motivation, was utilized. Therefore, teacher trainers may have more learning experiences using the SDT.

Keywords: learning experiences, innovation, traditional games, trainee teachers

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Authors: Edicio M. Faller

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The use of technology in the delivery of teaching and learning is vital nowadays especially in education. Computer Assisted Learning Module (CALM) software is the use of computer in the delivery of instruction with a tailored fit program intended for a specific lesson or a set of topics. The CALM software developed in this study is intended to supplement the traditional teaching methods in technical-vocational (TECH-VOC) instruction specifically the Consumer Electronics Servicing course. There are three specific objectives of this study. First is to create a learning enhancement and review materials on the selected lessons. Second, is to computerize the end-of-chapter quizzes. Third, is to generate a computerized mock exam and summative assessment. In order to obtain the objectives of the study the researcher adopted the Agile Model where the development of the study undergoes iterative and incremental process of the Software Development Life Cycle. The study conducted an acceptance testing using a survey questionnaire to evaluate the CALM software. The results showed that CALM software was generally interpreted as very satisfactory. To further improve the CALM software it is recommended that the program be updated, enhanced and lastly, be converted from stand-alone to a client/server architecture.

Keywords: computer assisted learning module, software development life cycle, computerized mock exam, consumer electronics servicing

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Authors: Shayan Mohajer Hamidi

Abstract:

Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

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Authors: Ira Slabodar

Abstract:

Autonomous learning in English as a Foreign Language (EFL) refers to the use of target language, learner collaboration and students’ responsibility for their learning. Teachers play a vital role of mediators and facilitators in self-regulated method. Thus, their perception of self-guided practices dictates their implementation of this approach. While research has predominantly focused on inadequate administration of autonomous learning in school mostly due to lack of appropriate teacher training, this study examined whether novice teachers who were exposed to extensive autonomous practices were likely to implement this method in their teaching. Twelve novice teachers were interviewed to examine their perception of learner autonomy and their administration of this method. It was found that three-thirds of the respondents experienced a gap between familiarity with autonomous learning and a favorable attitude to this approach and their deficient integration of self-directed learning. Although learner-related and institution-oriented factors played a role in this gap, it was mostly caused by the respondents’ not being genuinely autonomous. This may be due to indirect exposure rather than explicit introduction of the learner autonomy approach. The insights of this research may assist curriculum designers and heads of teacher training programs to rethink course composition to guarantee the transfer of methodologies into EFL classes.

Keywords: learner autonomy, teacher training, english as a foreign language (efl), genuinely autonomous teachers, explicit instruction, self-determination theory

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Authors: Issam Alnader, Aboubaker Lasebae, Rand Raheem

Abstract:

Wireless sensor networks (WSNs) are distributed network systems used in a wide range of applications, including safety-critical systems. The latter provide critical services, often concerned with human life or assets. Therefore, ensuring the dependability requirements of Safety critical systems is of paramount importance. The purpose of this paper is to utilize the Hidden Markov Model (HMM) to elongate the service availability of WSNs by increasing the time it takes a node to become obsolete via optimal load balancing. We propose an HMM algorithm that, given a WSN, analyses and predicts undesirable situations, notably, nodes dying unexpectedly or prematurely. We apply this technique to improve on C. Lius’ algorithm, a scheduling-based algorithm which has served to improve the lifetime of WSNs. Our experiments show that our HMM technique improves the lifetime of the network, achieved by detecting nodes that die early and rebalancing their load. Our technique can also be used for diagnosis and provide maintenance warnings to WSN system administrators. Finally, our technique can be used to improve algorithms other than C. Liu’s.

Keywords: wireless sensor networks, IoT, dependability of safety WSNs, energy conservation, sleep awake schedule

Procedia PDF Downloads 86