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STEAM Gamification learning Model to Enhance Vocational Students' Creativity and Innovation Skills 2nd

STEAM Gamification Learning Model to Enhance Vocational Stud...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Kummanee, Jiraphorn Nilsook, Prachyanun Piriyasurawong, Pallop Wannapiroon, Panita King Mongkuts Univ Technol North Bangkok Div Informat & Commun Technol Educ Bangkok Thailand King Mongkuts Univ Technol North Bangkok Fac Tech Educ Bangkok Thailand

ISBN: (纸本)9783030402716;9783030402709

This research purposed to (1) to design STEAM Gamification learning Model to Enhance Vocational Students' Creativity and Innovation Skills (2) to evaluate the appropriateness of STEAM Gamification learning Model to Enhance Vocational Students' Creativity and Innovation Skills. The result found that: STEAM Gamification learning Model had 4 elements categorize as follows (1) Input: 1.1 Creative activity plan, 1.2 learning objectives, 1.3 Students, 1.4 Teacher and 1.5 Classcraft programs, (2) learning processes: 2.1 Defining problems 2.2 Designing processes of solving problem using Mathematics and Technology 2.3 Creating the instruments and using projects to solve problems 2.4 Testing, transferring and improving methods to problem solving 2.5 Presenting works and solving problems, (3) Evaluation: the instruments which were used in the evaluation process 3.1 Questionnaire of creativity from the questionnaire in (TCT-DP) standard 3.2 Questionnaire of creative innovation skills and (4) Giving feedback: were shown as the students who possessed creativity and creative innovation skills. The appropriateness result of STEAM Gamification learning Model to Enhance Vocational Students' Creativity and Innovation Skills, which was evaluated by five experts found that: the STEAM Gamification learning Model for Vocational Students' Creativity and Innovation Skills of appropriateness was regarded as "excellent" in terms of the ratings. The expectation of the application was to apply STEAM Gamification learning Model to enhance vocational students' creativity and innovation skills.

关键词： STEAM education Gamification Creative innovation skills Vocational students

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Enhancing Diabetes Prediction with Advanced Machine learning Techniques 22nd

Enhancing Diabetes Prediction with Advanced Machine Learnin...

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22nd international conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Tian, Yuan Wang, Chuan Shi, Wen Zhou, Ying Zhou, Yi Vanderbilt University NashvilleTN37232 United States Department of Chemical Engineering Department of Biosystems Engineering Auburn University AuburnAL36849 United States Department of Economics TSYS School of Computer Science Columbus State University ColumbusGA31907 United States

ISBN: (纸本)9783031859014

Diabetes stands as one of the foremost causes of mortality in the United States. The imperative to predict diabetes in the country arises from its widespread prevalence, substantial healthcare expenses, potential severe complications, and the prospect of proactive prevention and early intervention. This study leverages data from the Framingham study to investigate the utilization of machine learning models in the realm of diabetes prediction. Consequently, the implementation of predictive models to combat diabetes holds the promise of yielding substantial positive outcomes for public health, optimizing healthcare resource allocation, and enhancing the overall health and welfare of individuals and communities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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An Effective Preprocess for Deep learning Based Intrusion Detection

An Effective Preprocess for Deep Learning Based Intrusion De...

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Chia-Ju Lin Ruey-Maw Chen National Chinyi University of Technology Taiping Taichung Taiwan

ISBN: (纸本)9781665447324

The data preprocess directly affects the classification results in various applications. In the field of intrusion detection, less research raised the problems or solutions of unequal metrics in data attributes. This study proposes an effective data preprocessing method for network packets with unequal metrics in packet attributes. A standard deviation standardization was first applied to standardize each attribute of KDDCUP'99 dataset, followed by quantizing it to the range of 0 to 255 interval for afterward use of the image. Meanwhile, the Zigzag arrangement coding and IDCT (Inverse Discrete Cosine Transform) were then used to convert the quantized data into images. Experimental results demonstrate that a more than 94% recall rate of the overall intrusion detection classifier can be yielded by the proposed preprocess method even without a complicated network model. Meanwhile, intrusion detection performance can be guaranteed by using small-size images of packet attributes.

关键词： Measurement Image coding Quantization (signal) data preprocessing Intrusion detection Feature extraction data models

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Backward Design with Virtual learning Ecosystem Model to Enhance Design Thinking and Innovation 2nd

Backward Design with Virtual Learning Ecosystem Model to Enh...

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作者： Chunpungsuk, Chananchida Piriyasurawong, Pallop Chatwattana, Pinanta King Mongkuts Univ Technol North Bangkok Fac Tech Educ Dept Informat & Commun Technol Educ Bangkok Thailand King Mongkuts Univ Technol North Bangkok Coll Ind Technol Dept Elect Engn Technol Bangkok Thailand

ISBN: (纸本)9783030402747;9783030402730

The purposes of this study were: 1. to develop Backward Design with Virtual learning Ecosystem Model to Enhance Design Thinking and Innovation, 2. to evaluate about the suitability of Backward Design with Virtual learning Ecosystem Model to Enhance Design Thinking and Innovation by 3 Educational experts and 4 Information and Communication Technology for Education experts. The findings indicated that the Backward Design with Virtual learning Ecosystem Model to Enhance Design Thinking and Innovation consists of 4 elements as follows: Input including 6 elements (1) objectives (2) lecturer (3) learner (4) hardware (5) software (6) network. Secondly, the process includes (1) goal (2) Identify desired results (3) Determine acceptable evidence (4) Plan learning experiences and instruction. Thirdly, output that is Summative Design Thinking skill and Innovation learning achievement and innovation. Fourthly, Feedback that use to verify the mistake in each procedure. From the evaluate of Backward Design with Virtual learning Ecosystem Model to Enhance Design Thinking and Innovation, It was found that the result of estimation about the learning model is proper in the highest level ((X) over bar = 4.67, S.D. = 0.47). It's shown that it can bring that model to use for the development of ability, design thinking, and innovation.

关键词： Backward Design Virtual learning Ecosystem Design Thinking Innovation

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Collaborative Multi-Object Tracking as an Edge Service using Transfer learning 22

Collaborative Multi-Object Tracking as an Edge Service using...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Sun, Han Chen, Yu Aved, Alexander Blasch, Erik Binghamton University Suny Dept. of Electrical and Computer Engineering BinghamtonNY13902 United States U.S. Air Force Research Laboratory RomeNY13441 United States

ISBN: (纸本)9781728176499

Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-Acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. The CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-The-Art methods. © 2020 IEEE.

关键词： Edge computing

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Efficiently recognition of vaginal micro-ecological environment based on Convolutional Neural Network 22

Efficiently recognition of vaginal micro-ecological environm...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Peng, Shaoliang Huang, Hao Cheng, Minxia Yang, Yaning Li, Fei College of Computer Science and Electronic Engineering Hunan University Changsha China Peng Cheng Lab Shenzhen China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728162676

Vaginal diseases caused by vaginal micro-ecological abnormalities mainly include Vulvovaginal Candidiasis (VVC), Aerobic Vaginitis (AV), and Bacterial Vaginosis (BV). Severe cases can lead to poor pregnancy outcomes and infertility. AI-based technologies are being deployed with an expectation to relieve doctors of routine, tedious work when implemented correctly in daily microscopy of vaginal micro-ecological abnormalities. In this paper, we built a clinical image dataset of the Gram stain of the vaginal discharge. By comparing the performance of state of art convolutional neural network models, we found the fine-tuning Inception ResNet V2 shows the best classification performance for vaginal diseases. It achieves 96%, 94%, 86% AUC in VVC, AV, BV classification respectively. The result shows that compared with human visual inspection, the method based on deep learning greatly improves the screening sensitivity. Besides, we found that transfer learning can reduce the required manual labeling by roughly 73% (about more than one thousand samples). But for BV, which is difficult to diagnose for both humans and AI. Unlike AV and VVC, it requires more labeled data and is insensitive to the transfer fine-tuning. © 2021 IEEE.

关键词： Convolutional neural networks

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Applying An Adaptive Multi-Population Optimization Algorithm to Enhance Machine learning Models for Computational Finance 22

Applying An Adaptive Multi-Population Optimization Algorithm...

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作者： Li, Zhixi Tam, Vincent Yeung, Lawrence K. Li, Zhenglong University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong

ISBN: (纸本)9781728176499

Developing effective machine learning models to maximize the generalization capability of the learned data patterns while minimizing the risk of overfitting and also the ultimate computational costs of the trained models are very challenging yet significant tasks in many real-world applications. Due to the high complexity and huge amount of data involved in financial applications, the critical task of constructing appropriate machine learning models will surely become more challenging. Typically, data scientists or researchers may employ various optimizers to enhance the feature selection and/or tuning of the involved hyper-parameters in order to improve the overall performance of the obtained learning models. In this paper, a very flexible optimizer, namely the Adaptive Multi-Population optimization Algorithm (AMPOA), utilizing multiple populations with different search strategies is considered in which two of its variants are carefully evaluated with the other state-of-Theart optimizers to enhance the feature selection process of the commonly used K-nearest-neighbor classifier approach on a set of 12 well-known benchmark problems. The first variant named the BAMPOA-R approach employs a rounding technique to determine on the binary decision of choosing an input feature or not for the underlying classifier while the other BAMPOA-T approach utilizes a specific transform function to decide on the feature selection. To demonstrate the feasibility of the proposed optimizer for financial applications, the BAMPOA-R approach with the best performance in the previous test is integrated with the Support Vector Regression (SVR) as the BAMPOA-R-SVR algorithm to compare against other regressors on 10 major financial market indexes. The evaluation results clearly demonstrate the remarkable performance of the proposed AMPOA approach to enhance the prediction performances of the underlying machine learning models through more vigorous feature selection for numerous real-world appl

关键词： Feature Selection

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Chat-Interviews as a Means to Explore Students' Attitudes and Perceptions on Developing Video Games with Unity in Computer Science Classes 2nd

Chat-Interviews as a Means to Explore Students' Attitudes an...

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作者： Comber, Oswald Motschnig, Renate Mayer, Hubert Univ Vienna Fac Comp Sci CSLEARN Educ Technol Vienna Austria

ISBN: (纸本)9783030402747;9783030402730

In this paper we explore how students perceived developing video games with Unity. The hypothesis behind the endeavour is that the prospect of being able to develop one's own video game in a computer science class would make it easier for students to overcome the steep learning curve typically in place when learning to program. As a means of research, we employed chat-interviews for answering the overall research question "How do students perceive a learning sequence aimed at the development of computational thinking and coding competencies through video-game development?". More specifically, we wanted to know whether students found the task of game-development motivating or not, which aspect or project-phase of game-development they found most motivating, most entertaining, most appealing, least appealing, most difficult, etc. Furthermore, we were interested in what students found most helpful while working with Unity, what they had learned, and which mode of learning - listening to lectures or independent work - appealed to them most. The analysis of the results of the first cycle of chat-interviews has shown that the main motivational factors were the focus on game-design and the option of cooperative work in small teams. The programming act itself using Visual Studio and C# was seen as the most difficult activity. The results of our research indicate that learners are highly motivated by the outlook of developing an interactive video game as opposed to other programming applications. Chat-interviews improved the research process by eliminating one of the most time-consuming steps - the transcription of interview data - and allowing some learners to more openly express their opinions.

关键词： Video game development K-12 education learning to code Chat-interviews Mixed methods

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international Network conference as an Efficient Way to Integrate Universities and Businesses in the Context of Digital Economy 2nd

International Network Conference as an Efficient Way to Inte...

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作者： Yushko, Sergey, V Galikhanov, Mansur F. Barabanova, Svetlana, V Kaybiyaynen, Alla A. Suntsova, Maria S. Kazan Natl Res Technol Univ Kazan Russia

ISBN: (纸本)9783030402747;9783030402730

Development of engineering education is closely related to the global development processes of post-industrial society in the context of Industry 4.0, digitalization of economy, and the vigorous growth of technology and media. The ideas become dominating that education must serve the goals of a sustainable and dynamic society experiencing serious crises of technogenic and social/political nature. Accordingly, the goal of higher engineering/ technology education be-comes turning out engineers that are ready to overcome the above challenges. This goal cannot be achieved without exchanging best practices and ideas among researchers and teachers representing the universities of various countries, involving the interested representatives of leading corporations. international engineering education forums and conferences may offer great opportunities for that. Issues and problems of engineering education are currently dis-cussed at many global forums and conferences, including the ICL/IGIP. Since 2016, the international Science and Practice SYNERGY Network conference has become a large discussion site in Russia. The conference applies best global practices, as well as identifies and accomplishes important goals, such as developing public-private partnership, modernizing curricula, upgrading the skills of faculty members, managing staff, and other conference participants, spreading new educational techniques and technology, using digital formats and education digitalization being especially important given the scale of Russia, and expanding partnership links to companies being the customers of universities and participating in the conference. Outcomes of the conferences already held demonstrate a significant effect pro-vided on the university teachers who adopt best practices and teaching methods, learn from each other, cross-pollinate, and get feedback from employers regarding the quality of professional training and necessary improvements.

关键词： engineering education Academic-industry partnership University collaboration

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Transformers fault identification by frequency response analysis using intelligent classifiers

Transformers fault identification by frequency response anal...

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22nd international Symposium on High Voltage engineering (ISH 2021)

作者： R. S. A. Ferreira H. Ezzaidi I. Fofana P. Picher Research Chair on the Aging of Power Network Infrastructure (ViAHT) Université du Québec à Chicoutimi Saguenay Canada Institut de recherche d'Hydro-Québec (IREQ) Varennes Canada

Failures in power transformers generates high financial loss inherent to equipment cost and power interruption. Thus, the industry has been working into developing efficient and reliable monitoring techniques with the objective of detecting incipient faults. Frequency Response Analysis (FRA) is among well-known methods to detect faults in transformers' active parts. FRA is very sensitive to changes in the winding geometry and a comparison between healthy and faulty measurements can present deviations that leads to the fault identification. However, until now, FRA interpretation is performed with the aid of experts. The development of FRA and the spreading of its application in transformers' diagnostics, have led studies to focus on the advancement of objective interpretation techniques. To contribute to this matter, intelligent classifiers are evaluated over its capabilities to classify transformers faults. For this purpose, a database of measurements performed on a laboratory winding model including different faults is used. Numerical indices are used as input for machine learning classifiers. This analysis indicated that the use of one individual index is appropriate for a good classification performance using Radial Basis Function neural network. Meanwhile Support-vector machine and Backpropagation neural network performed better with a combination of indices.

关键词： backpropagation condition monitoring fault diagnosis frequency response pattern classification power engineering computing power system faults power system reliability radial basis function networks support vector machines

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