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7th International Conference on Education and Technology, ICET 2021

作者： Prasetya, Didik Dwi Pinandito, Aryo Hayashi, Yusuke Hirashima, Tsukasa Universitas Negeri Malang Dept. of Electrical Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia Graduate School of Engineering Hiroshima University Dept. of Information Engineering Higashihiroshima Japan

ISBN: (纸本)9781665427814

Extension concept mapping is a technique to connect prior existing concept maps with a relevant new knowledge structure. It provides learners with the opportunity to review initial ideas, eliciting missing relationships, adding new components, and improve knowledge structure. While the current study proposed that the Extended Kit-Build design promotes students' achievements, it has yet to evaluate the students' perception. This study investigated the effects of Extended Kit-Build concept mapping design on students' perceptions using the Technology Acceptance Model. The perceived ease-of-use, perceived usefulness, and Behavioral Intention constructs were used to investigate users' intention to use the Extended Kit-Build tool in learning. The results reported that the learners perceived positive effects on Extended Kit-Build as ease of use and a useful learning tool. They also have a high intention to use the tool in the future. © 2021 IEEE.

关键词： Students

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Machine Learning Analysis for Temperature Classification using Bioelectric Potential of Plant

Machine Learning Analysis for Temperature Classification usi...

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International Conference on Information Technology, Information systems and Electrical engineering (ICITISEE)

作者： Imam Tahyudin Hasri Akbar Awal Rozaq Hidetaka Nambo Department of Information System Faculty of Computer Science Amikom Purwokerto University Banyumas Indonesia Faculty of Computer Science Graduate School of Informatics Gazi University Yenimahalle Ankara Turkey Division of Electrical Engineering and Computer Science Graduate School of NST Kanazawa University Kakumamachi Kanazawa Ishikawa Japan

Plants can respond to changes in their surroundings, such as temperature, humidity, and human behavior. This ability is due to photosynthesis and respiration in plants, resulting in low electrical signals. We focus on room temperature change by plant bioelectric potential detection. The temperature topic is interesting for the actual research because it is one of important factor in health aspect. This study aims to analysis of temperature of Bioelectric potential of plant using machine learning. This implementation identifies the plants' bioelectric potential in distinguishing three-room temperature categories: A, B, and C temperature. We conducted the experimental in room control. For the transformation process is using wavelet transform. Furthermore, the analysis process, we used the Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighborhood (K-NN), Naïve Bayes, and linear regression. Then, for the evaluation is using confusion Matrix. The result showed that the best level of transformation using Wavelet is 7 th level for reducing the noise. Furthermore, the best accuracy of analysis process is using Decision tree which obtained 75%.

关键词： Wavelet transforms Support vector machines Radio frequency Temperature Linear regression Bioelectric phenomena Decision trees

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Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification

Preliminary Study on the Effect of Traffic Representation on...

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Computing (ICOCO), IEEE International Conference on

作者： Nik Aqil Firdaus Afifi Faiz Zaki Nor Badrul Anuar Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu Malaysia

ISBN: (纸本)9781665489973

The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset.

关键词： Degradation Temperature sensors Performance evaluation Training data Feature extraction Temperature control Internet of Things

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Virtual Horizons: Pioneering the Metaverse in Education and Industry Across ASEAN and Beyond

Virtual Horizons: Pioneering the Metaverse in Education and ...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Ittipon Yamyuan Sushank Chaudhary Muhammad Saadi Piya Kovintavewat Juthatip Wisanmongkol Mitsunori Makino La-or Kovavisaruch Kamol Kaemarungsi Taweesak Sanpechuda Lunchakorn Wuttisittikulkij Nitthita Chirdchoo Petchpilin Tontiwinyoopong Pisit Vanichchanunt Pruk Sasithong Suebphong Noisri Avishi Waidyaratne Panithan La-aiddee Rosa Delima Department of Electrical Engineering Chulalongkorn University Thailand Department of Electrical Engineering University of Central Punjab Pakistan Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom Thailand National Electronics and Computer Technology Center Pathumthani Thailand Department of Information and System Engineering Chuo University Japan Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Informatics Department Universitas Kristen Duta Wacana Yogyakarta Indonesia

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The integration of the Metaverse into the educational sector heralds a transformative era in teaching and learning paradigms, with the ASEAN region at its forefront. This manuscript examines the Metaverse's potential to transcend traditional educational barriers such as resource limitations, geographic isolation, and the lack of personalized learning. Active case studies from Nakhon Pathom Rajabhat University (NPRU), Thailand, the University of Central Punjab (UCP), Pakistan, the National Electronics and computer Technology Center, Thailand, and Chuo University, Japan, are presented to offer a comprehensive overview of the Metaverse's current applications. These case studies showcase the Metaverse's role in creating inclusive, immersive, and engaging educational experiences. The paper identifies critical research gaps in technological feasibility, pedagogical adaptation, and equitable access that need bridging to harness the Metaverse's full potential. It underscores the necessity for robust technological infrastructure, innovative teaching methodologies for 3D learning environments, and inclusivity strategies for diverse socioeconomic and physical ability spectra. The inclusion of Chuo University's advancements extends the discourse beyond ASEAN, indicating the global momentum of Metaverse adoption in education. The paper posits that ASEAN's venture into this digital frontier, alongside international collaborations, prepares the ground for a more interconnected and technologically advanced future. This investigation into the ASEAN region and its collaboration with Japanese innovation underscores a unique global position, suggesting that the convergence of technology and education is key to resolving deep-rooted disparities in educational access and quality.

关键词： Industries Technological innovation Three-dimensional displays Metaverse Education Collaboration International collaboration

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches

Preferences for Using Canva Apps as a Presentation Media Cre...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Fairuz Iqbal Maulana Dian Lestari Agung Purnomo Rahman Arifuddin Vandha Pradwiyasma Widartha Puput Dani Prasetyo Adi Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga Surabaya 60132 Indonesia Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798350378573

ISBN: (纸本)9798350378580

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)" and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)" used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations.

关键词： Seminars Analytical models Technology acceptance model Accuracy Numerical analysis Media Mathematical models

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Application of Machine Learning Methods for Process Optimization in Electronic Packaging Processes

Application of Machine Learning Methods for Process Optimiza...

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European Microelectronics and Packaging Conference, EMPC

作者： Corinna Niegisch Sabine T. Haag Tanja Braun Ole Hülck Martin Schneider-Ramelow Corporate Sector Research and Advance Engineering Robert Bosch GmbH Renningen Germany Departement System Integration and Interconnection Technologies Fraunhofer IZM Berlin Germany Faculty IV Electrical Engineering and Computer Science Technical University Berlin Berlin Germany

Epoxy resins are commonly used as encapsulation materials in electronic packaging processes. Fluctuations in the materials lead to both changes in processability and to varying quality. Ideally, these variations should be identified, and measures taken as quickly as possible to reduce scrap parts and thus costs. A promising optimization approach for encapsulation processes are machine learning models, which have already shown good results in quality predictions, especially for injection molding. Subsequent quality measurements are avoided with good quality prediction models. With this type of models, not only predictions can be made, but also optimal parameter combinations can be found. In this paper, models for predicting quality criteria warpage and residual enthalpy of the epoxy molding compound were set up, trained and validated. Time series of in-situ sensors were used, from which relevant features were extracted and which, together with machine parameters, provide a dataset for prediction. The most promising prediction models are random forest regression and gradient boosting regression. They predict warpage with an accuracy of 90 % to 91 % and the residual enthalpy with an accuracy of 95 %. Subsequently, optimization models of the machine parameters were set up. All relevant target variables were considered in a cost function, through the minimization of which an optimal parameter set was found. The gradient boosted tree and Bayesian optimization were determined to be the most promising models, as they lead to the lowest values of the respective cost function.

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Supporting Test Case Design on Reasoning Scheme with Natural Language Processing Technique

Supporting Test Case Design on Reasoning Scheme with Natural...

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IEEE International Conference on Big Data

作者： Noriyuki Kushiro Yusuke Ogata Faculty of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781665445993

The purpose of the study is to support test case design by introducing natural language processing techniques and reasoning schemes for designing. Sentences containing a logical relationship of implication (key sentences) are extracted from a specification document with natural language processing techniques, and supplementary explanation sentences for the key sentence (subordinate sentences) that complement parameters appeared in the key sentence are placed in Toulmin's reasoning scheme based on "similarity" to the key sentence. As a result, we succeeded in automatically generating test cases for product testing, and also in supporting to extract parameters for each test case.

关键词： Conferences Production Big Data Cognition Natural language processing Complexity theory Compounds

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment 6

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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6th International Conference on Applied Computational Intelligence in Information systems, ACIIS 2023

作者： Zariful, Wafiq Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Basri, Aida Maryam Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Fakhrurroja, Hanif Universiti Teknologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei Universiti Teknologi Brunei School of Applied Sciences and Mathematics Bandar Seri Begawan Brunei Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Johor Bahru Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Telkom University Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350317428

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications. © 2023 IEEE.

关键词： Object detection

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Current Trends in Online Programming Languages Learning Tools: A systematic Literature Review

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Journal of Software engineering and Applications 2021年第7期14卷 277-297页

作者： Ahmad Alaqsam Fahad Ghabban Omair Ameerbakhsh Ibrahim Alfadli Amer Fayez Department of Information System Faculty of Computing The University of Tabuk Tabuk Saudi Arabia Information System Department Faculty of Computer Science and Engineering Taibah University Madinah Saudi Arabia

Students face difficulties in programming languages learning (PLL) which encourages many scholars to investigate the factors behind that. Although there a number of positive and negative factors found to be effective in PLL procedure, utilising online tools in PLL were recognized as a positive recommended means. This motivates many researchers to provide solutions and proposals which result in a number of choices and options. However, categorising those efforts and showing what has been done, would provide a better and clear image for future studies. Therefore, this paper aims to conduct a systematic literature review to show what studies have been done and then categorise them based on the type of online tools and the aims of the research. The study follows Kitchenham and Charters guidelines for writing SLR (Systematic Literature Review). The search result reached 1390 publications between 2013-09/2018. After the filtration which has been done through selected criteria, 160 publications were found to be adequate to answer the review questions. The main results of this systematic review are categorizing the aims of the studies in online PLL tools, classifying the tools and finding the current trends of the online PLL tools.

关键词： Online Programming Languages Online Learning Use of Information Technology Online Platforms Online Courses MOOC

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