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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

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Combining Gamification and Intelligent Tutoring systems for engineering Education 53

Combining Gamification and Intelligent Tutoring Systems for ...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Hare, Ryan Tang, Ying Zhu, Chengzhang Rowan University Dept. of Electrical and Computer Engineering GlassboroNJ United States

ISBN: (纸本)9798350336429

This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system's educational efficacy through early pre-post-test results from students who played the game with and without the personalized learning system integration. © 2023 IEEE.

关键词： Educational Software engineering Education Gamification Higher Education Learning Technology

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FedFuzzy: Incorporating Fuzzy Aggregation Model into Federated Learning

FedFuzzy: Incorporating Fuzzy Aggregation Model into Federat...

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international conference on knowledge and Smart Technology (KST)

作者： Uklid Yeesarapat Sansanee Auephanwiriyakul Nipon Theera-Umpon Department of Computer Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Faculty of Engineering Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Federated learning has emerged as a method for facilitating machine learning while safeguarding data privacy. This work proposes a novel model aggregation technique for federated learning based on fuzzy set theory to address challenges related to data diversity and model discrepancies. The proposed method, namely the FedFuzzy, aims to enhance the robustness and performance of federated learning systems by incorporating fuzzy membership functions into the aggregation process. The proposed model is implemented on the MNIST and CIFAR10 datasets. We also implement the existing models, such as the Federated Averaging (FedAvg), on the same datasets to show the comparison results. The results demonstrate that the proposed model outperforms the FedAvg in the case of MNIST dataset. Meanwhile, for the CIFAR10 dataset, our proposed method is comparable with the FedAvg.

关键词： data privacy Adaptation models Accuracy Uncertainty Federated learning Computational modeling Parallel processing data models Robustness Fuzzy set theory

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Semi-structured expert interview-based requirements elicitation for a digitalized production ramp-up 56

Semi-structured expert interview-based requirements elicitat...

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56th CIRP international conference on manufacturing systems, CIRP CMS 2023

作者： Stang, Julian Kroeger, Sebastian Zaeh, Michael F. Technical University of Munich TUM School of Engineering and Design Institute for Machine Tools and Industrial Management Boltzmannstr. 15 Garching near Munich85748 Germany

Due to the ever-shortening innovation cycles and the rapid change of customer demands, enterprises introduce new, innovative, and modern products at an increasingly faster frequency and desirably lower costs. The technological change of products and mass customization increase the product complexity. This development requires manufacturing enterprises to perform more frequent production ramp-ups. Many production ramp-up projects miss the time, cost, and quality targets, causing manufacturing enterprises to lose potential profit due to later market introduction. Although digital tools are widely applied during the serial production phase, their potential during production ramp-up is hardly investigated in the scientific literature. Moreover, no guidelines exist on how to use digital tools during the production ramp-up in various industries. To provide data-driven support for the production ramp-up phase, requirements for the functionality of digital tools need to be defined. Henceforth, this paper applies a requirements engineering (RE) approach to elicit the requirements for digital tools to support the production ramp-up phase based on a comprehensive literature review and semi-structured expert interviews. In total, 13 experts from seven different industries participated in the interviews. After introducing the necessary theoretical foundations and the current state of research in ramp-up management literature, the RE process is described in detail. The RE process is based on user stories - an agile development tool - and the ISO 29148, which outlines an international standard for RE processes. The result is a set of requirements for the functionality of digital tools during the production ramp-up phase. Finally, the paper presents an outlook on future research perspectives for applying the elicited requirements in practice. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/lice

关键词： Industry 4.0 production ramp-up requirements engineering semi-structured expert interviews

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A Review on Heart Diseases Using Machine Learning and Deep Learning Techniques 12th

A Review on Heart Diseases Using Machine Learning and Deep...

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12th international conference on Soft Computing for Problem Solving, SocProS 2023

作者： Mallikarjunamallu, K. Syed, Khasim School of Computer Science and Engineering VIT-AP University Amaravati Vijayawada522237 India

ISBN: (纸本)9789819732913

Heart disease is one of today’s major issues, as well as one of the main causes of mortality globally. Electrocardiogram (ECG) and patient data may be used to diagnose cardiac illness in its early stages, as shown by recent advancements in machine learning (ML), deep learning (DL) application. Many scholars and practitioners have uncovered numerous data level and algorithm level solutions throughout the years. An exhaustive literature review is presented here to reveal the difficulties posed by unbalanced data in heart disease forecasts and to offer a wider perspective on the available knowledge. Using 93 articles of reference that we had acquired between 2018 and 2023 from respectable journals, we conducted a meta-analysis. An extensive analysis of 30 literature references has been done, taking into account the kind of cardiac ailment, methods, programmes, and results. Our research showed that existing approaches have a few problems that have yet to be resolved when working with datasets, which ultimately reduces their real usefulness and efficacy. ML and DL methods are utilised to enhance data-driven decision-making for cardiac disease detection. For content analysis of 93 articles’ metadata, 30 articles about the diagnosis of heart disease were chosen. The research was primarily concerned with the models’ performance, as well as other issues including the machine learning and deep learning’s interpretability and explicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Intelligent Computer 3D Modeling Technology in 3D Printing manufacturing System

Intelligent Computer 3D Modeling Technology in 3D Printing M...

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Electrical engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Jing Liu Ying Li Yanhong Liu Jingjing Dong Zheng Wang Yingyu Wang High-tech Institute Qingzhou Shandong China

This article focuses on exploring the problems faced by 3D printing technology in the work process, and conducts in-depth research on this field by combining computer three-dimensional production technology and using modeling software as a research tool. First, this article analyzes the current status and development trends of relevant domestic knowledge. Then, through an in-depth understanding of its basic principles and characteristics, this article uses three-dimensional modeling software to generate a solid model and complete virtual prototype modeling. In addition, this article also uses CAD (Computer Aided Design) to draw two-dimensional drawings, part drawings and other files to implement the design of the 3D printing system, and verifies the final results through effect testing. The results show that the printing speed of this 3D printing manufacturing system is relatively fast and stable, and its printing resolution is also high, the adaptability of printing materials is good, with the overall adaptability being the lowest 80% and the highest 92 %. These test results ensure the printing quality and stability of the system.

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Classification Analysis of Microstrip Antennas for 700, 2600, and 3600 MHz frequencies based on Adaptive Neuro-Fuzzy Inference System 15

Classification Analysis of Microstrip Antennas for 700, 2600...

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15th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Galleto, Fredelino A. Africa, Aaron Don M. Bedruz, Rhen Anjerome Peradilla, Marnel De la Salle University Department of Electronics and Computer Engineering Manila1004 Philippines De la Salle University Department of Manufacturing Engineering and Management Manila1004 Philippines De la Salle University Department of Computer Technology Manila1004 Philippines

ISBN: (纸本)9798350381177

Communication antennas find extensive application in wireless communication domains, where data exchange relies heavily on the transmission and reception of electromagnetic waves. Microstrip antennas (MSA) are commonly employed for high-frequency operations among the various antenna types. Despite the existing body of literature on this antenna, achieving an efficient output design remains challenging for engineers due to numerous factors that must be considered. Consequently, this study sought to contribute to the existing research by employing a novel methodology to attain an effective antenna design. This research used a rectangular MSA with a copper conductor and air substrate. The simulation uses operating frequencies typical of 5G systems: 700, 2600, and 3600 MHz. Matlab's antenna designer obtained various parameters such as reactance, resistance, and S11. These parameters were then processed using the adaptive neuro-fuzzy inference system (ANFIS) feature. The Tagaki-Sugeno ANFIS model, in particular, generated 27 fuzzy rules for each operating frequency, employing distinct test and training data sets. The collected data was analyzed to classify and compare the different S11 values. The analysis involved surface plots and scatter plots to examine the results, and the results demonstrated that the algorithms performed according to the anticipated outcomes, hence validating their performance. © 2023 IEEE.

关键词： Fuzzy inference

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Performance Management and HR Appraisal System based on a Framework of BERT-BiGRU-CNN-A Approach 2

Performance Management and HR Appraisal System based on a Fr...

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2nd international conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Namdeorao, Milindkumar Kusuma Kumari, Ch.V.L.L. Mulla, Zameer Ahmed S. Kamble, Hrutuparna B. Rajeswari, M. Rudresh, S. Department of Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering Nagpur India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Management Ajinkya D. Y. Patil University Lohgaon Pune India Trinity Institute of Management & Research Pune India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Management -UG St Claret College Bangalore India

ISBN: (纸本)9798350340235

Part of an HRIS, a human resource appraisal system (HRAS) assesses employees in light of their duties and facilitates open communication about problems. The HRAS is a system for assessing the knowledge, skills, and personality attributes that companies demand from their employees. It's not meant to replace in-person meetings or other types of staff communication, though. Employee performance information stored in HRAS can facilitate pivotal business decisions. To aid and guide enterprises in this area, human resource (HR) performance standards have been developed. Preprocessing, feature extraction, and model training are all components of the proposed approach. Before feeding data into a learning system, the proposed approach use min-max normalization to make sure it's of high quality. Since the PCA is such a useful tool for extracting the most important features from a dataset, it is widely utilized in feature extraction across all types of study. After collecting the features, BERT-BiGRU-CNN-A is used to train the models. When compared to BiGRU and CNN, two popular methods, the proposed method performs better. © 2023 IEEE.

关键词： Human Resource Appraisal System (HRAS) Human Resource Management (HRM) Performance appraisal

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Promoting Energy-Conservation Behavior in a Smart Home App: Kano Analysis of User Satisfaction with Feedback Nudges 43

Promoting Energy-Conservation Behavior in a Smart Home App: ...

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43rd international conference on Information systems: Digitization for the Next Generation, ICIS 2022

作者： Berger, Michelle Schnaak, Feline Gimpel, Henner Wolf, Linda Research Center Finance & Information Management Branch Business & Information Systems Engineering of the Fraunhofer FIT University of Augsburg Augsburg Germany Research Center Finance & Information Management Branch Business & Information Systems Engineering of the Fraunhofer FIT University of Hohenheim Stuttgart Germany Research Center Finance & Information Management Branch Business & Information Systems Engineering of the Fraunhofer FIT University of Hohenheim Digital Management Stuttgart Germany Research Center Finance & Information Management Branch Business & Information Systems Engineering of the Fraunhofer FIT University of Bayreuth Bayreuth Germany

ISBN: (纸本)9781713893615

Smart home technologies and apps are on a rise. This allows to implement digital nudging elements to foster energy-conservation behavior and, thus, contribute to mitigating climate change. Digital nudging via feedback can be effective in improving energy-conservation behavior, as substantial prior research has shown. However, the investigation of users' preferences concerning feedback nudges is missing. This lack of knowledge is crucial, as user satisfaction influences their continuous app usage, a precondition for achieving positive effects. To close this gap, we perform a structured literature review, categorize the feedback nudge features from extant research, and conduct an online survey. Based on survey data and the Kano model, we analyze the effect of feedback nudge features on user satisfaction. Our study complements the traditional focus on the effectiveness of these nudges with a perspective on user satisfaction. The combination of both perspectives suggests which feedback nudge features should be considered for implementation. © 2022 international conference on Information systems, ICIS 2022: "Digitization for the Next Generation". All Rights Reserved.

关键词： Climate change

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Regression models based in optimized Ensemble of Extreme Learning Machine Networks

Regression models based in optimized Ensemble of Extreme Lea...

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2022 IEEE international conference on systems, Man, and Cybernetics, SMC 2022

作者： Soares, Wedson Lino Pereira De Carvalho, Halcyon Davys Santos, Wylliams Barbosa Andrade De A. Fagundes, Roberta University of Pernambuco Department of Computer Engineering Recife Brazil

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Predicting the failure rate in various educational contexts is an approach that can provide useful information about factors that can negatively influence the students' interaction with the academic environment. data mining provides a set of tools that can be used to approach this type of problem. It addresses educational data, searching for extracting knowledge and identifying patterns, featuring the process known as educational data mining. The current work proposes four different models using optimization algorithms, specifically artificial bee colony and genetic algorithm, combined with Extreme Learning Machine networks and ensemble learning to predict the failure rate of schools from Pernambuco, Brazil, in the elementary degree. The results' consistency is ratified by applying the models to different benchmark datasets from UCI. After obtaining the results related to the prediction error of each model, hypothesis tests were performed to ratify the results. The results and tests confirmed that the proposed models are statistically better at predicting school failure rates in the specified context. © 2022 IEEE.

关键词： data mining

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VizOPS: A data-driven ontology to represent public place surveillance data

VizOPS: A data-driven ontology to represent public place sur...

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2022 IEEE conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Patel, Ashish Singh Tiwari, Vivek Ojha, Muneendra Vyas, O.P. International Institute of Information Technology Department of Computer Science and Engineering Atal Nagar Naya Raipur India Indian Institute of Information Technology Department of Information Technology Prayagraj India

ISBN: (纸本)9781665477192

Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search, question answering and extraction of semantic information. However, most of the data in such knowledge-base(s) is based on the textual data with very emphasis on the visual data. Moreover, due to rapid deployment of surveillance systems in public places, massive amount of video data is being produced. Storage and analysis of this data in real time is a substantial challenge. Therefore, there is massive potential in representing the visual data in knowledge graph due to presence of hidden semantics present in images and videos. However, constructing knowledge graph requires ontology, which needs expertise and domain knowledge. In this paper, we present a novel data-driven ontology ('VizOPS') to represent the visual information of public-place surveillance data to automate ontology engineering process. The ontology is constructed by using the existing public domain benchmark datasets and its metadata. The Class for the ontology is obtained by the existing class from the various public place surveillance data. The relation between two classes is extracted using NLP-based algorithm image captioning datasets. This way the ontology is able to capture the most of the salient surveillance information of real-world public places. The proposed ontology is evaluated by representing the concepts of public places using Visual Genome dataset. © 2022 IEEE.

关键词： Ontology

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