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检索条件"机构=Postgraduate Program in Computer Science in Education"

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Exploring the Potential of Artificial Intelligence in Special education: A Bibliometric Analysis to Support Educators and Students with Autism Spectrum Disorder (ASD) 54

Exploring the Potential of Artificial Intelligence in Specia...

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54th IEEE Frontiers in education Conference, FIE 2024

作者： Silveira, Lisiane Corrêa Gomes De Alvarenga, Patricia Warnke Ribeiro, Luis Otoni Meireles Gonçalves, Eder Mateus Vicari, Rosa Barwaldt, Regina Federal Institute Sul-rio-grandense Postgraduate Program in Education Pelotas Brazil Federal University of Rio Grande Postgraduate Program in Science Education Rio Grande Brazil Federal University of Rio Grande Computer Science Center of Furg Rio Grande Brazil Federal University of Rio Grande Rio Grande Brazil Federal University of Rio Grande do Sul Computer Science Center Porto Alegre Brazil Federal University of Rio Grande Computer Science Center Rio Grande Brazil

ISBN: (纸本)9798350351507

This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in education to support educators in the teaching and learning processes in Special education in different contexts, becoming a tool with potential use in cases of students with Autism Spectrum Disorder (ASD). In this sense, this work aims to contribute to a better understanding of research associated with the area of Artificial Intelligence aimed at educational processes involving students with autism. To do this, we used the EndNote reference manager, an online tool designed to support researchers in conducting Literature Reviews, following the bibliometric protocol proposed by Guedes and Borschiver (2005). From the definition of search strings, ('artificial intelligence') AND ('autism' OR 'ASD' OR 'autism spectrum disorder') AND ('education' OR 'teaching'), scientific databases were explored like Web of science (WoS), Scopus, ERIC, Emerald, Scielo, Portal CAPES, and IEEE, to locate existing studies on the topic, between 2019 and 2024. As a result, 298 articles were mapped and 20 scientific works were selected that address the aforementioned specific theme, written by 89 authors belonging to 50 institutions in 19 countries on four continents, enabling the creation of a significant theoretical basis. By analyzing the information contained in scientific publications in this specific field, it was possible to infer that research is divided into distinct areas of concentration: 1) the exploration of algorithms and data analysis based on Artificial Intelligence for analytical-predictive issues in the field of special education;2) the use of robots in the teaching and learning processes of students with autism;3) the development of personalized educational intervention models for learning pedagogical, social and communication skills. The data show that research on th

关键词： Students

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A Method Based on Deep Neural Network for Instance Segmentation of Retinal Lesions Caused by Diabetic Retinopathy

A Method Based on Deep Neural Network for Instance Segmentat...

引用

2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Santos, Carlos Aguiar, Marilton Welfer, Daniel Silva, Marcelo Pereira, Alejandro Ribeiro, Marcelo Belloni, Bruno Computer Center Federal Institute of Education Science and Technology Farroupilha Alegrete Brazil Postgraduate Program in Computing Federal University of Pelotas Pelotas Brazil Postgraduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Federal University of Pelotas Pelotas Brazil Federal Institute of Education Science and Technology Sul-Rio-Grandense Passo Fundo Brazil

ISBN: (纸本)9798350320282

Diabetic Retinopathy is one of the main causes of vision loss and can be identified through ophthalmological exams that aim to locate the presence of retinal lesions such as microaneurysms, hemorrhages, soft exudates, and hard exudates. The development of computerized methods to perform the instance segmentation of lesions may support in the early diagnosis of the disease. However, the instance segmentation of retinal artifacts is a complex task due to factors such as the size of objects and their morphological characteristics. This article proposes a method based on a Mask R-CNN neural network architecture to perform instance segmentation of lesions associated with diabetic retinopathy. The proposed method was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets, and implemented with the Detectron2 and OpenCV libraries. The proposed method reached in the DDR dataset, using the SGD optimizer, the mAP of 0.2660 for the limit of I oU of 0.5 in the validation step. The results obtained in the experiments demonstrate that the proposed method showed promising results in the instance segmentation of fundus lesions. © 2022 IEEE.

关键词： Deep neural networks

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Interactive Multimedia Kolb Experiential Learning Model Using Logistic Regression Algorithm to Improve Student Cognitive

Interactive Multimedia Kolb Experiential Learning Model Usin...

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7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Khoirunnisa, Azizah Nurul Munir Rasim Rahman, Eka Fitrajaya Dewi, Laksmi Curriculum Development Study Program School of Postgraduate Studies Indonesia University of Education Bandung Indonesia Computer Science Education Study Program Department of Computer Science Education Indonesia University of Education Bandung Indonesia

ISBN: (纸本)9783030902346

The differences of each student become a challenge for educators in delivering learning materials. More often than not, students often find it difficult to process study materials. Conventional study-based learning may not always have a profound impact on students. Consideration of interactive multimedia as a tool for student-centered learning has not been widely discussed in scientific research. In this paper, the authors investigate interactive multimedia using Kolb’s experiential learning model in improving students’ cognitive abilities. This study uses a pre-experimental method and one-group pre-and post-test design type with the research population in class XI RPL and a sample of SMK students in Bandung. The results show that the application of interactive multimedia using Kolb’s experiential learning model is effective in improving students’ cognitive abilities. On the other hand, it is found that combining abstract learning materials with interactive visuals and activities that involve students’ participation could create exceptionally positive feedback from students. © 2021, Springer Nature Switzerland AG.

关键词： Students

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Optimizing Wireless Sensor Network Planning: Integrating Biased Random-Key Genetic Algorithm and Local Branching for Scalable Solutions

Optimizing Wireless Sensor Network Planning: Integrating Bia...

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International Conference on Information Fusion

作者： Almir Antônio Monteiro Junior Diego Brandão Felipe Da Rocha Henriques Claudio M. De Farias Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Computer Science Postgraduate Program Federal Center for Technological Education of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This study addresses the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations (WSNPMSD), an optimization challenge focused on reducing the sensor count within a network topology for a specified area, considering numerous sources and destinations. We introduce a hybrid strategy for tackling WSNP-MSD, particularly effective for large-scale scenarios, combining a Biased Random-key Genetic Algorithm with a Local Branching Technique. This methodology is justified by the limitations exact methods may encounter when the number of variables increases. Through computational experiments, we demonstrate the superiority of our proposed method over conventional exact methods in managing large instances of the WSNP-MSD.

关键词： Wireless communication Wireless sensor networks Network topology Planning Optimization Genetic algorithms

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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Exploring the Potential of Artificial Intelligence in Special education: A Bibliometric Analysis to Support Educators and Students with Autism Spectrum Disorder (ASD)

Exploring the Potential of Artificial Intelligence in Specia...

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Frontiers in education (FIE) Conference

作者： Lisiane Corrêa Gomes Silveira Patrícia Warnke de Alvarenga Luis Otoni Meireles Ribeiro Eder Mateus Gonçalves Rosa Vicari Regina Barwaldt Postgraduate Program in Education Federal Institute Sul-rio-grandense Pelotas Brazil Postgraduate Program in Science Education Federal University of Rio Grande Rio Grande Brazil Computer Science Center of FURG Federal University of Rio Grande Rio Grande Brazil Computer Engineering Federal University of Rio Grande Rio Grande Brazil Computer Science Center Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science Center Federal University of Rio Grande Rio Grande Brazil

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in education to support educators in the teaching and learning processes in Special education in different contexts, becoming a tool with potential use in cases of students with Autism Spectrum Disorder (ASD). In this sense, this work aims to contribute to a better understanding of research associated with the area of Artificial Intelligence aimed at educational processes involving students with autism. To do this, we used the EndNote reference manager, an online tool designed to support researchers in conducting Literature Reviews, following the bibliometric protocol proposed by Guedes and Borschiver (2005). From the definition of search strings, (“artificial intelligence”) AND (“autism” OR “ASD” OR “autism spectrum disorder”) AND (“education” OR “teaching”), scientific databases were explored like Web of science (WoS), Scopus, ERIC, Emerald, Scielo, Portal CAPES, and IEEE, to locate existing studies on the topic, between 2019 and 2024. As a result, 298 articles were mapped and 20 scientific works were selected that address the aforementioned specific theme, written by 89 authors belonging to 50 institutions in 19 countries on four continents, enabling the creation of a significant theoretical basis. By analyzing the information contained in scientific publications in this specific field, it was possible to infer that research is divided into distinct areas of concentration: 1) the exploration of algorithms and data analysis based on Artificial Intelligence for analytical-predictive issues in the field of special education; 2) the use of robots in the teaching and learning processes of students with autism; 3) the development of personalized educational intervention models for learning pedagogical, social and communication skills. The data show that research on

关键词： Autism Protocols Databases education Bibliometrics Learning (artificial intelligence) Data models Robots Portals Systematic literature review

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Publication of the society's sensitive personal data in the legitimate site of the general election committee 2

Publication of the society's sensitive personal data in the ...

引用

2nd International Conference on Vocational education and Technology, IConVET 2019

作者： Parwata, A. Aryanto, K.Y.E. Divayana, D.G.H. Study Program of Computer Science Postgraduate Program Ganesha University of Education Singaraja Indonesia

It is highly important to protect the society's sensitive personal data. The reason is that many personal data are published by the government institution without paying attention to the prevailing regulations. In this current study, the search for the data in the form of hyperlink with its contents in the legitimate site of the General Election Committees was conducted with the assistance of Crawling Web. The obtained contents were preprocessed using the Preprocessing Text, which were then weighted using the TF-IDF method before they were classified using the Nayve Bayes method. After that, an analysis on the types of the published sensitive personal and the extent of the publication based on the area groups was conducted. Out of 6,700 instances of the personal data which were analyzed, 6.430 were published. The personal data which were published were full name, place and date of birth, religion, marital status, ID Number of the government civil servants, identity card number, number of the tax payer, account number, mobile number, e-mail, address, position, and face photo. The level of publication based on the total data found was as follows: 11.45% in the Central General Election Committee, 21.60% in the eastern area, 17.01% in the central area and 49.94% in the eastern area. The accuracy of the Nave Bayes method averaged 96.99%. Prior to publication, the General Election Committee is recommended to respect someone's personal data as privacy and the data which would be published should obtain approval and an easily-contacted contact person. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Hypertext systems

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Optimization of artificial neural networks to improve accuracy of vocational competence selection of vocational school students using nguyen-widrow 2

Optimization of artificial neural networks to improve accura...

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2nd International Conference on Vocational education and Technology, IConVET 2019

作者： Christyaditama, I.G.P. Candiasa, I.M. Gunadi, I.G.A. Study Program of Computer Science Postgraduate Program Ganesha University of Education Singaraja Indonesia

The present study used Neural Network Backpropagation combined with Nguyen-Widrow to optimize the disadvantages of ANN causes, which is the difficulty in initializing initial weights. The test was conducted on a dataset of values in semesters 1 and 2. The test results show that the best performance in training model of artificial neural networks with Nguyen-widrow is the smallest average MSE error of 0.002 and the highest average accuracy of 96.38%. Training on artificial neural network model training data with Nguyen-widrow has the smallest MSE error, 0.000996 and the highest accuracy is 97.49% on architecture ANN 9-9.1 with training function parameters: traingdx, epoch: 1000, learning rate: 0.1, and error: 0.001. The best performance was also seen in testing the testing of artificial neural network models with Nguyen-widrow with the smallest average error-MSE of 0.026 and the highest average accuracy of 87.85%. Training data testing on artificial neural network models with Nguyen-widrow has the smallest error-MSE which is 0.004436 and the highest accuracy is 94.50% on architecture ANN 9-9.1 with training function parameters: traingdx, epoch: 1000, learning rate: 0.1, and error: 0.001. The artificial neural network model with Nguyen-widrow has an accuracy difference of 8.53% smaller than the artificial neural network model with an accuracy difference of 9.28%. It can be concluded that the Artificial Neural Network with Nguyen-Widrow can overcome the ANN problem in determining initial weights so that it gives an increase in the accuracy of the prediction of students' competency selection better than the Artificial Neural Network without Nguyen-Widrow. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Errors

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Evaluation of governance financial management information system (SIPKD) with framework of COBIT 5 in the government of Denpasar city 2

Evaluation of governance financial management information sy...

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2nd International Conference on Vocational education and Technology, IConVET 2019

作者： Vergantana, I.W.S.M. Dantes, I.G.R. Aryanto, K.Y.E. Study Program of Computer Science Postgraduate ProgramGanesha University of Education Singaraja Indonesia

Denpasar City Government is one of the government agencies that hav implemented the Regional Financial Management Information System (SIPKD). SIPKD is an integrated application that is used as a tool for regional governments for improving the effectiveness of the implementation of various regulations in the field of regional financial management based on the principles of efficiency, economic, effective, transparent, accountable and auditable. Research Evaluation of SIPKD governance in Denpasar City Government was conducted based on the framework of the COBIT 5 (Control Objective for Information & Related Technology) framework of ISACA 2012, which was modified according to the conditions at the study site. The research method used was descriptive-quantitative evaluation and cascading evaluation method at the COBIT 5 stage, by distributing questionnaires to each admin as many as 40 respondents, to get scores in the form of numbers and conducting structured interviews with BPKAD leaders to obtain the expected capability level conditions. Evaluation of SIPKD governance with COBIT 5 uses the PAM (Process Assessment Model) analysis as a reference in determining its attributes, with the results of the capability level currently at the level 3 established process in the APO01 process of 77% achieved, APO012 of 78% achiaved, DSS04 by 77% mostly achiaved, EDM03 by 78% largely achieved, and MEA03 by 77% largely achieved, which means that in the management of SIPKD the Denpasar City Government has been well established in managing SIPKD conducted by all SIPKD administrators and operators along with BPKAD leaders in Denpasar. And the results of the interview the desired results are at level 4 predictable process. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Information management

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A Method Based on Deep Neural Network for Instance Segmentation of Retinal Lesions Caused by Diabetic Retinopathy

A Method Based on Deep Neural Network for Instance Segmentat...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Silva Alejandro Pereira Marcelo Ribeiro Bruno Belloni Computer Center Federal Institute of Education Science and Technology Farroupilha Alegrete Brazil Postgraduate Program in Computing Federal University of Pelotas Pelotas Brazil Postgraduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Federal University of Pelotas Pelotas Brazil Federal Institute of Education Science and Technology Sul-Rio-Grandense Passo Fundo Brazil

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