• 제목/요약/키워드: Approaches to Learning

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Sentimental Analysis of Twitter Data Using Machine Learning and Deep Learning: Nickel Ore Export Restrictions to Europe Under Jokowi's Administration 2022

  • Sophiana Widiastutie;Dairatul Maarif;Adinda Aulia Hafizha
    • Asia pacific journal of information systems
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    • 제34권2호
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    • pp.400-420
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    • 2024
  • Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia's ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia's bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.

전자책 콘텐츠 제작을 통한 학습활동이 자기 주도적 학습 능력에 미치는 효과 (Effect of Learning Activities through Contents Creation for E-book on Self-directed Learning Ability)

  • 박상욱;유인환
    • 정보교육학회논문지
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    • 제18권1호
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    • pp.35-44
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    • 2014
  • 21세기 지식기반 정보화 사회에서 학교교육은 학습자 중심으로 변화되고 있다. 특히, 최근 스마트 교육을 위한 기반 구축으로 자기 주도적 학습이 보다 다양한 방법으로 실현 가능하게 되었다. 이에 본 연구에서는 ePUB 포맷 기반 전자책 저작도구를 교과학습에 적용하여 콘텐츠를 제작하도록 하여 학습자의 자기 주도적 학습 능력 향상에 미치는 효과를 분석해 보았다. 5학년 사회과 역사영역을 대상으로 하여 학습 결과물을 전자책으로 제작하는 활동을 하였다. 그 결과 실험집단이 통제집단보다 자기 주도적 학습 능력이 향상되었고 자기 주도적 학습 능력의 하위 요소인 내재적 동기, 학습 기회의 개방성, 자율성 등 모든 영역에서 높은 만족도를 보이는 것을 확인할 수 있었다.

Predicting idiopathic pulmonary fibrosis (IPF) disease in patients using machine approaches

  • Ali, Sikandar;Hussain, Ali;Kim, Hee-Cheol
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2021년도 춘계학술대회
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    • pp.144-146
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    • 2021
  • Idiopathic pulmonary fibrosis (IPF) is one of the most dreadful lung diseases which effects the performance of the lung unpredictably. There is no any authentic natural history discovered yet pertaining to this disease and it has been very difficult for the physicians to diagnosis this disease. With the advent of Artificial intelligent and its related technologies this task has become a little bit easier. The aim of this paper is to develop and to explore the machine learning models for the prediction and diagnosis of this mysterious disease. For our study, we got IPF dataset from Haeundae Paik hospital consisting of 2425 patients. This dataset consists of 502 features. We applied different data preprocessing techniques for data cleaning while making the data fit for the machine learning implementation. After the preprocessing of the data, 18 features were selected for the experiment. In our experiment, we used different machine learning classifiers i.e., Multilayer perceptron (MLP), Support vector machine (SVM), and Random forest (RF). we compared the performance of each classifier. The experimental results showed that MLP outperformed all other compared models with 91.24% accuracy.

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Analysis of Neural Network Approaches for Nonlinear Modeling of Switched Reluctance Motor Drive

  • Saravanan, P;Balaji, M;Balaji, Nagaraj K;Arumugam, R
    • Journal of Electrical Engineering and Technology
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    • 제12권4호
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    • pp.1548-1555
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    • 2017
  • This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.

Two person Interaction Recognition Based on Effective Hybrid Learning

  • Ahmed, Minhaz Uddin;Kim, Yeong Hyeon;Kim, Jin Woo;Bashar, Md Rezaul;Rhee, Phill Kyu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권2호
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    • pp.751-770
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    • 2019
  • Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

경로 탐색 기법과 강화학습을 사용한 주먹 지르기동작 생성 기법 (Punching Motion Generation using Reinforcement Learning and Trajectory Search Method)

  • 박현준;최위동;장승호;홍정모
    • 한국멀티미디어학회논문지
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    • 제21권8호
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    • pp.969-981
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    • 2018
  • Recent advances in machine learning approaches such as deep neural network and reinforcement learning offer significant performance improvements in generating detailed and varied motions in physically simulated virtual environments. The optimization methods are highly attractive because it allows for less understanding of underlying physics or mechanisms even for high-dimensional subtle control problems. In this paper, we propose an efficient learning method for stochastic policy represented as deep neural networks so that agent can generate various energetic motions adaptively to the changes of tasks and states without losing interactivity and robustness. This strategy could be realized by our novel trajectory search method motivated by the trust region policy optimization method. Our value-based trajectory smoothing technique finds stably learnable trajectories without consulting neural network responses directly. This policy is set as a trust region of the artificial neural network, so that it can learn the desired motion quickly.

선형변수 기계학습 기법을 활용한 저속비대선의 잉여저항계수 추정 (Prediction of Residual Resistance Coefficient of Low-Speed Full Ships Using Hull Form Variables and Machine Learning Approaches)

  • 김유철;양경규;김명수;이영연;김광수
    • 대한조선학회논문집
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    • 제57권6호
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    • pp.312-321
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    • 2020
  • In this study, machine learning techniques were applied to predict the residual resistance coefficient (Cr) of low-speed full ships. The used machine learning methods are Ridge regression, support vector regression, random forest, neural network and their ensemble model. 19 hull form variables were used as input variables for machine learning methods. The hull form variables and Cr data obtained from 139 hull forms of KRISO database were used in analysis. 80 % of the total data were used as training models and the rest as validation. Some non-linear models showed the overfitted results and the ensemble model showed better results than others.

긴급대응 시스템을 위한 심층 해석 가능 학습 (Deep Interpretable Learning for a Rapid Response System)

  • 우엔 쫑 니아;보탄헝;고보건;이귀상;양형정;김수형
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2021년도 추계학술발표대회
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    • pp.805-807
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    • 2021
  • In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

Covid-19 and Distance Education: Analysis of the Problems and Consequences of the Pandemic

  • Bida, Olena;Prokhorchuk, Oleksandr;Fedyaeva, Valentina;Radul, Olga;Yakimenko, Polina;Shevchenko, Olga
    • International Journal of Computer Science & Network Security
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    • 제21권12spc호
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    • pp.629-635
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    • 2021
  • In the spring, 2020, the pandemic caused quarantine and all educational institutions switched to distance learning, which led to significant changes in the field of education around the world. It has become necessary to build its capacity to provide distance learning to protect education and create opportunities for more individualized approaches to teaching and learning not only during future pandemics but also during other possible issues, such as natural disasters, when a developed flexible curricula could be taught face-to-face or online. The article presents an analysis of distance education in the world during a pandemic, analyzes significant changes, and implements measures in the field of education in Ukraine and around the world. The role of public and international organizations in the implementation of quarantine in the conditions of COVID-19, which partially took over the functions of state and local authorities, is emphasized. The closure of schools under COVID-19 has led to a de facto deterioration in learning outcomes, so we have analyzed the effects of distance learning and digital inequality in the world. It is shown how the COVID-19 pandemic affected access to public services in Ukraine.

Parameterization of the Company's Business Model for Machine Learning-Based Marketing Stress Testing

  • Menkova, Krystyna;Zozulov, Oleksandr
    • International Journal of Computer Science & Network Security
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    • 제22권2호
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    • pp.318-326
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    • 2022
  • Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.