• Journal of Information Processing Systems
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• 제16권5호
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• pp.1015-1033
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• 2020

With promising results and enormous capability, deep learning technology has attracted more and more attention to both theoretical research and applications for a variety of image processing and computer vision tasks. In this paper, we investigate 32 research contributions that apply deep learning techniques to the agriculture domain. Different types of deep neural network architectures in agriculture are surveyed and the current state-of-the-art methods are summarized. This paper ends with a discussion of the advantages and disadvantages of deep learning and future research topics. The survey shows that deep learning-based research has superior performance in terms of accuracy, which is beyond the standard machine learning techniques nowadays.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.151-155
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• 2023

The main purpose of the article is to analyze the practice of overcoming during distance learning of students-future teachers of a preschool education institution. The key aspects of practical activities to counter a stressful situation during distance learning of students-future teachers of a preschool education institution are identified. The research methodology includes a number of methods designed to analyze the practice of coping with stress during distance learning of students. The results of the study include the definition of the main elements of practical activities to counteract stress and stressful situations of different scales in the distance learning of students-future teachers of a preschool education institution. Further research requires the analysis of international experience in dealing with a stressful situation during distance learning of students.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 제11회 음성통신 및 신호처리 워크샵 논문집 (SCAS 11권 1호)
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• pp.407-410
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• 1994

In this paper, we describe the new unsuperivised learning algorithm, SASOM. It can solve the defects of the conventional SOM that the state of network can't converge to the minimum point. The proposed algorithm uses the object function which can evaluate the state of network in learning and adjusts the learning rate flexibly according to the evaluation of the object function. We implement the simulated annealing which is applied to the conventional network using the object function and the learning rate. Finally, the proposed algorithm can make the state of network converged to the global minimum. Using the two-dimensional input vectors with uniform distribution, we graphically compared the ordering ability of SOM with that of SASOM. We carried out the recognitioin on the new algorithm for all Korean phonemes and some continuous speech.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1517-1520
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• 2003

Generally, the environment we want to apply classifier system to is composed of several state spaces. So in this paper, we propose the layered classifier system having multifarious rule bases. From sensor's inputs, the lower layer of the layered classifier system learns strategies for each environmental state space. The higher layer learns how to allot each rule base of the strategy for environmental state space properly. To evaluate the proposed architecture of classifier system, we designed virtual environment having multifarious state spaces and from the analysis of the experimental results, we affirm that layered classifier system could find better strategies during a little time than other established classifier system's findings.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.407-409
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• 1998

This paper deals with temporal-difference learning that is a method for approximating long-term future cost as a function of current state in knowlege-poor environment, a function approximator is used to approximate the mapping from state to future cost, a linear function approximator is limited because mapping from state to future cost has a nonlinear characteristic, so a nonlinear function approximator is used to approximate the mapping from state to future cost in this paper, and that TD learning using a nonlinear function approximator is stable is proved.

• Asia pacific journal of information systems
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• 제31권2호
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• pp.185-196
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• 2021

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

• 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.

• 한국과학교육학회지
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• 제27권5호
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• pp.412-420
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• 2007

이 연구에서는 인식론적 신념이 개념변화 과정에 미치는 영향을 인지갈등, 상황흥미, 주의집중, 상태 학습 전략을 고려하여 조사했다. 사전 검사로 인식론적 신념 검사를 실시한 후, 선개념 검사를 통해 밀도에 대한 특정 오개념을 지닌 중학교 1학년 218명을 선발했다. 변칙사례에 대한 반응 검사와 상황흥미 검사를 실시하고, CAI 프로그램을 통해 밀도 개념학습을 진행했다. 사후 검사로 주의집중, 상태 학습전략, 개념 검사를 실시했다. 연구 결과, 인식론적 선념의 요소인 고정된 능력, 빠른 학습, 확실한 지식들 사이에는 밀접한 관련성이 있었으나, 확실한 지식만이 개념이해에 직접적으로 부정적인 영향을 주었다. 이 영향력보다 상대적으로 영향력은 작았지만, 확실한 지식은 직접적으로 또는 상황흥미를 매개로 주의집중에 영향을 줌으로써 개념이해에 긍정적인 영향을 미치기도 했다. 그러나 인식론적 신념이 얀지갈등과 상태 학습전략을 통해 개념이해에 미치는 영향은 매우 작았다.