• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.352-358
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• 2022

Text mining is an important process used for analyzing the data collected from different sources like videos, audio, social media, and so on. The tools like Natural Language Processing (NLP) are mostly used in real-time applications. In the earlier research, text mining approaches were implemented using long-short memory (LSTM) networks. In this paper, text mining is performed using average-stochastic gradient descent weight-dropped (AWD)-LSTM techniques to obtain better accuracy and performance. The proposed model is effectively demonstrated by considering the internet movie database (IMDB) reviews. To implement the proposed model Python language was used due to easy adaptability and flexibility while dealing with massive data sets/databases. From the results, it is seen that the proposed LSTM plus weight dropped plus embedding model demonstrated an accuracy of 88.36% as compared to the previous models of AWD LSTM as 85.64. This result proved to be far better when compared with the results obtained by just LSTM model (with 85.16%) accuracy. Finally, the loss function proved to decrease from 0.341 to 0.299 using the proposed model

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• Journal of The Korean Association of Information Education
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• v.23 no.4
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• pp.355-362
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• 2019

Elementary students are studying software training to teach coding education using text-based languages such as Python. In general, these higher-level languages support learning activities in combination with a kits for physical computing or various programming languages, in contrast to block-coding programming languages. In this study, we conducted a coding project based on computational thinking using the ARCS model to overcome the difficulties of text-based language. The results of the experiment show that students are generally confident and interested in programming. Especially, the understanding of repetition, function, and object was high in the change of computational thinking power, so this trend is believed to be due to the use of text-based languages and the Python module.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.247-253
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• 2021

There are various programming errors that occur in the course of programming practice for beginners in computer programming. At this time, since it is difficult for learners to recognize errors by themselves, they correct program errors through the instructor's feedback. However, as students learn programming techniques in an online learning environment due to the COVID-19 pandemic, there is a limit to interaction between the students and the instructor in comparison with offline classes, so it is necessary for learners to develop their own ability to solve programming errors by themselves. Therefore, in this study, error cases in online programming classes using the Python language are analyzed and an online programming education method that can improve learners' ability to correct programming errors is proposed based on the analysis results.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.395-396
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• 2022

본 논문에서는 블록 프로그래밍 언어를 선행조직자로 하여 텍스트 프로그래밍 언어를 학습하는 도구 활용 전략을 연구하였다. 텍스트 프로그래밍 언어는 파이썬이며, 블록 프로그래밍 언어는 엔트리, 활용하는 도구는 주피터 노트북으로 선정하였다. 주피터 노트북을 활용한 블록 프로그래밍 언어 선행조직자 학습 전략은 code cell에 IPython.display.IFrame 클래스를 활용하여 결과 창에 엔트리 작업환경을 불러와 선행조직자로 제시하여 엔트리를 학습 후 code cell에서 파이썬으로 학습한다. 주피터 노트북을 통해 블록 프로그래밍 언어를 선행조직자로 제시 후 텍스트 프로그래밍 언어를 제시함으로써 텍스트 프로그래밍 언어를 학습할 때 인지적 부담을 줄어들고 긍정적 전이가 일어나 효과적인 학습이 될 것으로 기대된다.

• Journal of KIISE
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• v.42 no.4
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• pp.496-503
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• 2015

Most programming languages are designed based on English. It becomes another barrier in learning programming languages in non-English speaking country. If a programming language is presented using a native language, the education cost of programming will be much cheaper and the programming itself can be much more fun. However, designing the programming languages based on native languages has not been much focused or published up to now. It is partly because the evolution of popular programming languages is so fast, and partly because the efficiency of programs is much stressed than the source code. But, the designing of programming languages based on native language is not a small issue, especially if we reflect on the education of programming. In fact, there have been significant efforts reported in the Korean programming languages so far, but it has not practically been used in the education. This paper introduces yet another Korean programming language, namely Sprout, which is concise and can be easily learned by beginners. To demonstrate the conciseness of Sprout, we have performed two experiments on Sprout. Firstly, we compared the sizes of the programs in Sprout with those in former Korean programming languages. Secondly, we compared the size of Sprout, the language itself, with those of popular programming languages such as C and Python. According to the experiments, Sprout programs are more concise to 10% on average than those in former Korean languages. Furthermore, Sprout itself is more compact to 24% on average than other popular programming languages.

• International Journal of Concrete Structures and Materials
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• v.7 no.2
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• pp.127-133
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• 2013

After reviewing the background and motivations for using modern computational methods for the design of reinforced concrete structures, an algorithm making use of the object oriented programming language Python and professionally developed finite element software is presented for the sizing and placement of the reinforcement in RC structures. The developed method is then used to design the reinforcement of a deep beam. To validate the design, two identical deep beam specimens were manufactured with the obtained steel, and then tested in the laboratory. It was found that the experimental results corroborated those predicted with the finite element design method.

• Journal of The Korean Association of Information Education
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• v.25 no.4
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• pp.603-610
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• 2021

As part of recent SW education methods, entry sites have been used in all practical textbooks in elementary schools. However, they are all learning block-type programming languages, making it difficult to produce programs that can be used in everyday life. This study is a study on the effects of learning programming using entry python on logical thinking ability and programming interest in elementary school students. Logical thinking ability and programming interest tests were conducted before and after the 8th class. Before and after classes, logical thinking ability score rose from an average of 6.6 to 9.4 and programming interests score also rose from an average of 46.7 to 59.1. This results in programming learning using Entry Python is significant for enhancing the logical thinking ability and programming interest of elementary school students.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.605-614
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• 2003

In this paper we introduce a new style multiparadigm language named Argos which applies a compositional approach [20] to multiparadigm programming. Argos is a superset of the Java, and its grammar has an extension point which allows other languages to be used in Argos programs. Therefore, Argos can support object-oriented programming and multiparadigm programming by enabling each method in a class to be implemented with one of the Java, C, Prolog, Python, and XML languages. Since Argos allows the existing languages to be used, it has advantages such as easiness of learning and high reusability. The Argos compiler is implemented according to the delegating compiler object (DCO) model[28,29]. The compiler partitions a program Into several parts according to the languages used in methods and delivers the parts the languages' processors which compile the parts.