• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• 지능정보연구
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• 제24권1호
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• pp.253-266
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• 2018

본 연구는 질의 응답(QA) 시스템에서 사용하는 개체명 인식(NER)의 성능을 향상시키기 위하여 시퀀스 태깅 방법론을 적용한 새로운 방법론을 제안한다. 사용자의 질의를 입력 받아 데이터베이스에 저장된 정답을 추출하기 위해서는 사람의 언어를 컴퓨터가 알아들을 수 있도록 구조화 질의어(SQL)와 같은 데이터베이스의 언어로 전환하는 과정이 필요한데, 개체명 인식은 사용자의 질의에서 데이터베이스에 포함된 클래스나 데이터 명을 식별하는 과정이다. 기존의 데이터베이스에서 질의에 포함된 단어를 검색하여 개체명을 인식하는 방식은 동음이의어와 문장성분 구를 문맥을 고려하여 식별하지 못한다. 다수의 검색 결과가 존재하면 그들 모두를 결과로 반환하기 때문에 질의에 대한 해석이 여러 가지가 나올 수 있고, 계산을 위한 시간복잡도가 커진다. 본 연구에서는 이러한 단점을 극복하기 위해 신경망 기반의 방법론을 사용하여 질의가 가지는 문맥적 의미를 반영함으로써 이러한 문제를 해결하고자 했고 신경망 기반의 방법론의 문제점인 학습되지 않은 단어에 대해서도 문맥을 통해 식별을 하고자 하였다. Sequence Tagging 분야에서 최신 기술인 Bidirectional LSTM-CRF 모델을 도입함으로써 신경망 모델이 가진 단점을 해결하였고, 학습되지 않은 단어에 대해서는 온톨로지 기반 특성치를 활용하여 문맥을 반영한 추론을 사용하였다. 음악 도메인의 온톨로지(Ontology) 지식베이스를 대상으로 실험을 진행하고 그 성능을 평가하였다. 본 연구에서 제안한 방법론인 L-Bidirectional LSTM-CRF의 성능을 정확하게 평가하기 위하여 학습에 포함된 단어들뿐만 아니라 학습에 포함되지 않은 단어들도 포함한 질의를 평가에 사용하였다. 그 결과 L-Bidirectional LSTM-CRF 모형을 재학습 시키지 않아도 학습에 포함되지 않은 단어를 포함한 질의에 대한 개체명 인식이 가능함을 확인하였고, 전체적으로 개체명 인식의 성능이 향상됨을 확인할 수 있었다.