http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Makara Mao (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Bi-directional Maximal Matching Algorithm to Segment Khmer Words in Sentence
Makara Mao,Sony Peng,Yixuan Yang,박두순 한국정보처리학회 2022 Journal of information processing systems Vol.18 No.4
In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to rightwithout a space separator; it is complicated and requires more analysis studies. Without clear standardguidelines, a space separator in the Khmer language is used inconsistently and informally to separate words insentences. Therefore, a segmented method should be discussed with the combination of the future Khmernatural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process inNLP with the capability of extensive data language analysis necessitates applying in this scenario. One of theessential components in Khmer language processing is how to split the word into a series of sentences andcount the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So,this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching(BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectionalimplementation of forward maximal matching (FMM) and backward maximal matching (BMM) toimprove word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie,enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracyof BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improvesdictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57%compared to FMM and BFF algorithms with 94,807 Khmer words.
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Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset
Sony Peng,Yixuan Yang,Makara Mao,Doo-Soon Park 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.2
A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.
ScienceON
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