• Title/Summary/Keyword: Neural Embedding Model

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Pairwise Neural Networks for Predicting Compound-Protein Interaction (약물-표적 단백질 연관관계 예측모델을 위한 쌍 기반 뉴럴네트워크)

  • Lee, Munhwan;Kim, Eunghee;Kim, Hong-Gee
    • Korean Journal of Cognitive Science
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    • v.28 no.4
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    • pp.299-314
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    • 2017
  • Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

Text Classification on Social Network Platforms Based on Deep Learning Models

  • YA, Chen;Tan, Juan;Hoekyung, Jung
    • Journal of information and communication convergence engineering
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    • v.21 no.1
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    • pp.9-16
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    • 2023
  • The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

A Study on Image Generation from Sentence Embedding Applying Self-Attention (Self-Attention을 적용한 문장 임베딩으로부터 이미지 생성 연구)

  • Yu, Kyungho;No, Juhyeon;Hong, Taekeun;Kim, Hyeong-Ju;Kim, Pankoo
    • Smart Media Journal
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    • v.10 no.1
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    • pp.63-69
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    • 2021
  • When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

Deepfake Detection using Supervised Temporal Feature Extraction model and LSTM (지도 학습한 시계열적 특징 추출 모델과 LSTM을 활용한 딥페이크 판별 방법)

  • Lee, Chunghwan;Kim, Jaihoon;Yoon, Kijung
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • fall
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    • pp.91-94
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    • 2021
  • As deep learning technologies becoming developed, realistic fake videos synthesized by deep learning models called "Deepfake" videos became even more difficult to distinguish from original videos. As fake news or Deepfake blackmailing are causing confusion and serious problems, this paper suggests a novel model detecting Deepfake videos. We chose Residual Convolutional Neural Network (Resnet50) as an extraction model and Long Short-Term Memory (LSTM) which is a form of Recurrent Neural Network (RNN) as a classification model. We adopted cosine similarity with hinge loss to train our extraction model in embedding the features of Deepfake and original video. The result in this paper demonstrates that temporal features in the videos are essential for detecting Deepfake videos.

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Using GA based Input Selection Method for Artificial Neural Network Modeling Application to Bankruptcy Prediction (유전자 알고리즘을 활용한 인공신경망 모형 최적입력변수의 선정 : 부도예측 모형을 중심으로)

  • 홍승현;신경식
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 1999.10a
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    • pp.365-373
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    • 1999
  • Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

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Hybrid Word-Character Neural Network Model for the Improvement of Document Classification (문서 분류의 개선을 위한 단어-문자 혼합 신경망 모델)

  • Hong, Daeyoung;Shim, Kyuseok
    • Journal of KIISE
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    • v.44 no.12
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    • pp.1290-1295
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    • 2017
  • Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

Extraction of Protein-Protein Interactions based on Convolutional Neural Network (CNN) (Convolutional Neural Network (CNN) 기반의 단백질 간 상호 작용 추출)

  • Choi, Sung-Pil
    • KIISE Transactions on Computing Practices
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    • v.23 no.3
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    • pp.194-198
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    • 2017
  • In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

Task Planning Algorithm with Graph-based State Representation (그래프 기반 상태 표현을 활용한 작업 계획 알고리즘 개발)

  • Seongwan Byeon;Yoonseon Oh
    • The Journal of Korea Robotics Society
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    • v.19 no.2
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    • pp.196-202
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    • 2024
  • The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

Automatic Classification and Vocabulary Analysis of Political Bias in News Articles by Using Subword Tokenization (부분 단어 토큰화 기법을 이용한 뉴스 기사 정치적 편향성 자동 분류 및 어휘 분석)

  • Cho, Dan Bi;Lee, Hyun Young;Jung, Won Sup;Kang, Seung Shik
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.1
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    • pp.1-8
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    • 2021
  • In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

Categorization of Korean News Articles Based on Convolutional Neural Network Using Doc2Vec and Word2Vec (Doc2Vec과 Word2Vec을 활용한 Convolutional Neural Network 기반 한국어 신문 기사 분류)

  • Kim, Dowoo;Koo, Myoung-Wan
    • Journal of KIISE
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    • v.44 no.7
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    • pp.742-747
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    • 2017
  • In this paper, we propose a novel approach to improve the performance of the Convolutional Neural Network(CNN) word embedding model on top of word2vec with the result of performing like doc2vec in conducting a document classification task. The Word Piece Model(WPM) is empirically proven to outperform other tokenization methods such as the phrase unit, a part-of-speech tagger with substantial experimental evidence (classification rate: 79.5%). Further, we conducted an experiment to classify ten categories of news articles written in Korean by feeding words and document vectors generated by an application of WPM to the baseline and the proposed model. From the results of the experiment, we report the model we proposed showed a higher classification rate (89.88%) than its counterpart model (86.89%), achieving a 22.80% improvement. Throughout this research, it is demonstrated that applying doc2vec in the document classification task yields more effective results because doc2vec generates similar document vector representation for documents belonging to the same category.