• Title/Summary/Keyword: Deep Learning Models

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Development of a model for predicting dyeing color results of polyester fibers based on deep learning (딥러닝 기반 폴리에스터 섬유의 염색색상 결과예측 모형 개발)

  • Lee, Woo Chang;Son, Hyunsik;Lee, Choong Kwon
    • Smart Media Journal
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    • v.11 no.3
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    • pp.74-89
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    • 2022
  • Due to the unique recipes and processes of each company, not only differences among the results of dyeing textile materials exist but they are also difficult to predict. This study attempted to develop a color prediction model based on deep learning to optimize color realization in the dyeing process. For this purpose, deep learning-based models such as multilayer perceptron, CNN and LSTM models were selected. Three forecasting models were trained by collecting a total of 376 data sets. The three predictive models were compared and analyzed using the cross-validation method. The mean of the CMC (2:1) color difference for the prediction results of the LSTM model was found to be the best.

Evaluating Unsupervised Deep Learning Models for Network Intrusion Detection Using Real Security Event Data

  • Jang, Jiho;Lim, Dongjun;Seong, Changmin;Lee, JongHun;Park, Jong-Geun;Cheong, Yun-Gyung
    • International journal of advanced smart convergence
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    • v.11 no.4
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    • pp.10-19
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    • 2022
  • AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried out an experiment. We report the results and discuss the findings.

Crop Leaf Disease Identification Using Deep Transfer Learning

  • Changjian Zhou;Yutong Zhang;Wenzhong Zhao
    • Journal of Information Processing Systems
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    • v.20 no.2
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    • pp.149-158
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    • 2024
  • Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

Analysis of Deep Learning-Based Lane Detection Models for Autonomous Driving (자율 주행을 위한 심층 학습 기반 차선 인식 모델 분석)

  • Hyunjong Lee;Euihyun Yoon;Jungmin Ha;Jaekoo Lee
    • IEMEK Journal of Embedded Systems and Applications
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    • v.18 no.5
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    • pp.225-231
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    • 2023
  • With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

Hangul Font Dataset for Korean Font Research Based on Deep Learning (딥러닝 기반의 한글 폰트 연구를 위한 한글 폰트 데이터셋)

  • Ko, Debbie Honghee;Lee, Hyunsoo;Suk, Jungjae;Hassan, Ammar Ul;Choi, Jaeyoung
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.2
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    • pp.73-78
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    • 2021
  • Recently, as interest in deep learning has increased, many researches in various fields using deep learning techniques have been conducted. Studies on automatic generation of fonts using deep learning-based generation models are limited to several languages such as Roman or Chinese characters. Generating Korean font is a very time-consuming and expensive task, and can be easily created using deep learning. For research on generating Korean fonts, it is important to prepare a Korean font dataset from the viewpoint of process automation in order to keep pace with deep learning-based generation models. In this paper, we propose a Korean font dataset for deep learning-based Korean font research and describe a method of constructing the dataset. Based on the Korean font data set proposed in this paper, we show the usefulness of the proposed dataset configuration through the process of applying it to a deep learning Korean font generation application.

Development of a Ream-time Facial Expression Recognition Model using Transfer Learning with MobileNet and TensorFlow.js (MobileNet과 TensorFlow.js를 활용한 전이 학습 기반 실시간 얼굴 표정 인식 모델 개발)

  • Cha Jooho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.3
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    • pp.245-251
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    • 2023
  • Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

Zero-anaphora resolution in Korean based on deep language representation model: BERT

  • Kim, Youngtae;Ra, Dongyul;Lim, Soojong
    • ETRI Journal
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    • v.43 no.2
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    • pp.299-312
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    • 2021
  • It is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep-learning-based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high-quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine-tuned a pretrained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence-transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end-to-end learning by disallowing any use of hand-crafted or dependency-parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR.

Application of Deep Learning: A Review for Firefighting

  • Shaikh, Muhammad Khalid
    • International Journal of Computer Science & Network Security
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    • v.22 no.5
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    • pp.73-78
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    • 2022
  • The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

Development of Deep Learning Models for Multi-class Sentiment Analysis (딥러닝 기반의 다범주 감성분석 모델 개발)

  • Syaekhoni, M. Alex;Seo, Sang Hyun;Kwon, Young S.
    • Journal of Information Technology Services
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    • v.16 no.4
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    • pp.149-160
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    • 2017
  • Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

Development of a Deep Learning Model for Detecting Fake Reviews Using Author Linguistic Features (작성자 언어적 특성 기반 가짜 리뷰 탐지 딥러닝 모델 개발)

  • Shin, Dong Hoon;Shin, Woo Sik;Kim, Hee Woong
    • The Journal of Information Systems
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    • v.31 no.4
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    • pp.01-23
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    • 2022
  • Purpose This study aims to propose a deep learning-based fake review detection model by combining authors' linguistic features and semantic information of reviews. Design/methodology/approach This study used 358,071 review data of Yelp to develop fake review detection model. We employed linguistic inquiry and word count (LIWC) to extract 24 linguistic features of authors. Then we used deep learning architectures such as multilayer perceptron(MLP), long short-term memory(LSTM) and transformer to learn linguistic features and semantic features for fake review detection. Findings The results of our study show that detection models using both linguistic and semantic features outperformed other models using single type of features. In addition, this study confirmed that differences in linguistic features between fake reviewer and authentic reviewer are significant. That is, we found that linguistic features complement semantic information of reviews and further enhance predictive power of fake detection model.