• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.175-177
• /
• 2024

OTT(Over-The-Top) 플랫폼의 부상은 미디어 콘텐츠 소비 방식을 혁명적으로 변화시키고 있다. 본 논문은 Netflix, Amazon Prime Video, Disney+, Hulu 등 주요 OTT 플랫폼에 등록된 영화들을 IMDb 평점과 러닝타임, Rotten Tomatoes 지수를 중심으로 분석한다. 이를 통해 현재의 영화 시장 트렌드와 소비자 선택, 시장 전략에 중요한 정보를 제공하려 한다. 분석 결과, 플랫폼별로 제공하는 영화의 품질과 러닝타임이 다양하며, 소비자들이 선호하는 영화 테마를 시각적으로 파악할 수 있는 워드 클라우드를 포함한다. 이러한 결과는 OTT 플랫폼의 전략적 콘텐츠 제공과 소비자 행동 이해에 기여할 수 있는 중요한 통찰력을 제공한다.

• The Journal of Society for e-Business Studies
• /
• v.19 no.3
• /
• pp.125-141
• /
• 2014

Nowadays, overflowing data produced every second from the internet make people to be difficult to search for the useful information. That's why people have invented and developed unique tools that they get some relevant information. In this paper, the recommender system, one of the effective tools, is used and it helps us to get the useful information that we want by using demographic information to predict new items of interest. The demographic recommender system in this paper computes users' similarity using demographic information, age and gender. So we performed demographic analysis on movie ratings on Internet Movie Database (IMDB) web site that movies are rated by thousands of people, where users submitted a movie rating after they watched a recent popular film. Meanwhile, we can understand that user's ratings, among various determinants of box office, is very essential factor in the study on recommendation of movie. This paper is aimed at analyzing movie average ratings directly given by film viewers, categorizing them into groups by sex and age, investigating the entire group and finding the representative group by examining it with F-test and T-test. This result is used to promote and recommend for the target group only. Therefore, this study is considerably significant as presenting utilization for movie business as well as showing how to analyze demographic information on movie ratings on the web.

• International journal of advanced smart convergence
• /
• v.8 no.1
• /
• pp.1-8
• /
• 2019

Coronaviruses are known respiratory pathogens. In the past, most human coronaviruses were thought to cause mild symptoms such as cold. However recently, as seen in the Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS), infectious diseases with severe pulmonary disease and respiratory symptoms are caused by coronaviruses, making research on coronaviruses become important. Considering previous studies, we constructed 'HCoV-IMDB (Human Corona Virus Immune Database)' to systematically provide genetic information on human coronavirus and host immune information, which can be used to analyze the interaction between human coronavirus and host immune proteins. The 'HCoV-IMDB' constructed in the study can be used to search for genetic information on human coronavirus and host immune protein and to download data. A BLAST search specific to the human coronavirus, one of the database functions, can be used to infer genetic information and evolutionary relationship about the query sequence.

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

• Polymer(Korea)
• /
• v.29 no.4
• /
• pp.369-374
• /
• 2005

In this work, the calcium alginate microcapsules containing lemon oil were prepared by emulsification-internal gelation and their potential use as aromatherapy was examined by the controlled release system. The lemon oil encapsulated in the alginate was successfully observed by Fourier transform (FT-IR) spectroscopy and differential scanning calorimeter (DSC) measurements. Analysis of the diameters and shapes of microcapsules was conducted by scanning electron microscopy (SEM). The mean diameters ranging from 4 to 7 um and encapsulation yield ranging from 50 to $85\%$ were obtained. The controlled release of the lemon oil at $37^{circ}$ was demonstrated by the infrared moisture determination (IMDB). It was found that the amount of released lemon oil decreased with increasing concentrations of alginate and $CaCl_2$ due to the higher the cross-linking density of the capsules prepared. The oil release from the capsule was measured as a function of physical force. We confirmed that the external factor could control the collapse of capsule wall and the release rate.

• Journal of the Korean Society for information Management
• /
• v.31 no.4
• /
• pp.161-178
• /
• 2014

Recently, many information services allow users to collaborate to produce and use information. Sharing information is also important for users who have similar taste or interest. As various channels are available for users to share their experiences and knowledge, users' data have also been accumulated within the information services. This study collected movie lists made by users of IMDB service. Co-word analysis and ego-centered network analysis were adapted to discover relevant information for users who chose a specific movie. Three factors of movies including movie title, director and genre were used to present related movie information. Movie title is an effective feature to present related movies with various aspects such as theme or characters and the popularity of directors affects on identifying related directors. Genre is not useful to find related movies due to the complexity in the topic of a movie.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.2
• /
• pp.156-163
• /
• 2006

CF systems are widely used in recommendation due to the easy implementation and the outstanding performance. They have several problems such as the sparsity problem, the first-rater problem, and recommending explanation. Many studies are suggested to resolve these problems. While the influence of the sparsity problem lessens as the users' data are accumulated, but the first-rater problem is originated from the CF systems and there are a number of researches to overcome the disadvantages of CF systems based on the content-based methods. Also CF systems are black boxes, providing no explanation of working of the recommendation. In this paper we present a content-based prediction system based on the preference words, which exposes the reasoning behind a recommendation. Our system predicts user's rating of a new movie and we suggest a semiotic network-based method to solve the mismatching problem between the items. For experimental comparison, we used EachMovie and IMDb dataset.

• The Journal of Society for e-Business Studies
• /
• v.26 no.3
• /
• pp.67-80
• /
• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.4065-4083
• /
• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• Proceedings of The KACE
• /
• 2017.08a
• /
• pp.253-256
• /
• 2017

딥러닝으로 만들어진 모델의 내부는 black box와 같은 특성을 가져 동작 규칙을 알기 어렵다. 최근 기계학습의 발전으로 인공지능이 전보다 더 복잡한 문제를 해결할 수 있으나 위와 같은 이유로, 모델이 내린 판단의 근거를 알기 어렵다. 그러므로 딥러닝의 동작 규칙을 사람이 이해할 수 있는 형식으로 나타내려는 노력이 필요하다. 본 연구에서는 Attention과 LIME 기법을 활용하여 IMDB 데이터를 감성 분석한 순환신경망의 의사결정 요인을 분석하였다. 각 기법을 활용했을 때의 장단점과 실제 구현에 있어 등장하는 문제에 대해 알아보고자 한다.