• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.25-34
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• 2009

As the trend of the Web is changing toward 'Web 2.0', OpenAPIs, Web 2.0's core technology, are used in many web sites. In the past, services in websites are used in its own, but recently it is possible to use services in other websites by using OpenAPI. In u-GIS many vendors also can provide combined service by using OpenAPI. There are already lots of OpenAPIs and the numer of OpenAPI increases very fast. So it is difficult to find a service that we want to use, and also difficult to find services for mashup. In this paper, we developed retrieval methods for OpenAPIs and mashup capable services based on similarity. First we define the integrated service information model to cover various protocols of OpenAPI, then developed a retrieval methods based on it. By implementing system according these methods by using relational database and JSP, we prove that the system can provide an ranked result sets based on similarity, OpenAPI's integration retrieval results and mashup capable service retrieval results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.744-747
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• 2004

Internet, media connecting global, has increased fast at every year. Many people have been used email as method of exchanging data. But, email has many problem. Existing email may reveal privacy and sensitive information because it ran read and modify email by simple method. So, It required development of strong cryptographic email system. This paper available that email system provide delivery and content proof and seure key-exchange using lava Crypto API

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.531-539
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• 2019

This paper proposes the training features for malware family analysis and analyzes the multi-classification performance of ensemble models. We construct training data by extracting API and DLL information from malware executables and use Random Forest and XGBoost algorithms which are based on decision tree. API, API-DLL, and DLL-CM features for malware detection and family classification are proposed by analyzing frequently used API and DLL information from malware and converting high-dimensional features to low-dimensional features. The proposed feature selection method provides the advantages of data dimension reduction and fast learning. In performance comparison, the malware detection rate is 93.0% for Random Forest, the accuracy of malware family dataset is 92.0% for XGBoost, and the false positive rate of malware family dataset including benign is about 3.5% for Random Forest and XGBoost.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.540-546
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• 2010

Since the stereoscopic 3-dimensional (3D) video that provides users with a realistic multimedia service requires twice as much data as 2-dimensional (2D) video, it is difficult to construct the fast system. In this paper, we propose a fast stereoscopic 3D broadcasting system based on the depth information. Before the transmission, we encode the input 2D+depth video using x264, an open source H.264/AVC fast encoder to reduce the size of the data. At the receiver, we decode the transmitted bitstream in real time using a compute unified device architecture (CUDA) video decoder API on NVIDIA graphics processing unit (GPU). Then, we apply a fast view synthesis method that generates the virtual view using GPU. The proposed system can display the output video in both 2DTV and 3DTV. From the experiment, we verified that the proposed system can service the stereoscopic 3D contents in 24 frames per second at most.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.28-33
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• 2011

Recently the market of smart phone applications grows very fast. And users want more various and rich experiments using enhanced smart phone functions. But the development of smart phone applications is not an easy job. Specially to control smart phone sensors can be realized by using each native programming languages in experts level. Moreover the process needs separate development based on each smart phone OS. Development of the Web-based smart phone application using Web Common APIs, known as 'WebApp', is one of solutions to overcome these problems. The method includes interface constructions with HTML, and Web Common API calls and accesses to smart phone device APIs with Java scripts. In this paper, the experience in development of the smart phone e-book application is introduced, which is implemented with comport and OS independent WebApp development method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.23-33
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• 2014

The smart phone are changing the way people communicate. And, the mobile app marketplace is greatly fast-growing. The app store continues its rapid growth, there are already more than 900,000 mobile apps on AppStore. We anticipate to see gained momentum throughout the business. Mobile is also becoming popular for marketers. Therefore, specialized app analysis systems are becoming important to how marketers and app developers invest, analyze and market their apps. App analysis systems enable users to discover and analyze behavior through data observations and meaningful patterns. In this paper, we introduce app analysis system and CMS System Open API, NugaLog. The NugaLog acquires users data and engages with them in a variety of ways. It will be essential for us to understand how users interact with and move through the app. The NugaLog will be able to see the number of users, smart phone model, smart phone OS, resolution, page views, and app version.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.11-18
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• 2009

Ajax based on Java Script receives attention as an alternative to ActiveX technology. Most portal sites in korea show a tendency to reopen existing services by combining the technology, because it supports most web browsers, and has the advantages of such a brilliant interface, excellent speed, and traffic reduction through asynchronous interaction. This paper modeled and implemented a multilingual meta-search service using the Ajax and open APIs provided by international famous sites. First, a Korean query is translated into one of the language of 54 countries around the world by Google translation API, and then the translated result is used to search the information of the social web sites such as Flickr, Youtube, Daum, and Naver. Searched results are displayed fast by dynamic loading of portion of the screen using Ajax. Our system can reduce server traffic and per-packet communications charges by preventing redundant transmission of unnecessary information.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.934-943
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• 2007

This paper presents a real-time fire simulation on the mobile phone using stable fluid animation techniques. Stable and fast fluid simulation methods are developed in PC and console games, but fluid simulation and interactive fluid models require too much system resources for applying on mobile environment. We studied and implemented physics-based models for fluids like fire and smoke effects using billboard and stable fluids simulation method on mobile 3D system. The mobile platform of our system is WIPI, which is the standard mobile platform in Korea, also we adopted NF3D API for our 3D programming API. We implemented real-time fire simulation and added it in mobile 3D game, "Rupee Story".

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.