• 한국컴퓨터정보학회논문지
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• 제26권2호
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• pp.61-68
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• 2021

최근 악성코드와 정상 바이너리를 분류하기 위해 기계학습을 이용하는 기술이 다양하게 연구되고 있다. 효과적인 기계학습을 위해서는 악성코드와 정상 바이너리를 식별하기 위한 Feature를 잘 추출하는 것이 무엇보다 중요하다. 본 논문에서는 재귀적인 방법을 이용하여 기계학습에 활용하기 위한 Feature 추출 방법인 RFA(Recursive Feature Addition) 제안한다. 제안하는 방법은 기계학습의 성능을 극대화 하기 위해 개별 Feature를 대상으로 재귀적인 방법을 사용하여 최종 Feature Set을 선정한다. 세부적으로는 매 단계마다 개별 Feature 중 최고성능을 내는 Feature를 추출하여, 추출한 Feature를 결합하는 방법을 사용한다. 제안하는 방법을 활용하여 Decision tree, SVM, Random forest, KNN등의 기계학습 알고리즘에 적용한 결과 단계가 지속될수록 기계학습의 성능이 향상되는 것을 검증하였다.

• 문화기술의 융합
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• 제4권2호
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• pp.197-204
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• 2018

현재 우리가 사는 21세기에서 가장 핫한 이슈중 하나는 AI이다. 농경사회에서 산업혁명을 통해 육체노동의 자동화를 이루었듯이 정보사회에서 SW혁명을 통해 지능정보사회가 도래햇다. Google '알파고'의 등장으로 인해 컴퓨터가 스스로 학습하고 예측하는 machine learning (머신러닝) 사례를 보면서 이제 바둑의 세계 까지 인간이 컴퓨터를 이길 수 없는, 다시 말하면 컴퓨터가 인간을 뛰어넘는 시대가 왔다. 기계학습ML(machine learning)은 인공 지능 분야로, 인공지능 컴퓨터가 인간을 뛰어넘는 시대가 도래했다. 기계학습ML(machine learning)은 인공지능의 분야로, 인공지능 컴퓨터가 혼자 학습 하도록 알고리즘 기술 개발을 하는 뜻을 의미하는데, 많은 기업들이 머신러닝을 바둑의 세계까지 인간이 컴퓨터를 이길 수 없는, 다시 말하면 컴퓨터가 인간을 뛰어넘는 시대가 왔다. 많은 기업들이 머신러닝을 용하는데 그 예로는 Facebook에서 이미지를 계속 학습하여 나중에 그 이미지가 누구인지 알려주는 것도 머신러닝의 한 사례이다. 또한 구글의 데이터 센터 최적화를 위해서 효율적인 에너지 사용 모델 구축을 위해 neural network(신경망)을 활용하였다. 또 다른 사례로 마이크로소프트의 실시간 통역 모델은 번역 학습을 통해 언어관련 인풋 데이터가 증가할수록 더 정교한 번역을 해주는 모델이다. 이처럼 많은 분야에 머신러닝이 점차 쓰이면서 이제 우리 21세기 사회에서 앞으로 나아가려면 AI산업으로 뛰어들어야 한다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.61-68
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• 2000

Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• 정보과학회 논문지
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• 제43권12호
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• pp.1325-1333
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• 2016

소프트웨어 분류 기법은 저작권 침해 탐지, 악성코드의 분류, 소프트웨어 보관소의 소프트웨어 자동분류 등에 활용할 수 있으며, 불법 소프트웨어의 전송을 차단하기 위한 소프트웨어 필터링 시스템에도 활용할 수 있다. 소프트웨어 필터링 시스템에서 유사도 측정을 통해 불법 소프트웨어를 식별할 경우, 소프트웨어 분류를 활용하여 탐색 범위를 축소하면 평균 비교 횟수를 줄일 수 있다. 본 논문은 API 호출 정보와 기계학습을 통한 윈도우즈 실행파일 분류를 연구한다. 다양한 API 호출 정보 정제 방식과 기계학습 알고리즘을 적용하여 실행파일 분류 성능을 평가한다. 실험 결과, PolyKernel을 사용한 SVM (Support Vector Machine)이 가장 높은 성공률을 보였다. API 호출 정보는 바이너리 실행파일에서 추출할 수 있는 정보이며, 기계학습을 적용하여 변조 프로그램을 식별하고 실행파일의 빠른 분류가 가능하다. 그러므로 API 호출 정보와 기계학습에 기반한 소프트웨어 분류는 소프트웨어 필터링 시스템에 활용하기에 적당하다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• 대한후두음성언어의학회지
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• 제33권3호
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.209-214
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• 2024

This is an extended research paper focusing on the applications of Machine Learing and Artificial Intelligence in virtual learning environment. The world is moving at a fast pace having the application of Machine Learning (ML) and Artificial Intelligence (AI) in all the major disciplines and the educational sector is also not untouched by its impact especially in an online learning environment. This paper attempts to elaborate on the benefits of ML and AI in E-Learning (EL) in general and explain how King Khalid University (KKU) EL Deanship is making the best of ML and AI in its practices. Also, researchers have focused on the future of ML and AI in any academic program. This research is descriptive in nature; results are based on qualitative analysis done through tools and techniques of EL applied in KKU as an example but the same modus operandi can be implemented by any institution in its EL platform. KKU is using Learning Management Services (LMS) for providing online learning practices and Blackboard (BB) for sharing online learning resources, therefore these tools are considered by the researchers for explaining the results of ML and AI.

• 농촌지도와개발
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• 제16권4호
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• pp.939-965
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• 2009

The Machine Learning has been identified as a promising approach to knowledge-based system development. This study aims to examine the ability of machine learning techniques for farmer's decision making and to develop the reference model for using pig farm data. We compared five machine learning techniques: logistic regression, decision tree, artificial neural network, k-nearest neighbor, and ensemble. All models are well performed to predict the sow's productivity in all parity, showing over 87.6% predictability. The model predictability of total litter size are highest at 91.3% in third parity and decreasing as parity increases. The ensemble is well performed to predict the sow's productivity. The neural network and logistic regression is excellent classifier for all parity. The decision tree and the k-nearest neighbor was not good classifier for all parity. Performance of models varies over models used, showing up to 104% difference in lift values. Artificial Neural network and ensemble models have resulted in highest lift values implying best performance among models.