• 제목/요약/키워드: machine learning framework

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A Pragmatic Framework for Predicting Change Prone Files Using Machine Learning Techniques with Java-based Software

  • Loveleen Kaur;Ashutosh Mishra
    • Asia pacific journal of information systems
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    • 제30권3호
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    • pp.457-496
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    • 2020
  • This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

TCAD-머신러닝 기반 나노시트 FETs 컴팩트 모델링 (Compact Modeling for Nanosheet FET Based on TCAD-Machine Learning)

  • 송준혁;이운복;이종환
    • 반도체디스플레이기술학회지
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    • 제22권4호
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    • pp.136-141
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    • 2023
  • The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

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Automated Analysis Approach for the Detection of High Survivable Ransomware

  • Ahmed, Yahye Abukar;Kocer, Baris;Al-rimy, Bander Ali Saleh
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제14권5호
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    • pp.2236-2257
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    • 2020
  • Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

시설물의 유지관리를 위한 기계학습 기반 콘크리트 균열 감지 프레임워크 (Machine Learning-based Concrete Crack Detection Framework for Facility Maintenance)

  • 지봉준
    • 한국지반환경공학회 논문집
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    • 제22권10호
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    • pp.5-12
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    • 2021
  • 시설물의 노후화는 피할 수 없는 현상이다. 노후화된 시설물의 관리를 위해 균열을 감지하고 이를 추적하면서 시설물의 상태를 간접적으로 추론할 수 있다. 따라서 균열 감지는 노후화된 시설물의 관리를 위해 필수적 역할을 하며 감지 결과를 바탕으로 더 이상의 노후화를 막기 위한 활동을 할 수 있다. 하지만, 현재 대부분의 균열 감지는 전문가의 판단에만 의존하기에 시설물의 면적이 큰 경우 비용과 시간이 과도하게 사용되고, 전문가의 역량에 따라 다른 판단 결과가 발생할 수 있어 신뢰성에 문제가 있었다. 본 논문에서는 이러한 한계를 극복하기 위해 기계학습 기반의 콘크리트 균열 감지 프레임워크를 제안한다. 제안된 프레임워크는 데이터 분류, 기계학습 모델 학습, 학습된 모델의 검증과 테스트를 포함하는 프레임워크로 완전 자동화된 콘크리트 균열 감지가 가능하다. 제안된 프레임워크를 통해 학습된 기계학습 모델은 콘크리트 균열 이미지와 정상 이미지를 96%의 높은 정확도로 분류할 수 있었다. 본 논문에서 제안된 프레임워크를 적용하여 기존의 전문가 중심의 시설물 유지관리보다 더욱 효과적이고 효율적인 시설물의 유지관리가 가능할 것으로 기대된다.

Classification of COVID-19 Disease: A Machine Learning Perspective

  • Kinza Sardar
    • International Journal of Computer Science & Network Security
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    • 제24권3호
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    • pp.107-112
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    • 2024
  • Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

Developing a Framework for Detecting Phishing URLs Using Machine Learning

  • Nguyen Tung Lam
    • International Journal of Computer Science & Network Security
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    • 제23권10호
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    • pp.157-163
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    • 2023
  • The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through end-users. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets.

Hand-crafted 특징 및 머신 러닝 기반의 은하 이미지 분류 기법 개발 (Development of Galaxy Image Classification Based on Hand-crafted Features and Machine Learning)

  • 오윤주;정희철
    • 대한임베디드공학회논문지
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    • 제16권1호
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    • pp.17-27
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    • 2021
  • In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

A review of Chinese named entity recognition

  • Cheng, Jieren;Liu, Jingxin;Xu, Xinbin;Xia, Dongwan;Liu, Le;Sheng, Victor S.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제15권6호
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    • pp.2012-2030
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    • 2021
  • Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

Regime 탐지 분석을 이용한 동적 자산 배분 기법 (Dynamic Asset Allocation by Applying Regime Detection Analysis)

  • 김우창
    • 대한산업공학회지
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    • 제38권4호
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    • pp.258-261
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    • 2012
  • In this paper, I propose a new asset allocation framework to cope with the dynamic nature of the financial market. The investment performance can be much improved by protecting the capital from the market crashes, and such crashes can be pre-identified with high probabilities by regime detection analysis via a specialized unsupervised machine learning technique.

빅데이터를 위한 H-RTGL 기반 단일 분류기 분산 처리 프레임워크 설계 (Design of Distributed Processing Framework Based on H-RTGL One-class Classifier for Big Data)

  • 김도균;최진영
    • 품질경영학회지
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    • 제48권4호
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    • pp.553-566
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    • 2020
  • Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.