• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.266-280
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• 2021

Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases. Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.110-116
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• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2019년도 제31회 한글 및 한국어 정보처리 학술대회
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• pp.326-329
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• 2019

기계독해를 실용화하기 위해 단락을 검색하는 검색 모델은 최근 기계독해 모델이 우수한 성능을 보임에 따라 그 필요성이 더 부각되고 있다. 그러나 기존 검색 모델은 질의와 단락의 어휘 일치도나 유사도만을 계산하므로, 기계독해에 필요한 질의 어휘의 문맥에 해당하는 단락 검색을 하지 못하는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 Word2vec의 입력 단어열의 벡터에 해당하는 IN Weight Matrix와 출력 단어열의 벡터에 해당하는 OUT Weight Matrix를 사용한 단락 검색 모델을 제안한다. 제안 방법은 기존 검색 모델에 비해 정확도를 측정하는 Precision@k에서 좋은 성능을 보였다.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.111-118
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• 2023

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.1-12
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• 2023

Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.

• Journal of applied mathematics & informatics
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• 제42권3호
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• pp.695-708
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• 2024

In this paper we present a non-standard finite difference method for solving a fractional decay model. The proposed NSFDM is constructed by incorporating a non-standard denominator function, resulting in an explicit numerical scheme as easy as the conventional Euler method, but it provides very accurate solutions and has unconditional stability. Two examples from the literature are presented to demonstrate the performance of the proposed numerical scheme, which is compared to three methods from the literature. It is found that the method's estimated errors are extremely minimal, such as within the machine precision.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• Journal of Digestive Cancer Research
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• 제11권3호
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• pp.157-164
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• 2023

Prediction is a significant topic in clinical research. The development and validation of a prediction model has been increasingly published in clinical research. In this review, we investigated analytical methods and validation schemes for a clinical prediction model used in digestive cancer research. Deep learning and logistic regression, with split-sample validation as an internal or external validation, were the most commonly used methods. Furthermore, we briefly introduced and summarized the advantages and disadvantages of each method. Finally, we discussed several points to consider when conducting prediction model studies.

• 화약ㆍ발파
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• 제37권2호
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• pp.22-30
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• 2019

화약을 이용하여 작업하는 현장에서는 발파에 의해 발생되는 소음과 진동의 영향으로 작업상 많은 제약을 받는다. 최근에 민원 발생 증가 및 보안물건에 대한 환경규제 기준이 대폭 강화되고 있는 추세이다. 때문에 보안물건이 근접해 있는 경우 일반적으로 기계식굴착에 의해 작업이 이루어지고 있다. 기계식굴착 공법은 발파공법에 비해 소음과 진동을 저감시키는 장점을 갖고 있으나 굴착하고자 하는 암반의 상태에 따라서 계획 보다 시공성이 떨어지는 경우가 발생되기도 한다. 일반적으로 굴착암이 극경암에 가까울수록 시공성이 낮아진다. 본고에서는 전자뇌관을 사용하여 보안물건이 초근접해 있는 공사구간을 시공한 사례에 대해 설명하고자 한다. 당 현장은 인근에 보안물건(철도)이 근접(9.9m)해 있어 암파쇄 굴착공법으로 설계가 되어 시공하던 중, 극경암 노출에 따른 시공성 저하 및 공사기간 단축을 위한 대안 공법으로 전자뇌관을 이용한 시공을 검토하게 되었다. 전자뇌관 이용한 발파작업으로 주변 보안물건에 미치는 영향을 최소화 하면서 시공성과 경제성을 극대화 할 수 있었다. 한화에서 생산하는 하이트로닉($HiTRONIC^{TM}$)은 혁신적인 안정성과 높은 기폭 신뢰성을 갖고 있어 초정밀발파가 가능하다. 전자뇌관은 철도 및 고속도로현장, 대형 석회석 광산을 비롯한 도심지 터파기 등에서 널리 사용되고 있다.