• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1778-1799
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• 2022

Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data. Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep. Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources. In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm. Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text. The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document. The user can select a compression ratio so that the system will output that much fraction of the summary. The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain. The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility. Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.

• Geomechanics and Engineering
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• 제30권2호
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• The Journal of Asian Finance, Economics and Business
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• 제9권3호
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• pp.33-42
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• 2022

Oil prices have become more volatile as a result of global economic contraction and control measures. Before and during the COVID-19 crisis, this study examines the relationship between oil price swings and daily stock returns in the power sector. The impact is investigated using a panel Vector Autoregressive (VAR) model. Granger causality tests are used to see if oil prices are effective in predicting returns. The dynamic impact of supply shocks is studied using Impulse Response Functions (IRFs). From January 2011 to May 2021, the study used daily data from all listed power sector enterprises on the Pakistan stock exchange. To investigate the differences in reactions between the Pre-COVID and COVID eras, the sample was separated into two groups. Oil shocks are inversely associated with daily firm stock returns. The conclusions are further supported by the lack of impact of stock prices on oil prices. The relationship, however, deteriorates during the COVID pandemic. We could not uncover any evidence of a significant relationship. In developing countries that rely on oil imports, the study sheds light on the utility of oil price shocks in daily stock return predictions.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2034-2046
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• 2023

Industrial control systems in nuclear facilities are facing increasing cyber threats due to the widespread use of information and communication equipment. To implement cyber security programs effectively through the RG 5.71, it is necessary to quantitatively assess cyber risks. However, this can be challenging due to limited historical data on threats and customized Critical Digital Assets (CDAs) in nuclear facilities. Previous works have focused on identifying data flows, the assets where the data is stored and processed, which means that the methods are heavily biased towards information security concerns. Additionally, in nuclear facilities, cyber threats need to be analyzed from a safety perspective. In this study, we use the system theoretic process analysis to identify system-level threat scenarios that could violate safety constraints. Instead of quantifying the likelihood of exploiting vulnerabilities, we quantify Security Control Measures (SCMs) against the identified threat scenarios. We classify the system and CDAs into four consequence-based classes, as presented in NEI 13-10, to analyze the adversary impact on CDAs. This allows for the ranking of identified threat scenarios according to the quantified SCMs. The proposed framework enables stakeholders to more effectively and accurately rank cyber risks, as well as establish security and response strategies.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.467-468
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• 2021

BLE 비콘을 이용한 실내측위 시스템의 성능 개선을 위해 BLE5.1에서 지원하는 방향탐지 기술 중 도래각을 측정하는 수신기를 제작하고 머신러닝으로 분석하여 최적의 위치를 측정하였다. 머신러닝 모델의 생성과 테스트를 위해 k-최근접 이웃 분류 및 회귀, 로지스틱 회귀, 서포트 벡터머신, 결정트리 인공신경망 및 심층신경망 등을 이용하여 학습하고 시험하였다. 결과로서, 연구에서 제작한 테스트 세트 4를 이용하는 경우 최대 99%의 정확도를 보였다.

• 아태비즈니스연구
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• 제15권1호
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• pp.225-237
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• 2024

Purpose - This study aims to explore the relationship between housing and land prices, with a specific emphasis on the impact of government policies on these factors such as land supply quantity and the ratio of residential land to total land supplied. The goal is to identify the most effective government intervention strategies for controlling both housing and land prices. Design/methodology/approach - Data from 70 primary and medium-sized cities in China spanning from 2003 to 2017 are utilized in this research. The analysis employs a panel vector autoregressive (PVAR) model, with a primary focus on examining the relationships among housing prices, land prices, and government intervention policies. Findings - Housing and land prices are influenced by various factors. Through impulse response analysis and variance decomposition, it is observed that both housing and land prices are predominantly influenced by their internal dynamics, with comparatively weaker effects attributed to policy interventions. Research implications or Originality - By investigating the impact of government policies on housing and land prices, This study establishes a foundation for effective price control measures. Our study advocates for a comprehensive examination of China's land supply mechanism to enhance understanding of the pathways through which government policies influence the markets.

• 응용통계연구
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• 제24권2호
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• pp.359-371
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• 2011

영상에서 에지검출은 영상분할 및 물체인식 등을 위한 영상처리의 전처리 과정으로 매우 중요한 단계이다. 본 논문에서는 영상에서 에지검출을 위해 웨이블렛 기반 하에서 로컬 히스토그램 분석을 이용한 새로운 에지검출법을 제안하고자 한다. 지금까지 웨이블렛 기반 에지검출은 수직과 수평성분으로부터 기울기 벡터를 구하고 임계값은 주로 글로벌 히스토그램 임계값 처리를 통하여 구하였다. 본 논문에서는 수직과 수평성분 외에 대각선 성분을 고려하여 기울기 벡터를 구하고 일반적인 영상에 적합한 로컬 히스토그램 임계값처리를 통하여 임계값을 구하였다. 제안된 에지검출법의 성능 평가를 위해 기존의 Sobel 방법, Canny 방법, Scale Multiplication 방법 그리고 Mallat의 웨이블렛 방법 등과 비교하였다. 영상실험 결과 제안된 방법은 잡음이 많고 적음에 관계없이 에지검출이 뛰어난 반면에 Canny 방법과 Sobel 방영은 잡음이 많을수록 급격하게 성능이 떨어짐을 알 수 있었다. 그리고 제안된 방법은 Scale Multiplication 방법과 Mallat 방법보다 좋은 성능을 갖고 있음을 알 수 있었다.

• 자원ㆍ환경경제연구
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• 제19권1호
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• pp.81-100
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• 2010

본고는 한 중 FTA가 체결되어 이행될 경우 대표적인 온실가스인 이산화탄소가 수출 증대를 통해 우리나라에 얼마나 더 많이 배출될 것인가를 분석하고자 하였다. 한 중 FTA 체결에 따른 관세율의 변화가 미래의 산업별 수출에 어떠한 경제적 파급효과를 가져올 것인지를 동태적으로 예측한 후 산업부문별 이산화탄소($CO_2$) 배출변화를 분석하였다. 한국의 대 중국 수출물량 추정을 위해 Bayesian Kalman Filter Vector Auto-Regression(BVAR) 모형을 이용하였다. 이 추정결과를 활용하여 이산화탄소 배출량 변화를 현행체제(Non FTA) 시나리오와 FTA 추진 시나리오를 대비한 결과, 산업 전체를 총합해 보면 2010년 4분기에 이르면 한 중 간 FTA 추진 시나리오(현행 대비 관세율 50% 감소)의 경우가 현행 시나리오보다 수출 증가를 통해 이산화탄소 배출량을 1.96% 증가시킬 것으로 나타났다. 또한 2012년부터 완전 무관세가 실시되는 것을 가정한 시나리오에 따라 2014년 4분기에 이르면 FTA 추진에 따라 이산화탄소 배출량이 현행 시나리오 경우보다 2.06% 증가 배출되는 것으로 예측되었다. 전체적으로 볼 때 한 중 간 FTA 추진에 따른 대 중국 수출액 순증가가 우리 국내에 추가적으로 배출시키는 이산화탄소량은 비교적 크지 않을 것으로 분석되었다.

• 정보처리학회논문지C
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• 제14C권5호
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• pp.425-430
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• 2007

Mobile Ad hoc Networks(MANET)는 구조 특성상 경로 구성을 위해 필요할 때마다 경로 구성 메시지를 브로드캐스팅하여 경로 정보를 얻는 것이 경로 정보를 계속 유지하고 있는 것보다 효율적이다. MANET의 라우팅 프로토콜 중 하나인 AODV에서 소스 노드는 목적지 노드를 효율적으로 찾기 위해 Expanding Ring Search(ERS) 알고리즘을 사용한다. ERS 알고리즘은 네트워크의 혼잡을 줄이기 위해 네트워크 전체를 대상으로 RREQ 메시지를 브로드캐스팅하는 것이 아니라 소스 노드는 목적지 노드로부터 타이머가 만료될 때까지 RREP 메시지가 도착하지 않는다면 TTL 값을 점차적으로 늘이면서 RREQ 메시지를 브로드캐스팅한다. 기존의 AODV는 고정적인 NODE_TRAVERSAL_TIME 값을 사용하기 때문에 목적지 노드를 찾는데 많은 비용이 소요된다. 본 논문은 기존의 AODV 프로토콜에 추가되는 메시지 없이 헬로우(HELLO) 메시지를 이용하여 이웃 노드들과의 메시지 지연시간을 측정한다. 측정된 메시지 지연시간을 NODE_TRAVERSAL_TIME에 적용하여 최적의 NET_TRAVERSAL_TIME을 구하는 적응적인 확장 링 검색(AERS : Adaptive ERS) 알고리즘을 제안한다. 본 논문에서는 AERS를 이용하여 최적의 NET_TRAVERSAL_TIME을 구하고, 이를 이용하여 불필요한 메시지 발생을 억제함으로써 네트워크 성능을 향상 시킬 수 있다. 시뮬레이션을 통해 제안한 방식의 효율성을 입증한다.