• The Journal of Asian Finance, Economics and Business
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• 제9권3호
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• pp.257-263
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• 2022

The objective of this study was to see how artificial intelligence applications affected organizational innovation in Jordanian commercial banks. Both independent and dependent variables were measured in three dimensions: expert systems, neural network systems, and fuzzy logic systems for artificial intelligence applications variable. Product innovation, process innovation, and management innovation for the organizational innovation variable. To achieve study objectives, a questionnaire was developed and distributed to a sample of one hundred fifty-three managers in Jordanian commercial banks, who were selected according to the simple random sampling method. Except for the neural network systems dimension, which comes in at an average level, the study indicated that there is a high level of organizational innovation and artificial intelligence applications. Furthermore, the findings revealed that artificial intelligence applications have a significant impact on organizational innovation in Jordanian commercial banks, with the most important artificial intelligence application being a fuzzy logic system. The study suggested keeping track of technological advancements in the field of artificial intelligence applications and incorporating them into banking operations by benchmarking with the best commercial bank practices and allocating a portion of the budget to technological applications and infrastructure development, as well as balancing between technology use and information security risks to ensure client privacy is protected.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.63-73
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• 2022

In today's financial economics literature, the impact of innovative family ownership and management on firm performance is a prominent concern. In this study, the existence of family firms in the listed sector of Pakistan's economy is investigated. The objective of this study is to examine the performance-oriented relationship of family ownership and active involvement of family member at the CEO position. The theoretical perspectives that underpin this research are agency and stewardship. This analysis used a sample of 315 publicly traded companies from 2009 to 2019. The study's primary independent variables include family influence on ownership and family CEO. Financial performance is the dependent variable that is divided into accounting and market measures. The proxy for accounting measure is return on asset and proxy for market measure is Tobin's Q. This study employs univariate and balanced panel data analysis. For robustness of the analysis random-effects GLS regression is carried out. The empirical results show that that Family Firms outperform Non-Family Firms both in terms of accounting and market measures. In the later part family CEOs firms outperform the firms that have either insider or outsider non-family CEOs. This superior performance is subjected to the positive and statistically significant association between family ownership, management, and financial performance.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.21-27
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• 2013

Construction safety is a predominant hindrance in in-situ workflow and considered an unresolved issue. Current methods used for safety optimization and prediction, with limited exceptions, are paper-based, thus error prone, as well as time and cost ineffective. In an attempt to exploit the potential of BIM for safety, the objective of the proposed methodology is to automatically predict hazardous on-site conditions related to the route that the dozers follow during the different phases of the project. For that purpose, safety routes used by construction equipment from an origin to multiple destinations are computed using video cameras and their cycle times are calculated. The cycle times and factors; including weather and light conditions, are considered to be independent and identically distributed random variables (iid); and simulated using the Arena software. The simulation clock is set to 100 to observe the minor changes occurring due to external parameters. The validation of this technology explores the capabilities of BIM combined with simulation for enhancing productivity and improving safety conditions a-priori. Preliminary results of 262 measurements indicate that the proposed methodology has the potential to predict with 87% the location of exclusion zones. Also, the cycle time is estimated with an accuracy of 89%.

• 한국정보시스템학회지:정보시스템연구
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• 제32권3호
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• 한국해안·해양공학회논문집
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• 제20권3호
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• pp.300-309
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• 2008

지금까지 연구된 피복석의 부분안전계수는 각 확률변수가 독립이라고 가정하여 계산하였다. 하지만 피복석의 안정공식 중 van der Meer 공식에서 파형경사와 파고는 독립이 아니며 상관성을 가지고 있다. 본 연구에서는 이들의 상관성을 고려한 부분안전계수를 산정하였고 이를 상관성을 고려하지 않은 다른 연구자들의 결과와 비교하였다. 파고와 파형경사의 상관성은 주기의 변동성과 밀접한 관계가 있다. 주기의 변동성이 작아짐에 따라 파고와 파형경사의 상관성은 커지며, 상관성 고려 여부에 따른 계산 결과의 차이가 커진다. 따라서 주기의 변동성이 작은 지역에서는 상관성을 충분히 검토하여 부분안전계수를 산정해야 할 것이다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권1호
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• pp.107-110
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• 2014

Background: Increasing uptake of human papillomavirus (HPV) vaccine should be a priority in developing countries since they suffer 88% of the world's cervical cancer burden. In many countries studies show that age at vaccination is an important determinate of parental acceptability. This study explores parental preferences on age-to-vaccinate for adolescent school-going girls. Materials and Methods: The sample was selected using a two-stage probability proportional to size cluster sampling methodology. Questionnaires were sent home with a random sample of 800 adolescent girls attending 12 schools in Mysore to be completed by parents. Descriptive statistics including frequencies, percentages and proportions were generated for independent variables and bivariate analyses (Chi square test) were used to assess the relationship between independent and appropriate age-to-vaccinate. Results: HPV vaccination acceptability was high at 71%. While 5.3% of parents felt girls should be vaccinated by 10 years or younger; 38.3% said 11-15 years; 14.8% said 16-18 years; 5.8% suggested over 19 years; and 33% didn't know. Only 2.8% of parents would not vaccinate their daughters. Conclusions: Delaying HPV vaccination until later ages may signifivantly increase uptake of the HPV vaccine in India.

• 한국정보처리학회논문지
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• 제7권2호
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• pp.529-535
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• 2000

본 논문에서는 웨이브렛 변환을 이용한 영상 부호화에서 입력 영상의 통계적 성질에 영향을 받지 않고 부호화 결과에 범용성을 갖는 새로운 벡터 양자화기 설계법을 제안한다. 기존의 벡터 양자화기의 가장 큰 문제점은 양자화대상 영상과 대표 벡터를 생성하기 위한 학습계열간의 통계적 성질의 불일치에 의한 부호화 성능의 열화이다. 그리하여, 본 논문에서는 벡터 양자화기의 대표벡터를 생성하기 위한 학습계열로 독립 난수에 영상의 상관과 에지 성분을 첨가한 모사 영상을 사용하여 종래 방식의 문제점을 해결하는 방법에 대하여 검토하였다. 제안방식에 의해 설계된 벡터양자화기와 대표 벡터 생성에 이용하는 학습계열에 부호화 대상이 되는 영상과 같은 실제의 영상을 사용한 종래 방식에 의해 설계된 벡터 양자화기와 부호화 성능을 컴퓨터 시뮬레이션을 통하여 비교하여 종래 방식의 문제점을 명확하게 밝힘과 동시에 제안 방식으로 설계된 벡터 양자화기가 부호화 성능이 뛰어남을 보인다.

• 한국정보전자통신기술학회논문지
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• 제12권6호
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• pp.629-635
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• 2019

여러 분야에서 사용되는 이미지 분류를 위한 딥러닝(Deep Learning) 모델은 오류 역전파 방법을 통해 미분을 구현하고 미분 값을 통해 예측 상의 오류를 학습한다. 엄청난 계산량을 향상된 계산 능력으로 해결하여, 복잡하게 설계된 모델에서도 파라미터의 전역 (혹은 국소) 최적점을 찾을 수 있다는 것이 장점이다. 하지만 정교하게 계산된 데이터를 만들어내면 이 딥러닝 모델을 '속여' 모델의 예측 정확도와 같은 성능을 저하시킬 수 있다. 이렇게 생성된 적대적 사례는 딥러닝을 저해할 수 있을 뿐 아니라, 사람의 눈으로는 쉽게 발견할 수 없도록 정교하게 계산되어 있다. 본 연구에서는 임의의 잡음 신호를 추가하는 방법을 통해 적대적으로 생성된 이미지 데이터셋을 탐지하는 방안을 제안한다. 임의의 잡음 신호를 추가하였을 때 일반적인 데이터셋은 예측 정확도가 거의 변하지 않는 반면, 적대적 데이터셋의 예측 정확도는 크게 변한다는 특성을 이용한다. 실험은 공격 기법(FGSM, Saliency Map)과 잡음 신호의 세기 수준(픽셀 최댓값 255 기준 0-19) 두 가지 변수를 독립 변수로 설정하고 임의의 잡음 신호를 추가하였을 때의 예측 정확도 차이를 종속 변수로 설정하여 시뮬레이션을 진행하였다. 각 변수별로 일반적 데이터셋과 적대적 데이터셋을 구분하는 탐지 역치를 도출하였으며, 이 탐지 역치를 통해 적대적 데이터셋을 탐지할 수 있었다.

• 한국방사선학회논문지
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• 제17권3호
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• pp.423-431
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• 2023

본 연구의 목적은 환자의 신체정보와 인공지능 기법을 활용하여 부작용에 영향을 미치는 인자들을 분석하고 조영제 부작용의 정도를 예측하여 이를 완화하는 기초자료로 활용되고자 한다. 연구에 사용한 데이터는 서울 소재 종합병원의 검진을 시행한 CT 검사 58,000건 중 조영제 부작용이 발생한 1,235건 중 과거력 조사에서 조영제 부작용이 없었던 606명의 검사자를 대상자로 하였다. 606개 샘플 중 70%는 훈련 셋으로 사용하고 나머지 30%는 검증을 위한 테스트 셋으로 사용하였다. 나이, BMI(Body Mass Index), GFR(Glomerular Filtration Rate), BUN(Blood Urea Nitrogen), GGT(Gamma Glutamyl Transgerase), AST(Aspartate Amino Transferase,), and ALT(Alanine Amiono Transferase)의 feature를 독립변수로 조영제 중증도를 목표변수로 사용하였다. AdaBoost, Tree, Neural network, SVM, Random foest 알고리즘을 통해 AUC(Area under curve), CA(Classification Accuracy), F1, Precision, Recall을 파악하였다. 분류 예측에 사용된 알고리즘 중 가장 높은 평가지표를 나타내 것은 AdaBoost와 Random Forest이다. 모든 모델의 예측에서 가장 큰 요인은 GFR, BMI, GGT 이였다. 이는 신장 여과 기능, 비만에 따라 주입되는 조영제 양의 차이와 대사증후군의 여부에 따라 조영제 부작용 중증도에 영향을 미치는 것을 알 수 있었다.

• Journal of Animal Science and Technology
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• 제54권6호
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• pp.463-469
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• 2012

This study utilized a standardization and cluster analysis technique for the selection and classification of beneficial bacteria. A set of synthetic data consisting of 100 individual variables with three characteristics was created for analysis. The three characteristics assigned to each independent variable were designated to have different numeric scales, averages, and standard deviations. The variables were bacterial isolates at random, and the three characteristics were fermentation products, including cell yield, antioxidant activity of culture, and enzyme production. A standardization method utilizing a standard normal distribution equation to record fermentation yields of each isolate was employed to weight their different numeric scales and deviations. Following transformation, the data set was analyzed by cluster analysis. The Manhattan method for dissimilarity matrix construction along with complete linkage technique, an agglomerative method for hierarchical cluster analysis, was employed using statistical computing program R. A total of 100 isolates were classified into groups A, B, and C. In a comparison of the characteristics of each group, all characteristics in groups A and C were higher than those of group B. Isolates displaying higher cell yield were classified as group A, whereas those isolates showing high antioxidant activity and enzyme production were assigned to group C. The results of the cluster analysis can be useful for the classification of numerous isolates and the preparation of an isolation pool using numerical or statistical tools. The present study suggests that a simple technique can be applied to screen and select beneficial microbes using the freely downloadable statistical computing program R.