• Agribusiness and Information Management
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• 제5권2호
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• pp.27-37
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• 2013

Referral marketing plays an important role in promoting new products. When it comes to innovative agricultural products, early adopter's review or recommendation has a more critical impact on follower's purchase decision making. Hence, understanding of consumer's characteristics and needs play more important role in success of innovation. More particularly, other researchers pay attention to the role of consumer innovativeness. This study attempts to fill this gap in knowledge between innovative propensity of consumer and her/his intention to generate positive word of mouth about new agricultural products. Furthermore, in this paper, we adopt Vandecasteele and Geunes' motivated consumer innovativeness model to investigate consumer innovativeness in extrinsic motive and intrinsic motive level, and examine the moderating role of referral efficacy. For empirical verification, survey method is used for data collection. Partial least square (PLS) is adopted to analyze the data. Finally, several theoretical contributions and practical implications are discussed.

• 전기학회논문지
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• 제57권9호
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• pp.1507-1515
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• 2008

It is essential for electric power companies to have a quick restoration system of the faulted pole transformers which occupy most of transformers to supply stable electricity. However, it takes too much time to restore it when a transformer is out of order suddenly because we now count on operator in investigating causes of failure and making decision of recovery methods. This paper presents the concept of 'Fault pattern analysis and Restoration prediction model using Data mining techniques’, which is based on accumulated fault record of pole transformers in the past. For this, it also suggests external and internal causes of fault which influence the fault pattern of pole transformers. It is expected that we can reduce not only defects in manufacturing procedure by upgrading quality but also the time of predicting fault patterns and recovering when faults occur by using the result.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.523-526
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• 2015

In recent years, building commissioning has often been part of a standard delivery practice in construction, particularly in the high-performance green building market, to ensure the building is designed and constructed per owner's requirements. Commissioning, therefore, intends to provide quality assurance that buildings perform as intended by the design and often helps achieve energy savings. Commissioning, however, is not as widely adopted as its potential benefits are perceived. Owners are still skeptical of the cost-effectiveness claims by energy management and commissioning professionals. One of the issues in the current commissioning practice is that not every project is guaranteed to benefit from the commissioning services. This, coupled with its added cost, the commissioning service is not acquired with great acceptance and confidence by building owners. To overcome this issue, this paper presents a unique methodology to enhance owner's predicting capability of the degree of success of commissioning service using the Bayesian theorem. The paper analyzes a situation where a future building owner wants to use a pre-commissioning in an attempt to refine the success rate of the future commissioned building performance. The author proposes the Bayesian theorem based framework to improve the current commissioning practice where building owners are not given accurate information how much successful their projects are going to be in terms of energy savings from the commissioning service. What should be provided to the building owners who consider their buildings to be commissioned is that they need some indicators how likely their projects benefit from the commissioning process. Based on this, the owners can make better informed decisions whether or not they acquire a commissioning service.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• 한국멀티미디어학회논문지
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• 제10권11호
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• pp.1483-1495
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• 2007

IEEE 802.11 기술 덕분에 학교와 대형 쇼핑몰을 비롯한 사무실, 병원, 역 등지에서도 무선 LAN을 통한 인터넷 접속이 가능하다. 본 논문은 무선 LAN에 현재 가장 많이 사용되는 2.4GHz 대역의 802.11b와 802.11g 프로토콜이 탑재된 액세스포인트(AP: Access Point)로부터 수신한 신호의 세기(RSSI: Received Signal Strength Indicator)를 판독할 수 있는 C# 라이브러리 함수를 제안한다. 위치기반서비스는 사용자의 현재 위치를 실시간으로 측정하여 현재 위치를 기반으로 길을 안내하거나, 현재 위치와 관련한 콘텐츠를 제공하는 등의 유용한 서비스를 제공한다. 옥내에서 위치기반서비스를 제공하려면 옥내에 있는 사용자의 위치를 판정하는 옥내측위가 반드시 선결되어야 한다. 옥내측위 기술로 적외선, 초음파, UDP 패킷의 신호세기 등을 이용하는 방법들이 소개된 바 있다. 이러한 방법들은 측위를 위한 특수 장비를 설비해야만 한다는 단점이 있다. 본 논문은 RSSI를 판독하는 라이브러리 함수를 제공할 뿐만 아니라 제공하는 함수를 이용한 옥내 측위 구현 예도 소개한다. 구현에 적용된 방법들은 이미 널리 알려진 K-NN(K Nearest Neighbors), 베이시안 방법 그리고 삼각측량법이다. K-NN 방법과 베이시안 방법은 일종의 지문방식인데, 지문방식은 준비단계와 실시간단계로 구성되며, 실시간 단계의 처리 과정은 처리속도가 빨라야만 한다. 본 논문은 실시간 단계의 속도를 개선하는 방법으로 판단나무 방법(Decision Tree Method)을 제안하고, 이러한 방법들의 성능을 실험적으로 평가한 결과를 소개한다.

• Neurospine
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• 제15권4호
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2027-2032
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• 2003

For action selection as well as learning, simple associations between stimulus and response have been employed in most of literatures. But, for a successful task accomplishment, it is required that an animat can learn and express behavioral sequences. In this paper, we propose a novel action-selection-mechanism to deal with sequential behaviors. For this, we define behavioral motivation as a primitive node for action selection, and then hierarchically construct a network with behavioral motivations. The vertical path of the network represents behavioral sequences. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, three 2-D grid world simulations will be illustrated.

• International Journal of Advanced Culture Technology
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• 제2권2호
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• pp.15-18
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• 2014

This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권3호
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• pp.165-169
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• 2010

This study seeks to find the factors associated with social workers' turnover intention and show us how to manage turnovers by looking for some rules affecting turnover intentions. Our investigation surveying 331 social workers reveals that social workers' turnover intentions are affected by organizational commitment, job satisfaction, and burnout. Our pattern analyses using fuzzy ID3 show that the higher their commitment, the higher their job satisfaction stemming from promotion opportunities, rewards, and personal relations with peers and bosses. In addition, turnover intentions decreases (even if burnouts--the job-related stress--are very serious) when organizational commitment increases. We come to understand that organizational commitment could be a more important variable than job satisfaction and burnouts. Such results suggest that it would be necessary to consider how to improve social workers' organization-wide commitment rather than satisfaction and burnout related to jobs and environments.

• 한국독성학회:학술대회논문집
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• 한국독성학회 2005년도 춘계 국제심포지엄 및 학술대회
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• pp.206-206
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• 2005