• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.91-103
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• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.281-288
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• 2018

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging startup companies in recent years. Despite the huge success of crowdfunding, it has been reported that only around 40% of crowdfunding campaigns successfully raise the desired goal amount. The purpose of this study is to investigate key factors influencing successful fundraising on crowdfunding platforms. To this end, we mainly focus on contents of project campaigns, particularly their linguistic cues as well as multiple features extracted from project information and multimedia contents. We reveal which of these features are useful for predicting success of crowdfunding campaigns, and then build a predictive model based on those selected features. Our experimental results demonstrate that the built model predicts the success or failure of a crowdfunding campaign with 86.15% accuracy.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.405-412
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• 2018

When a film company decides whether to invest or not in a scenario is the appropriate time to predict box office success. In response to market demands, AI based scenario analysis service has been launched, yet the algorithm is by no means perfect. The purpose of this study is to present a prediction model of movie scenario's box office hit based on human brain processing mechanism. In order to derive patterns of visual, auditory, and cognitive stimuli on the time spectrum of box office animation hit, this study applied Weber's law and brain mechanism. The results are as follow. First, the frequency of brain stimulation in the biggest box office movies was 1.79 times greater than that in the failure movies. Second, in the box office success, the cognitive stimuli codes are spread evenly, whereas in the failure, concentrated among few intervals. Third, in the box office success movie, cognitive stimuli which have big cognition load appeared alone, whereas visual and auditory stimuli which have little cognitive load appeared simultaneously.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• The Journal of Advanced Prosthodontics
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• v.16 no.3
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• pp.151-162
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• 2024

PURPOSE. This study aimed to analyze factors influencing the success and failure of implant prostheses and to estimate the lifespan of prostheses using standardized evaluation criteria. An online survey platform was utilized to efficiently gather large samples from multiple institutions. MATERIALS AND METHODS. During the one-year period, patients visiting 16 institutions were assessed using standardized evaluation criteria (KAP criteria). Data from these institutions were collected through an online platform, and various statistical analyses were conducted. Risk factors were assessed using both the Cox proportional hazard model and Cox regression analysis. Survival analysis was conducted using Kaplan-Meier analysis and nomogram, and lifespan prediction was performed using principal component analysis. RESULTS. The number of patients involved in this study was 485, with a total of 841 prostheses evaluated. The median survival was estimated to be 16 years with a 95% confidence interval. Factors found to be significantly associated with implant prosthesis failure, characterized by higher hazard ratios, included the 'type of clinic', 'type of antagonist', and 'plaque index'. The lifespan of implant prostheses that did not fail was estimated to exceed the projected lifespan by approximately 1.34 years. CONCLUSION. To ensure the success of implant prostheses, maintaining good oral hygiene is crucial. The estimated lifespan of implant prostheses is often underestimated by approximately 1.34 years. Furthermore, standardized form, online platform, and visualization tool, such as nomogram, can be effectively utilized in future follow-up studies.

• Journal of Korean Critical Care Nursing
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• v.7 no.2
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• pp.34-44
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• 2014

Purpose: The purpose of this study was to determine the factors related to airway failure during endotracheal intubation among patients with and without predicted airway difficulty. Methods: Medical records were examined retrospectively. 329 patients who were admitted to the general ward and underwent endotracheal intubation were included. The incidence of airway failure in the two groups was investigated. Results: The group predicted to have airway difficulty consisted of 79 patients (24.0%) and the group without airway difficulty, 250 (76.0%). The number of cases of airway failure was 50 (15.2%). The factors that were associated with airway failure in the group with predicted airway difficulty were the jaw relaxation score, Cormack-Lehane score, and the device of the first endotracheal intubation attempt. The factors that were associated with the airway failure in the group predicted not to have airway difficulty were the induction agent, jaw relaxation score, Cormack-Lehane score, level of training of the personnel with the first endotracheal intubation success, and the device of the first endotracheal intubation attempt. Conclusion: The prediction of airway difficulty during endotracheal intubation was not effective; however, it was meaningful from the perspective of patient safety.

• Journal of Veterinary Science
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• v.23 no.5
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• pp.65.1-65.10
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• 2022

Background: Persistent uroliths after a cystotomy in dogs are a common cause of surgical failure. Objectives: This study examined the following: the success rate of retrograde urohydropropulsion in male dogs using non-enhanced computed tomography (CT), whether the CT mean beam attenuation values in Hounsfield Units (mHU) measured in vivo could predict the urolithiasis composition and whether the selected reconstruction kernel may influence the measured mHU. Methods: All dogs and cats that presented with lower urinary tract uroliths and had a non-enhanced CT preceding surgery were included. In male dogs, CT was performed after retrograde urohydropropulsion to detect the remaining urethral calculi. The percentage and location of persistent calculi were recorded. The images were reconstructed using three kernels, from smooth to ultrasharp, and the calculi mHU were measured. Results: Sixty-five patients were included in the study. The success rate of retrograde urohydropropulsion in the 45 male dogs was 55.6% and 86.7% at the first and second attempts, respectively. The predominant components of the calculi were cystine (20), struvite (15), calcium oxalate (8), and urate (7). The convolution kernel influenced the mHU values (p < 0.05). The difference in mHU regarding the calculus composition was better assessed using the smoother kernel. A mHU greater than 1,000 HU was predictive of calcium oxalate calculi. Conclusions: Non-enhanced CT is useful for controlling the success of retrograde urohydropropulsion. The mHU could allow a prediction of the calculus composition, particularly for calcium oxalate, which may help determine the therapeutic strategy.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1259-1269
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• 2015

Recently, big data is positioning as a keyword in the academic circles. And usefulness of big data is carried into government, a local public body and enterprise as well as academic circles. Also they are endeavoring to obtain useful information in big data. This research mainly deals with analyses of box office success or failure of films using text mining. For data, it used a portal site 'D' and film review data, grade point average and the number of screens gained from the Korean Film Commission. The purpose of this paper is to propose a model to predict whether a film is success or not using these data. As a result of analysis, the correct classification rate by the prediction model method proposed in this paper is obtained 95.74%.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.