• Journal of Information Technology Applications and Management
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• 제17권2호
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• pp.133-154
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• 2010

Recently, Internet Telephony has become increasingly popular in telecommunication industry. However, previous research on Internet Telephony has focused on analyzing specific Internet Telephonysolutions, identifyingthe Internet Telephony movement itself. The research on prediction models about Internet Telephony adoption has been minimal. The main propose of this study is to develop models for predicting transition intention from using traditional telephones to using Internet Telephony. To do so, this study uses data mining methods to analyze demands in the IT communications market and to provide management strategies for Internet telephony providers. Especially this study uses discriminant analysis, logistic regression, classification tree, and neural nets to develop those prediction models toward Internet Telephony adoption. The models are compared with each other and a superior model is chosen.

• 대한인간공학회지
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• 제31권5호
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• pp.695-704
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• 2012

Objective: The purpose of this study was to investigate major factors influencing adoption of smartphone to promote its use by older adults. Background: Despite increasing proportion of elderly people and elderly market, the proportion of elderly smartphone user is still relatively small compared to whole smartphone users. Thus, we need to find out major factors influencing adoption of smartphone to increase proportion of elderly smartphone users. Method: Seven major factors were extracted from 36 survey questions using factor analysis. Regression analysis was also applied to determine specific factors affecting intention of use based on user versus non-user of smartphone, age, gender, and educational background. Results: As results of factor analysis and regression analysis, major factors influencing adoption of smartphone for elderly users were significantly different according to gender, age, educational background based on smartphone users or non-users. Conclusion: The result of this study identified major factors influencing adoption of smartphone for the elderly and provided basic information related to adoption of smartphone according to elderly people's characteristics. Consequently, we can expect to reduce the information gap and to improve quality of life for the elderly. Application: The development and marketing strategy could be applied differently based on the factors influencing adoption of smartphone. It is also possible to develop a prediction model for smartphone adoption according to elderly users' characteristics.

• 한국인공지능학회지
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• 제9권1호
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• pp.29-35
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• 2021

In this paper, we analyzed the factors of adoption and implemented a predictive model to activate the adoption of animals. Recently, animal shelters are saturated due to the abandonment and loss of companion animals. To address this, we need to find a way to encourage adoption. In this paper, a study was conducted using two data from an open data portal provided by Austin, Texas. First, a correlation analysis was conducted to identify the attributes that affect the result value, and it was found that Animal Type Intake, Intake Type, and Age upon Outcome influence the Outcome Type with correlation coefficients of 0.4, 0.26, and -0.2, respectively. For these attributes, the analysis was conducted using Multiclass Logistic Regression. As a result, dogs had a higher probability of Adoption than cats, and animals subjected to euthanasia were more likely to adopt. In the case of Public Assist and Stray, it was found that the Missing rate was high. Also, the length of stay for cats increased to 12.5 years of age, while dogs generally adopted smoothly at all ages. These results showed an overall accuracy of 62.7% and an average accuracy of 91.7%, showing a fairly reliable result. Therefore, it seems that it can be used to develop a plan to promote the adoption of animals according to various factors. Also, it can be expanded to various services by interlocking with the webserver.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 2010년도 춘계국제학술대회
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• pp.187-199
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• 2010

Recently, Internet Telephony has become increasingly popular in telecommunication industry. However, previous research on Internet Telephony has focused on analyzing specific Internet Telephony solutions, identifying the Internet Telephony movement itself. The research on prediction models about Internet Telephony adoption has been minimal. The main propose of this study is to develop models for predicting transition intention from using traditional telephones to using Internet Telephony. To do so, this study uses data mining methods to analyze demands in the IT communications market and to provide management strategies for Internet telephony providers. Especially this study uses discriminant analysis, logistic regression, classification tree, and neural nets to develop the prediction models for the Internet Telephony adoption. The models are compared with each other and a superior model is chosen.

• 경영과학
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• 제22권1호
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• pp.179-197
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• 2005

This study examines market acceptance for DMB service, one of the touted new business models in Korea's next-generation mobile communications service market, using adoption end diffusion of innovation as the theoretical framework. Market acceptance for DMB service was assessed by predicting the demand for the service using the Bass model, and the demand variability over time was then analyzed by integrating the innovation adoption model proposed by Rogers (2003). In our estimation of the Bass model, we derived the coefficient of innovation and coefficient of imitation, using actual diffusion data from the mobile telephone service market. The maximum number of subscribers was estimated based on the result of a survey on satellite DMB service. Furthermore, to test the difference in diffusion pattern between mobile phone service and satellite DMB service, we reorganized the demand data along the diffusion timeline according to Rogers' innovation adoption model, using the responses by survey subjects concerning their respective projected time of adoption. The comparison of the two demand prediction models revealed that diffusion for both took place forming a classical S-curve. Concerning variability in demand for DMB service, our findings, much in agreement with Rogers' view, indicated that demand was highly variable over time and depending on the adopter group. In distinguishing adopters into different groups by time of adoption of innovation, we found that income and lifestyle (opinion leadership, novelty seeking tendency and independent decision-making) were variables with measurable impact. Among the managerial variables, price of reception device, contents type, subscription fees were the variables resulting in statistically significant differences. This study, as an attempt to measure the market acceptance for satellite DMB service, a leading next-generation mobile communications service product, stands out from related studies in that it estimates the nature and level of acceptance for specific customer categories, using theories of innovation adoption and diffusion and based on the result of a survey conducted through one-to-one interviews. The authors of this paper believe that the theoretical framework elaborated in this study and its findings can be fruitfully reused in future attempts to predict demand for new mobile communications service products.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.95-95
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• 2023

Common hydrological problems of developing countries include poor data management, insufficient measuring devices and ungauged watersheds, leading to small or unreliable data availability. This has greatly affected the adoption of artificial intelligence techniques for flood risk mitigation and damage control in several developing countries. While climate datasets have recorded resounding applications, but they exhibit more uncertainties than ground-based measurements. To encourage AI adoption in developing countries with small ground-based dataset, we propose data augmentation for regression tasks and compare performance evaluation of different AI models with and without data augmentation. More focus is placed on simple models that offer lesser computational cost and higher accuracy than deeper models that train longer and consume computer resources, which may be insufficient in developing countries. To implement this approach, we modelled and predicted streamflow data of the Asa River Watershed located in Ilorin, Kwara State Nigeria. Results revealed that adequate hyperparameter tuning and proper model selection improve streamflow prediction on small water dataset. This approach can be implemented in data-scarce regions to ensure timely flood intervention and early warning systems are adopted in developing countries.

• ETRI Journal
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• 제30권4호
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• pp.506-515
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• 2008

The H.264/AVC standard has adopted new coding tools such as intra-prediction, variable block size, motion estimation with quarter-pixel-accuracy, loop filter, and so on. The adoption of these tools enables an H.264/AVC-coded bitstream to have more information than was possible with previous standards. In this paper, we propose an effective spatial error concealment method with low complexity in H.264/AVC intra-frame. From information included in an H.264/AVC-coded bitstream, we use prediction modes of intra-blocks to recover a damaged block. This is because the prediction direction in each prediction mode is highly correlated to the edge direction. We first estimate the edge direction of a damaged block using the prediction modes of the intra-blocks adjacent to a damaged block and classify the area inside the damaged block into edge and flat areas. Our method then recovers pixel values in the edge area using edge-directed interpolation, and recovers pixel values in the flat area using weighted interpolation. Simulation results show that the proposed method yields better video quality than conventional approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• ETRI Journal
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• 제39권3호
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• pp.310-318
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• 2017

Unified speech and audio coding (USAC) is one of the latest coding technologies. It is based on a switchable coding structure, and has demonstrated the highest levels of performance for both speech and music contents. In this paper, we propose an extended version of USAC with a single-mode of operation-which does not require a switching system-by extending the linear prediction-coding mode. The main concept of this extension is the adoption of the advantages of frequency-domain coding schemes, such as windowing and transition control. Subjective test results indicate that the proposed scheme covers speech, music, and mixed streams with adequate levels of performance. The obtained quality levels are comparable with those of USAC.