• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1518-1539
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• 2024

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

• 전자통신동향분석
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• 제38권6호
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• pp.22-30
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• 2023

Recently, artificial intelligence has been in the spotlight across various fields. Artificial intelligence uses massive amounts of data to train machine learning models and performs various tasks using the trained models. For model training, large, high-quality data sets are essential, and database systems have provided such data. Driven by advances in artificial intelligence, attempts are being made to improve various components of database systems using artificial intelligence. Replacing traditional complex algorithm-based database components with their artificial-intelligence-based counterparts can lead to substantial savings of resources and computation time, thereby improving the system performance and efficiency. We analyze trends in the application of artificial intelligence to database systems.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.

• 상하수도학회지
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• 제19권4호
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• ETRI Journal
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• 제46권1호
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• pp.106-117
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• 2024

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, pass-ratio@n, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the pass-ratio@n metric.

• 전자통신동향분석
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• 제38권6호
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• pp.84-94
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• 2023

Open source has many advantages and is widely used in various fields. However, legal disputes regarding copyright and licensing of datasets and learning models have recently arisen in artificial intelligence developments. We examine how datasets affect artificial intelligence learning and services from the perspective of copyrighting and licensing when datasets are used for training models. The licensing conditions of datasets can lead to copyright infringement and license violation, thus determining the scope of disclosure and commercialization of the trained model. In addition, we examine related legal issues.

• Asia pacific journal of information systems
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• 제32권4호
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• pp.916-944
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• 2022

Artificial Intelligence has been a conceptual area for several decades. It has been studied extensively through experiments by the Information Systems community. When Information Systems supported with Information Technology became all pervasive in business and other allied areas, gradually the advancements in Artificial Intelligence also emerged as innovations across domains. Artificial Intelligence by definition is expected to substitute Human Intelligence, thereby making a huge space for innovation. In fact, all processes effected by human intelligence are liable to be replaced by AI which in itself is a massive innovation space. This paper will study the publication's repository (Scopus and Google Scholar from 1983 till 2021) in the area of Artificial Intelligence and innovation, then analyze the trend to gain insight into the evolution of AI as a vehicle for innovation.

• 한국전자통신학회논문지
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• 제14권5호
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• pp.969-976
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• 2019

경량 인공지능 하드웨어는 다양한 문제의 해결을 위해 멀티모달 센서 데이터를 입력받아 특징 선택, 추출, 차원축소, 정규화 과정을 수행한 후 인공지능 엔진으로 예측 결과를 도출한다. 다양한 애플리케이션에서 높은 성능을 달성하기 위해서는 이러한 경량 인공지능 하드웨어의 초 매개변수와 전체적인 전처리 시스템의 구성을 데이터에 맞춰 최적화할 필요가 있다. 본 논문에서는 경량 인공지능 하드웨어의 효율적인 제어 및 최적화를 위한 통합 프레임워크를 제안한다. 제안된 통합 프레임워크는 데이터 전처리 및 뉴로모픽 기반 경량 인공지능 엔진을 유연하게 재구성할 수 있으며, 최적의 모델을 생성할 수 있다. 기능검증을 위해 손글씨 이미지 데이터 세트와 관성 센서 데이터 기반의 낙상 검출 데이터 세트를 사용하였으며, 실험 결과 제안하는 통합 프레임워크가 각각의 데이터 세트에서 90% 이상의 정확도를 갖는 최적의 모델을 생성함을 확인하였다.