• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2904-2926
• /
• 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%.

• Electronics and Telecommunications Trends
• /
• v.37 no.4
• /
• pp.28-35
• /
• 2022

To solve the challenge of waste plastics, this study investigated the related technologies and company trends along the plastic life cycle, and primarily describes ICT technologies to improve efficiency in the process of sorting and sorting waste plastics. Waste plastic discharge caused by the explosive increase in parcel traffic because of COVID-19 is also growing exponentially. Hence, waste treatment is emerging as a social challenge. Most of the domestic waste classification depends on the manual process according to the waste pollution level. The plastic material classification approach using the spectroscopy approach reveals a high error in the contaminated waste plastic classification, but if the Artificial Intelligence-based image classification technology is employed together, the classification precision can be enhanced because of the type of waste plastic product and the contaminated part can be differentiated.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.481-482
• /
• 2023

양자 컴퓨팅 환경에서 빅데이터를 이용하는 Quantum Artificial Intelligence(QAI)는 빠른 계산 속도를 추구한다. 최근 금융, 물류, 교통 분야의 QAI 모델과 이미지 분류용 quantum convolutional neural network가 소개됐지만 아직 완벽한 성능은 달성하지 못했다. 본 논문은 성능 향상을 위한 모듈을 새로 제시하고, 이를 소형 양자 컴퓨터에 적용하며 하이브리드 모델 구성을 가능하게 한다. 실험 결과, 제안하는 방법은 기존 네트워크와 비교해 우수한 성능을 보였다.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.126-133
• /
• 2023

Recent advancements in cloud service virtualization technologies have witnessed a shift from a Virtual Machine-centric approach to a container-centric paradigm, offering advantages such as faster deployment and enhanced portability. Container orchestration has emerged as a key technology for efficient management and scheduling of these containers. However, with the increasing complexity and diversity of heterogeneous workloads and service types, resource scheduling has become a challenging task. Various research endeavors are underway to address the challenges posed by diverse workloads and services. Yet, a systematic approach to container orchestration for effective cloud management has not been clearly defined. This paper proposes the DRA-Engine (Dynamic Resource Allocation Engine) for resource scheduling in container orchestration. The proposed engine comprises the Request Load Procedure, Required Resource Measurement Procedure, and Resource Provision Decision Procedure. Through these components, the DRA-Engine dynamically allocates resources according to the application's requirements, presenting a solution to the challenges of resource scheduling in container orchestration.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.25-34
• /
• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• Journal of Korea Artificial Intelligence Association
• /
• v.2 no.1
• /
• pp.15-24
• /
• 2024

This study is about the effect of chatbot service quality on customer satisfaction and continuous use intention. Data collection was conducted for 13 days from October 23 to November 5, 2023, and a survey was conducted on customers who have used chatbot services. A total of 572 questionnaires were targeted, of which 545 valid data were used for analysis, excluding those that responded insincerely or did not meet the purpose of the study. The analysis results of this study are as follows: First, chatbot service quality partially had a significant effect on satisfaction. Second, customer satisfaction had a significant effect on continuous use intention. Therefore, in order to have a positive impact on continuous use intention, it is necessary to focus on marketing strategies related to chatbot service quality. Also, research focusing on data analysis and performance evaluation is crucial for enhancing chatbot services, necessitating studies that address real-time changes. Through sophisticated data analysis and variable measurement, chatbot services can be effectively improved, leading to enhanced customer satisfaction.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.2
• /
• pp.81-86
• /
• 2024

Advancements in generative AI are reshaping graphic and 3D content design landscapes, where AI not only enriches graphic design but extends its reach to 3D content creation. Though 3D texture mapping through AI is advancing, AI-generated 3D modeling technology in this realm remains nascent. This paper presents AI 2D image-driven 3D modeling techniques, assessing their viability in 3D content design by scrutinizing various algorithms. Initially, four OBJ model-exporting AI algorithms are screened, and two are further evaluated. Results indicate that while AI-generated 3D models may not be directly usable, they effectively capture reference object structures, offering substantial time savings and enhanced design efficiency through manual refinements. This endeavor pioneers new avenues for 3D content creators, anticipating a dynamic fusion of AI and 3D design.

• Journal of Biosystems Engineering
• /
• v.25 no.3
• /
• pp.213-220
• /
• 2000

Grafting of fruit-bearing vegetables has been widely used to increase the resistance to soil-borne diseases, to increase the tolerance to low temperature or to soil salinity, to increase the plant vigor, and to extend the duration of economic harvest time. After grafting, it is important to control the environment around grafted seedlings for the robust joining of a scion and rootstock. Usually the shading materials and plastic films are used to keep the high relative humidity and low light intensity in greenhouse or tunnel. It is quite difficult to optimally control the environment for healing and acclimation of grafted seedlings under natural light. So the farmers or growers rely on their experience for the production of grafted seedling with high quality. If artificial light is used as a lighting source for graft-taking of grafted seedlings, the light intensity and photoperiod can be easily controlled. The purpose of this study was to develop a prototype system for the graft-taking enhancement of grafted seedlings using artificial lighting and to investigate the effect of air current speed on the distribution of air temperature and relative humidity in a graft-taking enhancement system. A prototype graft-taking system was consisted by polyurethane panels, air-conditioning unit, system controller and lighting unit. Three band fluorescent lamps (FL20SEX-D/18, Kumho Electric, Inc.) were used as a lighting source. Anemometer (Climomaster 6521, KANOMAX), T-type thermocouples and humidity sensors (CHS-UPS, TDK) were used to measure the air current speed, air temperature and relative humidity in a graft-taking system. In this system, air flow acted as a driving force for the diffusion of heat and water vapor. Air current speed, air temperature and relative humidity controlled by a programmable logic controller (UP750, Yokogawa Electric Co) and an inverter (MOSCON-G3, SAMSUNG) had an even distribution. Distribution of air temperature and relative humidity in a graft-taking enhancement system was fairly affected by air current speed. Air current speed higher than 0.1m/s was required to obtain the even distribution of environmental factors in this system. At low air current speed of 0.1m/s, the evapotranspiration rate of grafted seedlings would be suppressed and thus graft-taking would be enhanced. This system could be used to investigate the effects of air temperature, relative humidity, air current speed and light intensity on the evaportranspiration rate of grafted seedlings.

• Industry Promotion Research
• /
• v.8 no.2
• /
• pp.21-27
• /
• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• Journal of Life Science
• /
• v.30 no.5
• /
• pp.420-427
• /
• 2020

A repebody, an artificial non-immunoglobulin protein scaffold, is expected to be a solution in the search for faster, cheaper, and customizable antibodies. However, the production of medical repebodies remains difficult due to their low yield and the complex purification processes required. The Pseudomonas fluorescens ABC transporter system has been suggested as an efficient and cost-effective method for repebody production, but the total yield is low because of the secreted protein's positive charge; thus, a repebody with a high isoelectric point needs to be changed into a more negatively charged protein for better secretion. To achieve this, we first attached oligo-aspartic acids to the N- and C-terminals of the repebody, but secretion efficiency was not enhanced significantly. Subsequently, we devised an alternative method for improved secretion efficiency by engineering fifteen positively charged amino acids to aspartic acid in the non-antigen binding sites of the repebody to give a high net negative charge. As a result, secretion efficiency was greatly enhanced from 21.2% (wildtype) to 58.5% (negatively supercharged). The negatively supercharged repebody was succussfully produced extracellularly by ABC transporter secretion system in P. fluorescens.