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All Journal International Journal of Advances in Intelligent Informatics PROCEEDING IC-ITECHS 2014 Belitung Nursing Journal Journal of Applied Data Sciences
Yang Wang
Tongji University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Nursing

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AI Empowers Graphic Design Education: Innovation and Breakthrough Wang, Yang; Cheng, Lijia; Lu, Fei; Zeng, Ailifei; Lu, Ling
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1647

Abstract

In the 1950s, the scientific community first proposed the term "Artificial Intelligence" (AI). Against the backdrop of rapid technological development in the 21st century, the application fields of AI technology are becoming increasingly widespread, penetrating every corner of our work and life. From the perspective of AI's impact on graphic design education, it can provide designers with more inspiration, express more accurately what designers want to convey, and also promote the updating of graphic design course content, thereby continuously cultivating students' comprehensive abilities. AI's integration into graphic design education has led to the development of intelligent design tools that can automatically generate design drafts based on given parameters, thus significantly reducing the time required for the initial design phase. Moreover, AI can analyze trends and user preferences, offering designers insights that can guide their creative process. As a result, the educational curriculum must evolve to include the study of AI algorithms and their applications in design, ensuring that future designers are well-equipped to leverage these powerful tools. This shift not only enhances the efficiency and innovation in the design field but also prepares students for the demands of a future job market that will increasingly rely on AI-assisted design solutions.
Applying Factor Analysis to Assess Employment Competitiveness Strategies: A Data Science Perspective Wang, Yang; Sangsawang, Thosporn; Vipahasna, Piyanan Pannim; Vipahasna, Kitipoom; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.650

Abstract

This study aims to identify and analyze the factors influencing the employment competitiveness of graduates from higher vocational colleges in China and evaluate the impact of targeted programs designed to enhance these factors on graduates' employability. The research involved 17 experts and 100 instructors from Sichuan University of Science and Engineering, utilizing purposive sampling to explore effective career guidance models for improving employment ability. The Delphi technique was applied to synthesize expert opinions on key factors affecting graduate employment competitiveness. Additionally, a sample of undergraduate students participated in the study, with data collected through questionnaires. The findings demonstrate the transformative potential of focused career guidance programs, showing a significant improvement in students' employability post-intervention. These results emphasize the importance of targeted initiatives that equip students with the necessary skills, resources, and career insights to succeed in the job market. By bridging the gap between academia and industry expectations, such programs play a crucial role in preparing students for a smooth transition from university to the professional world, helping them secure meaningful employment opportunities.
Mediating role of self-efficacy in the relationship between family functioning and self-management behaviors in patients with coronary heart disease: A cross-sectional study in Jiangsu, China Wang, Yang; Masingboon, Khemaradee; Wacharasin, Chintana
Belitung Nursing Journal Vol. 11 No. 1 (2025): January - February
Publisher : Belitung Raya Foundation, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33546/bnj.3638

Abstract

Background: Self-management behaviors can prevent the negative consequences among patients with coronary heart disease (CHD). The reality of patients followed the self-management behaviors rate are unoptimistic. Objective: This study aimed to examine whether self-efficacy serves as a mediating role between family functioning and self-management behaviors among coronary heart disease patients. Methods: A cross-sectional approach was applied, and 140 patients with CHD were included using a cluster sampling strategy. Family functioning was assessed utilizing the Family APGAR Index, self-efficacy was evaluated using the Self-efficacy for Chronic Disease 6-item Scale, and self-management behaviors was examined utilizing the Coronary Artery Disease Self-Management Scale. Data were collected from July to October 2022 and analyzed using descriptive statistics and regression analyses to evaluate the mediating influence. Results: The degree of self-management behaviors among patients with CHD was at a low level (Mean = 82.23, SD = 11.863). Self-efficacy had a direct and positive impact on self-management behaviors (β = 0.39, p <0.001). Moreover, self-efficacy had a partially intermediary function in the relationship between family functioning and self-management behaviors (indirect effect = 0.14, 95% CI [0.04, 0.27]; direct effect = 0.39, p <0.001). Conclusion: Self-efficacy demonstrated an association with self-management behaviors and served as a mediation function in the relationship between self-management behaviors and family functioning. Therefore, the significance of family functioning and self-efficacy should be highlighted in nursing practice when developing methods to encourage patients with CHD to improve their self-management behaviors.
An automated learning method of semantic segmentation for train autonomous driving environment understanding Wang, Yang; Chen, Yihao; Yuan, Hao; Wu, Cheng
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1521

Abstract

One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As one of the most fundamental and challenging problems in autonomous driving, environment understanding has been widely studied. It directly determines whether the entire in-vehicle system can effectively identify surrounding objects of vehicles and make correct path planning. Semantic segmentation is the most important means of environment understanding among the many image recognition algorithms used in autonomous driving. However, the success of semantic segmentation models is highly dependent on human expertise in data preparation and hyperparameter optimization, and the tedious process of training is repeated over and over for each new scene. Automated machine learning (AutoML) is a research area for this problem that aims to automate the development of end-to-end ML models. In this paper, we propose an automatic learning method for semantic segmentation based on reinforcement learning (RL), which can realize automatic selection of training data and guide automatic training of semantic segmentation. The results show that our scheme converges faster and has higher accuracy than researchers manually training semantic segmentation models, while requiring no human involvement.
Co-Authors Chen, Yihao Cheng, Lijia Lu, Fei Lu, Ling Masingboon, Khemaradee Sangsawang, Thosporn Vipahasna, Kitipoom Vipahasna, Piyanan Pannim Wacharasin, Chintana Watkraw, Wasan Wu, Cheng Yuan, Hao Zeng, Ailifei