International Journal of Engineering Intelligent Systems

Incorporating Traditional Cultural Elements in Urban Streetscape Design Using the Internet of Things and Deep Learning

Dongli Si — 2024-11-01

With the development of the Internet of Things and deep learning, the styles and designs of urban streetscapes have undergone major changes. For instance, a new trend is emerging: the seamless integration of cultural elements with other streetscape characteristics. This can enhance the aesthetic characteristics of urban streetscape design, significantly improve the quality of urban streetscapes, emphasize traditional cultural elements, apply specific features to modern buildings, and improve the integration of various elements. In this study, we conduct a comprehensive investigation of the current status of urban street landscape settings and examine the integration of traditional cultural elements with modern architectural elements. Our
focus on this integration is anticipated to yield valuable insights for urban street landscape design and theory.

Keywords: application research, element, design, traditional culture, urban landscape

Cite As

D. Si, F. Tian, "Incorporating Traditional Cultural Elements in Urban Streetscape Design
Using the Internet of Things and Deep Learning", Engineering Intelligent Systems, vol. 32
no. 6, pp. 573-578, 2024.

One-Dimensional Convection Diffusion Equation Based on Operator Splitting

Qinghua Yao — 2024-11-01

Due to the phenomenon of numerical dispersion and oscillation used for solving one-dimensional convection diffusion equations, the accuracy of numerical simulation results is not high. Therefore, a method is proposed based on operator splitting for a one-dimensional convection diffusion equation. Using the operator splitting algorithm, the undetermined coefficient method is applied to the convection and diffusion steps, and dimensionless coefficients are introduced to minimize the numerical oscillation and numerical diffusion of the scheme. A new numerical solution scheme of one-dimensional convection diffusion equation is constructed by using the results of convection step calculation as the known value to solve the diffusion equation. The experimental results show that the proposed method can effectively control the numerical oscillation and numerical
diffusion, and has good convergence and stability, and high accuracy. It can effectively solve the one-dimensional convection diffusion equation, and has certain reference value.

Keywords: one-dimensional convection diffusion equation; solution; operator splitting; undetermined coefficient method

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Q. Yao, B. Qiu, L. Chu, "One-Dimensional Convection Diffusion Equation Based on Operator Splitting", Engineering Intelligent Systems, vol. 32 no. 6, pp. 579-586, 2024.

Promotion Effect of Sports Games Based on Deep Learning on Children’s Psychological Development

Zhihua Yu — 2024-11-01

With the wide application of deep learning technology in various fields, its potential role in the field of education, especially sports games, has aroused the interest of researchers. This study explores the impact on children’s psychological development of sports games based on deep learning. Through a combination of quantitative and qualitative research methods, using the SCL-90 scale and a deep learning model, the study analyzed changes in children’s mental health before and after participating in such games. The results showed that children who participated in deep-learning-enhanced sports games had significantly reduced anxiety and symptoms of depression, and better social interaction and teamwork skills. However, the study also had limitations, such as a small sample size and a lack of long-term impact assessment. Nevertheless, this study provides valuable insights for educators and game developers in terms of designing games that promote children’s mental health and social skills, while demonstrating the potential for deep learning models to be applied in the evaluation of educational games.

Keywords: Deep learning, sports games, children’s psychological development, SCL-90 scale, educational technology

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Z. Yu, Y. Ying, H. Liu, "Promotion Effect of Sports Games Based on Deep Learning on Children’s
Psychological Development", Engineering Intelligent Systems, vol. 32 no. 6, pp. 587-596, 2024.

Knowledge Transfer in the Teaching of English Translation Based on Deep Learning

Xinyi Cheng — 2024-11-01

This study was conducted to explore the utilization of deep learning concepts and techniques in the education domain as a means of improving the effectiveness of English translation teaching and the transfer and application of students’ translation knowledge and skills. The model-building process consists of three stages: the pre-training stage, the fine-tuning stage and the translation stage. The pre-training stage involves deep learning on a large-scale teaching dialog or text corpus; the fine-tuning stage involves knowledge transfer on a relatively small-scale real-world corpus; and the translation stage involves the translation of new real-world texts. This study collected and analyzed data collected by means of an experiment designed and implemented A deep-learning-based English translation teaching experiment was designed and implemented to compare three teaching models (a traditional model, a guided classroom model, and a transfer learning model) in terms of students’ translation competence, deep learning competence, and learning satisfaction. The results of the study suggest that the knowledge transfer model incorporating deep learning was the most effective, the guided classroom model was the second most effective, and the traditional model was the least effective.

Keywords: deep learning, English translation, pedagogical knowledge transfer

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X. Cheng, "Knowledge Transfer in the Teaching of English Translation Based on Deep Learning",
Engineering Intelligent Systems, vol. 32 no. 6, pp. 597-604, 2024.

Exploration and Optimization of a Deep Reinforcement Learning-based Model for the Creation of Children’s Literature

Jianing Yue — 2024-11-01

The creation of literature for children is an important research direction in natural language processing and one of the means of improving children’s academic outcomes. This paper explores and optimizes a deep reinforcement learning-based model for the creation of children’s literature; i.e., the technique of deep reinforcement learning is used to generate literary works for children that conform to the characteristics and principles of children’s literature, such as fairy tales, fables, and other fiction. In this paper, a deep reinforcement learning framework based on a generative adversarial network (GAN) is adopted to design a children’s literature creation model consisting of a generator and a discriminator, which is responsible for generating children’s literature. The discriminator is responsible for evaluating the quality of the generated works and giving reward signals to guide the generator to optimize its strategy. The model comprehensively considers the characteristics of theme, style, structure, language and other aspects of children’s literature, and designs a multi-dimensional evaluation index system, including theme relevance, style consistency, structural completeness, language fluency, etc., as well as a comprehensive evaluation index, which is used to measure the overall quality of the generated children’s literature. Moreover, by means of four experiments, this paper tested the ability of the model to generate children’s literature with different themes, styles, structures and lengths, and compared it with random generation, RNN generation, GPT-2 generation, manual generation and other methods. This study aims to provide innovative methods for children’s literature creation by exploring and optimizing models based on deep reinforcement learning, in order to generate more creative, educational and child-friendly story content.

Keywords: deep reinforcement learning, children’s literature creation, text generation

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J. Yue, B. Liu, "Exploration and Optimization of a Deep Reinforcement Learning-based
Model for the Creation of Children’s Literature", Engineering Intelligent Systems, vol. 32
no. 6, pp. 605-612, 2024.

Trade Network Pattern and Factors Influencing New Energy Vehicles in RCEP Agreement Countries

Weihua Pan — 2024-11-01

This study uses social network analysis as a tool to investigate the structure of the trade network and the factors that influence the trade of new energy vehicles in the 15 countries party to the 2022 RCEP agreement. The findings reveal that: (1) The density and correlation of the trade network for new energy vehicles among the countries in the agreement are significant, suggesting strong connection and considerable trade opportunities; (2) South Korea, China, and Japan occupy central positions within the trade network, and the “RCEP” trade network can be divided into three distinct trade clusters; (3) A country’s level of economic development and logistics performance index have a significant and positive impact on the establishment of new energy vehicle trade relationships; lithium battery trade discrepancies, fuel trade levels, the presence of a shared border between countries has a
significant impact on the establishment and strength of trade partnershipswith newenergy vehicles; and similarities in business convenience indices and geographical distance facilitate the establishment of new energy vehicle trade connections and enhance trade intensity among nations. Consequently, countries party to the agreement should continue to develop the construction of their transportation infrastructure and increase investment in industrial chain and infrastructure development.

Keywords: “RCEP” agreement; new energy vehicle trade; social network analysis; QAP analysis

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W. Pan, C. Liu, Y. Liu, "Trade Network Pattern and Factors Influencing New Energy Vehicles in RCEP
Agreement Countries", Engineering Intelligent Systems, vol. 32 no. 6, pp. 613-623, 2024.

Application of Big Data in English Teaching Evaluation and Feedback System

Xiaoqiong Feng — 2024-11-01

With the rapid development of information technology, big data has become a hot topic in the field of education. In English teaching, an evaluation and feedback system is key to improving teaching quality. This study explores the use of big data technology for the evaluation and feedback system of English teaching, with a view to improving teaching efficiency and students’ learning effectiveness. First, the feasibility and necessity of big data technology in English teaching evaluation were determined through a literature review and analysis of existing systems. Second, a prototype of a big data-based English teaching evaluation and feedback system was designed and implemented, comprised of a model for grammatical error recognition and scoring, a model for predicting students’ English proficiency development, and a model for implementing a feedback mechanism. Specifically, a neural network model was constructed using RNN, which is able to process time-series data to implement the grammatical error recognition kernel scoring. A prediction model of students’ English proficiency development is a support vector machine (SVM) used to predict students’ general English proficiency level, Y. Finally, a real-time feedback model based on Item Response Theory (IRT) was implemented using a dynamic adaptive strategy. Finally, the teaching and learning data before and after the use of the system were collected and analyzed through an experimental study. Results indicated that the English teaching evaluation and feedback system based on big data has a positive effect on college students’ learning of English.
The proposed system not only improves their learning outcomes, but also helps to stimulate learning motivation, change learning attitudes, and obtain better results at different knowledge points. The findings of this study demonstrate that big data technology can effectively integrate and analyze teaching evaluation information and provide teachers with real-time, personalized feedback.

Keywords: big data, English language teaching, evaluation and feedback

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X. Feng, "Application of Big Data in English Teaching Evaluation and Feedback System", Engineering Intelligent Systems,
vol. 32 no. 6, pp. 625-634, 2024.

Design of Intelligent Assistant System for English Teaching Based on Artificial Intelligence

Gongwei Dai — 2024-11-01

Driven by the wave of globalization, the importance of English as a facilitator of international communication is becoming more and more pronounced. Traditional English teaching is facing challenges such as the lack of teachers and uneven distribution of resources. Hence, there is an urgent need for innovative solutions. In this study, we develop an English teaching aid system based on artificial intelligence, with the aim of improving the quality of teaching and learners’ motivation. This study was carried out using a multi-dimensional approach comprising a literature review, requirement analysis, system design and prototype testing. First, the literature review revealed the core challenges of English teaching and the current status of AI application
in education. Then, the requirement analysis clarified the key requirements for system design. On this basis, we constructed a system architecture and developed a prototype system. In particular, in terms of teaching assistance, this system uses natural language processing technology and machine learning algorithms to achieve intelligent adaptation and personalized recommendation of course content to adapt to the needs of different learners. The results of the study show that the assistance system significantly improves the efficiency of English teaching and the learning outcomes of learners. The system dynamically adjusts teaching strategies and optimizes learning paths according to learners’ progress and feedback. Meanwhile, the system
provides teachers with learning data analysis, which enhances their understanding and responsiveness to learners’ needs.

Keywords: artificial intelligence, English language teaching, intelligent assistance system

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G. Dai, "Design of Intelligent Assistant System for English Teaching Based on Artificial Intelligence",
Engineering Intelligent Systems, vol. 32 no. 6, pp. 635-645, 2024.

English Learning Behaviour Pattern Mining and Personalized Teaching Strategies Based on Big Data Analysis

Yingying Xiao — 2024-11-01

With the development of information technology, data-driven decision-making in education is becoming increasingly important, especially in the personalization of English language teaching. In this study, large-scale English learning behavior data were deeply mined through a set of wellestablished analysis processes, using quantitative methods such as cluster analysis and association rule analysis. It was found that the careful delineation and parsing of students’ behavioral patterns revealed individual differences in terms of learning habits, preferences, and challenges faced by students when learning English. The experimental results show that the experimental group that implemented personalized teaching strategies demonstrated more significant improvement in the learning behaviors (e.g., online learning hours, number of interactions, task completion, etc.) and academic performance (e.g., test scores, homework grading) than those of the control group that were exposed traditional teaching modes. The case analyses of specific cases also demonstrated that the personalized teaching strategy designed according to students’ individual characteristics can effectively improve the learning outcomes of students.

Keywords: big data analytics, English learning, behavioral pattern mining, personalized instruction

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Y. Xiao, "English Learning Behaviour Pattern Mining and Personalized Teaching Strategies Based
on Big Data Analysis", Engineering Intelligent Systems, vol. 32 no. 6, pp. 647-657, 2024.

Factors Affecting the Quality of Online Open Course Teaching in Universities Based on Big Data Analysis

Genchao Wei — 2025-01-26

By means of big data analysis, this study systematically explores and discusses the factors affecting the educational quality of college open online courses (MOOCs). The research on the MOOC platform data of A University in Chengdu, China, reveals the significant impact of teaching content, teacher quality, student engagement and platform technology on the quality of teaching and, subsequently, on students’ learning outcomes. Results show that course content had the greatest influence on educational quality (regression coefficient 0.35, p = 0.000), followed by teacher quality (regression coefficient 0.30, p = 0.000), student engagement (regression coefficient 0.25, p = 0.006) and platform technology (regression coefficient 0.20, p = 0.001). The research shows that optimizing curriculum design, improving teachers’ professional level, strengthening students’ motivation to learn, and improving platform technology are key to improving the delivery of education via MOOCs. This study provides data support and a
scientific basis for improving the quality of online education, which has important theoretical and practical significance, and can provide references for education administrators and curriculum designers who wish to devise more effective teaching strategies and policies.

Keywords: online open course; influencing factors; big data analysis; colleges and universities

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G. Wei, "Factors Affecting the Quality of Online Open Course Teaching in Universities Based on
Big Data Analysis", Engineering Intelligent Systems, vol. 32 no. 6, pp. 659-669, 2024.