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article-light first"><h3><a href="/doi/10.4108/eetsis.5857">Evaluation of a Microcontroller-based Smart Wearable Device in College Students' Sports Forging Application</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Authors: </dt><dd class="value">Yong Che, Kaixuan Che, Qinlong Li</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">INTRODUCTION: The widespread use of smart wearable devices in various fields, including healthcare and sports, underscores the importance of their application in enhancing physical exercise among college students. Recent advancements in technology have facilitated the development of sophisticated m…</span><span class="full">INTRODUCTION: The widespread use of smart wearable devices in various fields, including healthcare and sports, underscores the importance of their application in enhancing physical exercise among college students. Recent advancements in technology have facilitated the development of sophisticated methods to assess and predict physical activity outcomes, making their evaluation increasingly critical. <br>OBJECTIVES: This study aims to develop a reliable assessment model for smart wearable devices used in college students' sports activities. The objective is to accurately predict and evaluate the effectiveness of these devices in improving students' physical health and promoting lifelong sports habits. Ultimately, the research seeks to integrate advanced computational methods to enhance the accuracy of physical exercise assessments. <br>METHODS: The research introduces a novel assessment model that combines a zebra behavior-based heuristic optimization algorithm with a convolutional neural network (CNN). By analyzing user behavior data from wearable devices, the model constructs an evaluation index system tailored for college sports activities. The approach optimizes the parameters of the CNN using the zebra optimization algorithm, ensuring enhanced prediction accuracy. <br>RESULTS: The evaluation model demonstrated high accuracy, with a significant improvement in predicting the outcomes of physical exercises among college students. Comparative analyses with traditional methods revealed that the new model reduced prediction errors and increased real-time performance metrics. Specifically, the model achieved a lower root mean square error (RMSE) in simulation tests, indicating more precise assessments. Figures and statistical data provided in the study illustrate the model's superior performance across various parameters. <br>CONCLUSION: The developed assessment model significantly advances the application of smart wearable devices in monitoring and enhancing college students' physical activities. By integrating cutting-edge algorithms, the study not only improves the accuracy of exercise assessments but also contributes to the broader understanding of technology's role in health and fitness education. Future research could further refine this model by incorporating additional sensors and data points to expand its applicability and robustness. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5636">The Digital Transformation of College English Classroom: Application of Artificial Intelligence and Data Science</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Yanling Li </dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study …</span><span class="full">A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5671">Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Authors: </dt><dd class="value">Yuanfang Wei , Li Song </dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy…</span><span class="full">The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5713">Realization of Urban Perception Art: Painting Expressions of Internet of Things Technologies in Urban Environments</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Authors: </dt><dd class="value">Hong Zhu, Lu Yao</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">INTRODUCTION: With the continuous progress of urbanization, people's perceptions and experiences of the urban environment are increasingly concerned. Traditional forms of artistic expression can no longer fully meet people's needs for urban perception. Therefore, it is especially important to explo…</span><span class="full">INTRODUCTION: With the continuous progress of urbanization, people's perceptions and experiences of the urban environment are increasingly concerned. Traditional forms of artistic expression can no longer fully meet people's needs for urban perception. Therefore, it is especially important to explore new possibilities of urban perception art with the help of modern technology, especially intelligent technology. <br>OBJECTIVES: The main purpose of this study is to explore the feasibility and effectiveness of utilizing advanced technology for urban perception art expression. Through an in-depth understanding of the urban environment and the perceptual needs of urban residents, as well as existing technological means, artistic expressions that can present urban perceptions more intuitively and vividly are developed. <br>METHODS: This study adopts a combination of field research and art practice. Through urban observation and questionnaire surveys, the subjective experience and needs of urban residents for urban perception were collected. Then, using digital painting and video technology, combined with the principles of perception psychology, urban perception works with artistic and technological senses were designed. <br>RESULTS: A series of urban perception artworks were designed in this study, covering all aspects of urban life, including architectural landscapes, transportation scenes, and humanistic customs. These works enable viewers to perceive the urban environment in a more intuitive and immersive way through digital painting and video technology, as well as real-time data and perceptual feedback. <br>CONCLUSION: By exploring new ways of artistic expression of urban perception, this study provides urban residents with a richer and deeper experience of urban perception. The application of digital painting and video technology, as well as the interaction and feedback with urban residents, opens up new possibilities for the development of urban perceptual art. <br> <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5764">Analysis of Learning Characteristics of Online Learners in the Context of Smart Education</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Weihua Weihua</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened"> This article aims to explore the learning characteristics of online learners within the smart education framework, with a specific emphasis on how they might use Internet of Things (IoT) technologies to improve their educational experience. The term "online learning" refers to the process of acq…</span><span class="full"> <br>This article aims to explore the learning characteristics of online learners within the smart education framework, with a specific emphasis on how they might use Internet of Things (IoT) technologies to improve their educational experience. The term "online learning" refers to the process of acquiring knowledge via electronic means, most often the global web. Online education, e-learning, web-based learning, and computer-assisted learning all share this term. The challenging characteristics of such online learners for students are technical issues, lack of motivation, and slow loading times in online courses. Hence, in this research, the Internet of Things-empowered Smart Education (IoT-SE) Framework has been improved for online learners for students by leveraging IoT tech that tracks how learners interact with learning resources and their environment. This paper aims to revolutionize web-based education through tailored instructions targeting individuals' unique needs and fads as availed by the IoT-SE system. This paper offers evaluation parameters such as level of engagement among learners, retention rates on knowledge acquired while studying e-courses, and satisfaction from an online program. Besides overcoming limitations associated with conventional e-learning approaches, such systems like IoT-SE technology promise more effective pedagogy and student satisfaction for online learners. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5765">Analysis of Employment Competitiveness of College Students Based on Binary Association Rule Extraction Algorithm</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Lixia Guo</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened"> Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures o…</span><span class="full"> <br>Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5771">Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Yujie Guo</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dyn…</span><span class="full">Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5785">Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Xiaoyan Wang</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the pr…</span><span class="full">INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production. <br>OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods. <br>METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis. <br>RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy. <br>CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system. <br> <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5826">Research and Design of Encryption Standards Based on IoT Network Layer Information Security of Data</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Jia Wang</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport l…</span><span class="full">INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport layer. Therefore, there is an urgent need to study and design an information security data enhancement security strategy for the transport layer of ubiquitous networks (i.e., IoT). OBJECTIVES: This thesis aims to research and create a data enhancement security strategy for the transport layer of the Ubiquitous Web to ensure the confidentiality and integrity of data transmitted in the Ubiquitous Web. Specific objectives include evaluating the advantages and disadvantages of current ubiquitous network transport layer lifting security techniques, proposing a new lifting security strategy applicable to the transport layer of ubiquitous networks, and verifying the feasibility and security of the proposed standard. <br>METHODS: First, a detailed study and evaluation of the current Ubiquitous Network Transport Layer Elevated Security Techniques is conducted, including analyzing and comparing the existing elevated security algorithms and protocols. Then, based on the obtained research results, a new lifting security strategy applicable to the transport layer of ubiquitous networks is proposed. The design process takes into account the characteristics and requirements of ubiquitous networks, such as resource constraints, dynamics of network topology, and cooperative communication of multiple devices. Subsequently, the feasibility and security of the proposed standard are verified through simulations and experiments. In the experiments, real ubiquitous network devices and network environments are used to evaluate the performance and attack resistance of the enhanced security algorithms. <br>RESULTS: Through the research and analysis of ubiquitous network transport layer lifting security techniques, some limitations of the existing lifting security algorithms are identified, such as high resource consumption, insufficient security, and limited ability to adapt to the characteristics of ubiquitous networks. Therefore, this thesis proposes a new lifting security strategy applicable to the transport layer of ubiquitous networks. The experimental results show that the standard can guarantee data confidentiality and integrity while possessing high efficiency and attack resistance. In addition, the proposed standard meets the needs of resource-constrained devices in ubiquitous networks and can operate properly under multiple network topologies and cooperative device communications. <br>CONCLUSION: This thesis proposes a new elevated security strategy applicable to ubiquitous networks through the study and design of transport layer elevated security techniques for ubiquitous networks. This standard can effectively protect the confidentiality and integrity of data transmitted in ubiquitous networks with high efficiency and attack resistance. The proposed standard is expected to provide a feasible solution for the information security of ubiquitous networks and a more reliable guarantee for developing and applying ubiquitous networks. Future work can further improve and optimize this enhanced security strategy and validate and apply it in a wider range of ubiquitous network environments. <br> <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5833">IoT Product Design for User Experience and Technological Innovation in Virtual Reality Environments</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Hao Zhang</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for inno…</span><span class="full">INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for innovative artistic expression. <br>OBJECTIVES: This study takes IoT products in the art field as an example to analyze the user experience in virtual reality environments and the impact of technological innovations on IoT products, as well as to explore the potentials and limitations of this emerging form of products and forms of painting. <br>METHODS: In this study, the author constructed a virtual reality painting environment, utilized IoT technology to collect data from the user's painting process, and combined quantitative and qualitative analysis methods to assess user experience and technological innovation comprehensively. <br>RESULTS: In the virtual reality environment, the user experience was significantly improved, and the users were more immersed in the painting process and felt more robust creativity and expression. Meanwhile, the application of Internet of Things (IoT) technology also provides more possibilities for drawing; for example, using smartpens makes the drawing process more smooth and natural. <br>CONCLUSION: IoT painting with user experience and technological innovation in a virtual reality environment can provide a new creative platform for artists and bring a richer artistic experience to the audience, showing the feasibility and broad prospect of IoT products based on a virtual reality environment. <br> <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5862">Design of Intelligent Political Test Paper Generation Method Based on Improved Intelligent Optimization Algorithm</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Qing Wan</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of politi…</span><span class="full">With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political thought grouping, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive t-distribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5867">Application Big Data and Intelligent Optimization Algorithms on Teaching Evaluation Method for Higher Vocational Institutions</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Author: </dt><dd class="value">Meijuan Huang</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. OBJ…</span><span class="full">INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. <br>OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities. <br>METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis. <br>RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. <br>CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li><li class="result-item article-light"><h3><a href="/doi/10.4108/eetsis.5872">Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis<span class="info-separator"> </span><strong>24</strong><span class="info-separator">(</span>5<span class="info-separator">)</span><span class="info-separator">: </span></dd><br><dt class="title">Authors: </dt><dd class="value">Yihua Liu, Weirong Wang</dd><br><dt class="title">Abstract: </dt><dd class="value abstract"><span class="shortened">The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to impr…</span><span class="full">The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority. <br></span> <span class="expander more"><a class="trigger">more »</a></span></dd></dl></li></ul></section></div><div name="meta"><h2>Scope</h2><div class="abstract"><div class="shortened"><p>EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editori…</p></div><div class="full"><p>EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish twelve issues per year. Authors are not charged for article submission and processing.</p> <p>INDEXING: ESCI-WoS (IF: 1.3), Scopus (CiteScore 2022: 2.6), Compendex, DOAJ, ProQuest, EBSCO</p></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Topics</h2><div class="abstract"><div class="shortened"><p>The scope of the journal includes:</p> <ul> <li>Scalable distributed information systems</li> <li>Scalable grid information systems</li> <li>Parallel information processing and systems</li> <li>Web information searching and retrieval</li> <li>Data mining</li> <li>Content delivery networks (CDN)</li> <li>VLDB</li> <li>P2P systems</li> <li>Scalable mobile…</li> </ul></div><div class="full"><p>The scope of the journal includes:</p> <ul> <li>Scalable distributed information systems</li> <li>Scalable grid information systems</li> <li>Parallel information processing and systems</li> <li>Web information searching and retrieval</li> <li>Data mining</li> <li>Content delivery networks (CDN)</li> <li>VLDB</li> <li>P2P systems</li> <li>Scalable mobile and wireless database systems</li> <li>Large scale sensor network systems</li> <li>Index compression methods</li> <li>Architectures for scalability</li> <li>Scalable information system applications</li> <li>Evaluation metrics for scalability</li> <li>Information security</li> </ul></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Indexing</h2><div class="abstract"><div class="shortened"><ul> <li><a href="https://mjl.clarivate.com/home">Web of Science Core Collection</a></li> <li><a href="https://www.engineeringvillage.com/home.url">Ei Compendex</a></li> <li><a href="https://doaj.org/toc/2032-9407">DOAJ</a></li> <li><a href="https://search.crossref.org/?q=2032-9407">CrossRef</a></li> <li>[EBSCO Discovery Service](https://www.ebsco.com/products/ebsco-disco…</li> </ul></div><div class="full"><ul> <li><a href="https://mjl.clarivate.com/home">Web of Science Core Collection</a></li> <li><a href="https://www.engineeringvillage.com/home.url">Ei Compendex</a></li> <li><a href="https://doaj.org/toc/2032-9407">DOAJ</a></li> <li><a href="https://search.crossref.org/?q=2032-9407">CrossRef</a></li> <li><a href="https://www.ebsco.com/products/ebsco-discovery-service">EBSCO Discovery Service</a></li> <li><a href="https://www.worldcat.org/title/eai-endorsed-transactions-on-scalable-information-systems/oclc/913714002&referer=brief_results">OCLC Discovery Services</a></li> <li><a href="https://europub.co.uk/journals/8124">EuroPub</a></li> <li><a href="http://miar.ub.edu/issn/2032-9407">MIAR</a></li> <li><a href="https://rzblx1.uni-regensburg.de/ezeit/detail.phtml?bibid=AAAAA&colors=7&lang=de&jour_id=237211">Elektronische Zeitschriftenbibliothek</a></li> <li><a href="https://publons.com/journal/37157/icst-transactions-on-scalable-information-systems">Publons</a></li> <li><a href="http://ulrichsweb.serialssolutions.com/login">UlrichsWEB</a></li> <li><a href="https://www.heal-link.gr/en/home-2/">Hellenic Academic Libraries Link</a></li> <li><a href="https://www.ingentaconnect.com/content/doaj/20329407">Ingenta Connect</a></li> <li><a href="https://www.proquest.com/products-services/Publicly-Available-Content-Database.html#overviewlinkSection">Publicly Available Content Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/adv_tech_aero.html">Advanced Technologies & Aerospace Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/databases/pq_scitech.html">SciTech Premium Collection (ProQuest)</a></li> <li><a href="https://scholar.google.sk/scholar?start=0&q=source:eai+source:endorsed+source:transactions+source:on+source:scalable+source:information+source:systems&hl=es&as_sdt=0,5&as_ylo=2018">Google Scholar</a></li> </ul></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Special Issues</h2><div class="abstract"><div class="shortened"><p><em>Call for Papers:</em> <a href="https://escripts.eai.eu/publication/366">Special issue on: Real-time image information processing with deep neural networks and data mining technologies</a> (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-…</p></div><div class="full"><p><em>Call for Papers:</em> <a href="https://escripts.eai.eu/publication/366">Special issue on: Real-time image information processing with deep neural networks and data mining technologies</a> (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-05-15; Publication of special issue (tentative): 2022-06-15)</p> <p><em>Guest Editor:</em> Dr. Prof. Hang Li (Northeastern University, China) <em>Guest Editor:</em> Dr. Prof. Jochen Schiewe (HafenCity Universität Hamburg, Germany)</p></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Editorial Board</h2><div class="abstract"><div class="shortened"><ul> <li>Editors-in-Chief</li> <li>Hua Wang, Victoria University, Australia</li> <li>Xiaohua Jia, City University of Hong Kong</li> <li>Editorial board</li> <li>Manik Sharma, DAV University, India</li> <li>Ajay Kattepur (Tata Consultancy Services)</li> <li>Aniello Castiglione (University of Salerno)</li> <li>Chang Choi (Chosun University)</li> <li>Cho-…</li> </ul></div><div class="full"><ul> <li>Editors-in-Chief</li> <li>Hua Wang, Victoria University, Australia</li> <li>Xiaohua Jia, City University of Hong Kong</li> <li>Editorial board</li> <li>Manik Sharma, DAV University, India</li> <li>Ajay Kattepur (Tata Consultancy Services)</li> <li>Aniello Castiglione (University of Salerno)</li> <li>Chang Choi (Chosun University)</li> <li>Cho-Li Wang (University of Hong Kong)</li> <li>Daniel S. Katz (University of Chicago)</li> <li>Fabrizio Silvestri (ISTI – CNR, Italy)</li> <li>Hamed Taherdoost (Hamta Business Solution Snd)</li> <li>Heng Tao Shen (University of Queensland)</li> <li>Houbing Song (Embry-Riddle Aeronautical University)</li> <li>José Manuel Machado (University of Minho, Portugal)</li> <li>Jose Merseguer (Universidad de Zaragoza)</li> <li>Jie Li (University of Tsukuba)</li> <li>Lin Yun (Harbin Engineering University)</li> <li>Phan Cong Vinh (Nguyen Tat Thanh University)</li> <li>Raj Gururajan (University of Southern Queensland)</li> <li>Sherman Chow (Chinese University of Hong Kong)</li> <li>Silva Fábio (University of Minho, Portugal)</li> <li>Steve Beitzel (Telcordia)</li> <li>Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)</li> <li>Wang-Chien Lee (The Pennsylvania State University)</li> <li>Weili Wu (The University of Texas at Dallas)</li> <li>Xueyan Tang (Nanyang Technological University)</li> <li>Vijayakumar Ponnusamy (SRM University, India)</li> <li>J Amudhavel (KL University, India)</li> <li>Yingshu Li (Georgia State University)</li> <li>Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)</li> <li>Karolj Skala (Ruđer Bošković Institute, Croatia)</li> <li>Xiao-Zhi Gao (University of Eastern Finland, Finland)</li> <li>Thaier Hayajneh (Fordham University, USA)</li> <li>Chin-Ling Chen (Chaoyang University of Technology, Taiwan)</li> <li>Nuno M. Garcia (Faculty of Sciences, University of Lisbon, Portugal)</li> <li>Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)</li> <li>Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)</li> <li>Hiep Xuan Huynh (Can Tho University, VietNam)</li> <li>Ji Zhang (University of Southern Queensland, Australia)</li> <li>Xiaohui Tao (University of Southern Queensland, Australia)</li> <li>Ye Wang (National University of Defense Technology, China)</li> <li>Nageswara Rao Moparthi (KL University, India)</li> <li>Shuai Liu (Hunan Normal University, China)</li> <li>Prof Xiaoming Fu (Georg-August-University of Goettingen, Germany)</li> <li>Prof Zhisheng Huang (Vrije University of Amsterdam)</li> <li>Prof Rose Quan (Northumbria University, UK)</li> <li>Prof Shi Dong (Zhoukou Normal University, China)</li> <li>Dr Limei Peng (Kyungpook National University, South Korea)</li> <li>Prof Hui Ma( Victoria University of Wellington, New Zealand)</li> <li>Dr. Venkatesan Subramanian (Indian Institute of Information Technology – Allahabad, India)</li> <li>Dr Pon Harshavardhanan (VIT Bhopal University, India)</li> <li>Dr. Manish Kumar (The Indian Institute of Information Technology, Allahabad, India)</li> <li>Muzammil Hussain, University of Management and Technology, Lahore, Pakistan</li> <li>Michael Bewong, Charles Sturt University, Australia</li> <li>Shabir Ahmad, Gachon University, Korea</li> <li>Vu Nguyen, University of Science, Vietnam</li> <li>Xiaodi Huang, Charles Sturt University, Australia</li> <li>Jianming Yong, University of Southern Queensland, Australia</li> <li>Yogeshwar Vijayakumar Navandar; National Institute of Technology, Indian.</li> <li>Zhengyi Chai, Tiangong University in China, China</li> <li>Chuanlong Wang, Taiyuan Normal University, China</li> <li>Chin-Feng Lee, Chaoyang University of Technology, Taiwan</li> <li>Hsing-Chung Chen (Jack Chen), Asia University, Taiwan</li> <li>Wen-Yang Lin, National University of Kaohsiung, Taiwan</li> <li>Chun-Hao Chen, National Kaohsiung University of Science and Technology, Taiwan</li> <li>Mudasir Mohd, University of Kashmir, India.</li> <li>BalaAnand Muthu, INTI International University, Malaysia.</li> <li>Md Rafiqul Islam, Australian Institute of Higher Education, Australia.</li> <li>Jin Wang, Institute of Applied Physics and Computational Mathematics, China.</li> <li>Chandu Thota, University of Nicosia, Cyprus.</li> <li>Haris M. Khalid, University of Dubai, UAE.</li> <li>Dr. G. Reza Nasiri, Alzahra University, Tehran, Iran.</li> <li>Siuly Siuly, Victoria University, Australia</li> <li>Bishnu Prasad Gautam, Kanazawa Gakuin University, Japan</li> <li>Sivaparthipan C B, Bharathiar University, India</li> <li>Ting-Chia Hsu, National Taiwan Normal University, Taiwan</li> <li>Punitha Palanisamy, Tagore IET, India</li> <li>Lakshmana Kumar R, Tagore IET, India</li> <li>Weiwei Jiang, Beijing University of Posts and Telecommunications, Taiwan</li> </ul></div> <span class="expander more"><a class="trigger">more »</a></span></div><h2>Journal Blurb</h2><div class="abstract"><div class="shortened"><p>Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index</p></div><div class="full"><p>Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index</p></div> <span class="expander more"><a class="trigger">more »</a></span></div></div></div></section><section class="publication-info"><dl class="metadata"><dt class="title">Publisher</dt> <dd class="value">EAI</dd> <dt class="title">ISSN</dt> <dd class="value">2032-9407</dd> <dt class="title">Volume</dt> <dd class="value">11</dd></dl><dl class="metadata"><dt class="title">Published</dt> <dd class="value">2024-05-02</dd></dl></section></section></form></section></section><div class="clear"></div><footer><div class="links"><a href="https://www.ebsco.com/" target="_blank"><img class="logo ebsco-logo" src="/images/ebsco.png" alt="EBSCO"></a><a href="https://www.proquest.com/" target="_blank"><img class="logo proquest-logo" src="/images/proquest.png" alt="ProQuest"></a><a href="https://dblp.uni-trier.de/db/journals/publ/icst.html" target="_blank"><img class="logo dblp-logo" src="/images/dblp.png" alt="DBLP"></a><a href="https://doaj.org/search?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22index.publisher.exact%22%3A%22European%20Alliance%20for%20Innovation%20(EAI)%22%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22european%20alliance%20for%20innovation%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22index.publisher%22%7D%7D%7D%7D%7Dj" target="_blank"><img class="logo doaj-logo" src="/images/doaj.jpg" alt="DOAJ"></a><a href="https://www.portico.org/publishers/eai/" target="_blank"><img class="logo portico-logo" src="/images/portico.png" alt="Portico"></a><a href="http://eai.eu/" target="_blank"><img class="logo eai-logo" src="/images/eai.png"></a></div></footer></div><div class="footer-container"><div class="footer-width"><div class="footer-column logo-column"><a href="https://eai.eu/"><img src="https://eudl.eu/images/logo_new-1-1.png" alt="EAI Logo"></a></div><div class="footer-column"><h4>About EAI</h4><ul><li><a href="https://eai.eu/who-we-are/">Who We Are</a></li><li><a href="https://eai.eu/leadership/">Leadership</a></li><li><a href="https://eai.eu/research-areas/">Research Areas</a></li><li><a href="https://eai.eu/partners/">Partners</a></li><li><a href="https://eai.eu/media-center/">Media Center</a></li></ul></div><div class="footer-column"><h4>Community</h4><ul><li><a href="https://eai.eu/eai-community/">Membership</a></li><li><a href="https://eai.eu/conferences/">Conference</a></li><li><a href="https://eai.eu/recognition/">Recognition</a></li><li><a href="https://eai.eu/corporate-sponsorship">Sponsor Us</a></li></ul></div><div class="footer-column"><h4>Publish with EAI</h4><ul><li><a href="https://eai.eu/publishing">Publishing</a></li><li><a href="https://eai.eu/journals/">Journals</a></li><li><a href="https://eai.eu/proceedings/">Proceedings</a></li><li><a href="https://eai.eu/books/">Books</a></li><li><a href="https://eudl.eu/">EUDL</a></li></ul></div></div></div><script type="text/javascript" src="https://eudl.eu/js/gacode.js"></script><script src="/js/highlight.pack.js"></script><script>hljs.initHighlightingOnLoad();</script><script type="application/ld+json">{"@context":"http://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"http://eudl.eu","name":"Home","image":null}},{"@type":"ListItem","position":2,"item":{"@id":"http://eudl.eu/journals","name":"Journals","image":null}},{"@type":"ListItem","position":3,"item":{"@id":"http://eudl.eu/journal/sis","name":"sis","image":null}},{"@type":"ListItem","position":4,"item":{"@id":"/issue/sis/11/5","name":"Issue 5","image":null}}]}</script></body></html>