International Journal of Embedded Systems (16 papers in press)

Regular Issues

• Design and application of digital network teaching resource system for network environment  
  by Guobin Jun 
  Abstract: As the information technique developing, resource construction has become an unavoidable practical problem in college education. The systematic integration of teaching resources has become an important breakthrough to solve this problem. Therefore, this study first extracts hidden structural features of digital network teaching resources through data pre-processing, and adds split and merge operations to K-means algorithm to extract main features. Then use LSTM to optimise CNN to form LSCN. Finally, LSCN is combined with the improved K-means algorithm and applied to the digital network teaching resource system. The results show that the objective function value of the final solution of the improved K-means algorithm is 115. The accuracy of LSCN model in online teaching resource database can reach 94.6% at most, and the running time is 38.6s. After combining the enhanced K-means with the LSCN model, the accuracy of the integration of online courses, digital materials and other resources in the college network education system is more than 93%. It shows that the teaching resources integration method proposed by the research has good effect and efficiency, and can provide a reference method for the further informatisation of the education system.
  Keywords: network environment; teaching resources; K-means; convolutional neural network; CNN; LSTM; data mining; K-means.
  DOI: 10.1504/IJES.2024.10063172
   
  
• Psychophysiological state recognition of middle school students based on vibraimage technology and k-means cluster analysis algorithm  
  by Rui Huang, Xiaoquan Liu, Yunzhen Xue, Zhu Zhang 
  Abstract: Adolescence is a special period for middle school students to have rebellious psychology. How to effectively evaluate the mental health of middle school students and help middle school students successfully pass adolescence has always been the focus and difficulty of psychologists’ research. The typical emotion recognition of middle school students in adolescence is the basis for completing this work. In order to identify the psychological and physiological state of middle school students in adolescence, this paper proposes a method of adolescent psychological and physiological state recognition based on vibration imaging technology-K-means clustering analysis algorithm. In order to verify the feasibility of this method, 74,011 middle school students from 59 schools in Taiyuan City were selected as experimental subjects, and the experimental data were obtained by face-to-face interviews and capturing the facial expression video stream of the interviewees. The research results show that the vibration imaging technology-K-means clustering combination model is feasible for the identification of the psychological and physiological state of middle school students in adolescence, and has certain reference significance for the research work in this field.
  Keywords: K-means clustering; vibration imaging technology; descriptive statistical analysis; adolescence.
  DOI: 10.1504/IJES.2024.10063193
   
  
• Construction and application of online learning mental state diagnosis model based on student learning behaviour data  
  by Xiaohui Ma, Zhongwang Li 
  Abstract: This study addresses the issue of burnout psychology in online learning, which has become prevalent due to educational reforms and the push for educational informatisation, leading to a disinterest in learning among students. It defines the concept and dimensions of online learning burnout psychology using student data, and develops an early warning model using the XGBoost algorithm to predict student burnout effectively. Results indicate the XGBoost algorithm outperforms three other classification algorithms in iteration quality, with minimal difference between actual and training loss, and demonstrates an average absolute error between 1.5 and 2.0, and a mean square error around 1.0. In tests, the model’s accuracy, recall rate, and F1 score were 93.1%, 93.5%, and 0.93, respectively, surpassing comparative models. Thus, this early warning model is highly effective for diagnosing online learning burnout, offering significant improvements over existing methods.
  Keywords: learning data; online diagnosis; educational psychology; promotion of information technology; reform in education.
  DOI: 10.1504/IJES.2024.10063285
   
  
• Chatbot for mental health diagnosis using data augmentation techniques and deep learning  
  by Neel Ghoshal, Vaibhav Bhartia, Balakrushna Tripathy, Anurag Tripathy 
  Abstract: Statistical results obtained during recent surveys have indicated that about 8% of the total mass of the people in the world suffer from mental health problems. A scalable option that offers an interactive way to engage consumers in behavioral health interventions powered by artificial intelligence might be chatbots Although several chatbots have showed early efficacy results that are encouraging, their efficient utilization by people is not properly confirmed. In this paper a customized chatbot framework is proposed and developed using natural language understanding (NLU) mechanisms. The framework comprises of a unique two-tier modular functionality of an empathetic conversational model with a simultaneous implementation of a classification model. Along with this, the framework uniquely works on a data-driven knowledge based and predictive pattern, providing a holistic service to any user. The dataset used is completely scraped and prepared manually to inculcate the various mental health diseases and the appropriate responses provided by professionals
  Keywords: chatbot; mental health; natural language processing; NLP; deep learning; soft computing.
  DOI: 10.1504/IJES.2024.10065115
   
  
• Strengthening childrens art education based on educational network technology and intelligent image recognition algorithms  
  by Kailei Zhang 
  Abstract: To improve the quality of children's art education, this paper combines education grid technology and an intelligent art image recognition algorithm to build a children's art education system. Moreover, this paper analyzes the advantages of the high computational efficiency of reconstructed meshes in ray tracing and the problem of poor adaptability of window types. To make the light transport model uniform, this paper fuses the external flow field and the virtual teaching hood into one refractive index field. According to the assumption of local uniformity of the refractive index gradient of the refractive index field, a linear refractive index gradient interpolation algorithm is proposed in this paper, which lays the foundation for the entire optical transmission calculation. In addition, this paper determines the specific steps and evaluation indicators of optical transmission. Finally, this paper constructs a children's art education system based on education network technology.
  Keywords: educational network technology; children; fine arts; education.
  DOI: 10.1504/IJES.2024.10065303
   
  
• The correction method of block authentication information in edge computing mode  
  by Jianbo Xu, Wei Jian, Hongbo Zhou, Wei Liang, Mengyen Hsieh, Changxu Wan 
  Abstract: In many industrial edge computing applications, terminal equipment often exhibits characteristics of remoteness and wide geographical distribution. To achieve cross-domain authentication of terminal devices, scholars often store authentication information in a blockchain network built by edge nodes. However, considering that terminal equipment may be damaged or require authentication parameter updates, it is essential to study a trusted solution that can delete and modify stored authentication information on the blockchain while retaining blockchain characteristics. By adopting chameleon hash trap technology in cryptography, this paper ensures blockchain integrity and enables correctability of block data when applying blockchain technology to edge computing. Experimental results demonstrate efficient generation and distribution of sub-private keys for modifying on-chain authentication data. The proposed modifiable blockchain scheme is well-suited for maintaining device authentication information, ensuring system security, maintainability, and communication efficiency.
  Keywords: blockchain; edge computing; authentication; key agreement.
  DOI: 10.1504/IJES.2024.10065506
   
  
• MOS-IRS: a novel minimum optimisation scheme based on IRS-assisted NOMA in wireless powered communication network  
  by Ming Ren, Xin-an Tong, Zeyu Sun, Xu Meng 
  Abstract: A novel minimum optimisation scheme for IRS-assisted NOMA (MOS-IRS) is proposed to minimise the power of the power station. Firstly, optimisation problem of minimising power is constructed with the users’ throughput requirements as constraints, and the optimisation variables are time allocation and IRS phase shift. Secondly, we simplify the optimisation problem and separate the phase shift optimisation problem for the wireless energy transfer (WET) and wireless information transfer (WIT) process. Then, the multi-variables optimisation problem is transformed into that with a single-variable, and the alternating optimisation method is used to solve the phase shift for the WET and WIT process. Finally, the optimal time allocation is obtained by using the functional extremum method with given IRS phase shift. Simulation results show that the required power of the proposed scheme is lower than that of the existing schemes for the same scenario when the other parameters are the same.
  Keywords: non-orthogonal multiple access; NOMA; intelligent reflecting surface; resource allocation; time; phase shift.
  DOI: 10.1504/IJES.2024.10065840
   
  
• Optimising routing using nature inspired grasshopper algorithm to improve performance of VANETS  
  by Abhishek Gupta, Jaspreet Singh 
  Abstract: The paper integrates grasshopper algorithm as a bio-inspired method to improve the performance of vehicle ad hoc networks (VANETs). VANETs are highly mobile with quick topology changes and limited communication range, where a large network architecture supported by roadside units (RSUs). Thus, need of customised routing strategies inspires the work to present modified pairing and evaluation behaviour. A unique decision-making mechanism within the grasshopper algorithm is designed and implemented. A fitness function is introduced that takes into account energy efficiency and delay for the broadcast response to evaluate the total cost including execution and idle time. The reduction of packet transmission delay forms the primary goal. The quality of service (QoS) parameters are evaluated against state-of-art algorithms to depict its significance in addressing the current challenges. The research focuses on the application of an advanced fitness function as essential elements in VANET performance optimisation using the grasshopper algorithm.
  Keywords: vehicular ad hoc network; road side units; quality of service; QoS; grasshopper algorithm; fitness function.
  DOI: 10.1504/IJES.2024.10065884
   
  
• Interaction design of mobile devices and psychological education course reform of distance education for college students based on cognitive psychology  
  by Xiaohui Ma, Zhongwang Li 
  Abstract: The study identified the characteristics of existing interactive pages in distance education through a questionnaire survey, and then optimised the interactive pages of distance psychological education courses in universities by combining the concepts of cognitive psychology. In order to determine the effectiveness of curriculum reform, this study also constructed a teaching quality evaluation model using a radial basis function neural network optimised by genetic algorithm. The evaluation model constructed was tested, and the research results showed that the evaluation model has high accuracy (95.1%) and recall rate (95.3%). In addition, the model only needs 26 iterations to reach a stable state. In the context of evaluating teaching methods, using interactive pages optimised based on cognitive principles can significantly improve students’ psychological satisfaction and overall performance, with a satisfaction rate of 96.1%. The innovative approach of this study provides new perspectives and tools for the future of online education.
  Keywords: mental health; distance education; cognitive psychology; curriculum reform; interaction design.
  DOI: 10.1504/IJES.2024.10066672
   
  
• Deep reinforcement learning-based collaborative computation offloading and caching decision for internet of things  
  by Jian Xin Li, Ke Yuan, Qian Wang, Si Guang Chen 
  Abstract: Currently, as an extension of cloud computing, edge computing has attracted much attention for its ability to reduce delay, energy and bandwidth consumption. For satisfying the demands of resource-intensive and delay-sensitive applications and solving the problems of existing computation offloading algorithms such as inability to handle massive amounts of data in time and the need of strong computing capability, an intelligent computation offloading, resource allocation and collaborative caching scheme with joint optimisation of delay and energy consumption is proposed. Specifically, we formulate an optimisation problem of minimising the weighted sum of all tasks' completion time and energy consumption under the constraints of delay, bandwidth, computing capability, and energy. To solve the above mixed integer nonlinear programming problem (MINLP), we develop a deep reinforcement learning (DRL)-based collaborative computation offloading and caching decision (DRL-CCOC) algorithm. The algorithm jointly optimises offloading decisions, caching decisions, the occupation ratio of the wireless channel bandwidth and the edge server's computing capability which is allocated to the task, it can generate the optimal policy and adapt to dynamic network environment with the ability of autodidacticism. Finally, the simulation results demonstrate that the DRL-CCOC can converge at a faster rate and reduce the total cost significantly compared with other methods, they also confirm the strong dynamic adaptability of our algorithm.
  Keywords: computing; computation offloading; caching; deep reinforcement learning; DRL; resource allocation.
  DOI: 10.1504/IJES.2024.10066673
   
  
• Enhancing sports trainer behaviour monitoring through IoT information processing and advanced deep neural networks  
  by Zhangying Li, Juan Song 
  Abstract: This study investigates the transformative potential of amalgamating IoT technology and deep learning to elevate athletes’ behavioural perceptions and revolutionise training programs. The research strategically selects sensors for intelligent wearables, meticulously collects nuanced behavioural data, and employs an innovative semi-supervised ensemble learning approach to handle unlabeled samples. The iterative use of classification information entropy is leveraged to minimise uncertainty, and a deep neural network (DNN) with gradient descent adapts the learning rate, accelerating convergence. The proposed system undergoes rigorous evaluation through cross-validation, demonstrating significant improvements over common methods, including accuracy (93.7%), response time ratio (94.2%), sensitivity ratio (96.2%), and prediction ratio (97.1%). These results underscore the profound impact of the research on advancing athlete training precision, making it a pivotal contribution with broad implications for sports science and performance optimisation.
  Keywords: internet of things; IoT; deep neural network; DNN; semi-supervised ensemble learning.
  DOI: 10.1504/IJES.2024.10066788
   
  
• A secure and lightweight hash-based mutual authentication scheme in fog-assisted healthcare network  
  by Upendra Verma, Hemant Gianey 
  Abstract: Security and privacy are considered the two main challenges in fog-assisted healthcare networks. Several authentication approaches have been presented in recent years to address the security issues in fog-assisted healthcare networks. To cope with these challenges and improve the safety of fog-assisted healthcare networks, we propose a secure and efficient mutual authentication scheme. In this paper, we design a lightweight hash-based authentication scheme for fog-assisted healthcare networks to provide security against various attacks. The informal security analysis illustrates that the proposed scheme has the capability to resist various security threats. In addition, the proposed scheme has been evaluated with a real-or-random (ROR) model to prove its resilience against cryptographic attacks. The performance study demonstrates that the proposed scheme is more effective and lightweight compared to existing schemes. Moreover, a comprehensive comparison analysis has been undertaken, which shows the proposed scheme provides better security features than existing schemes.
  Keywords: fog-assisted healthcare networks; real-or-random; ROR model; mutual authentication; hash function; cryptographic attacks.
  DOI: 10.1504/IJES.2024.10066866
   
  
• Information intelligent teaching method and its influence on vocal music teaching effect  
  by Bing Han 
  Abstract: Against the backdrop of rapid development of information technology, traditional vocal teaching models face challenges such as insufficient personalisation and limited efficiency improvement. The aim of this project is to explore and practice an intelligent teaching method based on hybrid algorithms. We have innovatively designed and developed an intelligent teaching system that uses cutting-edge algorithm technology, integrates multiple teaching resources, and constructs an intelligent platform that can automatically formulate teaching strategies based on real-time feedback from students. The experimental results show that compared with traditional vocal teaching methods, students using intelligent teaching systems have significantly improved their mastery of vocal skills, emotional expression in music, and learning efficiency. Especially, compared with traditional genetic algorithm driven teaching aids, the intelligent teaching system developed in this study has improved the accuracy of evaluating teaching effectiveness by 9.34%, fully demonstrating its significant advantages in improving the quality of vocal teaching.
  Keywords: intelligent teaching; vocal pedagogy teaching; information technology; hybrid algorithm.
  DOI: 10.1504/IJES.2024.10066884
   
  
• Analysis of the effect of the situational teaching model based on big data on the improvement of English translation ability of modern college students  
  by Shanshan Guan, Xiaocai Mu 
  Abstract: In order to promote the improvement of English translation ability of modern college students, this paper combines big data technology to explore the effect of situational teaching mode on the improvement of English translation ability of modern college students. By using problem analysis methods to study the problems existing in intelligent translation systems for college English, a reliable solution is proposed based on the theory of acoustic wave cancellation and adaptive filtering related to active noise control. On the basis of algorithm optimisation, an intelligent scenario translation system that can improve the ability of modern college English translation is proposed. This article combines intelligent models to study the application significance of situational design in English teaching in universities. In the future, incorporating information technology elements into the classroom and guiding students to conduct independent explorations experimental improve classroom can efficiency, cultivate innovative thinking, and enhance scientific literacy.
  Keywords: big data; situational teaching mode; English; translation ability.
  DOI: 10.1504/IJES.2024.10066942
   
  
• Design and application of intelligent teaching system for network and new media major driven by artificial intelligence technology  
  by Kun Zhang 
  Abstract: At present, the teaching method of this course is still traditional, and the teacher's dominance in teaching is not deep enough. On the basis of the traditional intelligent teaching system (ITS), the personalized network and new media professional ITS uses AI technology to make reasonable improvements in the expansion of educational resources and in-depth exploration of educational significance, making the ITS more intelligent. Therefore, this paper has completed the following work: 1) The research progress of ITS at home and abroad is introduced. 2) Design the ITS for network and new media, analyze and use the user interest model based on the deep learning algorithm, put forward the optimization scheme for the traditional ITS and give the system function realization. 3) The improved version of the user-interest model is a hybrid of two time-tested methods. Experiments show that the model has achieved good results.
  Keywords: network and new media; intelligent teaching system; AI technology; user interest model.
  DOI: 10.1504/IJES.2024.10067537
   
  
• Application of online MOOC education management technology in learning behaviour mining and dropout prediction  
  by Yongwo Yuan 
  Abstract: Internet education has become a new direction in modern education, and Massive Open Online Course will provide learners with comprehensive and personalized learning services. However, the low completion rate and a high dropout rate of online education in MOOC are important factors hindering its development. In order to solve the above problems, the K-means clustering algorithm (K-means) model is used to mine the behavior data of MOOC online learners. Considering the high dependence of the K-means model on the selection of initial centroids, a heuristic method is adopted to improve the K-means model. Due to the complexity of learning data, a combination of fuzzy theory and principal component analysis is used to screen the main behavioral special data, realizing the mining of MOOC learning behavior data for predicting the evaluation basis of the model. At the same time, an improved Long Short Term Memory (LSTM) network is used to construct a dropout prediction model based on learning behavior data, completing the prediction of learners' course situations.
  Keywords: K-means; MOOC online education; principal component analysis; learning behaviour; heuristics.
  DOI: 10.1504/IJES.2024.10067629