The environment of the epidemic is a life changer for everyone, and specifically for the educational system is progress. In order to overcome the challenges in the educational system, this study... Show More

The environment of the epidemic is a life changer for everyone, and specifically for the educational system is progress. In order to overcome the challenges in the educational system, this study proposes a new intelligent approach to teaching and learning. Teaching plays a vital role in higher education and can be advanced by utilising certain tools and technologies such as internet enabled mobile applications, automated scheduling of courses, assessment etc. The Emergency Distance Learning Methodology (EDLM) is proposed to improve the teaching-learning process by implementing artificial intelligence. Comparing the proposed system with the existing qualitative response analysis methods, it is observed that the proposed system provides 98.56% accuracy over the existing models. This study aims to assess the optimisation of teaching and learning models in higher education.

The modern educational landscape in colleges and universities demands efficient and dynamic management systems to handle the ever-increasing complexities of student enrollment, course scheduling... Show More

The modern educational landscape in colleges and universities demands efficient and dynamic management systems to handle the ever-increasing complexities of student enrollment, course scheduling, and academic performance assessment. In response to these challenges, this research project introduces a novel approach to education management through the application of Artificial Intelligence. By continuously developing through continuous improvement and optimization, this system is expected to provide modern higher education institutions with a path to sustainable development and a path to improve students' moral character and talents. This research provides higher education administrators and policymakers with valuable insights into how to address today's educational challenges.

The current classification is difficult to overcome the high-dimension classification problems. So, we will design the decreasing dimension method. Locally linear embedding is that the local optimum... Show More

The current classification is difficult to overcome the high-dimension classification problems. So, we will design the decreasing dimension method. Locally linear embedding is that the local optimum gradually approaches the global optimum, especially the complicated manifold learning problem used in big data dimensionality reduction needs to find an optimization method to adjust k-nearest neighbors and extract dimensionality. Therefore, we intend to use orthogonal mapping to find the optimization closest neighbors k, and the design is based on the Lebesgue measure constraint processing technology particle swarm locally linear embedding to improve the calculation accuracy of popular learning algorithms. So, we propose classification algorithm based on improved locally linear embedding. The experiment results show that the performance of proposed classification algorithm is best compared with the other algorithm.

Due to the complex distribution of web data and frequent updates under the cloud service architecture, the existing methods for global consistency of data ignore the global consistency of distance... Show More

Due to the complex distribution of web data and frequent updates under the cloud service architecture, the existing methods for global consistency of data ignore the global consistency of distance measurement and the inability to obtain neighborhood information of data. To overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model and solve it by a constrained particle swarm multi-objective optimization algorithm. While we measure the distance between data by manifold distance. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.

According to the spectral characteristics of different Chinese medicinal materials, the types of Chinese medicinal materials and the origin of Chinese medicinal materials are identified. Construct a... Show More

According to the spectral characteristics of different Chinese medicinal materials, the types of Chinese medicinal materials and the origin of Chinese medicinal materials are identified. Construct a fragmented clustering model. Firstly, the mid-infrared sample data is preprocessed, the Laida criterion model is established, and the abnormal data is eliminated; then the slicing model is used to divide the spectral wave into different regions according to the spectral characteristics. The data of each slice is clustered through the k-means clustering model. The origin of Chinese medicinal materials is identified by the support vector machine model. The data of Chinese medicinal materials with a known origin of a certain type of Chinese medicinal materials is used as the training sample set, and the data of Chinese medicinal materials with unknown origin is used as the test set.

Nowadays mobile computing has penetrated into our lives and work. Major companies have invested a lot of material and financial resources in the development of mobile terminals. For developers... Show More

Nowadays mobile computing has penetrated into our lives and work. Major companies have invested a lot of material and financial resources in the development of mobile terminals. For developers, querying the right API is extremely important. However, finding the right APIs can be time-consuming and laborious. In this paper, to solve the problems developers may face in the actual development process and improve the development efficiency, we propose RAMC (Recommendation APIs for Mobile Computing), an API recommendation approach leveraging word embedding technique and the information crawling from Stack Overflow posts and Java core packages, to recommend appropriate APIs for developers. Furthermore, RAMC also provides developers with label words, similar questions and relevant code. To evaluate the effectiveness of RAMC, we decided to analyze our system by simulating an instance. By testing a problem encountered during mobile computing development, the RAMC can effectively output the much-related API and tags for developers.

In vehicular ad hoc networks, vehicles regularly transmit information through beacons to raise awareness among nearby vehicles about their presence. However, as the number of beacons increases, the... Show More

In vehicular ad hoc networks, vehicles regularly transmit information through beacons to raise awareness among nearby vehicles about their presence. However, as the number of beacons increases, the wireless channel becomes congested, resulting in packet collisions and the loss of numerous beacons. This paper addresses the challenge of optimizing joint beaconing power and rate in VANETs. A joint utility-based beacon power and rate game is formulated, treated both as a non-cooperative and a cooperative game. To compute the desired equilibrium, three distributed and iterative algorithms (Best Response Algorithm, Cooperative Bargaining Algorithm) are introduced. These algorithms simultaneously update the optimal values of beaconing power and rate for each vehicle in each step. Extensive simulations showcase the convergence of the proposed algorithm to equilibrium and offer insights into how variations in game parameters may affect the game's outcome. The results demonstrate that the Cooperative Bargaining Algorithm is the most efficient in converging to equilibrium.

Due to data imbalances and the complexity of data, identifying outliers can be challenging. To address this issue, this paper introduces a new unsupervised model called MMD Fence GAN. An improved... Show More

Due to data imbalances and the complexity of data, identifying outliers can be challenging. To address this issue, this paper introduces a new unsupervised model called MMD Fence GAN. An improved loss function is incorporated into the generator to enhance anomaly detection accuracy. In the loss function of the generator, there are three main components: the encirclement loss limits the generated samples to lie at the decision boundary of the normal samples. The central dispersion loss maximizes the decision boundary's range. By measuring the maximum mean discrepancy between the generated samples and the given samples, the sample dispersion loss avoids issues like model collapse and outliers. Two datasets validate the effectiveness of the model: On the 2D synthetic dataset, it converges faster and pinpoints decision boundaries more precisely than Fence GAN. Both F1-score and recall improve over traditional and advanced deep learning methods on the KDD99 dataset.

This article analyzes the commonly used collision detection methods in game engines and designs a hybrid bounding box structure that is more suitable for human models based on their motion... Show More

This article analyzes the commonly used collision detection methods in game engines and designs a hybrid bounding box structure that is more suitable for human models based on their motion characteristics. In addition, this article also optimized the collision response algorithm after the system detects collisions, making the collision response process faster. Through experimental analysis, this approach has a good effect in addressing the problem of model penetration caused by continuously changing the model posture in shooting games; it also avoids the game fairness problem caused by model penetration and improves the realism of the game's virtual environment.

As China develops and grows, there are an increasing number of Chinese language learners worldwide, and Chinese learners are becoming more and more interested in the Chinese Proficiency Test (HSK).... Show More

As China develops and grows, there are an increasing number of Chinese language learners worldwide, and Chinese learners are becoming more and more interested in the Chinese Proficiency Test (HSK). The Chinese Proficiency Test (HSK) is also continuously evolving and improving. After more than 20 years of development, it has gradually been recognized by Chinese language learners at home and abroad. However, due to the short launch time, the existence of deficiencies is inevitable. But the IELTS exam was introduced in China in 1987 and has since amassed a wealth of knowledge and practice. Its framework is similar to HSK, so it can be used for HSK propositions and tests. One of the most challenging aspects of teaching Chinese is writing which involves the writing of Chinese characters, most Chinese learners have difficulty in writing. This paper mainly compares the test design and content in the writing part between HSK and IELTS, analyzes the effectiveness of the writing part of HSK, and puts forward some reference suggestions for the future writing test proposition and implementation of HSK.

Feature selection aims to select a subset of features from high-dimensional data, which can overcome the curse of dimensionality for the next dealing steps. However, the feature selection itself... Show More

Feature selection aims to select a subset of features from high-dimensional data, which can overcome the curse of dimensionality for the next dealing steps. However, the feature selection itself could face the curse of dimensionality. To overcome the above problem, in this paper, a new feature selection framework is designed according to a human processing in our daily life. In our daily life, to evaluate a candidate's ability to work, the related professional knowledge and the comprehensive ability of a candidate should be both evaluated. Actually, a candidate only with good professional knowledge often hardly solves new problems in the work. Based on the above analysis, in our new designed framework, the features are selected by evaluating its ability of global structure preservation and self-representation, which are respectively similar to the professional knowledge and comprehensive ability in evaluating candidate. As a result, the selected features can accommodate larger changes in test data. The conducted experiments validate the effectiveness of our feature selection.

At present, the combination of general evolutionary algorithms (EAs) and neural networks is limited to optimizing the framework or hyper parameters of neural networks. To further extend applications... Show More

At present, the combination of general evolutionary algorithms (EAs) and neural networks is limited to optimizing the framework or hyper parameters of neural networks. To further extend applications of EAs on neural networks, we propose a particle swarm optimization (PSO) based generative adversarial network(GAN), named as PGAN in this paper. In the study, PSO is utilized as a generator to generate fake data, while the discriminator is a traditional fully connected neural network. In the confrontation process, when the proposed PSO can generate a better fake image, this will react to the discriminator, so that the discriminator can improve the recognition effect of the image and the better discriminator also accelerates the evolution of the overall model. Through experiments, we explore the new application value of EAs in deep learning, so that the sample data in EAs and the sample data in deep learning are interconnected. The PSO algorithm is improved, so that it truly participates in the confrontation with multi-layer perceptrons.

Traditional evaluation methods based on static elastic-plastic analysis usually produce results with low fitting degree compared with actual data. In this paper, a new seismic performance evaluation... Show More

Traditional evaluation methods based on static elastic-plastic analysis usually produce results with low fitting degree compared with actual data. In this paper, a new seismic performance evaluation method is proposed. Firstly, several evaluation indexes of green ecological building structure are calculated. Subsequently, a cumulative damage model is established based on fatigue life curve and Miner criterion, which allows to determine the total dissipated strain energy and damage index. In order to verify the effectiveness of this method, it is compared with the traditional static elastic-plastic analysis method through experimental tests. The results show that compared with the traditional method of 50% to 60%, the proposed method achieves a significantly higher fitting degree with the actual data, ranging from 70% to 92%. This emphasizes the superiority and reliability of the proposed method in evaluating the seismic performance of green ecological building structures, and provides insights for safer and more flexible building practices.

The relationship between the emotional components associated with images and text is a crucial way of multimodal emotion analysis. However, most of the present multimodel affective cognitive models... Show More

The relationship between the emotional components associated with images and text is a crucial way of multimodal emotion analysis. However, most of the present multimodel affective cognitive models simply associate the features of images and texts without thoroughly investigating their interactions, resulting in poor recognition. Therefore, a multimodel emotion cognition method based on multi-channel graphic interaction is proposed. Text context features are extracted, scene and image information is encoded, and useful features are obtained. Based on these results, the modal alignment module be applied to obtain information about affective regions and words, and then the cross-modal gating module be applied to combine the multimodel features. In addition, we tested extensively on three open datasets, achieving an accuracy of 0.8122 for the MSA-single dataset, 0.7307 for the MSA-MULTIPLE dataset, and 0.7159 for TumEmo. The results show that this method is effective for multimodal emotion detection.

With the continuous development of society and economy, Internet thinking is gradually integrated into interior design, bringing new possibilities for interior design. This paper takes the interior... Show More

With the continuous development of society and economy, Internet thinking is gradually integrated into interior design, bringing new possibilities for interior design. This paper takes the interior design of general hospital outpatient department based on Internet thinking as an example to explore the interior design based on Internet thinking. Through the questionnaire survey and interview method and other scientific research methods, the author collected relevant data and carried out statistics and analyses, and concluded that the field of interior design should be combined with the current way of thinking about the Internet to innovate, and integrate the interior space into the combination of modern science and technology and interior design, so as to enable the interior design industry to develop rapidly. Under the influence of Internet thinking, interior design pays more attention to the interactive relationship between users and space. This mode of thinking enables designers to better understand user needs and provide users with more personalised and comfortable space design.

Person re-identification aims to retrieve pedestrians with the same identity across different cameras. However, current methods increase attention to interfering regions when dealing with complex... Show More

Person re-identification aims to retrieve pedestrians with the same identity across different cameras. However, current methods increase attention to interfering regions when dealing with complex backgrounds and occlusion, especially in the presence of similar interfering features. To enhance the robustness of the model, we propose the Reverse Pyramid Attention Guidance (RPAG) network, using a reverse pyramid structure to learn features at multiple granularities. To mitigate the impact of occlusion, we introduce the Similar Feature Filtering (SFF) attention module at the pixel level, using graph convolution to adaptively select occluded regions, thereby enhancing retrieval accuracy by filtering out irrelevant parts. Combining the reverse pyramid structure with the pixel-level attention module strengthens adaptability to complex scenes, guides multi-granularity feature learning, and effectively handles various occlusion scenarios. RPAG achieved Rank-1 accuracies of 96.2%, 93.2%, 88.7%, and 73.2% on the Market1501, DukeMTMC-ReID, MSMT17, and Occluded-Duke datasets, respectively.

In the long history, China has accumulated a large number of excellent traditional cultures, especially folk-art elements, which are very rich in content and are good design materials and... Show More

In the long history, China has accumulated a large number of excellent traditional cultures, especially folk-art elements, which are very rich in content and are good design materials and inspiration sources. With the development of China's economy and society, people's requirements for interior decoration have become higher, so multimedia graphic design to meet users' personalized needs is a typical implicit optimization problem. Therefore, this paper combines folk art elements with modern digital processing technology and interior design of image processing, and at the same time, the improved interactive genetic algorithm introduces users' subjective evaluation into the design process, allowing users to participate in the design work. Experiments show that the algorithm can effectively meet the personalized needs of customers in the process of graphic design. It has played a positive role in the integration of national art and modern interior design and the embodiment of national characteristics in modern multimedia design.