bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Credit Card Holders’ Age on the Credit Limit
Authors: Sun Yiduo
Published: April 2023

Credit Limit of the credit cards is one of the affecting factors influencing people’s credit scores, playing a great role in determining their ability of paying debts and could be involved in banks evaluation in mortgage. In order to help people better assess their credit scores quantitatively and be aware of the influencing factors that determine their specific credit limits, this paper analyzed 10127 bank customers’ data. Based on the estimation, the evaluation is presented according to the information given to obtain the formula used to evaluate people’s Credit Limit and its relationship with some influencing factors, e.g., age, gender, education and marital status, and education level. According to the analysis from STATA, it is shown that per unit age increase would increase the credit limit by 1.76 percentage. In addition, impacts of other variables (e.g., gender, education level, marital status, income level, card type, card utilization ratio) are also found. The model presented could be used to help evaluating a persons’ Credit Limit according to the data provided. Overall, these results shed light on guiding further exploration of credit limit designs.
How important was the Cold War in bringing about regime change?
Authors: Shen Yu Jia Audrey
Published: April 2023

Form after World War Two, through to the beginning of the 21st century, there were many international influences such as the Cold War that brough about regimes changes that were not desired by any individuals at the beginning of the process.  This paper analyses the importance of international conflicts on Eastern Europe, Eastern Asia, and America as a case study to analyse and identify the importance of the Cold War that caused regime changes.
The Rise of Far-right Politics in Europe: Examples from Italy, Hungary and France
Authors: Xiong Xin
Published: April 2023

Over the years, far-right politics has plateaued in terms of support in different parts of the world. The increasing acceptance of right-wing political agendas, such as in the US, Brazil, and New Zealand, has opened a conduit for the growth of far-right politics, especially in the European scene. The transformation of these politics to fighting an unfair system has gained resonance among the electorate, thus increasing its popularity. This essay studied the rise of far-right politics in Europe, exploring the recent wins of far-right parties in Italy, the case of Hungary’s decade-long right-wing political agenda and France’s recent election. The study relied on news articles, online data, and relevant literature to explain the topic. The findings suggested that economic inequity, the decline of democracy, immigration, multiculturalism, and opposition to the European Union were the primary factors leading to the rise of far-right politics in Europe. It was also evident that a far-right future would lead to the disintegration of liberal democracy, isolation due to nationalist policies, and cultural intolerance. Based on the findings, it was concluded that economic inequity was the driving factor, and far-right political parties were gaining momentum by proposing radical solutions to address this problem.
Minority shareholders’ participation and corporate green innovation
Authors: Jia Tianyu , Cao Zixian , Lu Jiayi
Published: April 2023

In recent years, the Internet has greatly reduced the cost of minority shareholders to obtain information, and has also enabled them to participate in corporate governance more conveniently and effectively. Based on the data of China’s A-share listed companies from 2011 to 2020, this paper examines the impact of minority shareholders’ participation in corporate governance on corporate green behavior. The study found that the minority shareholders can effectively promote green innovation in enterprises. This conclusion still holds true after robustness tests such as replacement variables, multiple fixed effects, addition of omitted variables, and propensity score matching test. Further research finds that state-owned enterprises and non-heavy polluting industries, the participation of minority shareholders in corporate governance will have a more significant role in promoting green innovation. This paper not only enriches the research on minority shareholders of listed companies and green behavior, but also has important theoretical and practical significance for minority investors to protect their own rights and interests.
Analysis of the Impact on International Trade through the Operation Mode of E-Commerce and Countermeasures
Authors: Ling Xiyue
Published: April 2023

With the gradual popularization of the Internet in the mass family, the continuous deepening of the information technology revolution, and the gradual improvement of people's daily consumption level, a new type of business model - e-commerce has emerged. In view of the unique trade means of e-commerce, the development speed of e-commerce exceeds most people's imagination. Starting from the positive and negative impacts of e-commerce on international trade, taking the development of cross-border e-commerce for small and medium-sized enterprises in Yiwu as an example, this essay is researched and explained by SWOT and STP analysis methods. In addition, in-depth research has been carried out, extended to the research of small and medium-sized enterprises in China. The advantages, disadvantages, opportunities and threats they face when developing e-commerce are analyzed and discussed. In the end, some strategic and reasonable development opinions are given according to the impact of China's current e-commerce operation mode on international trade. The study found that e-commerce plays an unexpected role in the development of international trade. The emerging e-commerce model has two different impacts on international trade, positive and negative, but without exception, it promotes the development of international trade. The negative impact can be come up with reasonable countermeasures for improvement and self-improvement, and the positive impact can continue to be maintained and developed steadily.
Comparison of old and new revenue recognition criteria and countermeasures——Take Evergrande Real Estate Group Co., Ltd. as an example
Authors: Cai Wenhui
Published: April 2023

In the current social development, transaction mode is constantly having to bring out the old, and the step and method of revenue recognition is becoming more and more complex. So the defects of the original income criterion are exposed gradually. In order to comply with the trend of Chinese economic development and perfect our accounting information standard, the Ministry of Finance made an innovation income recognition criterion decision. Therefore, the Accounting Standards for Business Enterprises No.14 Revenue (Finance and Accounting [2017] No.22) was released in July 2017. Compared with the original revenue recognition standards, the adjustment between the two standards and the revenue recognition standards in international financial reports basically converge, and the requirements for the way, time or time of revenue recognition of enterprises are significantly improved. Income is a very important indicator to evaluate the profitability of an enterprise in the process of operation, so the recognition and measurement of income is very important. The purpose of this study is to compare the differences between the old and new revenue recognition criteria, propose solutions to the problems, and clearly define how real estate enterprises recognize revenue under the new standard, what impact it will bring to real estate enterprises in practice, and what aspects real estate enterprises should pay attention to under the revised revenue criteria. This paper will also have a reference for other enterprises.
Forest Management Model based on Photosynthesis Model and Market Simulation
Authors: Gong Kaikang , Tong Zeheng , Zhang Zhaoguo
Published: April 2023

In order to mitigate the impact of climate change, it is necessary to make rational use of forest resources for carbon cycle. Firstly, based on the actual data of tomato single leaf light and rate, this paper establishes rectangular hyperbolic model, exponential model and non-rectangular hyperbolic model, and photosynthesis model based on light and effective radiation, temperature and carbon dioxide concentration of three environmental factors. Finally, it is analyzed through simulation experiments, The improved rectangular hyperbolic model with the best simulation effect is determined. The model based on photosynthesis designed in this paper is helpful to the decision-making of forest management.
Study on the Depth of Convolutional Neural Network for Pneumonia Detection
Authors: Qu Yunshan Nicole
Published: April 2023

Pneumonia is a dangerous but common disease to human. Due to the similar symptom between cold and pneumonia, it is essential to have an effective method to diagnose pneumonia. In this paper, a prevalent concept called computer vision was introduced to help detect pneumonia based on chest X-ray images. More specifically, a Convolutional Neural Network (CNN) was proposed to classify the images into normal class or pneumonia class. The image dataset was preprocessed by unifying, normalizing, shuffling and augmentation. To finally achieve a model with relatively reasonable and accurate prediction, four CNN models were built and compared. To keep them comparable and have certain similarity, a small architecture with one convolutional layer, one max pooling layer and one batch normalization layer was defined and piled up repeatedly. The channel numbers in the convolutional layer were increased by proportion. These four models were eventually evaluated on their accuracies both on training set and testing set and F1 scores on two categories. Experimental results indicated that the model with four convolutional layers had the best performance which achieved an accuracy of 0.8606 on test set. Also, the model had F1 scores of 0.86 on normal class and 0.88 on pneumonia class.
The Future Prospects of Importance of VR's Impact on Education
Authors: Fei Teng , Mao Qingyi , Wu Fangyuan , Yu Peiran
Published: April 2023

With the gradual progress of science and technology, virtual reality technology has matured and has been applied to many fields of people's lives. VR education has become a research hotspot, but relevant review articles are not comprehensive. This paper mainly researches the development prospects of VR game education, ideological and political education and art education. In game education, VR technology provides more gameplay so that students' interest can be attracted, which can help student to focus their attention. In ideological and political education, VR makes traditional boring lectures more vibrant and can better help students understand abstract concepts. Finally, in art education, different from traditional art teaching, VR technology can help students feel the artistic atmosphere immensely. At the end of the article, we made a summary and an outlook of VR Technology. Overall, virtual reality technology provides a more efficient method of learning, and it is hoped that the technology will become popular in the near future.
Transferring Music Pieces into Different Scales based on Nyquist Language
Authors: Li Jixin
Published: April 2023

As the increasingly need for art in the society and the current boost in technology, computer music is known as an increasingly important field in our life. While for music scales, which determine a music piece’s genre and style, is also important to any music pieces. The idea of this study is to combine the concepts of music scales and computer music, realizing the ability for music scales transformation. This article will use Nyquist language as an example to provide a clearer perspective of the way that computer algorithms are able to transfer music pieces into different music scales. This study combined the advantages programming within Nyquist, which will be the music synchronize functions and the presence of the unique data type “score” with our music ideas. Thus, it was easy for users to compose the notes in a clear and ordered manner. After composing the scales as functions in Nyquist, one can modify music pieces into different styles according to the scales. In addition, based on the auto backup function in Nyquist, it is easy to retrieve a .wav format video file after processing the program on our terminals. These results shed light on guiding further exploration of music pieces transferring based on computer techniques.
The Infinity Game with Virtual Reality Technology
Authors: Guan Yisheng , Tan Antonio Jiahui , Tan Yuheng , Zhang Jack
Published: April 2023

Virtual reality technology (VR), refers to the use of computers to generate a kind of visual and auditory directly on the participants, and allow them to observe and operate interactive virtual world technology. However, this work wants VR games to have a psychological impact on people. So, this work came up with a game for that purpose. The whole core feature of the game's design is infinity, and players will learn the true value of life: being brave and taking risks, or staying in your comfort zone. There are two main scenarios for the development of 3D workspaces and environments. The first is the aforementioned corridor, which is the main interactive environment where the player is trapped and needs to find a way out. The second is the hospital, where if the player chooses to leave, he will end up in a hospital bed, which means he will leave his family in the hallway and not be able to see them forever. In the end, this work interviewed nearly 30 different players. Most people gave us positive comments about the game, whether it was the music, the scenes, or the objects, it felt intense and exciting. It also causes them to think about life. Everyone has a different choice. This is in line with the expectations. Therefore, it can be concluded that the success of this game has caused a psychological impact on players and triggered their thinking about life.
Inclusive Web Design based on Gamified Learning and Color Psychology to Design Programming Learning Websites for People with Bipolar Disorders
Authors: Jin Zijing
Published: April 2023

Bipolar disorder is one of the top 20 causes of disability worldwide, and its symptoms are most common between the ages of 20 and 25. The objective of this study is to helpHelpBD patients who need to learn programming better through website design. The study was conducted using autoethnography and interviews. The research methods described above revealed that people with bipolar disorder who need to learn the program have great difficulty in learning to program. Therefore, in the design of the programming learning website, this study will be based on gamified learning, incorporating a game-like reward and encouragement mechanism, and breaking down large tasks into multiple small tasks that are easy to complete in order to maintain the patients' attention and interest and thus maintain the above-mentioned effects. By improving the learning environment and learning patterns it is hoped that normal learning can be gradually restored in patients with BD. This paper hopes to guide the design of learning websites for this group of patients, with a view to providing a reference for various learning fields. Learning disabilities are common among people with BD, and this distress can largely affect their future quality of life. With appropriate website design, it is possible to adapt BD patients to online programming learning and thus improve learning efficiency. And online programming learning websites derived from research methods of autobiographical ethnography and unstructured interviews as well as theories of gamified learning and color psychology can improve the programming learning experience of BD patients.
Researches Advanced in Finance based on Generative Adversarial Networks
Authors: Chen Zihan , Wang Yifei , Zhang Shuchen
Published: April 2023

Topics about how to apply Generative Adversarial Networks (GANs) to finance industry and research to promote financial market prosperity and progress with deep learning technology never fail to arouse public attention. For instance, financial academic realm such as portfolio optimization, trade execution strategies and financial information progressing have made use of machine learning. After a large number of literature retrieval and attempts to classify from different aspects, we ultimately find out that GAN is one of the most famous and appropriate technologies widely adopted in academic research and commercial applications. Aiming at assisting reader with having insight into how GAN improved financial work’s efficiency, in this paper, we propose three research perspectives of GAN in financial work: Stock Market Prediction, Fault Detection, and Time Series. By organizing from an innovative and macro perspective, we present and classify the application methods of GAN and the improvement of the basic model of GAN in order to adapt to the corresponding fields under the corresponding research directions. In general, the following improved models based on the basic GAN model for the corresponding financial perspective are involved: GAN of three-layer dense network, GAN with the MLP and LSTM separately as the discriminator and the generator, GAN-FD stock projection model, BERT and GAN in Stock Prediction; SSGANs, DAEGAN in Fault Detection; SeqGAN, Quant GAN, RNN-GAN, PSA-GAN in Time Series.
Perspective of EEG Signal Analysis for Depression Diagnosis
Authors: Zou Chendong
Published: April 2023

Depression has long been a severe threat to mental health and to a certain extent, it precludes the regular physical mechanism of patients susceptible. As neuroscience advancing, Electroencephalogram (EEG) was put into diagnosis of depression which once highly relied on subjective judgments of doctors. The usage of EEG perfectly circumvents subjectivity and serves as a noninvasive and inexpensive measure. With algorithm advancing, the signal would be filtered after which the features of it pass through extraction and classification. The whole procedure aims to analyze the symptom of depression from the perspective of waveform and reveal the intrinsic relationship between depression and signal’s feature. The paper would take a review on the current circumstances of depression, illustrating several methods based on EEG that have been proved effective in application at present stage and summarize brief drawback or limitations of current methods. This article may serve as an approach to rudimentary understanding of depression and the objective solutions using EEG.
Using Digital Storytelling to Improve Knowledge Acquisition of Circumscribed Interests of Children with Autism Spectrum Disorder
Authors: Fan Xiaoyu , Zhou Kaiwen
Published: April 2023

Children with Autism Spectrum Disorder face difficulties in many areas, such as academic performance and social integration. However, suppose the circumscribed interest of the children with Autism Spectrum Disorder could be adequately taken advantage of. In that case, more career opportunities could be open to them, which may facilitate their social integration. Hence, exploring how to better shape the knowledge acquisition process of the circumscribed interest areas is meaningful. Digital storytelling is considered to be an excellent pedagogical method for teaching children with autism. Therefore, this study aims to test whether digital storytelling positively influences knowledge acquisition in the CI area. Two websites with similar content but different design structures (one with digital storytelling and one without digital storytelling) are given to two children with autism between nine to twelve years old. It was predicted that a website with a digital storytelling design has a higher attraction level and thus leads to a better learning outcome. The result demonstrates that children with ASD prefer the design structure of digital storytelling and could correctly answer more questions on the website with digital storytelling. Future studies may use digital storytelling to smooth the knowledge acquisition in the CI areas.
Prospect and Application of Medical Robots in the Context of Epidemic
Authors: Li Xiaohang
Published: April 2023

Under the background of the modern epidemic, where exposed the disadvantages of epidemic prevention, and the safety of health care workers cannot be guaranteed, the medical efficiency can't satisfy people's basic needs, it also cannot reasonable allocation of medical resources. The robot by its efficient characteristics will assume greater responsibility. This article based on the current domestic and international policies and forms to analyze the key technology of the current medical robot. Several types of medical assistant robot in the market are analyzed, the advantages and disadvantages of the robot are given, some suggestions for improvement are put forward, the future development prospect of the robot is analyzed and the market is prospected. Through a series of investigations and studies, it is found that the medical robot can indeed play a very good help to the medical staff, and optimize the nucleic acid process. Moreover, it can effectively protect the safety of medical staff, reduce the possibility of novel coronavirus transmission, save people's time and improve handling efficiency. However, due to the insufficient development of the Internet technology in the past, there are still some problems in these robots. In conclusion, Robots are highly efficient, need less rest time than humans and have a lower probability of making mistakes.
Federated Learning based on Homomorphic Encryption and Digital Signatures
Authors: Chen Zao
Published: April 2023

Federated learning aims to train a centralized federated model using decentralized data sources and to ensure the security and privacy of user data during system training process. Federal learning still faces four challenges: high communication costs, system heterogeneity, data heterogeneity, and data security, The primary attacks facing federal learning include confidentiality, integrity, and usability attacks. Attackers mainly target confidential attacks, while malicious attackers target integrity attacks and usability attacks. Homomorphism encryption refers to the encryption algorithm that satisfies the nature of the homomorphism operation, that is, to the ciphertext of the data after the homomorphic encryption. This article first introduces the federated learning and its possible attacks in the process of modeling behavior, and then introduces how to use homomorphic encryption and digital signature algorithm to prevent the attack method, finally through the experimental reality of federated learning encryption and digital signature and analyzed the above operation on the performance of the federated learning system.
Stock Price Prediction based on Multiple Regression Models
Authors: Li Yuhan
Published: April 2023

Over the past two years, global stock markets have gradually recovered and new investors have entered the market. While there are many factors affecting stock prices and the stock market is changing rapidly, the way to accurately predict stock prices has become the focus of investors. This paper will use the concept of machine learning to predict the stock prices of three listed companies based on three different regression models (i.e., OLS, Ridge and XGBoost). According to the analysis, the OLS model and the Ridge model are very accurate in predicting stock prices, especially in the low and middle price ranges. In contrast to these typical linear regression models, the XGBoost model is not as accurate in predicting stock prices and even has a significant prediction bias in the high price range. These results will enable subsequent research to make better choices when selecting models for forecasting, especially for data sets with different characteristics.
Deep Learning for Detecting Quilt Status of Sleeping Children
Authors: Liu Ruiqi
Published: April 2023

Sleeping is important for the physical growth of infants and toddlers. However, because of the growth of their bodies, they may kick off the quilt when they fall asleep and catch a cold as a result. Because it is difficult to extract the all-appropriate features that insists the quilt is not covered in the images taken by the home camera manually, which is suitable for the old types of symbolic AI to recognize, there has not been a suitable solution to this problem in the past. However, thanks to the rapid development of artificial intelligence and neural networks, intelligent target detection, which was previously difficult to achieve, has finally been realized and several approaches to intelligent target detection have emerged, leading to many different perspectives and algorithms. In this paper, by analyzing and comparing the advantages and disadvantages of different algorithms in different application scenarios, we found that YOLO (v5 in this paper) is very suitable for quilt status detection and realized the program by applying YOLOv5 neural network and cosine difference algorithm simultaneously.
Research of the Path Finding Algorithm A* in Video Games
Authors: Liu Daohong
Published: April 2023

Pathfinding is an essential component of many video games. This review provides a simple overview of the classical pathfinding algorithm A*, including a brief summary of the A* algorithm, as well as a few optimizations and applications of A* in video games. In the gaming industry, A* is a widely used pathfinding algorithm. Based on breadth first search and heuristic search, A* uses a heuristic function to quickly find the shortest path in a game map. A common optimization of A* is to use a binary heap to save the open queue, a variety of heuristic functions are also commonly used to increase the accuracy and speed of A*. This paper summarizes work done on the A* algorithm. The goal of this paper is to give researchers and developers a simple overview of the A* algorithm and its applications in video games.
The Research of Different Algorithms on Path Planning
Authors: Fan Jianing
Published: April 2023

The evolution of modern existence is inextricably linked to path planning. Road transportation, urban road planning, and travel plans for people's travel requirements must all be carried out and assured on the basis of effective route planning implementation. However, the path planning algorithms are not ideal. This work brings together three contemporary popular algorithms: classical algorithms, graphics algorithms, and bionics algorithms. The article presents and evaluates their functions, as well as summarizes the benefits and drawbacks of each method. Following that, the experimental data from each algorithm is gathered and compared. The performance difference between different types of methods is analyzed. The collected results are integrated with the prior analysis to form a summary. Finally, the paper summarizes and looks forward to the full text.
Studies Advanced in Image Generation Techniques based on Deep Learning
Authors: Liu Chang
Published: April 2023

A popular area of study in computer vision is image generation. How to improve the authenticity and diversity of generated images, and how to reduce the use of hardware resources and the size of model storage to make it more conducive to model landing are the focus and difficulty of image generation algorithms. Moreover, in practical applications, many other tasks are closely related to the image generation task, utilizing the created picture can lower the cost of data collecting, and the produced picture's quality directly influences how well some activities work. This article first introduces the definition of the generated model in supervised learning and unsupervised learning, and analyzes several reasons why learning should be generated into a model. It also introduces the development of the generated models in China and abroad, and makes a simple classification of the generated models with the previous depth degree.
Applications of EEG-Based Brain-Computer Interface Devices in Rehabilitation
Authors: Wu Yuxuan
Published: April 2023

BCI techniques narrow the gap between the human brain and external devices. Previous investigation using Brain-Machine Interfaces to control devices such as wheelchairs has shown that the technology could be effective in limb rehabilitation. There has been limited research on using BCI in performing a grasp and reach task using a prosthetic arm. The authors of this paper thus sought to determine how possible and effective the procedure can be performed considering the multiple degrees of freedom of the human arm. Moreover, stroke is among the leading causes of death globally. Stroke events, in most cases, leave the patient with severe brain damage that usually includes impaired motor functions. BCI-based techniques have shown the capacity to restore the motor functions of patients. However, there is a need for the BCI methods to be coupled with cognitive training approaches to improve other bodily functions of stroke patients. Also, since the first electroencephalography (EEG) and electromyography (EMG) were implemented in the 1920s, their clinical applications, theories, and testing techniques have improved tremendously. Generally, brain imaging techniques have evolved rapidly since the 1920s. This paper explores the applications of EEG-based Brain-Computer Interfaces in medical rehabilitation.
Development, Application and Prospect of Artificial Intelligence in Biomedical Field
Authors: Lu Zhixian
Published: April 2023

The advancement of artificial intelligence, which has remarkable applications in numerous industries, is a key area of worldwide science and technology development. Among them, the application of artificial intelligence in new drug research and development and auxiliary disease diagnosis has made such outstanding achievements. Therefore, this paper analyzes the development and application of AI in biomedicine and looks forward to its future development. The detailed review included examples of how artificial intelligence has been used in biological medicine, the research and development of new pharmaceuticals, the development of new drugs, and the diagnosis of auxiliary diseases. Finally, this paper summarizes and prospects the future development of biomedicine.
Research on Glass Classification and Identification based on Machine Learning and Monte Carlo Algorithm
Authors: Zhou Jiasheng , Fan Zhihong , Zhang Wenxin
Published: April 2023

In order to classify and identify glass, we used logistic regression and various machine learning algorithms to classify glass of known composition and class, and used various metrics to evaluate the classification results comprehensively. To further improve the reliability and accuracy of the classification. In order to further improve the reliability and accuracy of the classification, we used grid search and Bayesian optimization to adjust the parameters of the SVM, and the optimized SVM was able to classify the glass with known components and classes with 99.25% accuracy. Finally, we applied the SVMs with grid search and Bayesian optimization to predict the type of glass with only the chemical composition identified and the category unknown.
The Application of Deep Learning for Network Traffic Classification
Authors: Yang Jingran
Published: April 2023

The classification, detection, and analysis of routine network traffic has been a hot topic for businesses and research institutions due to the proliferation of Internet of Things devices and the explosive development of networks. Traditional methods for categorizing network traffic primarily employ common machine learning algorithms e.g., decision trees and plain Bayes algorithms, but as deep learning technology advances, more and more traffic classifications are being successfully applied. This study examines existing deep learning-based network traffic classification techniques and focuses on the categorization of computer network traffic. Firstly, the research background of the topic is introduced, and then the traffic classification based on deep learning is mainly described, which includes traffic classification based on Stacked Autoencoder, traffic classification based on Convolutional Neural Network and traffic classification based on Recurrent Neural Networks.  Following investigation, this paper comes to the conclusion that Long Short-Term Memory and Convolutional Neural Network models are the best deep learning models for traffic classification, with three-dimensional Convolutional Neural Network outperforming the others.
Animal Movement Prediction based on the Corroboration of Object Detection and Pose Estimation
Authors: Chen Ke , Yu Yang , Zhou Haoxian
Published: April 2023

With the development of automated driving, driving safety has become a hot topic in the field of automated driving. However, the existing methods mainly define living objects as obstacle handling and mostly focus on the safety of pedestrians, thus ignoring the safety of animal movement in automatic driving. In this paper, we propose an animal movement prediction model with the corroboration of object detection and pose estimation, which aims, to help the autopilot perception system judge animal pose more accurately. First of all, we preprocess the datasets, use a Convolutional Neural Network based object detection method to detect animal images, and then use the current mainstream 2D animal pose estimation method to obtain the key points of animal images, and divide the resulting data set into three categories: running, walking and waiting. After preprocessing, we use Resnet and MLP models to classify the pose. For Resnet model, we will evaluate the accuracy of the model on the test set during training and compare the accuracy of the last model to determine the model with the highest training accuracy. The final model has achieved an accuracy of up to 75% for running evaluation. For MLP model, we tend to use the rank of 13 specific bone points’ angles as the standard of pose classification. Three types of test sets will be put into three trained MLPRegressor model for training to get the classification we want. Finally, the accuracy of all three poses is more than 80%. Finally, we choose the model with the highest accuracy as our classification method. This work is expected to apply animal pose estimation and animal and pose classification to automated driving to improve driving safety.
Housing Price Prediction Model and Impact Factors Analysis
Authors: Hao Jingxuan
Published: April 2023

Housing price forecast is usually used in macroeconomic regulation, which can effectively avoid the housing price explosion brought by economic growth and promote better housing market control. Considering the rapid growth of housing prices in China in the past decade, the prediction and analysis of housing prices have become a top priority. In this paper, we establish the VAR model to understand the rule of housing prices between different cities and reveal some potential factors affecting housing prices through variance decomposition and impulse response. The results of the experiment show that the factors affecting the change in housing prices are different for different regions, but it is undeniable that housing prices are often affected by the prices of previous years. These results will effectively assess the housing market in these cities and help the government make decisions.
Review of Breast Cancer Detection Method
Authors: Huang Jiahao , Qiao Qianfeng , Zhou Yu
Published: April 2023

The fierceness of breast cancer is obvious for the patients and the field of medical studies for its malignant destruction in the human body. The failure in diagnosis may put patients in suffering from high financial pressure and may cause the patients’ physical health to deteriorate. The key in this research of contrasting different kinds of algorithms and varied types of classifications is to support physicians to make a better judgment to prevent misdiagnosis. The research introduced many aspects of data on breast cancer detection to make the data inputs of algorithms able to satisfy doctors’ needs. Moreover, the varied kinds of features will specify and confirm the presence of breast cancer tumours instead of treating them as a part of the benign tissues. With the information above, the research found a difference in accuracy between the classification and tried to make a comparison to conclude that most satisfy and support the actual use of these data to physicians, who may see the symptoms with misunderstanding. Thus, Random Forest was qualified to be evaluated as the most applicable algorithm of its excellence in processing higher dimensional, which is dynamic, data.
Privacy Preserving Method for Image Recognition based on Denoising Autoencoder
Authors: Tian Yongshang
Published: April 2023

Machine learning based on neural networks have obtained great achievements in diverse domains. Training models usually requires large, labeled datasets, which are often crowdsourced and consist of private information. There is no doubt that any private information about the datasets should not be let out by the models. To realize the goal, this article introduces a composed model based on denoising autoencoder for classification. The difference between the reconstructed images by the decoder and the actual images is then set as the optimization object while training the encoder and decoder parts, and multiple fully linked layers are added to the pretrained encoder to create a composite model for classification. The entire assembled model is then trained once more with the noised images. The constructed model produces a satisfactory result since the characteristics extracted by the encoder were what the model used to do classification. The experiment results demonstrate that the composed model can protect privacy at a low cost in model quality and accuracy compared to the baseline models which take raw images as input.
Logistic Regression for Stroke Prediction: An Evaluation of its Accuracy and Validity
Authors: Wang Lechi
Published: April 2023

A stroke, also known as a brain attack, is a serious medical condition that occurs when the blood supply to the brain is disrupted. It is a leading cause of death globally, accounting for about 11% of all deaths. In this paper, the author opts to use logistic regression for predicting the stroke. The paper starts with introducing the methods used to preprocess the raw dataset, including data cleaning, label encoding, oversampling, splitting the dataset and finally feature scaling. Then it goes to the modeling section. In this section, the author introduces the process of constructing a logistic regression model in detail. The first step is building the sigmoid function, a foundation of the model. Then the cost function is built to measure the difference between the model's predicted values and the true values. After that, the author constructs the gradient-computing function to determine the rate and direction at each iteration when implementing gradient descent. Based on this function, the gradient descent function is finally built, and predictions are made according to the outcome after putting testing variables into the model. By analyzing the results, the author also compares the different performances of the model between the one without regularization and the one equipped with regularization and then draw a conclusion that using regularization can help improve the performance of the model. At the end of the study, the author gets a rather satisfying result with a prediction accuracy of over 95% in the collected dataset.
Research on 3D Object Reconstruction Method based on Deep Learning
Authors: Liu Xiaoyang
Published: April 2023

3D reconstruction is a classic task in the field of computer graphics. More and more researchers try to replicate the success of deep learning in 2D image processing tasks to 3D reconstruction tasks, so 3D reconstruction related research based on deep learning has gradually become a research hotspot. Compared with the traditional 3D reconstruction methods that require precision acquisition equipment and strict calibration of image information, the 3D reconstruction method based on deep learning completes the matching of 2D images to 3D models through deep neural networks, and can reconstruct 3D models of various categories of objects from RGB images obtained by ordinary acquisition equipment in a large number and quickly. This paper introduces the state of the art of 3D voxel reconstruction, 3D points cloud reconstruction and 3D mesh reconstruction, respectively. According to the different representation methods of 3D objects, the 3D object reconstruction methods based on deep learning are classified and reviewed, the characteristics and shortcomings of existing methods are analyzed, and three important research trends are summarized.
Studies Advanced in Salient Object Detection based on Deep Learning
Authors: Zhang Xiji
Published: April 2023

A very important job in the area of computer vision is salient object detection, its goal is to check salient or obvious objects in images or videos. Thanks to rapidly developing networks of convolutive neurons, especially generative adversarial networks, CGAN has become a popular framework for salient object detection, which has promoted breakthroughs in detection precision and velocity of protruding objects. Focusing on the topic of salient object detection, in this paper, we systematically introduce the research progress of salient object detection. Specifically, we first introduce methods for detecting protracted objects based on general object detection algorithms (such as Fast R-CNN, YOLO, and SSD), including their basic ideas, key steps, advantages and disadvantages, etc. We then focus on CGAN-based salient object detection methods, including method classification, typical method introduction, and application examples. Finally, we quantitatively compare the performance of different saliency detection algorithms on common datasets, and discuss the existing research problems and feasible solutions of the topic.
Research on Bluetooth AOA-based Indoor Navigation System for Hospitals
Authors: Yang Shuo , Zhu Yuhang , Gao Feiya , Wen Chao , Hao Guofang , Huang Wei
Published: April 2023

Bluetooth 5.1 introduces the direction-finding function which can detect the direction of Bluetooth signal, on this basis, this project combines the actual hospital scenario and uses Bluetooth AOA method and IoT technology to design the implementation of intelligent navigation system in hospital. The project uses WebGL technology to provide real-time cross-floor hybrid spatial guidance navigation for patients by interfacing with the hospital's HIS and realizing in-hospital intelligent navigation with the help of WeChat applets to help patients quickly and accurately locate their consultation locations. The application of the hospital indoor navigation system can shorten patients' in-hospital wayfinding time, reduce the work pressure of front desk staff, improve patients' medical experience, and effectively improve the digital management level of the hospital.
Research on the Application of Computer Technology in Sports Training
Authors: Jiang Dewei , Li Dongmei
Published: April 2023

The rapid development of modern sports has put forward higher requirements on the relevance, scientificity and rationality for physical training. The traditional physical training method that based on experience can no longer adapt to the new situation of sports development in the new era, which has obvious lag and limitations. Within this context, this paper discusses physical training and computer technology, analyzes computer-aided systems for physical training, and studies the application of computer technology in physical training.
From the Perspective of Computer Engineering: Research on Digestive Diseases based on Bioinformatics and Genetic Genetics
Authors: Qin Yi , Lin Yixian , Liu Zhenyu , Zhang Yan , Luo Jun , Huang Xushuang , Li Liangyu
Published: April 2023

Background: Irritable bowel syndrome needs long-term neuroscience detection after medical engineering modeling, during which time, computer engineering is needed to complete database collection. Research methods: The research team designed this model after constructing the illusion of intervention on different sweeteners and sugars. At the same time, the artificial intelligence neural system combined with data to predict which combination of sweeteners and sugars is likely to cause disease, which combination of sugar and glycogen may lead to irritable bowel syndrome in patients, and elaborate the neural network mechanism from the perspective of calculation. Results: Through experimental data processing and analysis, the research team completed the design of the model and established a complete computer network path. Conclusion: The model can be popularized in Europe, Russia, Belarus, China, Malaysia and other regions. After cooperation with Chifeng University, supported by the Autonomous University of Barcelona in Spain and the National University of Belarus, a more in-depth comparative experiment will be carried out to find a more accurate molecular mechanism.
Clustering model research based on composition analysis and identification of glass products
Authors: Lv Lei , Duan Suqing , Liu Liguo , Xu Hao
Published: April 2023

As we all know, ancient glass buried in the soil will be weathered, and a large number of chemical elements in it will exchange with the chemical elements of the soil. As a result, the proportion of chemical components inside the glass changes greatly, affecting the analysis and identification of ancient glass products. This paper selects the fresh data from weathering samples, establish a clustering model and use SPSS to cluster analysis, and classify the types of each cluster. If there is a significant difference, it shall be divided into subcategories; The K-Means algorithm is used to analyze again, and the observation results are compared with the results without disturbance.
Glass classification and identification based on lasso regression and K-means clustering
Authors: Xu Baowei , Li Boxun , Tian Siyu
Published: April 2023

Ancient glass is classified into two types, high potassium glass and lead-barium glass, which are highly susceptible to weathering by the burial environment. In order to protect the glass artifacts more safely, based on some data related to the chemical composition ratio of glass artifacts,by the lasso regression model based on the logit transformation, the classification rules of high potassium and lead-barium glass were analyzed, and the glass was divided into four subclasses based on the principal component analysis using K-means clustering, and sensitivity analysis was performed on the classification results. Finally, the validity of the model was verified by determining the type of glass to which a set of test data belonged.