bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Marketing Strategy Optimization Based on Financial Big Data
Authors: Chen Yinzhuo
Published: April 2023

The rapid rise of financial big data has brought subversive changes to the traditional financial field. Both traditional financial companies and emerging internet companies are eager to join the era of financial big data. The emerging consumer finance companies of industrial departments and mutual financial departments have used the Internet thinking, but their marketing strategies have caused problems such as large initial investment and poor results in a short time. If commercial banks want to keep up with the times, they must conduct in-depth research on the market, constantly innovate and enrich their own wealth management products, and design matching wealth management products according to the individual needs of different customers, thus improving the market turnover rate. This paper summarizes financial big data, analyzes the financial marketing strategies of commercial banks under the background of financial big data, and finally puts forward the optimization measures of marketing strategies based on financial big data, hoping to help promote the healthy and sustainable development of the financial industry.
The Modeling and Design of An Index Finger Rehabilitation Exoskeleton with High Suitability
Authors: Qian Rui , Yu Chengmin , Zhang Taihong
Published: April 2023

With the increasing demand of hand rehabilitation training, people pay more attention to the hand exoskeleton machine. In contrast to this growing demand, most exoskeletons have a weak adaptability, meaning that almost every exoskeleton is tailored to the patient's individual hand data. Through calculation, modeling, and simulation experiments, this paper aims to add additional sensors to the MCP and PIP joints to obtain more accurate data, and further increase joint mobility, flexibility, and adaptability based on the adjustable pulley to achieve better rehabilitation results and lower cost. Based on the existing underactuated exoskeleton, this work has added sensors, sliding contact modules and retractable link rods at the nodes between the MCP and PIP joints to improve the accuracy and adaptability of the exoskeleton.
Uncanny Valley Game in Virtual Environments
Authors: Cheng Muyang , Cui Heyan , Yu Dingze , Zhang Zhen
Published: April 2023

Nowadays, as VR technology is developing at an incredible rate, related topics such as the virtual environment, mobile equipment for VR, and metaverse are also becoming hot topics. The article will be depicting the process of researching the Uncanny Valley theory's demonstration in the virtual environment. We created a horror virtual reality game that simulates the environment in real life. In the early stage of making the game, we first discussed how to set a proper story background for the game to put in the environment we want for testing the theory. We got our inspiration from "Backroom". Based on our inspiration, we determined the art style of our game, and later, in 3D modeling parts, we designed several monsters. After finishing all the modeling and programming parts, we tested them in our group. Finally, we released it to the public with a questionnaire to test the public's response. After analyzing the collected data, most people will feel a feeling of horror with the environment we create. We can conclude that the Uncanny Valley theory might still be suitable for the virtual environment. It can show that the Uncanny Valley theory might suit more scales than we think. And for VR experiences, we can use this theory to better create a scary scene or experience, which could be used in in-game making or VR filmmaking.
Research on the Progress of VR in Game
Authors: Zhang Ruiqi
Published: April 2023

Virtual reality technology has evolved from the last century to the present, with huge improvements in performance, graphics quality and portability of headset devices. Major game manufacturers and studios have launched virtual reality games that use virtual reality technology. Compared with traditional games, virtual reality games have a strong sense of immersion, presence and interactivity, and are very popular among players. This paper summarizes the development history of VR devices and VR games, outlines the importance of VR in game design and development - the strong immersion and interactivity of VR games compared with traditional games, and explains the applications of VR in various types of games, among which are scenario-experiencing games with exploration games and social games, action-simulation games with sports and role-playing games, and educational games for teaching purposes, and points out that there are still shortcomings in the development and design of current virtual reality games, and developers of virtual reality devices and games need to work together to make virtual reality games have more application scenarios and meanings. The hotspots and development prospects of VR games at the current stage are prospected.
Analysis on Chatbot Performance based on Attention Mechanism
Authors: Fang Jiyang
Published: April 2023

The chatbot is a way to imitate the dialogue between people through natural language, enabling human beings to communicate with machines more naturally. The chatbot is a prevalent natural language processing task (NLP) because it has broad application prospects in real life. This is also a complex task involving many natural language processing tasks that must be studied. The chatbot is an intelligent dialogue system that can simulate human dialogue to achieve online guidance and support. The main work of this paper is to summarize the chatbot's academic background and research status and introduce the Cornell Movie-Dialogs Corpus dataset. The methods of artificial intelligence and natural language processing are outlined. Two attention mechanisms used to improve neural machine translation (NMT) are discussed. Finally, this paper tests the performance of chatbots under the influence of N_ITERATION and data scale summarizes the relevant optimization strategies and makes a prospect for the future of chatbots. The main work of this paper is to test the performance of the proposed method under different experimental Settings, including dialog templates, adjusting the amount of training data, and to adjust the number of iterations. The results show that the chatbot's vocabulary changes with N_ITERATION and that increasing the data in the training dataset improves the chatbot's understanding.
Gear-based Multimodal Underdriven Index Finger Exoskeleton Rehabilitation Device
Authors: Ma Wanli , Ye Zhang , Wang Junzhe
Published: April 2023

With the increase of stroke patients, wearable hand rehabilitation exoskeleton devices used to do mechanical rehabilitation training have received attention from researchers. Most of the rigid hand exoskeletons can only perform simple flexion and extension training of the finger as a whole, and not single joint training. Therefore, the subject of this paper is to develop a new multimodal underdriven index finger exoskeleton based on gear drive. The data analysis is compared to achieve the desired specific gesture. This device allows the user to further train the finger and also helps the medical practitioner to better determine the individual joint function of the patient.
Features and Recognition Algorithms in Sleep Staging and Drowsiness Detection by Using EEG
Authors: Luo Tianshu
Published: April 2023

Sleep quality is a significant problem worldwide, and drowsiness detection is meaningful to sleep staging as it can prevent many accidents with significant loss. Among all the methods, the method that combining the neuron science and the computer science to analyze the EEG signals has the best accuracy. The general steps of sleep staging include gathering the EEG signals, extracting the statistical features, putting the features into the model through recognition algorithms, and evaluating the model. The extraction of features and recognition algorithms are the most important parts of all the steps, which means the two steps can decide the precision of the method. This paper states and compares the different features and recognition algorithms used in this area and introduces some research using the related features and recognition algorithms. Thus, this paper can provide researchers valuable references of the feature and recognition algorithms and therefore develop more useful method for sleep staging in the future.
Oil Price Forecasting based on Improved SARIMA Model
Authors: Wang Yaobin
Published: April 2023

Oil Price is important to countries and people. As Black Gold, Oil is considered the blood of the industry. People in modern cities are surrounded by crude oil and its derivatives, such as petrol for cars, plastic products for storing food, etc. Thus, the changing price of oil can have a profound effect on people and countries. The prediction of oil prices can lessen the impact. Once people and the government know the future price of oil, then people and government can adjust their behavior. This study leverages the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to find the parameters of estimation and forecasts. The study uses Brent crude oil prices from May 1987 to July 2022. The study result shows that the price of oil is hard to predict based on the Brent crude oil prices. The study also discussed some protentional reasons why the SARIMA model cannot predict the price so accurately even if the method is correct.
COVID-19 Epidemic Trend Prediction using Long Short-term Memory Network
Authors: Zhang Tianren
Published: April 2023

The COVID-19 pandemic is continuously spreading in various countries and different regions. It produces serious economic shock worldwide and negatively impacts the life and work of people. Although many control measures are conducted to contain its spread, it is still not known when the epidemic will end. Predicting the trend of COVID-19 accurately is extremely important. It can improve the resource allocation rate and make better preventive and control measures for the epidemic. In this paper, Long Short-term Memory (LSTM) models are leveraged for predicting the epidemic in different countries, including Germany, Japan, Russia, and Italy. The LSTM is a type of recurrent neural network (RNN), which is effective for predicting sequential data such as the time series. In this work, a visualization analysis is firstly conducted for demonstrating the trends of COVID-19 in various countries. Then the performances of the LSTM network are validated on the data of four countries.
Transforming Music into Chinese Musical Style based on Nyquist
Authors: Lyu Xinying
Published: April 2023

As music recomposing becoming a popular way of artistic creation, computer-aided music making has generated many neoteric pieces through algorithms. Style transfer is one of the methods of recomposition based on an existing musical piece. This paper aims to develop a method that could implement music style transferring to Chinese style by changing the mode into a pentatonic scale. It includes two methods in note-to-note modification that both achieved the purpose well and are suited for different situations. The output of the approach is proved to be successful by certain comparison between the existing Chinese style music and the one that was modified by the method. It is convenient for musicians to utilize the analysis to improve the efficiency of recomposition based on style changing. In addition, they will be given a general view of what the changed music could be without excessive thinking prior to the start of their composing process. These results shed light on guiding further exploration of music composing based on Nyquist.
Prediction of Concrete Strength Using XGBoost
Authors: Li Gen
Published: April 2023

This paper aims to test the extent to which different materials affect concrete strength and to use Machine Learning to test the accuracy of different mathematical algorithms in predicting concrete strength to select the most appropriate method to predict concrete strength through a given database. There is a strong need to predict concrete strength, as cement can only show its solidity after it has solidified and been put into use. So, it is not possible to propose some samples for strength testing before each use of cement to ensure safety. Although this is indeed a method, it is too expensive and cost-intensive. Thus, it is not cost-effective to implement. In this paper, some data was found through the academic forum and analyzed using Python, the degree of correlation between different parameters for concrete strength was summarized, and different algorithms were used to compare the accuracy. Finally, XGBoost was successfully used to predict concrete strength.
White Wine Quality Prediction and Analysis with Machine Learning Techniques
Authors: Jiang Xianghui , Liu Xuanyu , Wu Yutong , Yang Dehuai
Published: April 2023

The wine originated 7,500 years ago, and it is one of the most popular alcoholic beverages in the world today. With modern advances in production processes, the quality of wine has been significantly improved. White wines can be graded by measuring various physical and chemical quantities. In reality, it is very rare for white wines to have dramatic quality differences, so precisely capturing these important characteristics will be difficult. In addition, the available dataset for wine quality is relatively small and imbalanced, so it is not easy to train a machine learning model for wine quality prediction. This paper explores the applicability of applying logistic regression machine learning models for wine quality prediction. The Synthetic Minority Oversampling Technique (SMOTE) algorithm is applied to address the imbalanced data issue. The key features in wine production are analyzed to the physical or chemical quantities that are most likely to influence the quality of white wines. In several tests, the random forest model gives good results, and free sulfur dioxide, chlorides, and fixed acidity are the most likely characteristics to influence the quality of white wines.
Research Perspective in Flexible Hand Exoskeleton
Authors: Li Tianran
Published: April 2023

The purpose of writing this review paper is to make it more convenient for the public to use and adapt to hand exoskeletons, especially to enhance the promotion of flexible hand exoskeletons, and to put forward some of my own views. In recent years, a flexible exoskeleton human-machine intelligent system has become a new research hotspot in the fields of robotics, electromechanical engineering, automatic control, bioengineering and artificial intelligence, and has been widely used in scientific research, industrial production, space or deep-sea exploration, entertainment, etc. Sports rehabilitation and daily life have gradually been widely used. Flexible exoskeletons are most commonly used in the medical and biological fields. The human-machine intelligent technology of a flexible exoskeleton takes man-machine integration technology as the core, and the advantages of people and intelligent machines can be fully utilized. Through organic human-machine coupling, perception and decision-making at the execution level, the performance of the system is enhanced. Teleoperated exoskeletons and augmented exoskeletons are two important research directions for flexible exoskeleton human-machine intelligent systems.
A Survey on Improved GAN based Image Inpainting for Different Aims
Authors: Wang Nanxiang
Published: April 2023

Computer Vision (CV) is an important field of Artificial Intelligence (AI) that focuses on processing and analyzing the visual data input like images or videos. And image inpainting technique is a task of reconstructing image with missing parts by using the information of the existing part, which becomes a crucial part in CV. It has various image processing applications such as digital cultural heritage preservation, old photograph restoration and object removal. The researchers found that using generative adversarial network can extract the feature from image without huge amount of computation which are two main challenges of image inpainting, therefore GAN-based inpainting method has recently been a hub for research. In this paper, we review most of the latest improved image inpainting methods and divide them into three categories according to their different aims: for semantic image inpainting, for generating higher diversity and for irregular or free-form image input. Firstly, we review the methods from structure and algorithm. Then we analyze the advantages and disadvantages of each method. Finally, we conclude the challenges or limitations raised from our findings and some potential development trend.
Analysis of Interaction Methods in VR Virtual Reality
Authors: Liu Yiwen
Published: April 2023

Virtual Reality is a new revolution in interaction. As a super technology that can trick the human brain, Virtual Reality is widely used in healthcare, education, business, medical, entertainment and industry. In the field of VR, developers are committed to giving users a full and realistic interactive experience, for example by transmitting the senses of touch, smell, sight and hearing to the user's brain and restoring the user's perception of the real world first-hand. This requires not only powerful technology, but also sophisticated interaction design. Many VR giants are currently working on various technologies and systems to enhance the user's interaction experience in the virtual world. This article analyses three of the main interaction methods (motion capture, eye tracking and haptic feedback) and their associated device products and provide a brief comparison and summary of them. Although the interaction methods in VR are not yet unified and the various virtual reality devices on the market have their own flaws, with more and more players getting involved and the rapid development of technology, it is not difficult to judge that in the near future virtual reality interaction devices will explode like a rocket.
Leveraging CNN and Vision Transformer with Transfer Learning to Diagnose Pigmented Skin Lesions
Authors: Yang Jujie
Published: April 2023

The transformer has emerged in computer vision, but whether it can rival or even replace the status of a convolutional neural network (CNN) still lacks deeper cognition. Therefore, this paper will study the feasibility and effectiveness of transformers in identifying the types of pigmented skin lesions by comparing one of the most widely known CNN architectures with three state-of-the-art Vision Transformer (ViT) models. The research results found that the transformer surprisingly outperforms the CNN. In other words, the overall accuracy and F1-score per class of its classifier are much higher than those of the CNN. In addition, the experimental data results show that if the ViT model has more training data input, its performance seems to have a large room for improvement. Conversely, the CNN model shows no indication that increasing training data will affect its performance. Therefore, if there are more dermoscopy images with corresponding label data to make the dataset larger and balanced, the transformer may completely replace CNN on this task.
Researches Advanced in Image Generation based on Deep Learning
Authors: Duan Jianing
Published: April 2023

Image generation has always been a study hotspot in machine learning, which aims to build models to learn specific semantic distributions from massive image data to generate realistic simulated images. Thanks to the deep learning technology’s quick development, generative models are constantly being developed and huge success has been achieved in image generation tasks. According to difference between generative models, the existing image generation methods based on deep learning can mainly be separated into three models: image generation based on Variational Autoencoder (VAE), image generation based on Generative Adversarial Network (GAN) and image generation combined the VAE and GAN. Focusing on the three frameworks, in this paper, the development process and related principles of each type of generation model are described respectively. After that, the different generation results of different generation models for the agreed training set are compared intuitively, the advantages and problems of various models are proposed, and reasonable improvement measures are proposed for some problems. Finally, the development prospects of various models are prospected.
Research on Online Education Web Design for Children with ADHD
Authors: Xie Yaqi
Published: April 2023

The education of ADHD children is one of the hot issues of concern to society. Some studies have found that ADHD children cannot focus on the online learning process, yet the current web design of online education cannot guide the group of ADHD children to focus on the learning process. Therefore, this study focuses on the online learning process of ADHD children’s patients for website interface design, based on the learning characteristics of ADHD children and children's aesthetic characteristics, to enhance the online learning experience of this group of children and improve their concentration on learning. The research methodology of this paper is as follows: first, we conducted a participatory observation of the online learning process of children with ADHD, and then interviewed the parents of the children to understand their needs and pain points, and analyzed the design principles of online learning for children with ADHD through the above-mentioned research process, which are: "game-based learning" and "ADHD child-centered", and the design strategies are: "fun", "interactive" and "reading." Based on the findings from the analysis, existing online learning websites were analyzed. The analysis revealed that the page design of the existing online learning websites does not create an immersive learning experience for children with ADHD. Their web design is not designed and optimized for the ADHD child population. Research shows that web design with fun, interactive, game-based online learning sites has a positive effect on the learning of children with ADHD. It is recommended that web designers and front-end development engineers pay more attention to the needs of ADHD children for online learning website design during the design and development process.
Correlation Analysis of the Global Trend between Nation GDP and Cancer Prevalence Rate
Authors: Li Bojie
Published: April 2023

Through the development of modern society, the prevalence of cancer gradually increased. Many researchers have done a lot of relevant research and experiments from the public health perspective. For example, liver cancer is found to be modifiable through changes in lifestyle. This paper discusses the relationship between cancer prevalence and macro factor, which is the national gross domestic product (GDP). More specifically, this paper discusses the relationship between cancer prevalence and national GDP. This paper applies polynomial regression to study the polynomial between the cancer rate of one country and its cancer rate. Through the collection of public cancer prevalence data and national GDP data, this work found that the cancer prevalence rate has a positive correlation with the national GDP. It is speculated that the reason for this result is that with the increase in national GDP and the development of medical technology, people are less likely to die from less dangerous diseases than before.
Image Classification Under Class-Imbalanced Situation
Authors: Bao Chunguang
Published: April 2023

Image classification technology processes and analyzes image data to extract valuable feature information to distinguish different types of images, thereby completing the process of machine cognition and understanding of image data. As the cornerstone of image application field, image classification technology involves a wide range of application fields. The class imbalance distribution is ubiquitous in the application of image classification and is one of the main problems in image classification research. This study summarizes the literature on class-imbalanced image classification methods in recent years, and analyzes the classification methods from both the data level and the algorithm level. In data-level methods, oversampling, under sampling and mixed sampling methods are introduced, and the performance of these literature algorithms is summarized and analyzed. The algorithm-level classification method is introduced and analyzed from the aspects of classifier optimization and ensemble learning. All image classification methods are analyzed in detail in terms of advantages, disadvantages and datasets.
Glass Classification and Identification based on K-means++ Clustering Analysis and BP Neural Network Method
Authors: Cai Guanfu , Li Haojun , Huang Jingming
Published: April 2023

The weathering caused by the burial environment will cause the ancient glass to exchange material with the environment, and the weathering layer produced by the material exchange will have an impact on the class judgment of ancient glass. In this paper, we analyze the classification rules of high potassium glass and lead-barium glass; select the appropriate chemical composition for subclass division and establish the classification model of glass. After this, a binary classification (high potassium/lead-barium) model is established by BP neural network training, and the subclassified main components of each type of glass derived from the classification model are used as adjustment variables to analyze the chemical composition of unknown categories of glass artifacts and identify the types to which they belong by controlling a single variable.
A CNN-Based Facial Expression Recognition System
Authors: Hu Ziyu
Published: April 2023

Facial expression recognition has long attracted researchers from a variety of domains, such as human-computer interaction, emotion analysis, intelligent medical care, and so on. Various human-designed features have been used in FER to extract image appearance features, but human-designed methods are difficult to be used to recognize new face images, which poses challenges for FER in uncontrolled environments. Recently, CNNs and FNNs have been used for facial expression recognition. The new CNN-based recognition approach yields excellent FER results. The numerous convolution and pooling layers that CNN possesses allow it to extract higher and multi-level features of the entire face or local regions. Additionally, CNN has excellent classification performance when it comes to the feature extraction of facial expression pictures. However, the mechanism through which FNN remains largely controversial. In this work, the experiments are carried out on the comparison of three facial expression recognition algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), and the experimental results show that, using CNN can get more stable recommendation results of better quality.
Research Status and Prospect Analysis of Autism Spectrum Disorders on the Basis of TMS and tDCS
Authors: Wang Qilong
Published: April 2023

Autism spectrum disorder (ASD) has become one of research hotspots in recent years. With the increasing number of patients, dominated by children, finding effective therapies has been under study, ranging from medication to stimulation treatments. Additionally, due to the heterogeneity of ASD symptoms, simple and universal biomarkers are also needed to be found to improve accuracy of diagnosis. This article reviews Transcranial Magnetic Stimulation (TMS) and transcranial Direct Current Stimulation (tDCS) with the key recording technique, Electroencephalogram (EEG), in the treatment of individuals with ASD. The author selects 10 representative research papers published on PubMed in the past 5 years for discussion. The author separately analyzes their major methods, results and new discoveries, followed by potential problems and future development directions of these two kinds of therapy methods. The Related Works in this review is organized in the order of different stimulation sites for each stimulation, including most common sites dorsolateral prefrontal cortex (DLPC) and right temporoparietal junction (rTPJ). In the end of this review, the expectations for future research and treatments of ASD are proposed from the perspective of the author. Based on recent studies and reasonable speculation by the author, homogenous subgroup analysis and more broadly available therapy research will be scientific trends for ASD therapy in the future.
The Analysis of DCDC BUCK Circuit based on Arduino
Authors: Lyu Shuntian , Zhang Yuwen
Published: April 2023

With the development of the times, digitally controlled power supplies are becoming the trend of today's DC power supplies. This paper details a step-down circuit with Mosfet switch controlled by Arduino. This power supply and DC-DC adopts advanced semiconductor technology and digital control technology, featuring small size, light weight, simple and easy adjustment, high efficiency, wide voltage range and complete shutdown of zero-point adjustment. First, this paper details the structure, principle, and many features of the Arduino platform. Second, this paper explains the principle of BOOST circuit for boost and BUCK circuit for buck with equations and structure diagrams, and presents real-life applications. By reading this article, beginners will have a basic understanding of the use of Arduino and know what the exact principles of this circuit are, as well as circuit design and software programming ideas.
The Advance of Distributed Computing Methods
Authors: Chen Shuo
Published: April 2023

As data computing techniques continue to advance and change, distributed computing has become more and more mature and widely used, and it has become an important and effective method for computing data in today's era. Since human beings entered the information age, effective data processing has always been a topic of concern, in the face of complex and huge data, distributed computing has always played its own role, and gradually has a significant impact on other fields such as the Internet of Things, medical care, artificial intelligence and other fields, and has a positive effect on making today's human life more convenient and faster. Starting from the development background of distributed computing, starting from the three typical distributed frameworks of distributed computing, this paper not only introduces distributed computing as a data processing method, but also uses divergent thinking, describes different computing methods, and analyzes them together. In addition, this paper briefly points out the advantages and disadvantages of distributed computing, as well as the challenges and challenges that still exist in distributed computing and puts forward expectations for the future development of distributed computing.
Precipitation Forecasting Using Transformer: A Comparative Study with Unet
Authors: Ang Sining
Published: April 2023

Accurate precipitation prediction has a huge socio-economic impact. With the development of algorithms, architectures, and hardware technologies in the field of machine learning, the disadvantages of traditional numerical weather prediction methods are becoming more and more obvious. Noting that related studies have attempted to use the latest machine learning architectures for precipitation prediction, this work uses Unet and Transformer for precipitation prediction based on cloud layer information and compares them. The results indicate that Transformer can achieve 94.13% accuracy, while Unet has 96.89%. Finally, in conducting the data comparison, a conjecture related to the lagging of Transformer data is proposed based on the results of this experiment.
Stock Price Prediction based on Long Short-Term Memory Model
Authors: Liu Ziji
Published: April 2023

The stock market is a place that brings investors opportunity to gain profit. Meanwhile, the stock market also brings investors high risks, which requires machine learning methods to improve the accuracy of prediction. This paper uses the Long Short-Term Memory (LSTM) model to predict stock prices. In the study, the daily stock historical data of Sinopec, Moutai and SPD Bank in the past 21 years are used as samples, including the date, the trade volume, the highest price, the lowest price, and the opening and closing prices. After the LSTM model has been trained, the three companies' predictions of opening price have achieved good results, and the predicted opening price curve and the actual opening price curve seem to be quite consistent. In terms of evaluation indicators, MAPE of the three companies is less than 1%. These results can provide some help for investors to predict stocks and shed light on guiding further studies.
AI Application in Computer Games
Authors: Geng Qianjun
Published: April 2023

It is well-established that artificial intelligence has already been used in nearly all kinds of computer games. The purpose of most games is to entertain (though there exists some educational games which aim to teach), and AI takes the role to improve the player’s experience. First, current use of artificial intelligence in computer games is introduced, then, some AI applications which aim to promote both entertainment and interactive environment is mentioned. Then, the future of artificial intelligence to find the possibility to create a kind of AI which can not only interact with players but also improve as number and experience of players grow. Lastly, the paper will analyse the future use of AI in different fields of computer games including, but not quite, the feedback of game bugs and solutions of players’ problems.
Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa
Authors: Zhou Xingchen
Published: April 2023

The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.
Mitigate Long-lasting Ethical Issues in the Healthcare Industry with Blockchain-based Solutions
Authors: Li Tongxin
Published: April 2023

As one of the most promising innovations in the recent computing paradigm, blockchain technology has been introduced into many fields such as finance. With the outbreak of the epidemic and the intensification of the global public health crisis, medical care has also undergone rapid revolutionary changes in replacing traditional systems with new technologies. Among them, the ethical issues exposed in the process of transformation are under intense debate. Traditional research and surveys focus on the privacy of patient data management and only include some roles in the medical ecosystem, which cannot meet the existing needs. To this end, based on system thinking, this paper studies blockchain technology and emphasizes its application potential in the field of health care to alleviate some long-standing ethical problems, mainly including manipulation of organ transplantation, counterfeit drugs, and medical insurance fraud. It also discussed the technical challenges and limitations of the implementation of the blockchain-based system and ended with the possibility of reimagining the future.
Proposal of a Bioinspired Patch for the Real-time Monitoring of Neurological Trauma
Authors: Luo Wei
Published: April 2023

Neurological trauma, damage to the brain, spine, or nerves, can happen as a result of both neurosurgery and vehicle accidents. It may be risky to cause damage during surgery because of humans' ineffective capacity to repair neurons; as a result, the market may require a medical device specifically made to address this problem. The accessory limb model (ALM), one of the most typical regeneration models, has demonstrated that it has significant potential to improve regeneration outcomes and might be used for the regeneration of human brain tissues. Nowadays, many implanted medical devices interact with circuitry outside the body via cables or wireless RF telemetry. However, wires usually result in a huge chance to increase surgical difficulties such as wire breakage, infection, and electrical noise. Intrabody communication, as a result, became a preferable and safer way of communication in the design of implantable medical devices, and is used in the device introduced in this article.
A Mobile Robot with a Manipulator to Alleviate the Shortage of Health Workers in Hospitals in COVID 19
Authors: Zhou Zichang
Published: April 2023

COVID-19 has brought an unprecedented burden to hospitals. Hospitals are facing many problems, such as understaffing and infected employees. But this has also promoted the development of hospital robots to a certain extent. This paper aimed to develop a robot that can move around the hospital and pick and send stuff to patients. The robot is mainly divided into 2 two parts: the moving part and the grasping part. The moving part includes map construction, localization, and navigation with gmapping, A*, and DWA algorithms that would stimulate on the ROS platform. For the grasping part, the forward and inverse kinematics of the robot arm and PD control would be analyzed using MATLAB with the Robotics System Toolbox, Simulink, and Simscape Multibody to simulate and control the robot arm. Using this approach, the robot can reach the target position and avoid dynamic obstacles. In addition, the robot arm can pick up and drop the ball with the designed trajectory.
Application of Blockchain and Fully Homomorphic Encryption in Online Questionnaires
Authors: Yu Xinhong
Published: April 2023

An online questionnaire is a simple and efficient method of acquiring diverse data for surveys. However, a proportion of respondents have strong concerns about whether questionnaires in this form will leak their privacy, which causes their unwillingness to fill out the questionnaires. Blockchain is well known for decentralization, traceability, and transparency and is now widely used in all kinds of platforms. Fully homomorphic encryption is another popular technique that ensures the safety of privacy, it helps to compute and use encrypted data without decrypting it. To solve privacy-preservation problems and enhance online data sharing, this paper dedicates to demonstrating how the merits of Blockchain and Fully Homomorphic Encryption (FHE) can be utilized to realize a secure, reliable, and decentralized online-questionnaire platform that guarantees the authenticity of data and privacy security for clients. This paper also proves that the combination of blockchain and FHE can successfully meet the expectation of an ideal online questionnaire platform.
Homomorphic Encryption-based Solution for Data Security in Smart Furniture
Authors: Liu Huanyu
Published: April 2023

Smart furniture is gradually entering people's lives. The special nature of smart furniture determines that it will be exposed to a large amount of private information, but the data processing behind smart furniture still lacks sufficient security. Therefore, the research topic of this paper is the possibility of using homomorphic encryption in smart furniture, and the research topic of this paper is a smart furniture data security solution based on homomorphic encryption. The research methodology of this paper is as follows: investigating the solutions and problems of mainstream smart furniture manufacturers in terms of data security and the possibility of enhancing protection with homomorphic encryption, as well as the impact on cost issues and applicability. The study shows that the use of homomorphic encryption improves the security of remote control and data transmission of smart furniture using cloud-based technologies. In addition, it increases the versatility and reduces the cost of smart furniture and improves the compatibility between smart furniture.
Solve the Problem of Expensive Drugs in Hospitals by Blockchain
Authors: Huang Hengyang
Published: April 2023

Effective treatment of major diseases has always been one of the key research topics for doctors. Some researchers have found that the lack of drugs and the proliferation of counterfeit drugs have a great impact on disease treatment, especially in developing countries. However, there is still a gap in research on drug cost control and hospital official drug pricing. As we all know, blockchain technology has been used in securities trading and other fields to reduce the costs of both parties. Therefore, the research topic of this paper is how to reduce the cost price of drugs purchased by hospitals nationwide or even globally, so that the price of drugs can be reduced to a win-win situation for hospital patients. The structure of this article is as follows: First, it introduces the current status of the application of blockchain technology at home and abroad, so that people can more clearly understand which transactions around them have used this technology. Later, this article will describe the working mechanism of blockchain technology, and finally introduce how to use this technology in drug trading. Through research, it is found that the point-to-point transaction of blockchain technology can greatly reduce the cost and time for hospitals to purchase drugs, reduce the price of expensive drugs in hospitals, enable more patients to get effective and rapid treatment of diseases, and reduce the phenomenon of family decline caused by illness. This shows that the effective reduction of drug purchase costs through blockchain technology is conducive to overcoming the problems of diseases and maintaining the stable income of hospitals.
Deep Learning-based Semantic Segmentation Methods in Medical Imaging
Authors: Li Jiaan , Li Peicheng , Li Houyuchen , Ying Tianheng
Published: April 2023

Image semantic segmentation based on deep learning attracted much attention in the field of computer vision in recent years. In the past few decades, the tremendous developments of medical imaging technology have made it possess an increasingly important role in diagnosis. Therefore, the image semantic segmentation method based on the deep learning algorithm is widely considered by many researchers in the medical image processing field. In this paper, first of all, we introduced some semantic segmentation methods based on deep learning, including point cloud data-based methods, Recurrent Neural Network (RNN) based methods, attention mechanism-based methods, transformer-based methods, and Generative Adversarial Network (GAN) based methods. Then, the characteristics of each method and the problems improved by each method in the field of medical imaging are shown respectively, for example, point cloud data can better express the information of biological tissue structure to improve the capacity of processing global contextual dependencies, attention mechanism assists the model quickly discover key information of input images, transformer improve the effect of segmentation tasks, and GAN has superior data generation capacity that they have excellent performance in generating realistic-looking images. And we summarize the performance of each method in the medical dataset by distinct metrics. Finally, some problems of theory, architecture and application in future research were discussed.
Study on Influencing Factors of Healing Music Records based on LightGBM
Authors: Chen Jiayu
Published: April 2023

At present, all kinds of music albums have become an indispensable product of spiritual consumption in public life. It can not only ease the pressure of life and work brought by external life, but also add some different interests to people's life. However, high-quality music is more popular with people, such as Dolby sound quality, and the demand for playing different types of high-quality music in different occasions and environments has greatly increased. This study will identify whether there is audience or artificial noise data during the recording of music albums, and explore the characteristics of various music albums, so that later music producers can better improve their music quality. To more specific, this study firstly collected the related dataset from Tianchi Platform. Then, data visualization e.g., correlation analysis is carried out to observe the data before passing it into the model. Subsequently, a typical machine learning algorithm called Light Gradient Boosting Machine is employed to train the dataset. The experimental results demonstrated the effectiveness of the model used in this study.
Multiple Machine Learning Algorithms for Spam Mail Detection
Authors: Chen Simou
Published: April 2023

Due to several issues including clogging mailboxes and obscuring critical personal correspondence caused by a large amount of spam mail, spam filtering application based on machine learning models has become more and more essential. This paper gives a comprehensive evaluation of 4 classic machine learning models including the Naïve Bayes classifier, support vector machine, neural network, and the decision tree in the task of spam email evaluation. In order to make fair comparisons between the algorithms, they were all built by learning from the same training dataset and asked to make predictions on the same testing dataset. More specifically, the Naïve Bayes classifier uses the conditional probability of the Naïve Bayes theorem and consumes the words that appear independently in the emails. The decision tree algorithm keeps recursively splitting the dataset to build a predictive theory. Support vector machine is able to make predictions by transforming the training dataset to a linear equation in n-dimensions. And neural networks can recognize the non-linear relationships between the inputs and outputs by constructing the front, hidden, and output layers. It is observed from the result that all machine learning models have successfully classified spam emails with high testing accuracy which are all above 90%. More specifically, the neural network achieves the highest testing accuracy followed by the support vector machine and decision tree. While the Naïve Bayes classifier does not perform well enough as it must assume the appearance of words happens independently.
Time-series Global Temperature Prediction and Visualization
Authors: Qiao Liuyiyan
Published: April 2023

Temperature prediction is a prevalent and practical research in recent years. However, due to the performance of mathematical models or the application of visualization technology, previous work either lacked accuracy and intuitively visualized results. This paper designs a pipeline for global temperature forecast, which has relatively ideal accuracy and visualized results, and makes prediction of the temperature of each country/ region from 2023 to 2100 based on the earth surface temperature dataset packaged by The Berkeley Earth Surface Temperature Study. The mathematic model used by this paper is Prophet, which is a high-performance time series forecasting model and extremely applicable for dataset with high periodicity. The forecast results are generated by using PyEcharts in the form of HTML pages, where the worldwide average temperature, the years, a drag-operable month setting bar and the colored temperature distribution map are shown intuitively. Experimental results based on the testing dataset indicate that the mean absolute error of this method is 0.735°C. Forecast results on wider dataset indicate that the worldwide average temperature in the future may continue growing at the speed of around 0.0248°C per year.