bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on short-term passenger flow prediction method of urban railway based on machine learning
Authors: Chen Yang
Published: October 2023

With the development of urban rail transit, the improvement of communication transmission technology, and big data processing level, the passenger flow data of urban rail transit continues to grow and can be effectively collected and stored, providing many basic data resources for analysis and passenger prediction research. With the development of artificial intelligence, short-term passenger flow prediction based on machine learning has received widespread attention. For this reason, based on the passenger flow data of Seoul Metro, this paper has carried out experiments and analysis on the existing models RNN-LSTM and ConvLSTM with better performance, providing theoretical support for short-term passenger flow prediction. The method in this paper solves the problem that a single traditional prediction model based on statistical theory is difficult to deal with large-scale complex data. It can complete the data processing task more accurately and efficiently, and has a good application prospect.
Predicting pulse wave age from cardiovascular characteristics using machine learning algorithms
Authors: Wang Ruihan
Published: October 2023

Arterial pulse waves are an essential and informative source of data for measuring cardiovascular health and are currently available on various devices, such as smartwatches. In addition, most experiments have focused on pulse wave velocity (PWV) and arterial pulse waveform (APW). However, in recent years, due to many factors, such as high work pressure among young people, cardiovascular diseases have become more prevalent. As a result, we need more research on pulse age to help patients focus on the consistency of their biological age with their legal age. Therefore, this paper introduces a machine learning framework to predict the age of patients according to the HR and SV data in the pulse wave of existing patients. At the beginning of the experiment, we attempted to use K-nearest neighbours (KNN), logistic regression (LR), and random forest (RF) models. Then we compared the accuracy of patient age estimates; RF had the best performance and was used as the final model. In addition, HR and SV were selected as the main features to predict patients' age according to the feature's importance. The final experimental results indicate that the RF model can predict the results with up to 1 accuracy only with two features, i.e., HR and SV. In particular, this is the point that we need to continue to pay attention to in the future to determine whether the prediction accuracy of this model can reach 1. Is it because the model is overly optimistic or because the selected HR and SV features strongly correlate with age? In the future, we can introduce the concept of actual body age and attract young people's attention to their body age. For example, more people will be urged to take a quick and efficient pulse diagnosis, complete a simple physical examination, and give rapid feedback by placing devices in places where young people gather, such as shopping malls. In this case, users can immediately know what part of their body may have problems. In general, our experiment demonstrates that patients' pulse wave ages could be well predicted based on their HR and SV data with high accuracy through the ML model of RF.
Design of asynchronous FIFO with adjustable depth based on Verilog
Authors: Li Xiangjie , Fu Shimeng , Wang Xuanbo , Wang Yinqiu
Published: October 2023

The integrated circuit industry is the basic industry of the digital economy and one of the basic sources of strength to build the new competitive advantage of China's economy in the future. With the development of microelectronic technology, IC technology has becoming more and more integrated, at the same time, the data flow in the different chips are getting complex. To overcome the restriction of the data transmission, FIFO become a way to solve the problem. However, different chips usually work in various clock domains, so the data transmission across clock domains is the key to make the communication between different chips efficient. Meanwhile, the rate of space usage is also a part of chip performance, so the depth of the FIFO is important. Because of the above, an asynchronous FIFO with adjustable depth is designed on Verilog, used Quartus II and Modelsim to draw an RTL diagram of the whole module and simulation.
Compartmental SICR model applied on study of celebrity worship behaviour
Authors: Xing Shuchang , Liu Muchen , Huang Kehan
Published: October 2023

We notice that there are lots of researches that studies celebrity worship behaviour and celebrity endorsement. Nevertheless, few go deep into a mathematical perspective, much less try to quantify this ambiguous process due to the lack of suitable models. In this research, we pioneer a new way, using SICR epidemic model, to fully describe the process of celebrity worship in details, through which we make predictions based on the basic reproduction number whether a celebrity could expand his or her fan base continuously. Meanwhile, not only do we further diversify the function of SICR model, but we also make improvements on the model itself. At the end of research, we also provide useful suggestions for celebrities and their brokerage companies about how to keep the number of their fans increasing rather than hit a bottleneck so quickly. The fact is that celebrities should emphasize the avoidance of negative events instead of positive publicity and be attractive to more groups of people rather than stick to one specific group only.
Introduction and analysis of bitcoin-based blockchain technology principle
Authors: Zhu Wangshu
Published: October 2023

Blockchain technology is a technology that inherently solves trust issues. It has the character-istics of decentralization, distributed storage, tamper resistance, security, and transparency. It ensures reliable communication between nodes that do not trust each other through consensus mechanisms, smart contracts, and other means. Blockchain stores each transaction data on each transaction node to make the data public and transparent, and generates the data into a blockchain.. As a relatively new distributed database system, blockchain has expanded to many other fields since the initial digital currency, but its development is seriously constrained by problems such as large storage overhead and low query efficiency. In order to find a suita-ble optimization method, select the representative blockchain system Bitcoin, analyze its data structure, data storage and data query processing mechanism, discuss the problems existing in the two functions of storage and query, summarize the existing relevant optimization methods, and look forward to the main research problems of the blockchain system represented by Bitcoin in the future.
UART communication protocol frame format explanation and application
Authors: Gong Jiajun , Guo Wenbo , Sun Weijian
Published: October 2023

Universal Asynchronous Transceiver (UART) is a Universal Serial Data Bus, hardware communication protocol using asynchronous serial communication in a configurable manner rate. In most embedded systems, microcontrollers and computers, UART plays an important communication role as a hardware communication protocol between devices to ensure the efficient transmission of communication data. The understanding and use of the UART protocol is crucial in our design process. In order to better understand. basic principle this protocol and realize the use of URAT, this paper introduces serial communication, UART frame format explanation, the working principle of UART, simulation of simple UART3. Depending on the source clock, also known as the Baud Clock, each heterogeneous device in a complete system may be able to create a distinct baud rate and connect with other devices over a UART interface. The fundamental tenet of the UART protocol stipulates that for proper communication, the transmitter and receiver must be set at the same baud rate. It also introduces several advanced designs and applications of UART with baud rate, namely UART with automatic frequency generator and frequency divider and Determination of UART Receiver Baud Rate Tolerance.
Analysis of convolutional neural networks and its application in object detection
Authors: Gao Yiming
Published: October 2023

With the development of times and technology, artificial intelligence-based neural network algorithms have been widely used in scientific research and life. Among them, Convolutional neural network (CNN) is the most classic and the most representative. CNN have been widely used in images classification in recent years. This article focuses on the basic information of the convolutional neural networks and its applications especially in object detection. Also, the advantages of the CNN and the possible future improvements will be told in this article. In the end of this article some research of experiments will be shown in order to prove the accuracy of convolutional neural networks in detecting objects and visualizing results. This article gathers basic knowledge of convolutional neural networks. As a result, it may help new starters to get to know the CNN and then make contributions to the development of deep learning especially the CNN according to the advantages and future improvements mentioned in the article.
Research on optimization technology based on mobile terminals convolutional neural networks
Authors: Zhong Jiahao
Published: October 2023

Convolutional neural networks play a very important role in computer vision, such as image classification, image segmentation, and handwriting recognition have been widely used. In daily life, this technology is used in the photo recognition of e-commerce platforms. However, the timing of the identification process grows into a major problem. Therefore, it is particularly important to reduce the recognition time by optimizing the deep learning model. To solve this problem, two experimental methods are proposed to optimize the volume of the convolutional neural network model. The first is to reduce the size of the model by scaling down the convolutional kernel. The second is to prune the model with L-1 norm to reduce the size of the model and improve the running speed. According to the experimental results, the two experimental methods have achieved remarkable optimization effects. In the first experiment, the method of scaling down convolutional kernel has an important optimization effect for training the deep learning model of small data sets. In another experiment using L-1 pruning algorithm greatly improves the running speed of models by reducing the size of models. To sum up, the optimization method proposed above for the convolutional neural network model on the mobile end can be applied in the field that requires a large amount of image classification, such as delivery package sorting. At the same time, to better improve the performance of the model, it will become feasible to use a variety of optimization methods to tune it.
Analysis of reversing image recognition problems based on lightweight model
Authors: Dai Dewei , Yang Jingyi
Published: October 2023

In recent years, people's living quality has improved, so the price of cars has fallen, and the number of cars in the world has been on the rise. In cities where land is expensive, there are fewer places to park, fewer parking Spaces, and parking itself is a difficult technology to learn. Therefore, the number of accidents caused by parking increases year by year. It is urgent to solve the safety problem of parking. Although some auxiliary astern tools have emerged as The Times require, these tools still have their shortcomings. For example, the commonly used astern radar and astern image cannot see objects behind, and astern image is a wide-angle lens that makes it difficult for the driver to judge the distance between the car and obstacles. In this environment, the current situation needs to be improved. In order to improve the accuracy of obstacle judgment, this paper chooses semantic segmentation as the function. This paper chooses lightweight model to speed up the process of deliver information, such as ShuffleNetV2.
The advance of non-digital-currency application with blockchain
Authors: Lyu Yi , Xing Tianlong
Published: October 2023

With the vigorous development of blockchain technology, its decentralization, privacy, convenience, and other characteristics make it shine in the financial field. Today, blockchain technology has gradually been widely integrated into government, the Internet of Things, supply chain management, healthcare, and other areas. It can change the traditional centralized data management model and provide distributed and decentralized data storage and management methods. This method has higher security, transparency, and reliability, which can solve the problems existing in traditional data management methods and improve the efficiency and reliability of data management. In the meantime, using new technologies also brings many practical problems to solve. These include insufficient technology maturity, privacy concerns, and volatile legal and regulatory policies. This paper delves into these real-world scenarios and summarizes some recommendations. This will give a greater understanding of the principles and characteristics of blockchain technology, promote the popularization of blockchain technology, have a more comprehensive understanding of the use of blockchain technology in different fields, and provide valuable research materials for researchers in related fields in the future.
Prediction of stock price leveraging Arima model
Authors: Lin Yeming
Published: October 2023

In today`s world, more and more machine learning methods and prediction methods have been proposed. There are also lots of investors who choose the more difficult and complex models to use as technical analysis tools to predict stock prices. However, Rome wasn't built in a day. So, it`s necessary to learn the traditional models penetratingly. To have a more thorough understanding of classic models in the past, this paper provides a great way to analyze the ARIMA model. It explains the methods of fitting the ARIMA model step by step and improves the ARIMA model by adding seasonal parameters to fit the SARIMA model, enabling readers to better understand the advantages and disadvantages of this model. Although the results of the ARIMA model are unfortunately similar, and difficult to demonstrate its predictive power in images, the SARIMA model presents a trend prediction that conforms to people's imagine. As of today, the change in the price of Netflix's stock has been visible, which is different from the predicted price. Therefore, while making technical predictions, investors also need to combine fundamental analyses like the supply and demand balance, changes in interest rates, government regulations, macroeconomic indicators, and unique features of the industry in question, among others. Rather than relying solely on a model.
Performance analysis of multiple erasure coding methods
Authors: Xia Bangyan
Published: October 2023

The blockchain is a decentralized digital ledger that provides tamper-proof and privacy fea-tures, making it a secure solution for various applications, including IoT homes and smart ag-riculture. However, these devices typically have limited storage resources, which poses chal-lenges in supporting the blockchain consensus process and storing the blockchain ledger. To address this issue, erasure coding can be employed, which reduces storage overhead and pro-vides data protection. Therefore, it is necessary to analyze the performance of multiple erasure coding methods. In this paper, a performance analysis was presented on (15,12) RS code and (16,12) pyramid code to evaluate their performance from three dimensions: fault tolerance, read reconstruction cost, and storage overhead. MATLAB simulations were conducted to in-vestigate the repair capabilities of the two encoding methods in response to different numbers of node failures and evaluate their performance. Finally, the performance of both codes was discussed based on the previous analysis.
A comparison of various deep learning models on blood glucose management of type 1 diabetes: A brief review
Authors: Jiang Z. , Yang N.
Published: October 2023

Type 1 diabetes has become one of the most common chronic diseases nowadays because patients pancreas cannot produce sufficient insulin, which helps the blood sugar to enter the cells, which will cause them to build up in the bloodstream and leads to complications and dis-eases. Therefore, a basal-bolus insulin therapy that contains daily insulin injections has be-come routine for patients with type 1 diabetes to help regulate the blood sugar level. To better monitor the blood glucose level, digital health monitors are becoming the trending method for type 1 diabetes patients. Meanwhile, all the data generated by the digital monitoring devices made researchers realize that deep learning algorithms could be implanted to help the device better predict a patients blood sugar levels. In this paper, we aim to present a review of testing several state-of-the-art deep-learning models on blood glucose prediction. We have identified a literature search and focused on the deep-learning algorithms for glucose management. After detailed explanations of each model, we employ them on one mutual dataset to identify the direct prediction results of each model and compare the pros and cons of these models accord-ing to the results report. While all these models have the most advanced frameworks, the lack of feature varieties and data availability becomes their limitations. However, followed by the increased focus on the digital health field, these challenges might soon get resolved, which leads to more comprehensive models that could be further deployed in clinical conditions.
Multi-Agent Reinforcement Learning in non-zero-sum games: Algorithms and applications
Authors: Ma Xu
Published: October 2023

Multi-Agent Reinforcement Learning (MARL) algorithms have been utilized in resource allocation modelling, game theory analysis, formation of alliances and so on. The potential for future research and applications of MARL in non-zero-sum games is vast, including integrating MARL with game theory and mechanism design, developing online learning algorithms for dynamic environments, and exploring applications in various domains. Currently, there is a relative lack of review literature on this area of research. This paper, therefore, aims to fill this gap. This work will first introduce the role of MARL in non-zero-sum games. Then it will discuss in detail the practical applications of MARL in economics, social sciences, and political science, which are typical of non-zero-sum games while presenting possibilities and challenges for future research. Finally, a summary is given at the end of the paper. This article provides insights into the current and future possibilities of using MARL in non-zero-sum games.
Research on path planning techniques based on deep reinforcement learning
Authors: Dong Tianyu
Published: October 2023

Path planning problem stands for a sort of problem that find a secure path from the departure position to the destination in the unique application area without conflicts with other objects under the premise of minimum time or distance cost. Path planning has many applications in real life. This paper summarizes and classifies the current main research results on path planning, which are classified according to conventional techniques, single-agent path planning techniques and multi-agent path planning techniques. Conventional techniques are classified into three different algorithms: Traditional techniques, Graphics techniques and Intelligent bionics techniques. For the single-agent path planning techniques, it is primarily broken down into two categories: value-based and strategy-based. For multi-agent techniques, according to the improvement techniques, it is split in three categories: expert demonstration type, improved communication type and task decomposition type. Based on above classification, the mechanisms, advantages, and disadvantages of the existing algorithms are summarized and compared, and the future work in this field is prospected.
A research on attack and defense models of network security
Authors: Zhang Bowen
Published: October 2023

This paper analyzes the attack and defense model in network security. For attack models, this paper focuses on Distributed Denial of Service (DDoS), Advanced Persistent Threat (APT), Man-in-the-Middle Attack (MITM), Trojan House, and Cross Site Script Attack (XSS). For each attack model, this article analyses the attack principle and attack effect and summarizes the advantages and disadvantages of various attack models. In the defense model, this paper studies policy, protection, detection, response (P2DR) and the zero-trust model. This paper introduces the basic principle and defense strategy of the P2DR model and analyzes the advantages and disadvantages of this model. At the same time, this paper also introduces the concept and basic principle of the zero-trust model and analyzes its advantages over the traditional security model. The comprehensive analysis shows that different defense strategies need to be adopted for different attack models. When choosing a defense model, it is necessary to choose the most suitable model according to the specific situation. The research of this paper has certain practical significance for improving the level of network security.
Research and improvement of movie recommendation based on a collaborative filtering algorithm
Authors: Zhao Junxiang
Published: October 2023

Among various recommendation algorithms, collaborative filtering stands out as a popular and effective technique that analyzes user behavior to suggest personalized items. The goal of studying recommendation algorithms is to enhance the exactness of recommendation outcomes and provide users with tailored suggestions. In this paper, the algorithm of collaborative filtering is divided into User-CF and Item-CF, and their algorithm principles and implementation steps are introduced. The similarity calculation of both methods is improved, and a comparison of their advantages and disadvantages is conducted. Experiments are conducted on the MovieLens-latest-small movie dataset to compare the performance of User-CF and Item-CF before and after their similarity calculation is improved. The results show that the similarity improvement of User-CF can substantially enhance the algorithms inclusiveness, while the similarity improvement of Item-CF does not have a considerable impact on the algorithms performance indicators. Finally, the comparison shows that Item-CF has better coverage and popularity, while User-CF has better precision and recall.
Performance analysis of several common error correction codes
Authors: Du Xiwen
Published: October 2023

With the rapid development of information technology and advances in science and technology, people are constantly interacting with data and coding. Error correction codes are integral to ensuring the security, robustness, and accuracy of information and systems, used not only in various engineering fields but in our daily lives as well. Systems that incorporate error correction codes have been proven to perform significantly better than those that do not, which makes it essential to understand the principles behind coding, encoding techniques, and their performance in specific domains.This paper focuses on three widely used error correction codes: Hamming code, RS code, and convolutional code. By introducing each code separately and evaluating their performance in different practical areas using proper simulations and relevant research results, this paper provides a comprehensive understanding of the vital role of error correction codes in channel encoding. Each code has its strengths, weaknesses, and specific applications. Hamming code, for example, is suitable for correcting bit errors in computer memories and communication systems. RS code is widely used in digital storage and broadcast systems due to its high level of error correction capability. Convolutional code is ideal for correcting bit-errors in continuous channels, making it an excellent choice for wireless communication systems. Overall, the results demonstrate that these error correction codes are crucial to guaranteeing data transmission integrity and system robustness. Understanding their principles and performance can guide future research and development of coding techniques in advancing technological progress and improving our daily lives.
Maturity level determination and optimization model of D&A system based on support vector machine and multi-objective programming
Authors: Yao Shunyu
Published: October 2023

In today's era of fragmented information, good data management is essential for companies. Optimizing data integration and analysis (D&A) is crucial to unleashing the full potential of data value. An evaluation was conducted on Intercontinental Freight Company's (ICM) D&A system, with three key assessments identified: importance, connectivity, and availability nature. Via multi-level fuzzy evaluation metrics, key points that affect the system's development were determined. Multiple regression lines were used to analyze relationships between people, process, and technology. Potential performance was measured via average growth in business profits, and an index of 11 variables was created to measure the system's maturity. A growth phase distribution model was developed utilizing a support vector machine and tested on a sample of 50 US ports. The optimization model, based on multi-purpose functions, maximizes efficiency and can be seen by comparing factors before and after port optimization. This experiment proves the effectiveness of the D&A system development evaluation model.
Analysis for RS-coding and fountain-code for erasure coding
Authors: Li Yusheng
Published: October 2023

This research provides an in-depth analysis of RS-coding and fountain-coding for erasure cod-ing, focusing on the different circumstances that data storage may face during daily usage. The essay outlines the fundamental information of RS-code and fountain-code, and com-pares their effectiveness in addressing varying degrees of erasure in the data disk. The research includes case studies and content analysis to identify appropriate circumstances for using dif-ferent coding methods. Based on these findings, the research concludes that RS-coding is more effective when fixing erasures up to a certain limit, beyond which fountain-coding is a better option. Finally, the research presents a research example to demonstrate the practical usage of different coding methods in specific circumstances, highlighting the advantages and disad-vantages between the two coding systems. Overall, this research provides valuable insights into erasure coding and can aid in optimizing data storage systems for efficient and reliable per-formance.
Structural analysis of U-Net and its variants in the field of medical image segmentation
Authors: Kan Yanjia
Published: October 2023

Medical image segmentation can provide valuable information for doctors, it has important research value in the medical field. Meanwhile, U-Net, as the fundamental networks for such tasks, brings a substantial improvement in the segmentation performance of traditional medical images. With the increasingly widespread use of U-Net, researchers have designed various U-Net variants according to different task requirements. However, most of the current summaries of U-Net variants are divided according to the direction of network applications, and the structural relationship between the variant networks and U-Net is not elaborated. Therefore, this paper classifies U-Net variants according to their network framework by elaborating the principles of U-Net structure. According to the U-Net network structure, it is divided into three main categories: backbone improvement, module addition and cross-network fusion. Further, the characteristics, advantages and disadvantages of different categories of variants are introduced, and the directions of the variants for U-Net optimization are analyzed. Finally, the article summarizes the current development direction of U-Net variants and provides an outlook on the future directions that can continue to be optimized.
Research on robot path planning methods
Authors: Chen Tianhao , Jiang Guanhong
Published: October 2023

Mobile robots are used extensively across a variety of industries and fields. How to discover a start-to-end path without colliding has become a hot topic in recent years due to the complexity and uncertainty of the workplace. In various environments, a path planning technique should demonstrate high efficiency and speed. And this can reduce the energy consumption of the robots and greatly increase their working efficiency. This paper will conclude the presently popular path planning algorithm. Based on the different features of these algorithms, they are divided into three types: traditional path planning algorithm, neural-work-based algorithm, and sampling-based algorithm. Based on the new papers in these years, detailed introduction of the algorithms and their variants will be given. At the end of the paper, the thesis is summarized and the future research trend is prospected.
Research of different feature detection and matching algorithms on panoramic image
Authors: Xiao Jindong
Published: October 2023

Image stitching is the process of combining numerous photos to make a panorama. The technique of image stitching has rapidly advanced and grown to be a significant area of digital image processing. Many image stitching methods have been proposed and studied in prior study. In this paper, the image stitching process is implemented using different algorithms. For keypoints detection, the algorithms of Harris corner detection, SIFT(Scale-Invariant Feature Transform), SURF(Speeded Up Robust Feature) and ORB(Oriented FAST and Rotated BRIEF) algorithms are applied, then use different methods (i.e., Brute Force, etc.) for feature matching. The RANSAC(Random Sample Consensus) method is used to calculate a homography matrix from matched feature vectors and use it to warp the images. Image blending and cropping methods are proposed to enhance the image quality. Given groups of the self-captured images, experiments have been down to shown the performance of different techniques.
A content-based collaborative filtering algorithm for movies and TVS recommendation
Authors: Wang Ziqi
Published: October 2023

With the rapid development of multimedia technology and the constant upgrading of film and television libraries, users' demand for movies and television is increasing. How to accurately and timely find favorite movies from massive movie and television resources according to user's preferences and needs has become a great challenge. In recent years, the recommendation of movies and TVs has attracted a lot of research interest from academia and industry. The existing recommendation algorithms mainly include content based and collaborative filtering. The former recommends projects through collaborative learning of others' interests, while the content-based method examines the rich context of the project. In this paper, to further improve the performance of recommendations, a content based collaborative filtering method is proposed to provide recommendations for movies and television. Specifically, we extract and vectorize feature and category information from movies based on TF-IDF and apply truncated SVD to reduce the dimensions of the rating and TF-IDF matrix to retain the most representative information. We calculate the cosine similarity between the vectors from these two matrices. The final recommendation is to list 10 movies based on the average similarity of content and ratings. Extensive experiments on Amazon review data have proven the effectiveness of this method.
Modeling and numerical simulation optimization of gain spectrum of thulium-doped broadband fiber amplifier based on cat swarm algorithm
Authors: Guo Rui , Liu Zhuoer , Quan Yuchen
Published: October 2023

With the development of light technology, especially the maturation of WDM/DWDM technology, the demand for optical amplification of the S-band and S+ band (1450nm~1520nm) is increasing day by day, and the energy level structure of Tm3+ has energy level transitions to meet the requirements of S-band and S+ band amplification. Although thulium ion has a very complex energy level structure, TDFA is one of the most promising optical fiber amplifiers for S and S+ bands. At the same time, with the continuous development of computer technology and mathematical theory, the optimization algorithm has been rapidly developed and widely used in recent decades, based on genetic algorithm, simulated annealing algorithm, and other traditional optimization algorithms that have been proved to get good convergence speed and optimization results. In this paper, the thulium-doped fiber amplifier's gain is optimized by using a cat swarm intelligent optimization algorithm to obtain the maximum fiber length and doping concentration.
Using end-to-end learning and PyAutoGUI to apply gesture recognition for human-computer interaction
Authors: Tang Junhao
Published: October 2023

Contact-less human-machine interaction is becoming increasingly important due to the growing number of special environmental needs and accessibility situations. Gesture recognition has also been a hot topic in computer vision and machine learning in recent years. In this paper, a real-time computer manipulation system based on hand gesture recognition is studied and deployed. A relatively mature end-to-end target recognition model, the YOLOv5 model, is trained in this paper to achieve real-time detection and recognition of hand gestures. According to the result of the recognition, it is translated into the corresponding operation on the computer according to a set of rules, and then PyAutoGUI is used to actually control the computer. At the end of the research, the trained YOLOv5 model exhibited excellent performance and verified the feasibility and scalability of the solution. This is a good inspiration for developing a more convenient and efficient related software.
Deep learning methods used in movie recommendation systems
Authors: Liu Lexi
Published: October 2023

As the amount of internet movie data grows rapidly, traditional movie recommendation systems face increasing challenges. They typically rely on statistical algorithms such as item-based or user-based collaborative filtering. However, these algorithms struggle to handle large-scale data and often fail to capture the complexity and contextual information of user behavior. Therefore, deep learning techniques have been widely applied to movie recommendation systems. This paper reviews movie recommendation algorithms based on traditional statistical models and introduces three main deep learning techniques: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). ANN can extract features at different levels of users and movies; CNN can capture features of movie posters and movie data to recommend similar movies; RNN can consider user historical behavior and contextual information to better understand user interests and demands. The application of these deep learning techniques can enhance the accuracy and user experience of movie recommendation systems. This paper also demonstrates the advantages and disadvantages of these models and their specific application methods in movie recommendation systems, and points out the direction for further development and improvement of deep learning models in this field.
Comparison the effects of KNN and linear regression models in lung cancer prediction
Authors: Zhou Yi
Published: October 2023

Lung cancer has a range of major factors like smoking, yellow gingers, anxiety, etc. now, the problem of this research is that prediction for lung cancer. Prediction for lung cancer is a complex problem that is not suitable for human prediction. This research using a dataset was from Kaggle. There are 16 rows and 309 columns. To determine the k nearest neighbors (KNN) algorithm and linear regression algorithm, which one is better for prediction for lung cancer, and which coefficient will be best effective. This research uses mixed method research. In this work, when the K of the KNN algorithm equals 7 or 2, the effectiveness of the KNN model is best, when the alpha of the linear regression algorithm equals 20, the effectiveness of the linear regression model is best. The KNN model is better than the linear regression model, though the difference is negligible. In the future, more emphasis can be placed on using a wider range of algorithms or using more extensive and generalized dataset, as well as assessing the efficiency of the algorithm on larger datasets.
Research on the diagnosis of corona virus disease 2019(COVID-19) based on machine-learning
Authors: Du Fang
Published: October 2023

In recent years, the Corona Virus Disease 2019(COVID-19) has brought a huge impact on people's daily life and the normal operation of society, and as machine learning research deepens, this technology could help detect viruses such as the novel coronavirus. According to this, how to accurately, quickly and effectively analyze and classify laboratory results with multiple indicators through the method of machine learning is the object of this paper. In order to explore the classification performance of various machine learning classification algorithms on laboratory results, linear and non-linear methods were used respectively to analyze and classify the laboratory results. In linear analysis, distance discriminant and fisher discriminant were used to explore the classification effect of linear classification on laboratory results. The non-linear analysis mainly used Adaptive Boosting(AdaBoost) and Random Forest algorithm which are widely used to test the classification effect under the influence of multiple indexes. In this paper, python and other tools were used to classify samples of different combinations by using the idea of cross validation. By comparing the running time and detection accuracy, it was found that Adaboost algorithm is applicable in most cases and was a relatively fast and accurate classification method. In addition, Random Forest algorithm had similar accuracy, but it might have better performance on a large data set.
Homography transform enhance CNN prediction accuracy on image classification
Authors: Li Chang
Published: October 2023

Convolutional Neural Network (CNN) image classification is a well-established algorithm that has been implemented in many fields. Benefit from the digitalization process and the exponential increase in the base of smart devices, this algorithm can be applied to even more traditional or casual contexts in the future driven by the trend of Internet of Things. Thus, the needs for optimizing image classification in specific domains may have turned out to be ongoing valuable research direction. This paper focuses on providing optimization under one example context which is using traffic signs as the experimental target. For the randomly selected traffic sign samples in the experiment, the accuracy obtained from the samples treated by the homography transform compared to control group passed all three statistical tests: Two Sample t-test, McNemar's test, and Fisher's exact test. Therefore, research direction has achieved small-scale validation and presents optimism for large-sample experiments and further research in optimization using the introduced strategy in the paper.
Fruit 360 classification based on the convolutional neural network
Authors: Zhang Dehui
Published: October 2023

This research paper focuses on the Fruit360 Classification challenge, a task aimed at developing a fruit classification model capable of accurately identifying various fruits and distinguishing them from each other. In this study, the Fruit360 dataset is used, consisting of 90380 images of 131 fruits and vegetable classes. Prior to training the CNN model, the images are preprocessed by resizing, normalizing, and augmenting them. The authors employ a pre-trained CNN model called ResNet-50 using the PyTorch deep learning framework and add a custom fully connected layer on top to adapt the model to the specific classification task. The authors conclude that the proposed model achieved excellent performance on the Fruit360 dataset. The study highlights the importance of the Fruit360 Classification challenge in advancing the field of computer vision, specifically in the development of deep learning algorithms for image classification tasks. The proposed model has the potential to improve the efficiency and accuracy of fruit classification, which can benefit the fruit industry in terms of enhanced productivity and cost-effectiveness.
Chinese stock price prediction under COVID-19 period based on linear Regression Model
Authors: Zhang Yijia , Yang Yiran
Published: October 2023

To anticipate and assess the price trend of scientific and technical stocks in a time of high volatility following the breakdown of China's COVID-19 Epidemic Economic Policy at the end of 2022, we plan to utilize a training linear regression model in this work. assisting people and businesses in making better analyses and decisions during this time of high risk, thereby lowering investment risks. This paper uses the stock price data of Alibaba, Tencent, and Xiaomi in Quandl during the new crown epidemic to represent the general trend of Chinese technology stock prices. We preprocessed the data to retain features that better reflect the characteristics of the data and remove features that have less impact on the data analysis. This study chose a linear regression model to model various relationships through the classifier used by Scikit-Learn for regression and estimated the unknown parameters in the linear regression model from the data. The data from the training and test sets are used to train the linear regression model, and the results are visualized as graphs, which can more intuitively convey the overall trend and local volatility of stock prices. In this study, the accuracy of the model can reach more than 85%. The visualized figure shows that although stock values recovered quickly when the regulation was lifted, they are still lower than they were before the outbreak due to the impact of China's past epidemic policies on their long-term fall. The experimental findings demonstrate the great accuracy with which the advanced regression model in this work can forecast the price trend of technology stocks throughout the period of high volatility following the policy's unsealing and the extent to which it may represent price volatility.
Spam filter based on naive bayes algorithm
Authors: Han Mengyuan
Published: October 2023

The widespread use of Electronic Mail (E-mail) has led to a significant increase in spam, which has severely impeded the growth and well-being of the Internet. To mitigate this issue, the implementation of email filtering techniques has become necessary, requiring the use of specific technological tools. Presently, the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) algorithms are commonly used in probability statistical classification methods for email filtering. Among these, the NB algorithm is the most classical, with its rich mathematical theory as the basis, high classification efficiency, and straightforward algorithmic approach. However, the algorithm relies on the conditional independence assumption, making the accuracy susceptible to the correlation between attributes. This study focuses on email filtering techniques based on the NB algorithm, conducting experiments to evaluate the classification accuracy and proposing feasible improvements to weaken the independence assumption. The experimental results demonstrated the effectiveness of the employed method.
The stock price forecast under the failure of silicon valley bank based on the ARIMA model
Authors: Gu Dengyi
Published: October 2023

The collapse of Silicon Valley Bank on March 10, 2023, had a profound impact on the stock prices of many companies in the United States. This study aims to examine the response of other banks in the US to this event by utilizing the Autoregressive Integrated Moving Average (ARIMA) model to forecast their stock prices. The research demonstrates that the ARIMA model effectively predicts the general trend of these banks' stock prices, with Root Mean Squared Error (RMSE) values below 1 for four out of six major US banks. These findings indicate that the proposed method is a promising tool for managing sudden fluctuations in stock prices, outperforming traditional linear regression models. Consequently, this research provides valuable insights for investors and financial institutions in managing and mitigating risks associated with abrupt market changes. Additionally, the study contributes to a greater understanding of the effects of bank collapses on the stock market. Overall, the research highlights the significance of incorporating advanced forecasting methods, such as ARIMA, in analyzing and predicting stock price movements in volatile market conditions.
A research on the consensus mechanisms
Authors: Peng Jiawei , Wu Yijun , Yuan Kunfeng
Published: October 2023

A distributed and decentralized ledger widely used in the computer science and financial fields called blockchain has provided safe and fast transactions for multiple parties. Also, check the transaction by each node on the blockchain. The consensus mechanism is the core of the blockchain. It lets all the nodes reach an agreement for those transactions, which ensures security and accuracy and make Bitcoin valuable and popular. Two of the most mainstream Consensus mechanisms are Proof of Work (PoW) and Proof of Stake (PoS), and Proof of Authority (PoA) is the new one that will apply in the future. Many discourses talk about consensus mechanisms, most of which are review papers. Those papers mainly show a specific aspect of a consensus mechanism or introduce the primary notion, but they rarely explain the corresponding relationship between theories and cryptocurrency. So, the purpose is to give a clear structure, connect the consensus mechanism to its application and simplify the reader's understanding. This paper aims to provide an overview of the consensus mechanism, including its general definition, concepts of different mechanism variants, and advantages and disadvantages. For the structure below, the essay introduces the notion of consensus mechanism and how PoW, PoS, and PoA work. Then summarize the papers based on these three consensus mechanisms, describing the theories of many consensus mechanisms and comparing the advantages and disadvantages. The essay also creates a comparison table about these three consensus mechanisms to embody the content above the stem better.
A research on digital signature schemes
Authors: Li Jinhan
Published: October 2023

Currently, a lot of studies have been done on the core of Bitcoin, the blockchain. It offers a wide range of distribution mechanisms and infrastructure that keeps the data constant, unchanging, and time consistent. The blockchain is an ideal tool for assertion class applications to offer digital proof of ownership and time stamps as a result of the creation of digital summaries of physical or digital assets. So it is possible to apply the block chain to a wide range of fields, for example, online paying, trading and so on. As the block chain develops, the safety issues directly affect the effectiveness and integrity of the trade. In essence, these questions are about the safety of information. To ensure the security of the data, this paper studies and uses the security of the digital signature. The classification and characteristics of each kind of digital signature are introduced in this article, as well as some other scholars' achievements in this field are analyzed.