bluuebunny/crossref_metadata_2025 huggingface dataset:
Bogdan, Geometry and Quanta
Authors:   Stockholms Universitet, Fysikum, Bengtsson Ingemar , Życzkowski Karol ,   Institute of Theoretical Physics, Jagiellonian University, Cracow & Center forTheoretical Physics, Polish Academy of Sciences, Warsaw
Published: August 2024

We review selected achievements of the late Bogdan Mielnik in the field of theoretical physics, with an emphasis on his attempts to go beyond standard quantum mechanics. Some of his original views on the problems of contemporary society and organization of science are also recalled.
Bildning för hållbar utveckling – och dess potential för svenskämnets undervisning
Authors: Wessbo Simon , Uhrqvist Ola
Published: August 2024

Den här artikeln visar på hur arbete med berättelser inom ramen för vad vi kallarBildning för hållbar utveckling, kan ge svenskämnet en mer framträdande roll i ämnesövergripande undervisning. Första delen av artikeln visar hur det välkända men samtidigt mångtydiga begreppet bildning kan ha stora likheter med grundtankar i det framväxande fältet Lärande för hållbar utveckling. Artikeln argumenterar för hur en sammanslagning gör kärnan i Lärande för hållbar utveckling synlig och relevant för bredare lärargrupper. Samtidigt aktualiseras innehållet i bildning i ljuset av människans nya roll och ansvar i den antropocena epoken. Vi menar att bildning för hållbar utveckling karaktäriseras av ledorden komplexitet, öppenhet, meningsskapande och engagemang. Den andra halvan av artikeln utmanar och konkretiserar detta föreslagna begrepp med hjälp av tre exempel där berättelser och berättande står i centrum.
Abstract
This article shows how stories and storytelling can be used within the framework of what we call Bildung for Sustainable Development. This concept may contribute to the school subject of Swedish by providing it with a more prominent role in interdisciplinary teaching. The first part of the article shows how the well-known and ambiguous concept of Bildung has several similarities with basic ideas in the emerging field of Education for Sustainable Development. The article argues for the benefits of these concepts in making the core of Learning for Sustainable Development visible and relevant to a wider spectrum of teachers. At the same time, the content of Bildung is brought to the fore in the light of the new role of humankind and our responsibilities in the Anthropocene era. We believe that Bildung for Sustainable Development is characterized by complexity, openness, meaning-making and commitment. The second half of the article challenges and concretizes this proposed concept with the help of three examples in which stories and narration are considered.
Att göra sin röst hörd – en didaktiskt orienterad bilder-boksanalys av Naturen och Mitt bottenliv – av en ensam axolotl
Authors: Lyngfelt Anna , Söderberg Eva
Published: August 2024

Genom narratologisk analys utforskas didaktiska möjligheter att utveckla kunskap om relationen mellan ekologisk, ekonomisk och socialt hållbar utveckling. Två bilderböcker utgör utgångspunkt för analysen: Naturen (2020), av Emma AdBåge, och Mitt bottenliv – av en ensam axolotl (2020), av Linda Bondestam. Forskningsfrågan lyder: Hur kan analys av begreppet röst vara didaktiskt användbart för att bilderböckernas läsare ska kunna positionera sig själva i relation till böckernas innehåll och det omgivande samhället? De bilderböcker som används rymmer en stor tolkningspotential. Detta visar sig genom bilduppslagens rikedom av representationer i form av exempel hämtade från vår ”civilisation”. Dessa möjliggör prövande, tänkbara positioneringar för böckernas läsare. Genom att lyfta fram möjligheter med den narratologiska analysen visar vi vad klassrumsarbete skulle kunna innebära, med utgångspunkt i UNESCO:s Education for Sustainable Development Goals. Learning Objectives (www.en.unesco.org).
Making your voice heard. A didactically oriented analysis of the picture books Naturen and Mitt bottenliv – av en ensam axolotl
The purpose of this article is to explore didactic possibilities for developing knowledge about the relationship between ecological, economic and social sustainable development based on narratological analysis of Swedish picture books. For this purpose, the picture books Naturen (2020), by Emma AdBåge, and Mitt bottenliv – av en ensam axolotl (2020), by Linda Bondestam are used. The research question is: How can the narratological concept of voice be didactically useful for the readers of the picture books to be able to position themselves in relation to the content of the books and the surrounding society? The analyses of the books show that it is impossible to distinguish ecological conditions from economic and social circumstances, and that becoming a subject does not necessarily lead to emancipation. In connection with this, the analyses show that the voices of the books - if seen as part of a public discourse where teaching is included - can be twisted together and develop into a communal voice through the voices of students. It is through the process of positioning, by making use of the interpretive potential of the picture books, that the possibilities for developing knowledge about the relationship between ecological, economic and social sustainability are to be found.
Research and Design of Toy Chinese Dragon Model based on OpenSCAD and Render based on Three.js
Authors: Zhu Jiaqi
Published: August 2024

Nowadays, there are a lot of 3D models in the world, but how to build a Chinese Dragon is relatively few methods.Aiming at this problem, this paper put forward a method which made up for the deficiency in this aspect. In order to build this model, OpenSCAN software was used in this paper. This paper also divided Chinese Dragon into four parts: head, body, claws and tail, and used different methods to model these four parts. At the same time, in order to make the Chinese Dragon look more realistic, this paper also implemented basic lighting and shadows on Three.js. The methods used are the Phong model and shadow maps. What's more, In order to achieve the dragon in Chinese people's imagination, this paper also simulated the appearance of dragons breathing fire and surround fire. In the process of doing this, in order to make the effect of fire more realistic, this paper used Perlin noise to simulate the burning of fire. Meanwhile, in order to compare the effects of different surround fires, some people were invited to evaluate the effects of various surround fires. For the final experimental results, a cartoon style Chinese Dragon was made, and the fire effect was achieved very well.
A Research on Image Recognition and Classification Based on Traditional Machine Learning and Deep Learning
Authors: Huang Zelong
Published: August 2024

With the rapid development of smart devices, a huge amount of images are appearing in people's lives, so the processing of images, especially recognition and classification processing, becomes more and more important. This paper aims to provide researchers with an overview of current traditional machine learning (ML) and deep learning (DL) in solving image recognition and classification problems, specifically analyzing architectures such as KNN, SVM, Random forest algorithms, and CNN Architecture, and finding available data training results for comparative analysis. In summary, it is found that deep learning algorithms perform well in handling large-scale complex datasets and automatic feature extraction, and are applied to most image recognition and classification problems, but the traditional machines have obvious advantages of accessibility and speed in handling small-scale data, and deep learning algorithms are still not an effective choice in some special cases or resource-limited environments. At the end of the article, the full text is summarized and outlook.
Research on Sales Decision of Vegetable Products Based on Time Series Analysis
Authors: Ma Wenting , Yang Jiawei , Zhao Haitao
Published: August 2024

This article studies the automatic pricing and replenishment decision-making of vegetable products. Through factor analysis and time series analysis, analyze the inherent laws, and combine mathematical programming with genetic algorithm to make sales decisions for vegetable products. Firstly, the data is preprocessed using time series analysis to predict and fill in missing values from September 12, 2022 to October 14, 2022, when the data is empty. Secondly, the relationship between vegetable types was analyzed. JB and Q_Q tests were conducted on the daily sales of each category, and it was found that the data did not follow a normal distribution. Therefore, this article uses Spearman correlation coefficient to analyze the correlation between different categories and finds that the flavor, taste, or cooking characteristics of edible mushrooms and water rhizomes are complementary and can be used in combination. To analyze the relationship between the sales volume and price of vegetable varieties, this article uses linear regression analysis to find that there is a negative correlation between sales volume and price. Finally, considering the shortcomings of time series analysis and factor analysis models, combined with practical considerations, the data to be collected includes competitor data, seasonal sales data, market trends and consumer insights data, inventory data, weather data, promotion and discount data, purchasing power data, customer feedback and demand data, vegetable supply chain data, and loss rate data.
Prediction of Vacant Parking Spaces in the City based on LSTM
Authors: Zhang Yuhao
Published: August 2024

With the continuous progress of modern society, more and more families have their own private cars for the convenience of travel and other needs, making it easier and faster to go anywhere. However, with the increase in the number of private cars, parking spaces in parking lots cannot keep up with the growing number of cars. It is increasingly difficult to find parking spaces everywhere when driving, and even if they are found, they are often full. The problems of chaotic management in parking lots have become more serious, greatly affecting people's pace of life. This study aims to predict the vehicle access situation of a parking lot in the future by constructing a prediction model. Based on the collected information of nearly 9,000 vehicles entering and leaving a parking lot, this paper uses the LSTM model to analyze the data by comparing the entry and exit times of different vehicles and whether the vehicles leave the parking lot, and obtain a highly fitted parameter model. The results show that the distribution state of the fitted value of the model is quite close to the real value. The model predicts the data of different time periods in the next 6 days, and obtains relatively accurate prediction results.
Fast and High-resolution Image Generation Based on Improved DCGAN
Authors: Xin Yixuan
Published: August 2024

Among generative models, Generative Adversarial Network (GAN) has been sought after by researchers since its proposal. The researching fields of image generation, style transformation, data augmentation, super-resolution, image restoration, and image transformation have shined because of GAN. Deep Convolution Generative Adversarial Network (DCGAN), as an early neural network to improve GAN, solved the problem of unstableness during training. It can be easily scaled to deal with larger datasets and more sophisticated tasks, so various image generation and manipulation tasks can be tackled by this powerful tool. Nevertheless, it still has certain problems. This research investigates the hyperparameters, label smoothing and improved model’s effect on the quality and speed of image generation, and finally selects the appropriate hyperparameters and label smoothing to cooperate with the improved model to quickly generate clearer images with DCGAN in the case of few samples and few number of trainings. This work can bring some ideas for saving computational resources and data for training.
Research on rapid SAR solution for submarines based on Particle Swarm Optimization
Authors: Wu Minxiao , Liu Yuchen , He Yuren
Published: August 2024

This paper uses particle swarm and neural network algorithms to construct an efficient submarine search and rescue (SAR) solution based on an unmanned underwater detector cluster, aiming to provide stronger safety guarantees for tourists who experience the submarine project. Specifically, this article constructs a position prediction model and a search optimization model respectively. The former will accurately simulate the submarine's motion trajectory and various parameters through a combination of interpolation fitting and neural network learning. The latter will use the above data to Particle swarm algorithm optimizes the group behavior of unmanned detectors to derive the optimal search and rescue plan. Finally, upon analyzing the success rate of this strategy, it’s surprised to find that its increasing trends complies with Logistic growth law. This indicates that the strategy has promising advantages in terms of time efficiency and accuracy. The research conducted in this article provides an application direction for the particle swarm algorithm and a feasible model for the construction of deep-sea search and rescue strategies, which has a great use value.
Prediction of USD/RMB Price Change Based on BP Neural Network
Authors: Li Xiaorui
Published: August 2024

The market position of the RMB has gradually increased, and the price change of the US dollar against the RMB has become increasingly important. Therefore, it is vital to use scientific means to forecast the exchange rate forecasting model. In this thesis, the BP neural network model is established by MATLAB, and the price change of the USD/RMB exchange rate is analyzed and processed according to the characteristics of the exchange rate prediction model. Part of the data is elected for the network training, the other part is used to verify the accuracy of neural network prediction. The results indicate that the fitting accuracy is 94.6%. Continuing to modify some arguments of the model including changing the number of training rounds to 100000, and changing the training target to 1e-15 with the learning rate remaining unchanged, The equal accuracy could be further increased to 96%. In the results, the model can precisely, mirror the trend of fluctuations, verify the effectiveness of this forecasting method, provide an important reference for financial institutions and personnel engaged in the financial industry, and have great significance for foreign exchange management to effectively avoid market risks and cope with the impact of exchange rate fluctuations.
BERT Model with Fuzzy Logic Optimization on Multivariate Sentiment Analysis Tasks
Authors: Sun Chengxiang
Published: August 2024

With the development of natural language processing techniques, sentiment analysis techniques are constantly updated. However, most of the sentiment analysis techniques are limited to handling binary sentiment analysis and perform poorly on multivariate sentiment analysis tasks. This paper explores the optimization in the field of sentiment analysis by using the BERT model combined with fuzzy logic to improve the performance of the model on multivariate sentiment classification tasks. The study uses the Twitter comments dataset and aims to gain insights into the challenges and optimization strategies of sentiment analysis models when dealing with multivariate sentiment. First, a single BERT model is experimentally demonstrated to perform better in binary classification tasks compared to multivariate classification tasks, highlighting its adaptability for simple sentiment classification and providing a basis for comparative experiments for multivariate classification experiments. To further improve the performance in multivariate sentiment analysis, fuzzy logic is introduced in this paper, and through the integration of this fuzzy logic, the accuracy of the BERT model on the multivariate sentiment classification task is successfully improved. The results of the study not only reveal the superiority of a single BERT model in binary classification but also emphasize the importance of incorporating fuzzy logic when facing multivariate sentiment analysis.
Deep Convolutional Neural Network enabled unmanned agricultural machine visual navigation system: architecture design, model optimization and empirical evaluation
Authors: Gao Xin , Yuan Shaojia , Zhu Jiazheng , Zhao Yifei
Published: August 2024

In response to the pressing need for intelligent navigation technology in modern agriculture, this paper introduces a visual navigation system for unmanned agricultural machinery based on deep convolutional neural networks (CNNs). The system integrates the strong representational power of deep learning with rich visual information to facilitate high-precision, adaptive autonomous navigation in complex agricultural environments. Key innovations encompass: (1) the development of a customized deep CNN model proficient in extracting critical features from agricultural images, such as obstacles, landmarks, and crop rows; (2) the integration of the CNN model with visual SLAM (Simultaneous Localization and Mapping) technology for real-time localization and three-dimensional mapping of agricultural landscapes; (3) the creation of a decision-making system that merges deep learning predictions with conventional path planning algorithms to ensure machinery navigates around obstacles while adhering to optimal trajectories. Experimental validation across diverse agricultural scenarios demonstrates that the proposed visual navigation system sustains high localization accuracy (RMSE < 0.2 meters) and robust obstacle avoidance performance (success rate exceeding 95%) even in GPS-denied or weak GPS environments. Furthermore, the system significantly improves the continuity and efficiency of machinery operations by reducing unnecessary repositioning and redundant tasks. Practically, it is readily integrable into existing agricultural machinery platforms, offering broad applicability and potential for widespread adoption.
Fatigue Driving Detection Methods based on Human-computer Interaction
Authors: An Guanzheng
Published: August 2024

Drivers' fatigue driving detection is very important to traffic safety, and is closely related to the safety of human life and property. It is a key research topic for researchers. Effective fatigue identification technology can effectively reduce the traffic accidents caused by fatigue. This paper provides a systematic review of the detection methods for driver fatigue driving. The concept of driver fatigue and its necessity for detection are introduced, and the characteristics of fatigue driving behavior are described and classified. This paper summarizes several of the widely used public data sets for fatigue driving in detail, and analyzes the characteristics of each data set to compare its applicability and limitations, providing a valuable resource for subsequent research. Finally, the driver fatigue driving detection method based on facial features, physiological signal features, vehicle features and multiple features fusion is comprehensively analyzed. By comparing the advantages and disadvantages of various methods, this paper summarizes the problems and challenges faced by drivers in the field of fatigue driving detection, and prospects the future development direction.
Predicting Color Schemes Based on Genetic Algorithm and Deep Feedforward Neural Network Model
Authors: Wang Yongchao , Wu Xingcheng , Hou Jianni , Hu Mengda
Published: August 2024

To enhance the accuracy and efficiency of color matching for opaque products, a relevant model is employed to predict their color schemes. By extracting the spectral reflectance of dyed panels as input and dyeing ratios as output, it is possible to reduce color matching errors and improve production efficiency, thereby advancing the dyeing technology of opaque products and achieving color matching freedom. This study initially applies a logarithmic transformation to the K/S (absorption coefficient/scattering coefficient) ratio and concentration, converting the resultant equations into linear forms. The least squares method is then employed to solve the linear equations describing the relationship between K/S and concentration. A comparison is made between linear regression fitting and nonlinear regression fitting methods, revealing that the former yields better fitting results and higher correlation coefficients in the range of 400nm-700nm for K/S versus concentration. Thus, linear fitting is chosen. In addressing the issue of predicting color differences with minimal deviation based on target samples, this paper unifies variables using the Beer-Lambert color matching equation, Kubelka-Munk theory, and the Simpson discrete variable integration formula to obtain a training set. Leveraging the CIELAB color space model, a deep feedforward neural network is trained on the L, a, and b values for red, yellow, and blue. Finally, a predictive model combining genetic algorithm and deep feedforward neural network (GA-DFNN) is utilized to forecast color matching schemes for the original samples, resulting in 5 sets of color matching schemes with color differences of no more than 1 unit.
Design of Client Interactive Interface for Exoskeleton Rehabilitation Robot
Authors: Hu Ruiyun , Shi Haoming , Wen Jiandong
Published: August 2024

With the improvement of contemporary biotechnology and the rapid development of exoskeleton robot technology, the demand for exoskeleton rehabilitation robots is constantly increasing, and a large number of movements in daily life require the participation of the limbs. Therefore, it is of profound significance to study limb exoskeleton rehabilitation robots, but there are few products on the market to help customers recover more smoothly. This paper designs an interactive client framework that can help exoskeleton rehabilitation robots to better serve customers. This study mainly for the main purpose of exoskeleton rehabilitation robot, with the support of interaction design theory, through the exoskeleton rehabilitation robot and some medical customers design research and design questionnaire user needs, the analysis of exoskeleton rehabilitation robot client interface interaction, put forward a new exoskeleton rehabilitation robot interaction interface design. By establishing an interactive prediction model for design evaluation, the feasibility and necessity of the client interaction interface of the exoskeleton rehabilitation robot are verified, and the needs of users for such products are summarized. It provides a design idea for the interface interaction design of similar robot products.
Application of Artificial Intelligence in the Diagnosis of Neurodegenerative Diseases: The Case of Alzheimer's Disease
Authors: Wang Chengyu , Zhong Zihe
Published: August 2024

In the arena of Alzheimer's Disease (AD) research, the utilization of Artificial Intelligence (AI) for the early diagnosis and prediction of the disease is identified as a crucial avenue of exploration. The impetus for this line of inquiry is rooted in the urgent need for treatments and strategies that can slow the progression of AD, particularly through early detection and prognostication. The application of AI, in conjunction with big data, facilitates the integration of diverse datasets related to AD and actual clinical cases. This approach is instrumental in identifying genes associated with AD, thereby uncovering the disease's biological underpinnings and potentially leading to the development of novel therapeutic modalities. Such advancements promise not only to reduce healthcare costs but also to alleviate the emotional burden on families affected by this condition. The primary research methodology of this paper is an extensive review of existing literature, focusing on the evolution, challenges, and future prospects of AI in the early diagnosis and prediction of AD. Research to date has demonstrated that various teams have proposed a multitude of models, each with its unique strengths, showcasing significant progress in the application of AI within this field. The findings reveal that while AI's application in the early diagnosis and prediction of AD has made commendable strides, the models employed still offer considerable room for improvement. Moreover, several challenges remain unresolved. Hence, the current body of research not only highlights AI's contributions to AD research but also underscores the necessity for further investigation and refinement in this vital area. In conclusion, this review emphasizes the developments achieved in the application of AI for the diagnosis and prediction of AD, while also identifying areas for potential enhancement. The significance of this study lies in its detailed depiction of the current landscape of AI in AD research, accentuating the importance of continued exploration and optimization in combating this formidable disease. Ultimately, this research seeks to forge more effective strategies against AD, underscoring the potential of AI as a powerful tool in understanding and addressing this debilitating condition.
Exploiting Topological Priors for Boosting Point Cloud Generation
Authors: Chen Baiyuan
Published: August 2024

This paper presents an innovative enhancement to the Sphere as Prior Generative Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud generation. A novel method is introduced for point cloud generation that elevates the structural integrity and overall quality of the generated point clouds by incorporating topological priors into the training process of the generator. Specifically, this work utilizes the K-means algorithm to segment a point cloud from the repository into clusters and extract centroids, which are then used as priors in the generation process of the SP-GAN. Furthermore, the discriminator component of the SP-GAN utilizes the identical point cloud that contributed the centroids, ensuring a coherent and consistent learning environment. This strategic use of centroids as intuitive guides not only boosts the efficiency of global feature learning but also substantially improves the structural coherence and fidelity of the generated point clouds. By applying the K-means algorithm to generate centroids as the prior, the work intuitively and experimentally demonstrates that such a prior enhances the quality of generated point clouds.
Survival prediction and analysis of Titanic based on logistic regression and KNN
Authors: Li Jiale
Published: August 2024

The main purpose of this paper is to use machine learning methods to identify factors related to the survival of Titanic passengers and analyze model parameters. The study first speculated on the survival factors of Titanic passengers and conducted data visualization tests on these speculations. Subsequently, this article cleaned up the relevant data, abandoned the factor of excessive missing data, made up for a small amount of missing data, and then constructed a relevant machine model. Finally, by comparing the accuracy of the two models, a more efficient and accurate model was selected. Through relevant work, it has been concluded that the logistic regression model is more effective than the K-nearest neighbor (KNN) model and that passenger age, economic status, and cabin class are closely related to passenger survival. This article provides valuable references for relevant experimental research by introducing machine learning methods for effective survival prediction. In the future, research will further delve into the methods and solutions of deep learning.
Utilize the Machine Learning Models to Forecast Home Values.:Seattle U.S.
Authors: Zhang Yunqi
Published: August 2024

Understanding housing price predictions assists the government in better adjusting and formulating relevant policies to promote economic stability and sustainable social development. In this study, researchers collected a large amount of data on the Seattle real estate market, including housing characteristics (such as area, number of bedrooms, number of bathrooms), geographical location (such as neighborhood, nearby facilities), and historical price data. This paper used this data to train and test linear regression, and KNN prediction models to forecast future housing price trends. The linear regression model, on the other hand, models the linear relationship between a single independent variable and the dependent variable, predicting housing prices by fitting an optimal line. The KNN prediction model, based on the nearest neighbor algorithm, predicts by searching for the K nearest neighbor samples closest to the target sample. Researchers will compare the accuracy and effectiveness of these three methods in predicting Seattle housing prices to determine which method is most suitable for housing price forecasting. Through this analysis, they aim to provide a reliable housing price prediction model for local residents to help them make wiser real estate decisions.
Study on the management program of Massai Mara national reserve based on indicator analysis
Authors: Wang Junjie , Yu Yu , Wang Xuechun
Published: August 2024

The Maasai Mara National Reserve is rated as one of the seven natural wonders of Africa and one of the ten wonders of the world. To reduce the impact of human activities on wildlife and further consolidate the balance between humans and nature, this paper proposes a research analysis method for the Massai Mara National Reserve. First, the influencing factors were divided into four indicators: wildlife, economy, resident life and non-animal resources. For these four indicators, eight main factors are further divided, including employment, native resource destruction, poaching, injuries to personnel, crop destruction, causing dissatisfaction among tourists, revenue brought in by tourists, the cost of conserving biological resources. The relationship between these factors and indicators produced the influence on the number of visitors and the development trend of human footprint. Subsequently, the reasons for these important indicators are analyzed, and corresponding solutions are put forward, such as strengthening the management of protected areas, expanding the scope of protected areas, and formulating unified charging standards.
Research on Training Performance Prediction Model Based on LightGBM
Authors: Peng Jingzhe , Pan Feiyang , Li Jiaxing , Duan Yixin
Published: August 2024

This study aims to build an accurate performance prediction model to assist the training and development of skilled workers. This article uses the advanced LightGBM algorithm and combines it with rich technical worker training data in a certain industry to predict trainees' training results. In the data processing stage, this article conducted detailed data cleaning to eliminate outliers and ensure the accuracy and reliability of the data. At the same time, by introducing new variables, this paper further enhances the feature expression ability of the model, thereby improving the accuracy of prediction. To further optimize the model performance, this article innovatively uses the particle swarm algorithm to fine-tune the parameters of LightGBM. Experimental results show that the optimized model performs well in predicting students' training performance, with not only high prediction accuracy but also good stability. This model is expected to become a powerful tool for the training and development of skilled workers, helping various industries to evaluate and improve training effects more efficiently, thereby better meeting the needs of modern production and promoting the improvement of the overall quality of the skilled worker team.
The Application of SLAM Technology in Indoor Navigation in Complex Indoor Environment
Authors: Yu Changju
Published: August 2024

This paper describes the application of SLAM in complex indoor environment. Through the joint work of sensor, LiDAR and visual camera, the real-time self-positioning and map construction are realized at the same time. However, in the practical application, there are also many challenges, the layout of the environment, the brightness of the environment, the presence of noise, the presence of dynamic objects and other environmental factors will lead to different degrees of data errors in SLAM technology. The small errors generated by a long time of work will gradually accumulate into potentially dangerous huge errors, and this is one of the biggest challenges for the current SLAM technology. Not only has that, the high configuration conditions required by SLAM technology also become one of the reasons why people do not choose it. However, the advantages of SLAM technology far outweigh the disadvantages. And almost all of these problems have corresponding solutions, SLAM technology is still very reliable, very widely used, the future of a great prospect of a technology.
Word data prediction based on statistical method
Authors: Jiang Lingwen
Published: August 2024

Puzzle game wordle has been wildly popular since its launch, its simple and unique game rules have generated a lot of data while gaining a large number of users in a short period of time, making wordle prediction a hot topic. In this paper, we construct four models based on the properties of words: interpretive prediction report counting model, correlation judgment model, percentage prediction model and word difficulty category classification model to provide a solution for predicting words. In order to measure the difficulty of words, we evaluated the words from two aspects of commonality and structure, and defined sub-indicators such as the harmony of word pronunciation (both American and British), the number of vowels, the number of repeated letters, and the word frequency of words to measure the difficulty of words comprehensively. We built a BP neural network model for multivalue prediction and predicted for a given future puzzle word its correlation to the percentage distribution of each attempt (1, 2, 3, 4, 5, 6, X). We developed a K-means clustering model to classify the difficulty of words. We found that words fall into two categories. The closer a word is to the center of the category; the word belongs to that category. We suggest that in addition to players taking the initiative to advertise and attract traffic, newspapers should also take the initiative to promote the game (such as advertising), firstly warming up the publicity before the game goes live. According to the popularity trend curve fitted by the differential equation model, mass interest in emerging games rises rapidly and, after peaking for a short time, slowly declines due to the loss of novelty. Therefore, in order to maintain public enthusiasm as much as possible, publicity should be stepped up after the peak.
Overview of Research Progress and Challenges in Federated Learning
Authors: Guo Wenxin
Published: August 2024

In recent years, federated learning has become a hot research topic in the machine learning community. It aims to reduce the potential data security and privacy risks caused by the centralized training paradigm of traditional machine learning through local training and global aggregation. Although federated learning methods have been widely applied in numerous fields such as finance, healthcare, autonomous driving, and smart retail, there are still urgent issues to be addressed in the field of federated learning, including data privacy leakage, malicious node attacks, model security, and the trustworthiness of participants. By delving into and discussing federated learning, this paper aims to provide researchers and practitioners in related fields with a comprehensive understanding and the latest progress of this technology. Based on the characteristics of the data distribution of the parties involved in federated learning training, this paper categorizes existing federated learning methods into horizontal federated learning, vertical federated learning, and federated transfer learning. It also introduces representative federated learning algorithms under different types, including their design concepts, basic processes, and advantages and disadvantages. Combining different application scenarios, this paper further discusses the challenges of federated learning and looks forward to the future development direction of this topic.
Machine Learning Based Stock Market Trend Prediction and Analysis
Authors: Wang Yilin
Published: August 2024

Stock price prediction can help investors to create initial pre-scenarios. The topic of this research is to predict the stock market scenario through machine learning methods. By successfully predicting the stock market situation, the movement of different stocks, etc., the possibility of buying the wrong stocks can be greatly reduced, making it possible to make huge profits by buying and selling stocks. The purpose of the research is threefold. This paper uses a market capitalization weighted index consisting of the most important 40 stocks out of the top 100 stocks with the largest market capitalization on the Paris Stock Exchange as a dataset to analyze the movement of stocks and the possibility of buying them. The stocks are analyzed and subsequently forecasted through data visualization techniques such as candlestick charts and moving average charts. Long Short-Term Memory (LSTM) was used as a benchmark. This study has three main objectives: first, to study the temporal evolution of the closing prices of these stocks; second, to make stock forecasts using candlestick charts, which is the traditional method of stock analysis; and third, to analyze the closing prices of the stocks and the moving average charts, which are studied and evaluated.  Stock prediction can reduce risk, guide investment decisions, realize risk management, make market references, assess the value of stocks and enhance strategic vision.
Preface: 2nd International Conference on Artificial Intelligence, Database and Machine Learning (AIDML 2024)
Authors: Arthur Humphrey , Huang Fan
Published: August 2024

The 2024 2nd International Conference on Artificial Intelligence, Database and Machine Learning (AIDML 2024) was held in Rotterdam, Netherlands during July 06-07, 2024. This annual event aims to serve as an international forum to gather academicians, scientists, engineers, and researchers working in the fields of algorithms, big data, computing, artificial intelligence, and machine learning to exchange views, share their professional knowledge, experience, and research results, and discuss the challenges and future directions in their professional fields.
AIDML 2024 will mainly feature keynote speeches and peer-reviewed paper speeches. In addition, social activities or academic visits will be arranged to encourage exchanges, discussions, or cooperation between researchers. The conference also had many invited lectures and workshops which discuss many in-depth topics related to the main themes.
We sincerely hope that AIDML 2024 would not only show the participants a broad overview of the latest research results, but also provide them with a significant platform for academic connection and exchange. We would like to express our sincere gratitude to all the keynote speakers, reviewers, and editors for their hard work, precious time and endeavor preparing for the conference.
AIDML 2024 Organizing Committee
Rotterdam, Netherlands
A Study on Predicting Tennis Player's Competition Direction Based on Logistic Regression and LSTM Model
Authors: Ding Yan , Zhang Chi
Published: August 2024

A tennis player's performance during a match affects the outcome of the match. The study of the issue can provide advice to tennis players on how to prepare for the tournament, and to coaches and their teams on how to develop training programs that will help athletes get better results in the tournament. Initially, according to the entropy weight method to assess the player's performance on the field. Two factor layers were used to finally get a score measuring the performance at each point. Next, based on the score and making use of conditional probabilities in logistic regression models to predict the winning rate in each point and then in each game. The result shows high accuracy in the logistic regression in both points and games. Ultimately, by assuming the existence of average effect of the players’ performance in all points of one game, deep learning with long short-term memory is used to predict game fluctuations and identify factors that contribute to them.
A study of the competitive relationship between trout and bass based on differential equations
Authors: Mu Sayang
Published: August 2024

In this paper, the competitive relationship between trout and perch populations was analyzed by model differential equations, and the stability and final stabilization points under different parameters were explored. It is found that under the premise that global stability is better than local stability, trout and perch can coexist and reach the final stable state when the competition factors satisfy specific conditions; while under other competition conditions, it may lead to the number of one species tends to zero or the number of the other species tends to the maximum capacity. This has important implications for the understanding of biological population competition and the construction of competition models for software products.
Transformation of infusion system to automatically complete infusion tasks
Authors: Yao Mingyu
Published: August 2024

In the realm of medical technology advancements, there has been a significant enhancement in methodologies for investigating patient ailments and devising therapeutic interventions, surmounting myriad complex challenges in the process. Concurrently, the healthcare sector has adopted a diverse array of tools to accommodate various treatment modalities. Among these, the infusion device, a staple in clinical settings, is ubiquitously employed yet is considered to be archaic in its operational methodology. Predominantly utilized within hospital environments, the traditional infusion paradigm is notably labor-intensive, necessitating constant supervision to mitigate the risk of errors, a responsibility often delegated to patients or their guardians. This scholarly discourse aims to delineate the extant landscape and historical context of infusion device technology. It will methodically review and analyze existing literature on advancements in automation within the domain of infusion therapy, with the intention of synthesizing a comprehensive solution that epitomizes the zenith of current research findings. By comparing and contrasting various scholarly contributions on the subject, the paper endeavors to construct a cohesive overview of the advancements in automated infusion systems, thereby contributing to the ongoing discourse and potentially influencing future innovations in medical device technology.
Based on the Gemini Large-scale Model, Enhanced Accuracy in Semantic Similarity Detection With the Ernie Model
Authors: Li Zihang
Published: August 2024

The rapid development of deep learning models has been a hallmark of recent years. As the world moves towards greater intelligence, there is an urgent need for advancements in technologies like question-answering systems, chatbots, and search and recommendation engines, especially within the service and e-commerce sectors. At the heart of these advancements lies the task of detecting semantic similarity in natural language processing. Against this backdrop, this paper examines the accuracy of semantic similarity detection across different deep learning models, focusing specifically on the LSTM, Transformer, and ERNIE models under identical hyperparameters and configuration settings. The study reveals a common challenge among these models in achieving effective data generalization on the LCQMC dataset. To address this, the paper introduces an innovative approach by combining the highest-performing ERNIE model with the Gemini large-scale model and employing data augmentation techniques to enhance accuracy. This strategy increased accuracy from 82% with the ERNIE model alone to 85%.
Prediction of Medium-duration Subway Passenger Flow Volume based on the ARIMA Model
Authors: Yang Mingyue
Published: August 2024

The subway industry has brought about a significant change in transportation by effectively reducing ground traffic congestion, resulting in an increasing demand for predicting subway passenger flow based on historical data. Researchers are constantly striving to improve the diversity and accuracy of data prediction models. This paper examines the daily passenger flow data of Nanjing Metro in 2023 and makes medium-term predictions using the ARIMA model to explore the feasibility and effectiveness of this approach. ADF, ACF, and PACF tests are conducted on the data to ensure that the parameters input into the model can optimize its accuracy. Then the ARIMA model is utilized to fit the data, resulting in a highly accurate parameter model. The results predict the passenger flow of the Nanjing subway in the next ten days, showing that the model's fitted values closely resemble the true values' distribution. By utilizing the ARIMA model, predictions for the next 10 days are made, yielding relatively accurate results. This paper demonstrates that the ARIMA model can be effectively applied to predict subway passenger flow in the medium term, thereby improving the precision of the forecasts.
Effects of sex ratio changes in Lampreys on ecosystem based on modeling techniques
Authors: Liu Sitong , Tong Ziwen , Liu Yuhe
Published: August 2024

In this paper, a number of mathematical models were established to study the effects of sex ratio changes in lampreys on this ecosystem. Logistic and Verhulst models were established to describe the population dynamics of Lampreys. Then a food web model and a niche model were construct-ed to describe the food relationships and interactions between different species. The effects of sex ratio of lampreys on ecosystem stability and species interaction were analyzed. Finally, TOPSIS evaluation model was used to evaluate the contribution of sex ratio changes in lampreys to other species. The results show that the change of sex ratio of lampreys will affect their growth rate, and then affect the balance of the whole ecosystem. The model established in this paper provides an important reference for revealing the ecological adaptation significance of the sex ratio change in lampreys.
Study on Freight Volume Prediction of Routes Based on Random Forest Model
Authors: Du Tianyu , Yin Qingwen
Published: August 2024

With the rise of e-commerce and the ever-changing consumer demand, logistic network sorting centers face increasingly complex and significant challenges in cargo volume forecasting. To predict the cargo volume for various routes, this paper selects the Random Forest model for forecasting. Feature data were extracted, and the dataset was divided into a training set and a test set. The model was evaluated using the test set. Ultimately, a high coefficient of determination and low mean squared error were achieved, demonstrating the accuracy and reliability of the Random Forest model's predictions. The transportation scenario was extended to changes in transportation routes, focusing on the impact of adding and canceling transportation routes on cargo volume forecasting. By establishing model features, conducting feature synthesis, and calculating impacts, the adjusted cargo volume for each sorting center was obtained, and an analysis of changes in transportation routes was conducted. The influence of added and canceled routes was integrated into the cargo volume forecast, resulting in the final cargo volume prediction. By distributing the adjusted volume evenly on a daily basis, the accuracy and reliability of the forecast results were ensured. Finally, a visual representation of the forecast results was provided, intuitively showcasing the model's effectiveness.