Lung cancer has the highest morbidity and mortality rates worldwide. Pulmonary nodules are an early manifestation of lung cancer. Therefore, accurate classification of pulmonary nodules is of great significance for the early diagnosis and treatment of lung cancer. However, the classification of lung nodules is a complex and time-consuming task requiring extensive image reading and analysis by expert radiologists. Therefore, using deep learning technology to assist doctors in detecting and classifying pulmonary nodules has become a current research trend. A lightweight classification model named Res-VGG is proposed for classifying lung nodules as benign or malignant. The Res-VGG model improves on VGG16 by reducing the use of convolutional and fully connected layers. To reduce overfitting, residual connections are introduced. The training of the model was performed on the LUNA16 database, and a ten-fold cross-validation method was used to evaluate the performance of the model. In addition, the Res-VGG model was compared with three other common classification networks, and the results showed that the Res-VGG model outperformed the other models in terms of accuracy, sensitivity, and specificity.
{"title":"A Hybrid Deep Learning Approach for Lung Nodule Classification","authors":"Cheng Ren, Shouming Hou","doi":"10.54097/498fxm65","DOIUrl":"https://doi.org/10.54097/498fxm65","url":null,"abstract":"Lung cancer has the highest morbidity and mortality rates worldwide. Pulmonary nodules are an early manifestation of lung cancer. Therefore, accurate classification of pulmonary nodules is of great significance for the early diagnosis and treatment of lung cancer. However, the classification of lung nodules is a complex and time-consuming task requiring extensive image reading and analysis by expert radiologists. Therefore, using deep learning technology to assist doctors in detecting and classifying pulmonary nodules has become a current research trend. A lightweight classification model named Res-VGG is proposed for classifying lung nodules as benign or malignant. The Res-VGG model improves on VGG16 by reducing the use of convolutional and fully connected layers. To reduce overfitting, residual connections are introduced. The training of the model was performed on the LUNA16 database, and a ten-fold cross-validation method was used to evaluate the performance of the model. In addition, the Res-VGG model was compared with three other common classification networks, and the results showed that the Res-VGG model outperformed the other models in terms of accuracy, sensitivity, and specificity.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence introduces a fresh research perspective to digital image processing. However, its integration into the curriculum of colleges and universities for teaching digital image processing remains scarce. This lack of incorporation results in outdated course content, reliance on singular teaching methods, and simplistic course experiments, consequently impeding effective teaching and hindering the development of well-rounded and innovative individuals. Digital image processing technology expands the horizons of communication engineering, facilitating more convenient modes of communication for people. For instance, video calls and photo transmissions diversify everyday communication methods, transcending the constraints of time and space by enabling online meetings and fostering enhanced communication possibilities. Despite these advancements, numerous challenges and methodologies merit thorough exploration. Therefore, this paper aims to comprehensively grasp both traditional and deep learning approaches to digital image processing, enhancing practical project proficiency and fostering scientific research exploration capabilities, thus serving as a valuable reference for similar research endeavors.
{"title":"Using Artificial Intelligence to Refine the Implementation Trajectory of Digital Image Processing Technology","authors":"Chen Li, Zengyi Huang","doi":"10.54097/6sn88t34","DOIUrl":"https://doi.org/10.54097/6sn88t34","url":null,"abstract":"Artificial intelligence introduces a fresh research perspective to digital image processing. However, its integration into the curriculum of colleges and universities for teaching digital image processing remains scarce. This lack of incorporation results in outdated course content, reliance on singular teaching methods, and simplistic course experiments, consequently impeding effective teaching and hindering the development of well-rounded and innovative individuals. Digital image processing technology expands the horizons of communication engineering, facilitating more convenient modes of communication for people. For instance, video calls and photo transmissions diversify everyday communication methods, transcending the constraints of time and space by enabling online meetings and fostering enhanced communication possibilities. Despite these advancements, numerous challenges and methodologies merit thorough exploration. Therefore, this paper aims to comprehensively grasp both traditional and deep learning approaches to digital image processing, enhancing practical project proficiency and fostering scientific research exploration capabilities, thus serving as a valuable reference for similar research endeavors.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the low-altitude economy rapidly expands, the demand for UAVs is increasingly growing, and their operational scenarios are becoming more complex, with higher requirements for endurance and short-distance take-off and landing performance. Tiltrotor UAVs, characterized by vertical take-off and landing and long endurance, have attracted widespread attention for their potential applications. However, the dynamics and flight paths of tiltrotor UAVs are highly nonlinear, and traditional linear flight controllers cannot fully utilize the real-time performance capabilities of tiltrotor UAVs. Under the conditions of model uncertainty and input saturation in tiltrotor UAVs, traditional LOS+PID control strategies exhibit characteristics of insufficient responsiveness and excessive overshoot. To improve the performance of tiltrotor UAVs in completing path tracking tasks, we have developed a new control strategy. By establishing an error model for three-dimensional space path tracking, we propose a cascaded control strategy of motion controllers and dynamic controllers. The motion controller is designed based on model predictive control, generating a series of speed-limited signals. Then, in the dynamic controller part, an adaptive radial basis function neural network is used to estimate the model uncertainty caused by aerodynamic parameters to enhance its robustness. Finally, the proposed algorithm is compared with the LOS guidance method and PID controller through simulation experiments. The comparison results show that the proposed algorithm can improve the path tracking effect, increase the response speed, and reduce the overshoot.
随着低空经济的迅速发展，对无人机的需求日益增长，无人机的作战场景也越来越复杂，对续航能力和短距离起降性能的要求也越来越高。倾转旋翼无人机具有垂直起降、续航时间长等特点，其潜在应用已引起广泛关注。然而，倾转翼无人机的动力学和飞行轨迹是高度非线性的，传统的线性飞行控制器无法充分利用倾转翼无人机的实时性能。在倾转翼无人机模型不确定和输入饱和的条件下，传统的 LOS+PID 控制策略表现出响应速度不足和过冲过大的特点。为了提高倾转翼无人机完成路径跟踪任务的性能，我们开发了一种新的控制策略。通过建立三维空间路径跟踪误差模型，我们提出了一种由运动控制器和动态控制器组成的级联控制策略。运动控制器基于模型预测控制设计，产生一系列限速信号。然后，在动态控制器部分，使用自适应径向基函数神经网络来估计由空气动力参数引起的模型不确定性，以增强其鲁棒性。最后，通过仿真实验将提出的算法与 LOS 制导方法和 PID 控制器进行了比较。比较结果表明，所提出的算法可以改善路径跟踪效果，提高响应速度，减少过冲。
{"title":"Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV","authors":"Zijing Ouyang, Sheng Xu, Chengyue Su","doi":"10.54097/6xca9783","DOIUrl":"https://doi.org/10.54097/6xca9783","url":null,"abstract":" As the low-altitude economy rapidly expands, the demand for UAVs is increasingly growing, and their operational scenarios are becoming more complex, with higher requirements for endurance and short-distance take-off and landing performance. Tiltrotor UAVs, characterized by vertical take-off and landing and long endurance, have attracted widespread attention for their potential applications. However, the dynamics and flight paths of tiltrotor UAVs are highly nonlinear, and traditional linear flight controllers cannot fully utilize the real-time performance capabilities of tiltrotor UAVs. Under the conditions of model uncertainty and input saturation in tiltrotor UAVs, traditional LOS+PID control strategies exhibit characteristics of insufficient responsiveness and excessive overshoot. To improve the performance of tiltrotor UAVs in completing path tracking tasks, we have developed a new control strategy. By establishing an error model for three-dimensional space path tracking, we propose a cascaded control strategy of motion controllers and dynamic controllers. The motion controller is designed based on model predictive control, generating a series of speed-limited signals. Then, in the dynamic controller part, an adaptive radial basis function neural network is used to estimate the model uncertainty caused by aerodynamic parameters to enhance its robustness. Finally, the proposed algorithm is compared with the LOS guidance method and PID controller through simulation experiments. The comparison results show that the proposed algorithm can improve the path tracking effect, increase the response speed, and reduce the overshoot.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the public's demand for the accuracy of meteorological services is increasing, the scale of meteorological network in Qinghai Province is expanding, the depth of the network level is extending, the topology is becoming more and more complex, and the security problems are becoming more and more prominent. Traditional security protection measures are unable to detect the problems in Qinghai Province meteorological network as a whole. Network Security Situational Awareness is an effective means to guarantee the security of meteorological network at the present stage by collecting comprehensive and macro security elements in the network environment and carrying out big data analysis and processing to have a macro and comprehensive judgment of the security situation of the network and to predict the security trend of the network system. This paper mainly focuses on the network security situational awareness system used in Qinghai meteorological network and gives a brief introduction to the deployment of the situational awareness platform and a brief overview of the supporting applications.
{"title":"Application Analysis of Security Situational Awareness System in Qinghai Provincial Meteorological Network","authors":"Yanping Chang, Qibin Li, Jianan Zhang","doi":"10.54097/33gnp941","DOIUrl":"https://doi.org/10.54097/33gnp941","url":null,"abstract":"As the public's demand for the accuracy of meteorological services is increasing, the scale of meteorological network in Qinghai Province is expanding, the depth of the network level is extending, the topology is becoming more and more complex, and the security problems are becoming more and more prominent. Traditional security protection measures are unable to detect the problems in Qinghai Province meteorological network as a whole. Network Security Situational Awareness is an effective means to guarantee the security of meteorological network at the present stage by collecting comprehensive and macro security elements in the network environment and carrying out big data analysis and processing to have a macro and comprehensive judgment of the security situation of the network and to predict the security trend of the network system. This paper mainly focuses on the network security situational awareness system used in Qinghai meteorological network and gives a brief introduction to the deployment of the situational awareness platform and a brief overview of the supporting applications.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine Learning, as one of the key technologies in the field of artificial intelligence, has made significant advancements in recent years. This study provides a relatively systematic introduction to machine learning. Firstly, it gives an overview of the historical development of machine learning, and then focuses on the analysis of classical algorithms in machine learning. Subsequently, it elucidates the latest research advancements in machine learning, aiming to comprehensively explore the applications of machine learning in various domains and discuss potential future directions.
{"title":"Research on Development of Generative Artificial Intelligence","authors":"Junliang Wang","doi":"10.54097/d24rqq11","DOIUrl":"https://doi.org/10.54097/d24rqq11","url":null,"abstract":"Machine Learning, as one of the key technologies in the field of artificial intelligence, has made significant advancements in recent years. This study provides a relatively systematic introduction to machine learning. Firstly, it gives an overview of the historical development of machine learning, and then focuses on the analysis of classical algorithms in machine learning. Subsequently, it elucidates the latest research advancements in machine learning, aiming to comprehensively explore the applications of machine learning in various domains and discuss potential future directions. ","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research delves into the application of the Autoregressive Integrated Moving Average (ARIMA) model for predicting climate change and its subsequent implications for decision-making within the insurance industry. The study introduces a comprehensive approach to forecast climatic variables such as temperature, rainfall, and relative humidity, which are critical factors in assessing insurance risks and formulating underwriting strategies. The ARIMA model, recognized for its efficacy in time series analysis, is employed to capture the seasonal patterns and trends in climatic data. The model is calibrated using historical weather records from two distinct regions, Dali and New York, to account for geographical variability in climate sensitivity. By integrating the model's predictions with economic indicators and industry-specific data, the research constructs a Weather Composite Index (WCI) that quantifies the potential impact of climate change on local economies and insurance claims. The paper meticulously describes the model's parameters, including the order of differencing (d), the number of autoregressive terms (p), and the number of moving average terms (q), which are selected to optimize the model's fit and predictive accuracy. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are utilized to evaluate and compare the performance of different ARIMA configurations, ensuring that the chosen model minimizes the forecast error and provides the most reliable predictions.
{"title":"Research on Climate Change Prediction based on ARIMA Model and its Impact on Insurance Industry Decision-Making","authors":"Haihui Xu, Zhiyuan Ge, Wenjie Ao","doi":"10.54097/3r7nkd35","DOIUrl":"https://doi.org/10.54097/3r7nkd35","url":null,"abstract":"This research delves into the application of the Autoregressive Integrated Moving Average (ARIMA) model for predicting climate change and its subsequent implications for decision-making within the insurance industry. The study introduces a comprehensive approach to forecast climatic variables such as temperature, rainfall, and relative humidity, which are critical factors in assessing insurance risks and formulating underwriting strategies. The ARIMA model, recognized for its efficacy in time series analysis, is employed to capture the seasonal patterns and trends in climatic data. The model is calibrated using historical weather records from two distinct regions, Dali and New York, to account for geographical variability in climate sensitivity. By integrating the model's predictions with economic indicators and industry-specific data, the research constructs a Weather Composite Index (WCI) that quantifies the potential impact of climate change on local economies and insurance claims. The paper meticulously describes the model's parameters, including the order of differencing (d), the number of autoregressive terms (p), and the number of moving average terms (q), which are selected to optimize the model's fit and predictive accuracy. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are utilized to evaluate and compare the performance of different ARIMA configurations, ensuring that the chosen model minimizes the forecast error and provides the most reliable predictions.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A Case-Based Reasoning (CBR) intelligent process design system is developed through Visual Studio development tools to improve the processing efficiency of mechanical parts and the recurrence rate of corporate knowledge. The key factor in improving the accuracy of case matching in the CBR system is the similarity calculation of parts. In this paper, similarity calculation models for different attribute types are presented by combining the nearest neighbor method. And the improved AHP method and matrix calculation of MATLAB are used to determine the weighting coefficient. The most similar cases are matched according to the overall similarity of the cases and the set threshold, and the method is applied to the intelligent process design of shafts. The results show that this method is conducive to shortening the development cycle and quickly responding to the market, which provides a reference for intelligent manufacturing of mechanical parts.
通过 Visual Studio 开发工具开发了基于案例推理（CBR）的智能工艺设计系统，以提高机械零件的处理效率和企业知识的复现率。在 CBR 系统中，提高案例匹配准确性的关键因素是零件的相似性计算。本文结合近邻法，提出了不同属性类型的相似度计算模型。并利用改进的 AHP 方法和 MATLAB 的矩阵计算来确定权重系数。根据案例的总体相似度和设定的阈值，匹配出最相似的案例，并将该方法应用于竖井的智能工艺设计。结果表明，该方法有利于缩短开发周期，快速响应市场，为机械零件的智能制造提供了参考。
{"title":"Research on the Application of CBR Technology in Intelligent Process Design System","authors":"Junli Liu, Hui Lu, Guanhui Cui, Xibin An","doi":"10.54097/592p3296","DOIUrl":"https://doi.org/10.54097/592p3296","url":null,"abstract":"A Case-Based Reasoning (CBR) intelligent process design system is developed through Visual Studio development tools to improve the processing efficiency of mechanical parts and the recurrence rate of corporate knowledge. The key factor in improving the accuracy of case matching in the CBR system is the similarity calculation of parts. In this paper, similarity calculation models for different attribute types are presented by combining the nearest neighbor method. And the improved AHP method and matrix calculation of MATLAB are used to determine the weighting coefficient. The most similar cases are matched according to the overall similarity of the cases and the set threshold, and the method is applied to the intelligent process design of shafts. The results show that this method is conducive to shortening the development cycle and quickly responding to the market, which provides a reference for intelligent manufacturing of mechanical parts.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image sentiment analysis is a large class of tasks for classifying or regressing images containing emotional stimuli, and it is believed in psychological research that different groups produce different emotions for the same stimuli. In order to study the influence of cultural background on image sentiment analysis, it is necessary to introduce a dataset of image sentiment stimuli that can represent cultural groups. In this paper, we introduce the Chinese Affective Picture System (CAPS), which represents Chinese culture, and revise and test this dataset. The PDANet model has the best performance among the current image sentiment regression models, but due to the difficulty of extracting cross-channel information from the attention module it uses, image long-distance information correlation and other shortcomings, this paper proposes an image emotion regression multiple attention networks, introduces the SimAM attention mechanism, and improves the loss function to make it more consistent with the psychological theory, and proposes a 10-fold cross-validation for CAPS. The network achieves MSE=0.0188, R2=0.359 on IAPS, and MSE=0.0169, R2=0.463 on NAPS, which is better than PDANet; the best training result of CAPS is MSE=0.0083, R2=0.625, and the paired-sample t-test of the results shows that all the three dimensions are significantly positively correlated, with correlation coefficients r=0.942, 0.895 and 0.943, respectively, showing good internal consistency and excellent application prospect of CAPS.
{"title":"Improved Multi-attention Neural Networks for Image Emotion Regression and the Initial Introduction of CAPS","authors":"Rending Wang, Dongmei Ma","doi":"10.54097/92w2rc31","DOIUrl":"https://doi.org/10.54097/92w2rc31","url":null,"abstract":"Image sentiment analysis is a large class of tasks for classifying or regressing images containing emotional stimuli, and it is believed in psychological research that different groups produce different emotions for the same stimuli. In order to study the influence of cultural background on image sentiment analysis, it is necessary to introduce a dataset of image sentiment stimuli that can represent cultural groups. In this paper, we introduce the Chinese Affective Picture System (CAPS), which represents Chinese culture, and revise and test this dataset. The PDANet model has the best performance among the current image sentiment regression models, but due to the difficulty of extracting cross-channel information from the attention module it uses, image long-distance information correlation and other shortcomings, this paper proposes an image emotion regression multiple attention networks, introduces the SimAM attention mechanism, and improves the loss function to make it more consistent with the psychological theory, and proposes a 10-fold cross-validation for CAPS. The network achieves MSE=0.0188, R2=0.359 on IAPS, and MSE=0.0169, R2=0.463 on NAPS, which is better than PDANet; the best training result of CAPS is MSE=0.0083, R2=0.625, and the paired-sample t-test of the results shows that all the three dimensions are significantly positively correlated, with correlation coefficients r=0.942, 0.895 and 0.943, respectively, showing good internal consistency and excellent application prospect of CAPS.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article provides a detailed elaboration on the design and development of the Housing Selection Platform, an online platform that responds to current real estate market demands and integrates modern technologies. The paper comprehensively introduces the platform's system modules, including online housing rental, buying and selling, as well as related shopping mall experiences. The platform adopts a front-end/back-end separation and microservices architecture, making development efficient and the system easy to maintain. It also emphasizes performance optimization through technologies like Redis and has adopted the latest authentication and authorization measures for security. The article widely discusses the implementation of the system and the technical challenges faced, providing solutions such as API gateways and event-driven architectures. The conclusion revisits key learned points and successful experiences, predicting that the introduction of innovative technologies like artificial intelligence and machine learning will drive the platform's development. The importance of user experience throughout the developmental process is emphasized, looking forward to how the Housing Selection Platform will continue to lead the industry in the future.
本文详细阐述了 "选房平台 "的设计与开发。"选房平台 "是一个顺应当前房地产市场需求、融合现代技术的在线平台。本文全面介绍了该平台的系统模块，包括在线房屋租赁、买卖以及相关的商城体验。该平台采用前后端分离和微服务架构，开发效率高，系统易于维护。该平台还强调通过 Redis 等技术优化性能，并采用了最新的身份验证和授权措施以确保安全。文章广泛讨论了系统的实施和面临的技术挑战，提供了 API 网关和事件驱动架构等解决方案。结论部分重温了关键的学习要点和成功经验，预测人工智能和机器学习等创新技术的引入将推动平台的发展。文章强调了用户体验在整个开发过程中的重要性，并展望了选房平台在未来将如何继续引领行业发展。
{"title":"Digital Transformation in Real Estate Services: Development and Implementation of the Housing Selection Platform","authors":"Siyu Wang, Haishan Wang","doi":"10.54097/yyw4jr63","DOIUrl":"https://doi.org/10.54097/yyw4jr63","url":null,"abstract":"This article provides a detailed elaboration on the design and development of the Housing Selection Platform, an online platform that responds to current real estate market demands and integrates modern technologies. The paper comprehensively introduces the platform's system modules, including online housing rental, buying and selling, as well as related shopping mall experiences. The platform adopts a front-end/back-end separation and microservices architecture, making development efficient and the system easy to maintain. It also emphasizes performance optimization through technologies like Redis and has adopted the latest authentication and authorization measures for security. The article widely discusses the implementation of the system and the technical challenges faced, providing solutions such as API gateways and event-driven architectures. The conclusion revisits key learned points and successful experiences, predicting that the introduction of innovative technologies like artificial intelligence and machine learning will drive the platform's development. The importance of user experience throughout the developmental process is emphasized, looking forward to how the Housing Selection Platform will continue to lead the industry in the future.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In view of the increasingly serious air pollution problem, to alleviate the harmful effects of air pollution on human body and society, this paper studies the prediction of air quality. Due to the nonlinear, regional and dispersive characteristics of pollutant data, the effective utilization rate of data is low and the prediction process is extremely complicated. How to effectively build a prediction model and improve the prediction accuracy of air quality is a hot issue in current research. This paper mainly introduces the current research status of air quality prediction.
{"title":"Research on Air Quality Prediction Based on Neural Networks","authors":"Ruihao Wan","doi":"10.54097/w80vg420","DOIUrl":"https://doi.org/10.54097/w80vg420","url":null,"abstract":"In view of the increasingly serious air pollution problem, to alleviate the harmful effects of air pollution on human body and society, this paper studies the prediction of air quality. Due to the nonlinear, regional and dispersive characteristics of pollutant data, the effective utilization rate of data is low and the prediction process is extremely complicated. How to effectively build a prediction model and improve the prediction accuracy of air quality is a hot issue in current research. This paper mainly introduces the current research status of air quality prediction.","PeriodicalId":504530,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141128745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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