Frontiers in Computing and Intelligent Systems最新文献

A Hybrid Deep Learning Approach for Lung Nodule Classification 用于肺结节分类的混合深度学习方法

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/498fxm65

Cheng Ren, Shouming Hou

{"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}

引用次数: 0

Using Artificial Intelligence to Refine the Implementation Trajectory of Digital Image Processing Technology 利用人工智能完善数字图像处理技术的实施轨迹

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/6sn88t34

Chen Li, Zengyi Huang

{"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}

引用次数: 0

Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV 基于模型预测和自适应神经网络滑动模式的倾转旋翼无人机跟踪控制

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/6xca9783

Zijing Ouyang, Sheng Xu, Chengyue Su

{"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}

引用次数: 0

Application Analysis of Security Situational Awareness System in Qinghai Provincial Meteorological Network 安全态势感知系统在青海省气象网络中的应用分析

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/33gnp941

Yanping Chang, Qibin Li, Jianan Zhang

引用次数: 0

Research on Development of Generative Artificial Intelligence 关于开发生成式人工智能的研究

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/d24rqq11

Junliang Wang

引用次数: 0

Research on Climate Change Prediction based on ARIMA Model and its Impact on Insurance Industry Decision-Making 基于 ARIMA 模型的气候变化预测及其对保险业决策的影响研究

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/3r7nkd35

Haihui Xu, Zhiyuan Ge, Wenjie Ao

{"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}

引用次数: 0

Research on the Application of CBR Technology in Intelligent Process Design System CBR 技术在智能工艺设计系统中的应用研究

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/592p3296

Junli Liu, Hui Lu, Guanhui Cui, Xibin An

引用次数: 0

Improved Multi-attention Neural Networks for Image Emotion Regression and the Initial Introduction of CAPS 用于图像情感回归的改进型多注意神经网络和 CAPS 的初步引入

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/92w2rc31

Rending Wang, Dongmei Ma

{"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}

引用次数: 0

Digital Transformation in Real Estate Services: Development and Implementation of the Housing Selection Platform 房地产服务的数字化转型：选房平台的开发与实施

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/yyw4jr63

Siyu Wang, Haishan Wang

{"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}

引用次数: 0

Research on Air Quality Prediction Based on Neural Networks 基于神经网络的空气质量预测研究

Frontiers in Computing and Intelligent Systems Pub Date : 2024-05-10 DOI: 10.54097/w80vg420

Ruihao Wan

引用次数: 2