Frontiers in Computing and Intelligent Systems最新文献_第4页

Robot Path Planning Algorithm based on Improved A* and DWA 基于改进型 A* 和 DWA 的机器人路径规划算法

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-27 DOI: 10.54097/fcis.v6i1.07

Haisheng Song, Deyang Zhang

引用次数: 0

Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network 神经网络驱动的云计算资源动态分配机制

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-27 DOI: 10.54097/fcis.v6i1.03

Yining Ou

{"title":"Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network","authors":"Yining Ou","doi":"10.54097/fcis.v6i1.03","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.03","url":null,"abstract":"With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234910","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

Trajectory Tracking Control Algorithm of Two-wheel Rutting Model 双轮车辙模型的轨迹跟踪控制算法

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-27 DOI: 10.54097/fcis.v6i1.09

Yuanhong Dan, Haiyang Zhong

引用次数: 0

Classification Method for Railway Tunnel Secondary Lining Cold Joint Detection based on CNN-BiLSTM-SVM Model with Improved Hybrid Leader Algorithm 基于 CNN-BiLSTM-SVM 模型与改进型混合先导算法的铁路隧道二次衬砌冷接缝检测分类方法

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-27 DOI: 10.54097/fcis.v6i1.05

Honggu Zhu, Jiaye Wu

{"title":"Classification Method for Railway Tunnel Secondary Lining Cold Joint Detection based on CNN-BiLSTM-SVM Model with Improved Hybrid Leader Algorithm","authors":"Honggu Zhu, Jiaye Wu","doi":"10.54097/fcis.v6i1.05","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.05","url":null,"abstract":"Cold joints pose great safety risks to the safe operation of railways. In view of the existing cold joint detection methods, which have low detection efficiency and difficulty in data analysis, a tunnel secondary lining cold joint detection classification method based on the improved hybrid leader CNN-BiLSTM-SVM model is proposed. First, the Rayleigh wave method is used to extract the waveform information of the cold joints. Secondly, CNN-BILSTM is used to perform feature extraction and fusion processing on the waveform information and then input into the support vector machine, and the improved hybrid leader algorithm is used to optimize the parameters in the SVM. Finally, the information is input into the optimized CNN-BiLSTM-SVM to obtain the cold joints detection classification results. In order to verify the effectiveness of this method, the waveform data collected using the Rayleigh wave method in the tunnel under construction and the verified coring detection results are used as the data set. The results show that the results of this method are higher than the unoptimized CNN-BILSTM-SVM and the CNN-BILSTM-SVM optimized by the seagull optimization algorithm and the sparrow search optimization algorithm.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233471","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

The Mechanical Performance Prediction of Steel Materials based on Random Forest 基于随机森林的钢铁材料力学性能预测

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-27 DOI: 10.54097/fcis.v6i1.01

Shihao Wang, Xiangxiang Wu

引用次数: 0

Service Quality Evaluation of Air China based on SERVPERF Model 基于 SERVPERF 模型的国航服务质量评价

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-26 DOI: 10.54097/fcis.v6i1.10

Ying Wang, Nutteera Phakdeephirot

{"title":"Service Quality Evaluation of Air China based on SERVPERF Model","authors":"Ying Wang, Nutteera Phakdeephirot","doi":"10.54097/fcis.v6i1.10","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.10","url":null,"abstract":"In order to effectively carry out aviation service quality evaluation, based on the service quality characteristics of Air China, through research on service quality evaluation models and methods, a questionnaire based on SERVPREF and a fuzzy comprehensive evaluation method were designed to evaluate the service quality of Air China. evaluate. Through literature analysis and expert consultation, the service quality evaluation indicators of Air China were screened and a service quality evaluation indicator system was established. Through the questionnaire survey method, a questionnaire is designed from the two dimensions of passenger satisfaction and indicator importance, and the questionnaire is distributed to collect data. Based on the questionnaire, the analytic hierarchy process was used to determine the weight of the service quality evaluation indicators of Air China. Through statistical analysis of the results of the questionnaire, the results of the comprehensive evaluation of Air China were sorted and analyzed, and it was concluded that Air China The advantages and disadvantages of the company's service quality are analyzed using the fuzzy evaluation method to comprehensively evaluate the service quality of Air China. Finally, it provides the basis for strategic research on the improvement of service quality of Air China.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235180","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

Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer 人工智能在乳腺癌超声诊断中的应用进展

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-26 DOI: 10.54097/fcis.v6i1.11

Jerry Yao, Yuan Zou, Shuqian Du, Hong Wu, Bin Yuan

{"title":"Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer","authors":"Jerry Yao, Yuan Zou, Shuqian Du, Hong Wu, Bin Yuan","doi":"10.54097/fcis.v6i1.11","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.11","url":null,"abstract":"The mammary gland is an important human organ that secretes milk and feeds offspring, while breast tumors are benign or malignant tumors that occur in the breast tissue. There are many causes of breast cancer, and the incidence continues to rise, which is an important killer that threatens women's health. In recent years, a large number of researchers have been interested in the study of AI diagnosis of breast cancer. Artificial intelligence uses a specific algorithm to intelligently process ultrasound images, and develops a high-precision and high-efficiency breast cancer recognition model through training and optimization of the algorithm. At present, the application of computer-aided detection methods in breast cancer ultrasound has been gradually promoted, and the combined application of artificial intelligence has played an advantageous role in the field of breast disease ultrasound diagnosis, such as shortening the examination time, effectively improving the detection rate and diagnostic accuracy rate. The main reasons are: the accuracy of traditional AI diagnosis model of breast cancer based on machine learning is not high; The aim of this paper is to improve the accuracy of early diagnosis of breast diseases effectively and reduce the misdiagnosis rate caused by overwork of doctors on the basis of clear medical images and computer-aided diagnosis technology.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235434","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

Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks 基于 TPUGRAPHS 使用图神经网络预测模型运行时间

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-26 DOI: 10.54097/fcis.v6i1.13

Jingyu Xu, Linying Pan, Qiang Zeng, Wenjian Sun, Weixiang Wan

引用次数: 0

Image Emotion Analysis Combining Attention Mechanism and Multi-level Correlation 结合注意机制和多级相关性的图像情感分析

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-20 DOI: 10.54097/fcis.v6i1.12

Shuxia Ren, Simin Li

引用次数: 0

Optimization of Artificial Intelligence Algorithm based on Neural Network in Complex System 复杂系统中基于神经网络的人工智能算法优化

Frontiers in Computing and Intelligent Systems Pub Date : 2023-11-14 DOI: 10.54097/fcis.v5i3.13801

Xinqiang Yu

{"title":"Optimization of Artificial Intelligence Algorithm based on Neural Network in Complex System","authors":"Xinqiang Yu","doi":"10.54097/fcis.v5i3.13801","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13801","url":null,"abstract":"The purpose of this paper is to discuss the application of AI (Artificial Intelligence) algorithm with NN (Neural Network) in complex system optimization. Therefore, this paper takes the optimization of power system as the research object and designs a hybrid NN model. The model can effectively extract features from historical power data and predict future power demand and supply. At the same time, the NN model is optimized by genetic algorithm. The algorithm can efficiently search the optimal solution in a large solution space and has the ability to deal with multi-objective optimization problems. Finally, it is verified by experiments. By using test data sets to evaluate the model, it is found that the algorithm in this paper has high accuracy and applicability in dealing with power system optimization problems. At the same time, the model can effectively reduce the cost of power generation and improve the stability of the system. These achievements provide new ideas and methods for future complex system optimization, and provide useful reference for promoting the development and application of AI technology. In order to provide some guidance for practical application.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"53 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276983","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