检索结果-内蒙古大学图书馆

2022 International Conference on Algorithms, Microchips and Network Applications

作者： Tang, Weiyu Ding, Hui Huang, Hongliang Lv, Wei School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China Regional Teaching Operation Center Huike Education Technology Group Co. Ltd Beijing100086 China School of Public Basic and Applied Statistics Zhuhai College of Science and Technology Zhuhai519041 China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai519041 China

ISBN: (纸本)9781510653290

With the rapid development of tourism, more and more people choose to travel by air, and the price fluctuation of air tickets is large and the law is not significant, which makes it necessary to use some big data technical means to predict the change law of prices. The research and analysis of the characteristics of air tickets are helpful to analyze the internal value of tourism. Based on the web crawler technology, this paper obtains a large number of flight information from Shanghai to Guangzhou. Through data exploration and preprocessing, Random Forest classification model is established to predict the purchase of air tickets. The prediction accuracy of whether to buy or not is 96.96%. Then, ARIMA model is established to predict the ticket price trend. The deviation rate of the model is less than 18%. The future ticket price can be effectively predicted through Time Series. The machine learning model established in this paper provides model support for the study of the characteristics of air tickets. The machine learning model established in this paper provides model support for the study of the characteristics of air tickets and technical support for the specified price strategy of the aviation industry, so as to promote the development of tourism. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Web crawler

来源：评论

学校读者我要写书评

暂无评论

ENSIOT: A Stacking Ensemble Learning Approach for IoT Device Identification

ENSIOT: A Stacking Ensemble Learning Approach for IoT Device...

引用

International Workshop on Quality of Service

作者： Kangli Niu Shenghao Liu Lingzhi Yi Xianjun Deng Suning Chen Laurence T. Yang Minmin Cheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Safety Engineering Zhongnan University of Economics and Law

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In order to resist network attacks on IoT devices, identifying IoT devices is the first step for ensuring device security. The traditional passive method identifies IoT devices by mining the potential relationship between traffic characteristics and devices. However, the form of selected traffic features are too singular without considering device behavioral characteristics and the classifier used is too specific with simple structure in these methods. This paper proposes a stacking ensemble learning approach for IoT device identification, ENSIOT, which fully considering the behavioral characteristics of devices and integrating the advantages of various machine learning methods to achieve efficient identification of IoT devices. Firstly, in the process of traffic processing, our method selects features from activity cycles, port numbers, signalling patterns, and cipher suites. Then, in model integration, many machine learning methods are used as base models to learn features selected, and output preliminary recognition results. Finally, the meta model learns the relationship between label and the recognition results of each base model and outputs the final device identification result. This stacking structure stacks the base models and the meta model to make a classifier with strong identification and generalization ability. Incremental learning is used to improve identification accuracy when traffic pattern changing. Comparative experiments are conducted on two datasets of UNSW and TMA-2021. The experimental results verify the effectiveness of ENSIOT, which achieve the accuracy of over 98% on two dataset and bring a noticeable improvement in terms of both accuracy and macro F1 score.

关键词： Ciphers Accuracy Incremental learning Stacking Resists Traffic control Internet of Things

来源：评论

学校读者我要写书评

暂无评论

A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation

arXiv

引用

arXiv 2024年

作者： Yuan, Li Cai, Yi Ren, Haopeng Wang, Jiexin School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibits a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and data are released at https://***/YuanLi95/T5-LMPM. © 2024, CC BY-NC-ND.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Diversified Sequential Recommendation via Evolutionary Multi-Objective Transfer Optimization

Diversified Sequential Recommendation via Evolutionary Multi...

引用

Artificial Intelligence (CAI), IEEE Conference on

作者： Wei Zhou Xiaolong Luo Hongyue Bao Zexuan Zhu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Sequential Recommendation (SR) intends to model user interests based on historical behavior sequences and suggest the next item. Most existing sequential models primarily focus on learning users’ preferences on the relevance objective of the target item. However, the investigation of personalized preferences on the diversity objective of SR results is usually ignored. Evolutionary algorithms have amply demonstrated effectiveness in solving multi-objective problems but encounter efficiency bottlenecks when applied to multi-objective recommendation tasks due to the limited scalability at the user level. To address the problem, this study proposes to facilitate diversified SR via a multi-objective transfer optimization algorithm, in which each optimization task corresponds to the recommendation for a target user. With the optimization knowledge of user preference transferred within and across tasks, the diversified SR of a set of users can synchronously proceed. The novelty of our proposed algorithm is fully utilizing the outstanding global search capability of evolutionary multi-objective optimization without hindering the efficiency of sequential models.

关键词： Scalability Evolutionary computation Task analysis Artificial intelligence Optimization

来源：评论

学校读者我要写书评

暂无评论

Solving the Model Unavailable MARE using Q-Learning Algorithm

arXiv

引用

arXiv 2024年

作者： Yan, Fei Gao, Jie Feng, Tao Liu, Jianxing School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

In this paper, the discrete-time modified algebraic Riccati equation (MARE) is solved when the system model is completely unavailable. To achieve this, firstly a brand new iterative method based on the standard discrete-time algebraic Riccati equation (DARE) and its input weighting matrix is proposed to solve the MARE. For the single-input case, the iteration can be initialized by an arbitrary positive input weighting if and only if the MARE has a stabilizing solution;nevertheless a pre-given input weighting matrix of a sufficiently large magnitude is used to perform the iteration for the multi-input case when the characteristic parameter belongs to a specified subset. Benefit from the developed specific iteration structure, the Q-learning (QL) algorithm can be employed to subtly solve the MARE where only the system input/output data is used thus the system model is not required. Finally, a numerical simulation example is given to verify the effectiveness of the theoretical results and the algorithm. © 2024, CC BY.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Deep Time-Series Prediction of Complications and Deterioration in ICU Patients Based on the MSIPA Model

Deep Time-Series Prediction of Complications and Deteriorati...

引用

Cross Strait Quad-Regional Radio science and Wireless technology Conference (CSRWTC)

作者： Duanyang Feng Wennan Wang Junjie Yan Shiyang Song Yiwen Liu Xiaomin Guo Faculty of Data Science City University of Macau Macau China Academy of Management Guangdong University of Science and Technology Dongguan China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China Information Technology School Hebei University of Economics and Business Shijiazhuang China

In this research, a novel deep time-series model, MSIPA, is introduced for predicting complications and deterioration in ICU patients. Unlike conventional models, MSIPA efficiently handles data across varied time intervals, offering enhanced accuracy, particularly with discontinuous time series. It outperforms other deep learning models in multiple tasks, emphasizing the promise of deep time series prediction in healthcare.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A comprehensive dataset for dynamic analysis of ocean front

引用

Intelligent Marine technology and Systems 2024年第1期2卷 1-13页

作者： Yang, Yuting Gao, Ying Sun, Xin Ju, Yakun Zhang, Cong Lam, Kin-Man College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China School of Data Science Qingdao University of Science and Technology Qingdao China Data Science in Earth Observation Technical University of Munich Munich Germany School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper proposes an ocean front database and a method for its construction tailored for studying the dynamic evolution of ocean fronts. Ocean fronts play a crucial role in the interactions between the ocean and atmosphere, affecting the transfer of heat and matter in the ocean. In recent years, research on ocean fronts has emerged as a significant and rapidly evolving area within oceanography. With the development of ocean remote sensing technology, the amount of available ocean remote sensing data has been increasing. However, the potential of this expanding volume of ocean front data remains largely untapped. The lag in data processing technology has hindered research progress in understanding ocean fronts despite the growing amount of data available. To bridge this gap, this paper proposes an ocean front dynamic evolution database along with a method for its construction to further promote research into the variations and interactions of ocean fronts. This is especially relevant for studies utilizing deep learning to explore the dynamic evolution of ocean fronts. Specifically, the proposed database is designed to capture the variation processes of ocean front enhancement and attenuation, as well as the interactions during ocean front splitting and merging. The proposed database construction method allows for the segmentation and extraction of specific ocean fronts of interest from ocean front images. The proposed method is beneficial for analyzing the dynamic evolution between multiple ocean fronts on the same timeline.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Decomposing Source Codes by Program Slicing for Bug Localization

Decomposing Source Codes by Program Slicing for Bug Localiza...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jian Yong Ziye Zhu Yun Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications

Bug localization, which aims to automatically locate buggy source code files based on the given bug report, is a critical yet time-consuming task in the software engineering field. Existing advanced bug localization methods have successfully leveraged deep learning to bridge the lexical gap between bug reports and source code files at the semantic level. These methods usually first build the entire source code file semantic representation and then match it with the bug report. However, the bug described in the bug report may be related to only part of the source code file semantics. Directly constructing a semantic representation of the entire source code file would increase the difficulty of semantic matching between bug reports and source code files. In this paper, we propose a novel model named S-BugLocator, which decomposes source code file with the help of program slicing. Especially, our proposed S-BugLocator incorporates two distinctly structured slice feature extraction components in processing source code files to cope with the significant discrepancy between multi-row slices and single-row slices. For each multi-row slice, a CNN and Bi-LSTM network is firstly employed to extract its semantics and then a keywords supervised attention mechanism is designed to build its semantic representation by focusing on slices that have strong relevance with the bug report. For each single-row slice, the semantic representation is obtained by fusing word embeddings in single-row slices. The experimental results on four real-world large-scale projects indicate that our proposed model outperforms existing state-of-the-art bug localization methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

引用

8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Regional research based on big data of hot spring industry in Dawan District of Guangdong, Hong Kong and Macao 2

Regional research based on big data of hot spring industry i...

引用

2021 2nd International Conference on Computer Information and big data Applications, CIBDA 2021

作者： Zhang, Bo Zhang, Zhiqin School of Computer Science Wuhan Donghu University Hubei Key Laboratory of Big Data in Science and Technology Wuhan Library of Chinese Academy of Science Wuhan430212 China

One belt, one road area, tourism research in the Tai Wan District of Guangdong, Hong Kong and Macao is mainly focused on the integration mechanism of culture, commerce and tourism in Guangdong, Hong Kong and Macau, and the leisure and recreational zone structure of the city group of Guangdong, Hong Kong and Macau. In this paper, combined with the actual needs of hot spring scenic spots in Guangdong, Hong Kong and Macao, we use the concept of global tourism to achieve information integration, marketing accuracy, intelligent management, and create humanized service, so as to create a new benchmark of smart hot spring scenic spots. We need a support platform and four kinds of application-oriented business systems, including technical service, technical management, technical marketing, and technical management. Using the methods of theoretical analysis, simulation test or implementation, this paper studies the regional scientific and technological research based on the scientific and technological big data of hot spring industry in Dawan District of Guangdong, Hong Kong and Macao. © Published under licence by IOP Publishing Ltd.

关键词： Hot springs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：