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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1361 条记录，以下是781-790 订阅

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4th international conference on Innovative data Communication Technology and Application, ICIDCA 2022

4th International Conference on Innovative Data Communicatio...

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4th international conference on Innovative data Communication Technologies and Application, ICIDCA 2022

The proceedings contain 100 papers. The topics discussed include: security and privacy preservation for data communication network;insights into NoSQL databases using financial data: a comparative analysis;a dynamic fuzzy engine for adaptive control towards improvement of network performance in big data environment;a resource-optimized and accelerated sentiment analysis method using serverless computing;tuning multi-layer perceptron by hybridized arithmetic optimization algorithm for healthcare 4.0;digital evidence management system for cybercrime investigation using proxy re-encryption and blockchain;hybrid neural network architecture for multi-label object recognition using feature fusion;a review on vulnerabilities to modern processors and its mitigation for various variants;metaheuristics approach to improve data analysis process for the healthcare sector;swarm intelligence in data science: challenges, opportunities and applications;and analysis of in-place matrix rotation of square matrix for information security applications.

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Research on Sentiment Analysis of hotel reviews based on Word2vec method 3

Research on Sentiment Analysis of hotel reviews based on Wor...

引用

3rd international conference on Artificial intelligence and Electromechanical Automation, AIEA 2022

作者： Wan, Jiahua Yao, Lisha Tang, Qiang School of Big Data and Artificial Intelligence Anhui Xinhua University Anhui Hefei China College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781510657281

This paper takes sentiment analysis as the research direction and collects the data of users' comments on hotel rooms. The research methods used include text corpus preprocessing, Word2vec model, support vector machine, AUC value calculation, feature classification based on supervised learning method, emotion analysis and ordered logistic regression model, etc. Word2vec is used to map words into k-dimensional vector space, and PCA algorithm is used to reduce the dimensionality of the results. According to Word2vec, the comment corpus can be divided into 7 categories of feature dimensions, and then feature recognition is carried out, and emotion scores are calculated by emotion analysis technology. Finally, classify and summarize the emotional scores of each comment in different feature categories. In this paper, through the establishment of Word2vec model, the analysis of consumers' emotional tendency to the hotel, to provide the direction of improving the service level for the hotel, is conducive to the management and development of the hotel. © 2022 SPIE.

关键词： Support vector machines

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5th IEEE international conference on Computer and Communication Engineering Technology, CCET 2022

5th IEEE International Conference on Computer and Communicat...

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5th IEEE international conference on Computer and Communication Engineering Technology, CCET 2022

ISBN: (纸本)9781665467353

The proceedings contain 58 papers. The topics discussed include: an improved lion swarm optimization algorithm based on tent-map and differential evolution;a self-attentive interest retrieval recommender;marine predators algorithm based on trigonometric function;research on hybrid intelligence wargame method;automatic current transformer verification technology for high-speed railway power based on edge-cloud collaborative computing;grasshopper optimization algorithm based on adaptive curve and reverse learning;research on a user context model based on data mining;large-scale distance matrix calculation method based on contraction hierarchies;epidemic data analysis based on data visualization technology;improving aerospace big data infrastructure and applications with distributed file system and massive parallel calculation;arbitrary-shaped text detection with gaussian probability distance distribution;similarity computation of heterogeneous ontology based on graph attention network;and a small target detection method based on feature enhancement and positioning optimization.

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Efficient Transmission based on Genetic Evolutionary Algorithm 4

Efficient Transmission based on Genetic Evolutionary Algorit...

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4th IEEE international conference on Artificial intelligence in Engineering and Technology, IICAIET 2022

作者： Fan, Jin Lim, Kit Guan Ee Chuo, Helen Sin Tan, Min Keng Farzamnia, Ali Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively. © 2022 IEEE.

关键词： Genetic algorithms

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Online and Offline Mixed Teaching Reform in the Practical Course of Artificial intelligence and Application 3

Online and Offline Mixed Teaching Reform in the Practical Co...

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3rd international conference on Sensing, Measurement and data Analytics in the Era of Artificial intelligence, ICSMD 2022

作者： Zheng, Wenbin Liu, Bing Feng, Lei Wu, Yan Qiao, Jiaqing Yin, Hongtao Meng, Shengwei Fu, Ping School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665492812

It is necessary to ensure the quality of students' courses, especially practical courses, which is an important part of higher education, and plays a positive role in promoting and popularizing the improvement of innovation and entrepreneurship in the face of the non suspension of classes and schools under the COVID-19. This paper explores the mode of online and offline combined with ideological and political education mixed teaching reform in the course, in order to explore the educational functions and ideological and political elements of the course from the practical contents and objectives from the practical course of artificial intelligence foundation, explore the implementation methods and teaching concepts of ideological and political education in the course, so that students can better master and understand knowledge comprehensively, improve the results of students' ideological and moral education, and explore the reform mode which satisfy the requirements of talent training. © 2022 IEEE.

关键词： artificial intelligence curriculum ideological and political education Online and offline mixed teaching reform practical course

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High-dimensional Feature Selection in Classification: A Length-Adaptive Evolutionary Approach 19

High-dimensional Feature Selection in Classification: A Leng...

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19th international conference on Networking Sensing and Control

作者： Zhou, Junhai Lu, Jianyun Wu, Quanwang Wen, Junhao Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Coll Elect Engn Sch Artificial Intelligence & Big Data Chongqing Peoples R China Chongqing Univ Coll Software Engn Chongqing Peoples R China

ISBN: (纸本)9781665472449;9781665472432

Feature selection is an essential technique which has been widely applied in data mining. Recent research has shown that a good feature subset can be obtained by using evolutionary computing (EC) approaches as a wrapper. However, most feature selection methods based on EC use a fixed-length encoding to represent feature subsets. When this fixed length representation is applied to high-dimensional data, it requires a large amount of memory space as well as a high computational cost. Moreover, this representation is inflexible and may limit the performance of EC because of a too huge search space. In this paper, we propose an Adaptive-Variable-Length Genetic Algorithm (AVLGA), which adopts a variable-length individual encoding and enables individuals with different lengths in a population to evolve in their own search space. An adaptive length changing mechanism is introduced which can extend or shorten an individual to guide it to explore in a better search space. Thus, AVLGA is able to adaptively concentrate on a smaller but more fruitful search space and yield better solutions more quickly. Experimental results on 6 high-dimensional datasets reveal that AVLGA performs significantly better than existing methods.

关键词： classification evolutionary computing feature selection high-dimensional data length-adaptive

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conference Committee

Conference Committee

引用

big data intelligence and computing (datacom), 2019 IEEE 5th international conference on

Provides a listing of current committee members and society officers.

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Automatic Optimization of Variational Quantum Algorithm-Based Classifiers 5

Automatic Optimization of Variational Quantum Algorithm-Base...

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5th international conference on Artificial intelligence and Pattern Recognition, AIPR 2022

作者： Jia, Ruihong Yang, Guang Nie, Min Liu, Yuanhua Zhang, Meiling School of Communication and Information Engineering School of Artificial Intelligence Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. This paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. This model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. The simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise. © 2022 ACM.

关键词： Classification (of information)

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Industrial data-Driven Model of Tobacco Leaf Loose Moisture Regain 22

Industrial Data-Driven Model of Tobacco Leaf Loose Moisture ...

引用

5th international conference on big data Technologies, ICBDT 2022

作者： Zhou, Jiaxian Ma, Zongze China Tobacco Yunnan Industrial Co. Ltd. China

ISBN: (纸本)9781450396875

With the development of advanced information technology such as cloud computing, big data, micro services and artificial intelligence, the manufacturing industry has also tried to introduce a large number of these technologies to achieve industrial transformation and upgrading. In the process of cigarette processing, there are many pain points that cannot be solved by the traditional manufacturing process, such as the loose moisture control in the silk making process and the balance control of the temperature, hot air and moisture in the thin sheet drying cylinder. The traditional manufacturing process requires continuous pre drying control based on experience. Due to the differences in the process level of manual operation, the quality of cut tobacco produced is also uneven, which can't meet the requirements of homogenization of cigarette production. Therefore, this paper proposes a loose regain model of tobacco leaf based on industrial data. By analyzing the dependence of various processing parameters, we obtain important parameters that affect the amount of water added to the loose regain, use these parameters to learn the characteristics of the loose regain process, automatically predict the amount of water added, and improve the cigarette homogenization processing level. Experiments show that our proposed model can effectively improve the quality rate of products. Finally, based on the CPS architecture and the proposed model, an intelligent control system of loose moisture regain is developed, which has been successfully deployed in the actual production process.. © 2022 ACM.

关键词： Multiple linear regression

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data Mining Analysis Based on Cloud computing Technology 11th

Data Mining Analysis Based on Cloud Computing Technology

引用

11th international conference on Frontier computing, FC 2021

作者： Li, Yizhi College of Internet of Things Jiangxi Teachers College Jiangxi Yingtan China

ISBN: (纸本)9789811680519

With the rapid development of computer technology, Internet technology and artificial intelligence technology, the amount of global data has exploded, and the era of big data has come. This paper mainly studies the data mining analysis based on cloud computing technology. All the data needed in the experiment are put into the name node, which is responsible for copy management to other machines. All the intermediate data generated during the experiment are stored in the distributed file system of the cluster. Firstly, the data set to be mined is uploaded to Hadoop distributed file system (HDFS), and the program reads the data from HDFS when using it. At the same time, the output file name is set so that the system can store the calculation results in HDFS. The selection of indicators follows the scientific design principle to ensure that the indicators are comprehensive, scientific and operable, and the indicators are verified and selected by expert analysis. In the case of using complete 150g data, 72% of the single machine does not use Hadoop platform, and the accuracy of using Hadoop platform is 71%. The results show that the combination of cloud computing technology and data mining can significantly improve the operation effect of the algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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