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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是191-200 订阅

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Analysis of the U.S. Military's Tactical Cloud application Based on Zero Trust

Analysis of the U.S. Military's Tactical Cloud Application B...

引用

icmlca 2021;2nd international conference on machine learning and computer application

作者： Na Zhang Tongjun Wang Jingzhou Ji

Firstly, this paper studies the characteristics of the information environment in the tactical environment in the information age, and elaborates the definition, principles, characteristics, supporting technology and application of zero trust technology; secondly, it summarizes the development and evolution of the U.S. military's tactical cloud, service model, and the proficiency test raises the problem; once again, it analyzes the main structure and application scenarios of the Zscaler tactical cloud solution based on zero trust, and analyzes (Zscaler Private Access) and ZIA (Zscaler Internet Access, Zscaler Internet Access) solution, and summarized its characteristics and advantages; finally, put forward the enlightenment and suggestions based on the zero-trust tactical cloud for the future development of our army’s network information system.

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application and Teaching Practice of Virtual Simulation Technology Based on Cognitive Perspective 2

Application and Teaching Practice of Virtual Simulation Tech...

引用

2nd international conference on Big Data Engineering and Education, BDEE 2022

作者： Zhe, Wang Lijuan, Xu Juan, Huang Xue, Li Yunmei, Zhou School of Economics and Management Sichuan Tourism University Chengdu China University of Technology Mara Faculty of Business and Management Selangor Malaysia School of Transportation and Logistics Southwest Jiaotong University Chengdu China

ISBN: (数字)9781665481847

ISBN: (纸本)9781665481847

On the one hand, it is beneficial for students to complete the internalization of knowledge by constructing a real learning environment and designing a reasonable human-computer interaction way to let students' body senses and actions participate in the process of mental cognition, so as to increase the meaningful interaction between human-computer (object) environment. On the other hand, currently immersive VR mostly belongs to the customization of application scenarios. Individual VR learning takes a long time, and VR independent devices are still unable to support students' centralized parallel learning. In particular, the cognitive laws of limited attention model, cognitive load and attention shift, embodied cognition and so on suggest that existing educators can carry out practical training after the teaching of system theory to strengthen the internalization of practical cognition through immersive application scenarios. As for the centralized class teaching, the combination of early theory (including case) explanation with course ideology and politics, teachers' own knowledge and experience, external output ability, and the diversity, expansion and flexibility of teaching methods and tools application still have advantages for students' systematic theoretical learning under the guidance of teachers. This paper takes the fire safety training of the warehouse management course combined with the VR intelligent warehouse practice training system as an example, the teaching practice is explored. © 2022 IEEE.

关键词： Students

来源：评论

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Large-scale Text Multiclass Classification Using Spark ML Packages 2

Large-scale Text Multiclass Classification Using Spark ML Pa...

引用

2nd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2021

作者： Wang, Shuang Luo, Jian Luo, Liangfu College of Computer Science Wuhan Qingchuan University Wuhan430204 China

machine learning is widely used in the field of natural language processing. Text classification is an important branch of natural language processing, mainly utilized to determine the topic of news information and judge the emotional tendency reflected by social data. However, with the expansion of data scale, traditional machine learning algorithms will encounter various performance bottlenecks due to the limitation of single node resource. The machine learning library ML provided by the distributed memory computing framework spark can efficiently process large-scale datasets. Based on Spark ML, this paper uses logistic regression to classify the preprocessed data originated from standard dataset 20newsgroups. The classification accuracy and F1-score can reach 89.64% and 89.64% respectively. Meanwhile, 5-fold cross validation experiments prove that the result is reliable and procedure design is feasible. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： machine learning

来源：评论

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Image Search System Based on machine learning 2

Image Search System Based on Machine Learning

引用

2021 2nd international conference on Artificial Intelligence, computer Networks and Communications, AICNC 2021

作者： Wang, HongLin School of Information Engineering Yancheng Teachers University Jiangsu Yancheng224002 China

Since the 21st century, with the continuous maturity of network technology and its integration with the education field, traditional face-to-face communication has gradually expanded to the virtual network environment. In the online learning environment, students can use the online platform to communicate directly with teachers, no longer limited by time and region. The time and space breakthrough of teacher-student interaction has brought development opportunities for teachers to constantly contact students with a long-term management mechanism. Based on this situation, this article uses artificial intelligence technology to build a network communication platform. This article first analyzes the application status of artificial intelligence technology in the network communication platform, and then introduces the artificial intelligence technology applied in this article. Then, this article uses artificial intelligence technology to design a network communication platform, and test the function and performance of the platform. The test results show that the function of the system is very accurate and reliable, and the performance of the system is sufficient to support nearly 10,000 users at the same time. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Review on evaluation techniques for better student learning outcomes using machine learning 2

Review on evaluation techniques for better student learning ...

引用

2nd international conference on Intelligent Engineering and Management, ICIEM 2021

作者： Rana, Pooja Raj Gupta, Lovi Dubey, Mithilesh Kumar Kumar, Gulshan Lovely Professional University Department of Computer Applications Phagwara India

ISBN: (纸本)9781665414500

The paper represents review on student learning outcomes on the basis of various evaluation parameters which plays an important role in an education system. Student learning outcomes along with other attributes are taken into consideration like learner factor, learner engagement, learning strategies use, teacher experience, motivational beliefs and technology in learning etc. With the help of examination and evaluation we can measure student learning outcome. Classification Algorithms like Decision Tree, Naïve Bayes and Support Vector machine can help us to classify student's performance. This classifier helps in tracking student performance. With the use of machine learning techniques we are trying to identify whether learning outcome is achieved or not. Students learning evaluation should be done on regular basis so that true learning outcomes can be measure. Once learning outcome is evaluated on regular basis, its aggregation should be done to sum up the learning outcome of course. © 2021 IEEE.

关键词： Support vector machines

来源：评论

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A distributed and unified API service for machine learning models 2

A distributed and unified API service for machine learning m...

引用

2nd international conference on Intelligent Engineering and Management, ICIEM 2021

作者： Nandigramwar, Harshal Mittal, Akshay Bhatnagar, Apoorv Rashid, Mamoon Lovely Professional University School of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9781665414500

machine learning models are usually based on a single dataset and a single architecture approach. But this approach falls behind in many practical scenarios. Many techniques such as boosting and bagging have been developed in the past that enables better predictions by ensembling multiple models. In this paper, we propose an extension to the existing ensembling techniques by developing a community-driven system of distributed API that unifies several models to produce an ensembling effect along with several other benefits such as wider availability, greater usability, lesser resource constraints and an infrastructure for future developments in the field. © 2021 IEEE.

关键词： Measurement computer architecture Predictive models Logic gates Prediction algorithms Boosting Software

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Real-Time Detection of Edge Defects on a Rolled Steel Sheet Using Transfer learning Technique 2nd

Real-Time Detection of Edge Defects on a Rolled Steel Sheet ...

引用

Proceedings of the 2nd international conference on Future Technologies on Manufacturing, Automation, Design and Energy 2021

作者： Suresh, V. V. N. Satya Kalyani, Y. Kumar, C. Ankith Mahatma Gandhi Institute of Technology Hyderabad Gandipet India

ISBN: (纸本)9789819912872

Automation of the manufacturing processes especially in the rolling process of steel needs better quality assessment methods so that proper inspections are carried out before the end product is released. In order to achieve higher productivity through non-contact inspection, it is important to identify edge defects during the rolling process itself with the help of computer vision and machine learning techniques. In this work, a new approach consisting of an improved convolutional neural network (CNN) as classifier using Visual Geometry Group-19(VGG-19) algorithm as feature extractor is used to detect the edge defects under the framework of tensor flow. About 4000 images have been collected for model testing and training. By adopting the recently developed VGG-19 trained CNN network model, 3 Fully Connected layers were reduced into one flat layer and 2 Fully Connected Layers with reduced parameters. Testing accuracy of the model was 87% and the model achieved 98% training accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Botnet Detection Based on Flow Summary and Graph Sampling with machine learning 2

Botnet Detection Based on Flow Summary and Graph Sampling wi...

引用

2nd international conference on computer Engineering and application, ICCEA 2021

作者： Long, Chun Xiao, Xisheng Wan, Wei Zhao, Jing Wei, Jinxia Du, Guanyao University of Chinese Academy of Sciences Computer Network Information Center Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426169

With the development of botnets, detecting and preventing botnet attacks has become an important task of network security research. Existing works rarely consider timing patterns in botnets, and thus are not effective in realistic botnet detection, nor can they detect unknown botnets. To deal with these problems, this paper proposes a flow summary and graph sampling based botnet detection method using machine learning algorithms. Firstly, the network flow data is aggregated according to the source host IPs, and the flow summary records are generated within a duration of time window. Meanwhile, we use graph sampling technology to obtain a subset of entire graph, obtaining 4 graph features which are added to the flow summary records. Afterwards, decision tree, random forest and XGBoost machine learning classification models are built to validate the performance of our method. The experimental results on the Bot-IoT and CTU-13 datasets show that the method we proposed can effectively detect botnet traffic and unknown botnets. © 2021 IEEE.

关键词： machine learning

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Research on the application of RBF Neural Network in Image Compression

Research on the Application of RBF Neural Network in Image C...

引用

icmlca 2021;2nd international conference on machine learning and computer application

作者： Fang Liu Tianyun Wang Mowen Cheng Yongxin Feng

In contemporary life, a large number of images need to be stored and transmitted. In order to save transmission resources and improve transmission speed, image compression is particularly important, and the application of intelligent algorithms such as neural networks provides new ideas and methods for image compression. Considering the serious block effect of traditional BP neural network in image compression and the poor performance of compression rate, RBF radial basis neural network quantization image compression method (RQIC) is proposed. After testing, RQIC has the ability to quickly locate the optimal number of nodes in the hidden layer relative to the traditional BP neural network compression algorithm and can reduce the block effect and improve the compression rate by about 0.05 under the premise of similar PSNR.

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Anomalous Behavior Detection in Galaxies and Exoplanets using ML DL Techniques 2

Anomalous Behavior Detection in Galaxies and Exoplanets usin...

引用

2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Dere, Shivani Fatima, Maziya Jagtap, Rutuja Inamdar, Unzela Shardoor, Nikhilkumar MIT ADT University Computer Science and Engineering MIT School of Engineering Pune India

ISBN: (纸本)9781665433686

There has been a rapid increase in astronomical data in terms of both size and complexity. To process this extremely large amount of data, machine learning and deep learning methods can be instrumental in arriving at results and further processing relevant data needed by the researchers. In this project, the Inception V3 and Faster RCNN models are employed for determining the morphological type of galaxies along with detection of galaxies in images for the Galaxy Zoo dataset. For detecting the exoplanets, Astronet is used to determine whether the astronomical body is an exoplanet or not on the Kepler time series data. The machine learning method: SGD classifier is predicting the exoplanet habitability, which is tested on PHL's exoplanet catalogue. © 2021 IEEE.

关键词： Deep learning

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