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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是471-480 订阅

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Quantum machine learning with Quantum Topological Data Analysis 4

Quantum Machine Learning with Quantum Topological Data Analy...

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Khandelwal, Ankit Chandra, M Girish Tata Consultancy Services Tcs Research India

ISBN: (纸本)9798350343236

Topological Data Analysis (TDA) is a powerful but computationally expensive tool for data analysis. Quantum Topological Data Analysis (QTDA) promises to reduce this cost while giving comparable results. this paper explores using QTDA for quantum machine learning (QML) and provides a pipeline for QML where QTDA's quantum state output is fed to the QML module. We also show how reduced Hamiltonians can be used to recover most of the performance for machine learning while using much fewer resources in terms of the number of qubits, depth, and gate counts. © 2023 IEEE.

关键词： machine learning

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proceedings - 2023 international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

Proceedings - 2023 International Conference on Advances in E...

引用

4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

ISBN: (纸本)9798350308082

the proceedings contain 159 papers. the topics discussed include: cloud edge interaction protocol detection system for distribution IoT based on metadata definition;a data mining method based on natural language processing and machine learning;simulation and intelligent fault diagnosis of bidirectional LLC resonant converter;an autonomous exploration method integrating local multi-point TOF data and edge following;regional integrated energy system control strategy considering orderly charging and discharging of electric vehicles;photogrammetric method for calculating spatial coordinates of transmission line galloping;analysis of the mechanism of soft fault in cables based on transmission line theory;short-term photovoltaic power prediction based on similar daily clustering and ISSA optimized neural network;and leveraging profiling for facial recognition optimization.

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Geospatial Data Exploration Using machine learning 4

Geospatial Data Exploration Using Machine Learning

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Geetha, K. Srivani, A. Gunasekaran, S. Ananthi, S. Sangeetha, S. Mam College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy India Mahendra Collegeo of Engineering Department of Computer Science and Engineering Tamil Nadu India V.S.B. Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy India

ISBN: (纸本)9798350300888

Geospatial data exploration's major objective is to learn more about spatial characteristics in order to decide accommodation of students in various locations. the K-means technique, which is frequently used in geospatial analysis to find spatial patterns or hotspots of particular phenomena, is the algorithm utilised in this research. It is used to organise data points into clusters based their similarity. Urban planning, transportation analysis, and epidemiological research, among other subjects, can all benefit greatly from this kind of analysis. the process of looking into and displaying patterns, trends, and relationships within data that has been gathered and documented using geographic information is known as exploratory analysis of geolocational data. It will be possible to accommodate in this study. © 2023 IEEE.

关键词： Urban transportation

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proceedings - 2023 4th international conference on Frontiers of computers and Communication Engineering, FCCE 2023

Proceedings - 2023 4th International Conference on Frontiers...

引用

4th international conference on Frontiers of computers and Communication Engineering, FCCE 2023

ISBN: (纸本)9798350333169

the proceedings contain 8 papers. the topics discussed include: running P4 programs on general programmable network interconnection chips;research on flotation level detection based on EIT technology;deadline sensitive cloud computing resource scheduling method for scene rendering;performance characteristics of selected network topology in a software-defined networking QoS testing framework;research on student achievement prediction method based on machine learning;mini-Savi: realistic satellite network simulation platform based on open-source tools;research on key technologies of face recognition data storage security;and analysis on influencing factors of performance of NSM-CFRP sheets reinforcement ancient building timber beams based on digital simulation.

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Conducting and Mitigating Portable thermal Imaging Attacks on User Authentication using AI-driven Methods 23

Conducting and Mitigating Portable Thermal Imaging Attacks o...

引用

4th Augmented Humans international conference (AHs)

作者： Macdonald, Shaun Alexander Alotaibi, Norah Islam, Md Shafiqul Khamis, Mohamed Univ Glasgow Glasgow Scotland

ISBN: (纸本)9781450399845

thermal cameras have become portable enough to integrate into wearables, such as glasses, and can be used maliciously to infer passwords observing heat traces left on keyboards, keypads and screens. While prior work showed how AI-driven approaches can be used to further enhance the effectiveness of these attacks, we use similar approaches to detect vulnerable interfaces and obfuscate heat traces to defend against thermal attacks. At our Augmented Humans 2023 demo, attendees will have the chance to use a thermal camera to observe thermal traces on a keyboard, and observe how machine learning can both automatically identify keys pressed based and identify, then obfuscate, thermal images of a keyboard to prevent thermal attacks. this demo will provoke thought and discussion about the security risks presented by discrete, wearable thermal cameras and how these risks can be mitigated by both designers and users.

关键词： thermal Attacks Usable Security machine learning

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Neural machine Translation Research Progress and Its Implications for Education Technology: A Bibliometric Analysis 4

Neural Machine Translation Research Progress and Its Implica...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Jiang, Qingbo Huang, Yong School of Humanities Jinan University Guangdong Zhuhai519070 China Library of Zhuhai Campus Jinan University Guangdong Zhuhai519070 China

ISBN: (纸本)9798400709449

Neural machine translation (NMT) stands as one of the most prominent domains in contemporary natural language processing research. In this study, employing VOSviewer software, we conducted a bibliometric analysis based on 1386 NMT-related publications retrieved from the Web of Science database as of September 2023. Co-citation analysis revealed that influential sources in the NMT field were primarily concentrated in top-tier conferences and journals in areas such as artificial intelligence, neural computing, and computer vision. Notably influential authors focused on machine translation techniques, deep learning, and natural language processing technologies. Co-occurrence analysis delineated two prominent research directions within the NMT domain: machine translation and deep learning. Based on the outcomes of bibliometric analysis, we provide insights into the current state, challenges, and prospects of NMT research and offer an understanding of NMT applications in the realm of educational technology. © 2023 ACM.

关键词： Deep learning

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Research on the application of computer Intelligent 3D Aided Technology in Design Modeling System 4

Research on the Application of Computer Intelligent 3D Aided...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Fan, Chong Zhang, Wei Art Design College Hubei University of Technology Wuhan China

ISBN: (纸本)9798350342710

this paper presents the architecture of computer aided industrial design based on application Service provider pattern. the system designed and developed in this paper consists of four toolsets, each of which is developed using reusable component technology and dynamically loaded into the computer aided industrial design platform. Process model and process diagram of parts were generated by reverse reconstruction method. the process model and 2D process diagram of shaft sleeve parts in reverse order are generated automatically. the key technologies in toolset development, such as style and shape layout template, color scheme library, man-machine operation component library and appearance evaluation, are studied. the experimental simulation shows that the system can improve the design ability of enterprises and is an important part of regional networked manufacturing. © 2023 IEEE.

关键词： three dimensional computer graphics

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Rice Varieties Classification Using Neural Network and Transfer learning with MobileNetV2 4

Rice Varieties Classification Using Neural Network and Trans...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Vania, Vanessa Setyadi, Anthony Widyatama, I Made Danendra Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Rice plays a vital role in global food security, feeding over 8 billion people worldwide. the classification of rice varieties based on visual characteristics, such as color, shape, and texture, is crucial for quality inspection and efficient farming practices. However, existing classification methods heavily rely on subjective and time-consuming human visual inspection, leading to errors and inefficiencies. this research paper explores the potential of deep learning approaches, specifically neural network and transfer learning with MobileNetV2, for accurate and efficient rice variety classification. the paper builds upon previous studies that have demonstrated the superiority of deep learning over traditional machine learning methods. Experimental results using a publicly available dataset of 75,000 rice grain images, labeled with five rice varieties, show that both the neural network and MobileNetV2 models achieve high accuracy rates. the neural network model is trained using stochastic gradient descent with 250 epochs, while the MobileNetV2 model leverages pretraining on the ImageNet dataset and is trained with the Adam optimizer for 10 epochs. the comparative analysis reveals that the MobileNetV2 model performs slightly better, benefiting from its ability to extract features from images learned from the large-scale ImageNet dataset. the findings highlight the potential of deep learning methods for rice variety classification, offering improved accuracy and efficiency in rice quality inspection. the choice between the neural network and MobileNetV2 models depends on the specific requirements and priorities of the application, with the neural network offering flexibility and transparency, and MobileNetV2 providing high accuracy through pre-trained feature extraction. © 2023 IEEE.

关键词： Textures

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Multi-fault feature extraction method based on Manifold learning 4

Multi-fault feature extraction method based on Manifold Lear...

引用

4th international conference on computer Engineering and application, ICCEA 2023

作者： Huang, Lin Wang, Wei Gong, Li Liu, Yajie Shi, Lianhui Navy University of Engineering Ship Comprehensive Test and Training Base Wuhan China 91278 Army of Pla Dalian China

ISBN: (纸本)9798350347548

Aiming at the difficulty of signal processing and feature extraction in multi-fault mode, a multi-fault feature extraction method based on manifold learning is proposed. On the basis of maintaining the linear relationship between neighborhood sample points in low dimensional space, the nearest neighborhood sample points are found based on Euclidean distance through locally linear embedding (LLE) algorithm and the optimal local geometric embedding is obtained by linear combination. In the problem of regularization, by using the method of multiple weight vectors, the optimal solution is ensured to identify when it converges to the desired embedding. Finally, the multi-fault feature extraction is visually displayed in two-dimensional and three-dimensional images respectively to verify the effectiveness of the method based on the CMAPSS data set provided by NASA. © 2023 IEEE.

关键词： Feature extraction

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A QoS and Load Balancing Predictive Model Based on LSTM and Random Forest Regression in SDN: A Rest API Approach 4th

A QoS and Load Balancing Predictive Model Based on LSTM and ...

引用

4th international conference on Data and Information in Online Environments

作者： Ansari, Muhammad Salman Zhang, JianXun Shaban Tianjin Univ Technol & Educ Tianjin Peoples R China Univ Elect Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9783031807121;9783031807138

Fog Computing, Software Define Networking, RESTful API and machine learning are new technologies in the area of ICT as well as in Networking. Fog computing brought high performance computing at the edge of network and Software Defined networking. As we intended to head towards QoS and Load balancing in SDN. In this work we cover an initial segment which is machine leaning model implementation on a network traffic information to predict future outcomes for traffic flow. Meanwhile, in comprehensive research we intended to reduce network congestion, jitter, QoS and load balancing by learning past traffic flow information. As well as, we aim to improve maximum utilization of link-bandwidth. through RESTful API of SDN, we can embed machine learning based prediction model to the network server which can modify the network policies by learning from past experiences. In this paper, we proposed a QoS and load balancing framework. A Server and SDN based application for network monitoring, management and controlling the policies over network gateway for better performance in regard of traffic flow. Experiment result shows that implementation of machine learning over network traffic flow information immensely important for new emerging technologies. the evaluation results of machine learning model we implemented in this work depicts that model performs well. Meanwhile the model improves by increasing with the number of epochs.

关键词： machine learning in Networking SDN RESTful API QoS and Load balancing

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