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检索条件"任意字段=10th International Conference on Computer-Aided Design and Computer Graphics"

共 10291 条记录，以下是381-390 订阅

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Discussion on the Construction of Power 3D design Platform for Nested Virtualized Hybrid Cloud 10th

Discussion on the Construction of Power 3D Design Platform f...

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10th international conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Liu, Yi Cao, Sheng School of Computer Science and Technology Shanghai University of Electric Power Shanghai China East China Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group Shanghai China

ISBN: (纸本)9789819787425

this article addresses the challenges associated with three-dimensional (3D) design models in the power industry, such as substantial data volume and complex calculations. Traditional data exchange methods pose difficulties in coordinating design efforts among project participants, and current computing systems cannot meet the performance demands of 3D power design. To overcome these challenges, this study thoroughly evaluates both public and private clouds and ultimately selects a hybrid cloud infrastructure for a custom-built 3D design platform that addresses the specific needs of the power industry. Employing optimization algorithms for 3D design models, we reduce model size and implement automated synchronization of platform data through code. We also propose different levels of cloud services and identify the necessary functionalities to be fulfilled by each layer of the 3D design cloud platform. Nested virtualization technology is used to create cloud desktops with different hardware requirements. Ultimately, we successfully establish a comprehensive framework for a 3D design platform specifically tailored to power design enterprises. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud platforms

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Application of Machine Learning Techniques for Decision Making Process in Human Resource Management 10

Application of Machine Learning Techniques for Decision Maki...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Krishna, C.Balarama Joshi, Meeta Kumar, K. Sathesh Kalyan, Nalla Bala Bhardwaj, Shivani Hinge, Punamkumar School of Science Sr Universtity Telangana Warangal India Marwadi University Faculty of Management Studies India Alliance College of Engineering and Design Alliance University Central Campus Computer Science and Engineering Bengaluru India Department of Management Studies India Manav Rachna International Institute of Research and Studies Fms Faridabad India Indus Business School Business Management Pune India

ISBN: (纸本)9798350382471

the strategic alignment between organisational objectives and human resource management is stronger in contemporary organizations. As deep learning methods and machine learning solutions play a larger role in managing human resource management operations, organizations are focusing on more applicable sets of solutions. Models based on machine learning are now making progress in a variety of HRM-related fields. Machine learning is being used in human resource management to anticipate who will remain and who will depart the company, as well as to gauge workers' interest in their specific *** scraping methods are used to extract the data, which is then saved in CSV format. With the aid of ML algorithms, the many characteristics in the data acquired using this method may be used to make predictions. the management may develop a strategy to keep a deserving person in the organization by using the analysis to draw conclusions about who will remain or depart the *** used a variety of methods in our investigation, including feature scaling and SMOTE. the recommended techniques, such as random forest and XG boost classifier, are supported by the findings. We'll arrive to a judgment based on the accuracy rate (%) numbers for the results generated by the offered approaches. © 2023 IEEE.

关键词： decision making Human resource management machine learning random forest SVM

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Outage Probability Analysis of Reconfigurable Intelligent Surface (RIS)-Enabled NOMA Network 10

Outage Probability Analysis of Reconfigurable Intelligent Su...

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10th international conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Sadia, Haleema Abbas, Ziaul Haq Hassan, Ahmad Kamal Abbas, Ghulam Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Topi23640 Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Topi23640 Pakistan

ISBN: (纸本)9798350329674

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for the next generation of wireless networks. However, the random nature of wireless channels can significantly reduce performance. Recently, reconfigurable intelligent surfaces (RISs) have emerged as a passive smart solution to combat the effects of wireless radio links. Integrating NOMA with RIS is a smart solution to control the stochastic behaviour of wireless channels with improved coverage and enhanced throughput. It can substantially improve the outage probability (OP) of wireless networks. this paper investigates the outage performance of a multi-antenna downlink NOMA system with RIS assistance. We determine the OP utilising the statistical characteristics of the signal-to-noise ratio of the reflection channel from the base station to the users through RIS for both near and far users. the proposed RIS-aided system outperforms conventional NOMA and orthogonal multiple access (OMA) systems without RIS. We validate our analytical results using Monte Carlo simulations. the simulation results show that the RIS-aided NOMA system outperforms conventional systems and the scenario without RIS assistance. © 2023 IEEE.

关键词： Antennas

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Trafficnet: A Novel Network Performance Prediction Model via Aggregator-Based Enhancement 10th

Trafficnet: A Novel Network Performance Prediction Model via...

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10th international conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Li, Ming Du, Haizhou Department of Computer Science and Technology Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9789819787487

Network performance estimation is a key enabler in achieving efficient network operation. It can assist in important tasks such as topology design, parameter tuning, and capacity planning. the most popular methods have recently been based on Convolutional Neural Network(CNN) or Recurrent Neural Network(RNN). However, many of these methods focus excessively on particular aspects of network features. they often overlook the diversity and complexity in network performance evaluation. this paper proposes a novel model TrafficNet for network performance estimation. this model uses aggregators to learn inter-flow correlations and intra-flow dependencies. By creating specialized feature extraction components for different types of network traffic and using the adaptive mechanism to fuse these features, we aim to improve the accuracy of network performance evaluation. Furthermore, our extensive experiments have shown that TrafficNet can improve the Mean Squared Error(MSE) by 58.3% compared with the SOTA models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Big Data Approach for Sustainable Environmental design 10

Big Data Approach for Sustainable Environmental Design

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Jani, Kruti Rajyalaxmi, M. Gehlot, Anita Halwani, Marwah A. Marwah, Aparna Bawa, Daljeet Singh Shri Chimanbhai Patel Post Graduate Institute of Computer Application Ahmedabad India School of Business Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India King Abdulaziz University Management Information System Department Rabigh Saudi Arabia Bvimr Department of Management Studies Delhi India

ISBN: (纸本)9789380544472

the concept of sustainable environment enhancement has drawn attention on a global scale. this study examines the connection between environmental design as well as the long-term development of a sustainable environment in the modern era using a big data approach. Big data analytics is the challenging process of sifting through vast amounts of data to discover subtle data like market patterns, connections, and client preferences. To evaluate commercial big data, we proposed a novel, highly efficient approach for controlling the sustainable environmental supply system. Additionally, this lessens the hazard that comes with using hazardous substances. this research covers both economic expenses and carbon emissions in 3 contexts. the results show that the environment's sustainability scale will expand as the Binhai region of China is further developed, the commercial structure is changed, and the public's view of urban sustainable advancement steadily rises. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Big data

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QNN Learning Algorithm to Diversify the Framework in Deep Learning 10

QNN Learning Algorithm to Diversify the Framework in Deep Le...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Sharma, Aastha Sharma, Harsh Kumar Sharma, Kirti Abes Engineering College Cse Department Ghaziabad India Krishna Engineering College It Department Ghaziabad India Chandigarh University Interior Design Department Chandigarh India

ISBN: (纸本)9798350382471

the QNN (quantum neural network) algorithmic approach is to interpreted the forward networking in the dataset. Alike, some classical dataset structure take input from the layer of qubit and directed to another layer of qubit. i.e. back propagation technique. the nature of QNN is an algorithmic based on parameter quantum circuit to train the dataset in an sequence manner by using classical optimizers to design an unique algorithmic computing technique in quantum learning, social network modeling, memory devices, automated control system, fast computing technique in executing program. It can be implemented in various sectors to learn hidden agenda layers -financial, health, economic etc. It decreases the limit of qubits, time & space complexity and compute within a second. It mainly optimizes the wave function, qubits representation in the QNN computing. the QNN is entitled for NISQ (noisy intermediate scale quantum computer) to remove the noise from the dataset. In machine learning dataset gather and trained in system with the model of QNN it enhanced the computing technique and optimizing strategies to remove noise from the data while modulating the signal approach. the layer of QNN introduces in this paper to approach the bit neural network, cellular network & associative neural network approach to classify the quantum learning optimization. this algorithmic approach implemented in the breast cancer dataset & predicts the 88.44 % accuracy on predicting the disease in patient. the aim of this paper is to categories the QNN properties in machine learning algorithm & provides new evidence of optimization. © 2023 IEEE.

关键词： Machine Learning Neural network neuro Computing QNN Quantum computing quantum cryptography Quantum learning

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Interaction design in Residence Mode of Health and Tourism 10th

Interaction Design in Residence Mode of Health and Tourism

引用

10th international conference on Distributed, Ambient and Pervasive Interactions (DAPI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Liu, Yanlin Wang, Ruichao Zhao, Tingwei Univ Jinan 336 West Rd Nan Xinzhuang Jinan Shandong Peoples R China Shandong Ruisheng Investment Co Ltd Weihai Peoples R China CDI Coll 3D Modeling & Animat Design Montreal PQ Canada

ISBN: (纸本)9783031054631;9783031054624

Under the background of the healthy country strategy, new health industries, new business forms and new models have emerged and expanded, and the big health industry has entered a golden period of rapid development. the integrated development of new technologies such as artificial intelligence, interactive design, health maintenance, tourism and elderly care, community e-commerce, cloud computing technology and big data makes human-computer interaction have a wider range of application fields and burst out new vitality. With the support of artificial intelligence technology, the big health industry, because of its strong relevance, a wide range of areas, deep fusion innovation with interaction design, culture, tourism, electricity, medical, big data, etc., makes the comprehensive service mode of interactive design + intelligent tourism + health maintenance + community e-commerce ushered in a new challenge. Relying on big data "design + service + text brigade + maintenance + intelligent terminal", with the support of government service platform, with artificial intelligence, integration of community electricity resources, efforts to build to "human-computer interaction, kang, brigade service and health records management" as the center, intelligent health country service network, more effectively provide more support and services for the society.

关键词： Health country Interactive design Community e-commerce Health care Artificial intelligence

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Website and Mobile Based Application Utilization to Increase & Upscale MunchUp Selling Margin 10

Website and Mobile Based Application Utilization to Increase...

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10th international conference on ICT for Smart Society, ICISS 2023

作者： Putra W, Muhammad Faadhil Pramono, Andi Gabriella, Jerica Zahirah, Avicena Zalfa Rosalinda, Elina Purnomo, Agung School of Computer Science Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Indonesia's Food and Beverage (F&B) development is increasing rapidly every day. Our surroundings demonstrate that more and more food and beverage items, particularly light snacks, are being sold. As a result, the company's chances of connecting with a potential customer are indirectly reduced as market competition in the F &B industry heats up. To solve this problem, MunchUp, which specializes in selling light snacks F &B, tries to fully shift its marketing strategy to utilize the latest technology. the technology that MunchUp uses is a website and mobile-based applications. this research aims to investigate the impact of website and mobile app usage on the selling margin of a business. the study was conducted by analyzing sales data from a sample of the companies that have implemented a website and mobile app for their sales process. the study results indicate that businesses with a website and mobile app have seen an increase in their selling margin compared to companies that do not have a website and mobile app. the study also found that features such as push notifications, loyalty programs, and personalized recommendations can help keep customers engaged and encourage repeat purchases. this research suggests that a well-designed and user-friendly website and mobile app can be a powerful tool for any business looking to boost its sales and increase its revenue. © 2023 IEEE.

关键词： Websites

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Urban Walkability: A Digital Twin Approach for Urban Regenerative Development 10th

Urban Walkability: A Digital Twin Approach for Urban Regener...

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10th IFIP WG 13.2 international Working conference on Human-Centered Software Engineering (HCSE)

作者： Viet Hung Pham Wagenfeld, Malte Bernhaupt, Regina Eindhoven Univ Technol Het Eeuwsel 53 NL-5612 AZ Eindhoven Netherlands RMIT Univ 124 La Trobe St Melbourne Vic 3000 Australia

ISBN: (纸本)9783031645754;9783031645761

Traditional urban planning methods fall short in addressing the complexities of modern urbanization and environmental challenges. By adopting Generative design, Virtual Reality, and Digital Twins, the aim is to make urban planning more interactive, accessible, and inclusive. the research is grounded in a comprehensive literature review across related fields, including Digital Twins' dynamic modeling capabilities and Virtual Reality's immersive visualization potential. Initial findings from sit-down interviews with Eindhoven residents reveal critical insights into pedestrian preferences. In my next steps in the PhD, I will focus on developing Virtual Reality prototypes to further examine walkability and Generative design prototypes to generate urban planning aligning with regenerative development toward walkability. At the end of my PhD, the knowledge from previous prototypes is used to create a tool to simplify urban planning, including multiple stakeholders, to improve walkability in an urban context. this innovative approach promises to contribute significantly to the creation of livable, sustainable urban environments by leveraging cutting-edge technologies to address key challenges in urban walkability and planning.

关键词： Urban Planning Virtual Reality Walkability Generative design Digital Twin

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Customizing Graph Neural Network for CAD Assembly Recommendation 24

Customizing Graph Neural Network for CAD Assembly Recommenda...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Liang, Fengqi Zhao, Huan Quan, Yuhan Fang, Wei Shi, Chuan Beijing Univ Posts & Telecommun Beijing Peoples R China 4Paradigm Inc Beijing Peoples R China

ISBN: (纸本)9798400704901

CAD assembly modeling, which refers to using CAD software to design new products from a catalog of existing machine components, is important in the industrial field. the graph neural network (GNN) based recommender system for CAD assembly modeling can help designers make decisions and speed up the design process by recommending the next required component based on the existing components in CAD software. these components can be represented as a graph naturally. However, present recommender systems for CAD assembly modeling adopt fixed GNN architectures, which may be sub-optimal for different manufacturers with different data distribution. therefore, to customize a well-suited recommender system for different manufacturers, we propose a novel neural architecture search (NAS) framework, dubbed CusGNN, which can design data-specific GNN automatically. Specifically, we design a search space from three dimensions (i.e., aggregation, fusion, and readout functions), which contains a wide variety of GNN architectures. then, we develop an effective differentiable search algorithm to search high-performing GNN from the search space. Experimental results show that the customized GNNs achieve 1.5-5.1% higher top-10 accuracy compared to previous manual designed methods, demonstrating the superiority of the proposed approach. Code and data are available at https://***/BUPT-GAMMA/CusGNN.

关键词： computer-aided design Recommender System Graph Neural Networks Neural Architecture Search

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