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共 14948 条记录，以下是61-70 订阅

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Predictive Intelligence Enhanced Fuzzy Model for Underwater network Optimization 5th

Predictive Intelligence Enhanced Fuzzy Model for Underwater ...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Praveena, K.N. Mishra, Awakash Kamalraj, R. Belwal, S. Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper gives a predictive intelligence-enhanced fuzzy version for underwater network optimization. It combines predictive intelligence technology with fuzzy common sense, which can lessen the functionality gap of underwater community optimization. This proposed version is implemented in an underwater cooperative tracking community. With the proposed model, objectives can be tracked with sensors within the network, and their national statistics can also be appropriately anticipated. Similarly, the version has excessive scalability and robustness because of the mixing of predictive intelligence. In the end, simulation experiments suggest that the proposed model correctly improves the precision of tracking challenges and improves the scalability of the network. The Predictive Intelligence superior Fuzzy model (PIE-FUZMO) for underwater network optimization is a singular method developed to cope with the complex optimization issues confronted through underwater networks. The PIE-FUZMO uses a couple of laptops with imaginative and prescient sample recognition techniques and fuzzy good judgment to enhance the underwater community performance while decreasing the running fee. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalability

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Multi-spatial Scales Delay Time Index Calculation Method Based on Digital Road networks

Multi-spatial Scales Delay Time Index Calculation Method Bas...

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international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Lu, Kai Zeng, Kun Wu, Yaoting Wei, Jingfeng Deng, Xingdong School of Civil Engineering and Transportation South China University of Technology Guangzhou510640 China State Key Laboratory of Subtropical Building and Urban Science Guangzhou510640 China Guangzhou Urban Planning and Design Survey Research Institute Guangzhou510050 China Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou Guangzhou510060 China

ISBN: (纸本)9789819639724

In the context of digital road networks, lane-level data support and dynamic traffic perception enable the precise calculation of operational parameters for different lanes during various time periods. Delay metrics play a crucial role in evaluating traffic states. The delay time index defined in this paper intuitively reflects traffic delay states and eliminates the influence of network scale on delay assessment, making it an appropriate evaluation metric. By establishing relationships among the indices at different levels of the road network through weighting, and considering factors such as traffic flow and free-flow travel time, this paper presents definitions and calculation methods for the weight coefficients of various evaluation objects within the network. Based on the constructed weight system, a multi-scale delay time index calculation method based on weight coefficients is derived. Case results indicate that the traffic states evaluation metrics obtained through weight coefficient estimation are consistent with those derived from simulation and defined calculations. Additionally, when the data acquisition rate for the road network is 10%, the relative accuracy of the estimates can reach 96.0% at an 80% cumulative probability. Utilizing foundational operational data provided by digital road networks, not only enables the step-by-step calculation of the delay time index across multi-spatial scales of the road network, but the overall traffic states of the network can also be estimated based on partial traffic operation data, effectively supporting the assessment needs of the overall traffic states of the network in scenarios where the digital road network construction is incomplete or coverage is insufficient. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Travel time

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Research on Clothing Recommendation Technology Integrating Multimodal data 24

Research on Clothing Recommendation Technology Integrating M...

引用

2nd international conference on Artificial Intelligence, Systems and network Security, AISNS 2024

作者： Lin, Rong He, Yi Tang, Jiale Tang, Zhihang School of Computer and Communication Hunan Institute of Engineering Hunan Xiangtan China

ISBN: (纸本)9798400711237

Due to the rapid increase in the volume of text data, traditional recommendation systems that rely solely on textual information are struggling to keep up with the evolving fashion trends and the personalized needs of users. In today’s age of information overload, users are no longer satisfied with simple text descriptions when making clothing choices. They seek a comprehensive evaluation of information through various means such as text descriptions, images, and user comments. This paper introduces a clothing recommendation algorithm that utilizes a multimodal data mining technique, integrating it with a multi-vector semantic segmentation method for a clothing product recommendation system. By conducting in-depth multimodal data analysis and introducing topic words, the algorithm generates personalized recommendations that align with users’ needs. The experimental results demonstrate the algorithm’s effectiveness in reducing confusion between different topics. With the LOP parameter set to 20, the Precision and MAP values of the model proposed in this paper are better than the comparison model. This algorithm can effectively mine potential information within multimodal data, identify users’ potential points of interest, and accurately predict user preferences, boosting merchandise sales and user growth on the apparel e-commerce platform. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Privacy-Preserving Publishing Mechanism for Real-Time Vehicle Trajectory in Vehicle-to-Grid networks 5

Privacy-Preserving Publishing Mechanism for Real-Time Vehicl...

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5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Wang, Yulong Xu, Jianbo Gong, Bo School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331533694

The Vehicle-to-Grid (V2G) network is a smart grid technology generated under the background of the rapid development of new energy technology, which allows mobile energy storage vehicles (MESVs) to realize bidirectional energy exchange with the grid system. As distributed energy storage units within the V2G network, MESVs help meet the grid's load-balancing demands. To accurately schedule grid loads, V2G networks rely on MESVs' real-time trajectory data. After collecting this data, the central authority (CA) publishes it to the grid enterprise (GM) for load-balancing decisions. However, before publication, the CA must process the data to protect the location privacy of MESVs and prevent GM from extracting sensitive information. To address this privacy protection challenge during data publication, we propose a differential privacy-based mechanism. Our mechanism uses the unscented Kalman filter to predict and correct the published data at the sampling point and intelligently adjusts the sampling interval. Additionally, we introduce a privacy budget allocation method based on regional access frequency and travel time, enhancing privacy protection of trajectory in high-access areas and peak hours. Through security analysis and experimental validation, our mechanism effectively preserves the privacy of real-time vehicle trajectory data while ensuring data validity. © 2025 IEEE.

关键词： Vehicle-to-grid

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Steel Defect Detection Algorithm Based on Multiobjective ENAS Sharing Strategy 24

Steel Defect Detection Algorithm Based on Multiobjective ENA...

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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Measuring the Impact of Flexible Communications Software Design in network Applications 5th

Measuring the Impact of Flexible Communications Software Des...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Prabhu, A. Yadav, Renu Pravinthraja, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electronic and Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This study studies the impact of bendy communications software design on network applications. Through a case observation method, it looks at how a community protocol like HTTP may be redesigned while permitted to modify elements of the protocol to suit the characteristics of a disbursed system. It examines the impact of particular protocol modifications, consisting of retransmission strategies, on person and device performance and the general effects of protocol flexibility. Such flexibility enables networks to alter and scale without problems, allowing for more excellent, innovative, dependable community packages. The consequences of this research will assist in understanding how redesigning the communications software program can benefit both customers and network packages. The results endorse that a better-stage verbal exchange layer could offer first-rate upgrades in designing community programs and conversation protocols. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： HTTP

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Application and research of deep learning algorithms in the core technology of photovoltaic inverter diagnosis

Application and research of deep learning algorithms in the ...

引用

2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Wei, Cuihua Guangdong University of Science and Technology Guangdong Province Dongguan City523668 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

This study aims to explore and analyze the application effects of deep learning algorithms in the core technology of photovoltaic (PV) inverter diagnosis.[Methodology] A combined approach of literature review and experimental evaluation was adopted. By organizing and summarizing relevant literature, the research direction was clarified, the research structure was outlined, and the conduct of the study was guided, thereby providing theoretical support for the research. Furthermore, experimental evaluation was conducted to test the application effects of the research content, ensuring the practical research value of the study.[Results] In this research, the direct processing of raw data and automated feature extraction capabilities of deep learning algorithms were found to be suitable for various application scenarios in PV inverter diagnosis technology. Consequently, during experimental evaluation, the Empirical Mode Decomposition-Convolutional Neural network (EMD-CNN) demonstrated robust performance in open-circuit fault diagnosis of PV inverters, offering a practical and effective method for inverter fault diagnosis.[Conclusion] The research findings indicate that the application of deep learning algorithms in the core technology of PV inverter diagnosis can effectively enhance the fault diagnosis efficiency and accuracy of PV inverter systems. This, in turn, strengthens the management level and maintenance quality of renewable energy PV arrays, providing stable facility support for the development and utilization of new energy sources. © 2025 SPIE.

关键词： Photons

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Implementing Encryption Algorithms for the Protection of Wireless networks 5th

Implementing Encryption Algorithms for the Protection of Wir...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahmad, Naseem Madhura, K. Agarwal, Trapty Zaidi, Taskeen Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

The use of encryption algorithms is more and more turning into a crucial issue of defensive wireless networks. Encryption algorithms may be used to cozy facts that is transferred over a Wi-Fi network while also presenting authentication and integrity protocols for the systems connected to the network. This paper discusses the significance of enforcing encryption algorithms for the safety of a wireless community. It describes the distinct encryption algorithms and protocols which are available, and the criteria for selecting the proper algorithm for exclusive varieties of Wi-Fi networks. It additionally provides unique implementation eventualities based totally on numerous sorts of Wi-Fi community structure in addition to the vast security threats in Wi-Fi networks that may be averted by imposing encryption algorithms. Finally, this paper offers guidelines on selecting and imposing the perfect encryption set of rules for safety of wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Comparing MAE and RMSE as Fitness of Genetic Algorithm for Optimizing Echo State network Hyperparameters with Different Probabilistic Distributions 25th

Comparing MAE and RMSE as Fitness of Genetic Algorithm for O...

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25th international conference on Intelligent data engineering and Automated Learning

作者： de Araujo, Henrique Vaz Cardoso, Fabian Correa de Mattos, Viviane Leite Dias Borges, Eduardo Nunes Lucca, Giancarlo Dalmazo, Bruno Lopes Berri, Rafael Alceste Fed Univ Rio Grande BR-96203900 Rio Grande RS Brazil Fed Univ Rio Grande Computat Modeling Grad Program BR-96203900 Rio Grande RS Brazil Univ Rio Verde Fac Software Engn BR-75901970 Rio Verde Go Brazil Univ Catolica Pelotas BR-96015560 Pelotas RS Brazil

ISBN: (纸本)9783031777370;9783031777387

This work aims to compare Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as fitness functions in a genetic algorithm used to optimize the hyperparameters of an Echo State network (ESN) with five probabilistic distributions. We have used financial time series data extracted from the Brazilian Stock Market. The ESN proved to be excellent for predicting time series. However, using MAE and RMSE within the genetic algorithm was ineffective since the results did not differ significantly despite the asymmetrical data distribution. Two of the five distributions stood out, Uniform and Student's t, showing low variability both with the use of RMSE and MAE, where we graphically observed that Uniform presented better prediction using the MAE fitness.

关键词： Stock price forecasting Financial Time Series ESN

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Investigating Wireless networks with network Coding Algorithms 5th

Investigating Wireless Networks with Network Coding Algorith...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Ahmed, Zahid Gupta, Anjali Preethi, D. Singh, Akhilendra Pratap Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronic Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Community coding algorithms had been demonstrated to have capability to improve the performance of wireless networks. This paper offers a research, assessment, and implementation of community coding algorithms for Wi-Fi networks. The paper affords an overview of community coding algorithms and discusses the related blessings, drawbacks, and tradeoffs that have to be taken into account while evaluating the performance of these algorithms. Furthermore, simulation outcomes are offered to assess the overall performance of one of a kind network coding algorithms while implemented in a wireless community surroundings. Ultimately, the paper outlines the hardware and software elements required to correctly put into effect and test those algorithms in an operational wireless community. The promising consequences of the experiments imply that these community coding algorithms can be used to efficiently improve the overall performance of Wi-Fi networks within the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： network coding

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