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共 1872 条记录，以下是61-70 订阅

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intelligent recognition Algorithm of Specific pattern Content Information based on Mobile Terminal Equipment 4

Intelligent Recognition Algorithm of Specific Pattern Conten...

引用

4th IEEE international conference on Power, intelligent Computing and systems, ICPICS 2022

作者： Sheng, Lei Wu, Lijie Wang, Lei Wang, Chunying Quan, Yizhan Information & Telecommunication Company State Grid Henan Electric Power Company Zhengzhou China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

In order to solve the problem of poor intelligent recognition effect of specific mode content information, an intelligent recognition algorithm of specific mode content information based on mobile terminal equipment is proposed. the mobile terminal equipment is used to collect network information, identify the unidirectional characteristics of specific mode content information, formulate a specific mode content information feature evaluation system based on single features, mine and classify multi-directional information, and optimize the intelligent recognition steps of specific mode content information. Finally, experiments show that the intelligent recognition algorithm of specific mode content information based on mobile terminal equipment has high practicability and accuracy, Fully meet the research requirements. © 2022 IEEE.

关键词： Classification (of information)

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Optimizing the Human-Computer Interaction Interface of Warehouse Management systems Using Automatic Speech recognition Technology 4

Optimizing the Human-Computer Interaction Interface of Wareh...

引用

4th international conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2024

作者： Song, Xue Fanatics Inc. Department of Technology - User Experience San Mateo94403 United States

ISBN: (纸本)9798350378542

the exponential growth of the e-commerce and logistics industries in recent years has underscored the necessity for a warehouse management system (WMS) that is more efficient and intelligent. the prevailing WMSs are dependent on manual input and the use of handheld devices, which can result in inefficiencies and the potential for human error. In this work, we propose a human-computer interaction model for speech recognition optimized for the warehouse environment. this model integrates noise suppression technology based on an adaptive filter, which can dynamically detect and filter background noise, such as forklift operation sounds, human voices, and mechanical operation sounds. Furthermore, to address the variability in pronunciation, speech rate, and accent among different users, the system incorporates a speech enhancement model based on the variational autoencoder (V AE) technique. this approach enables the system to adaptively adjust the input speech features, thereby enhancing the robustness of recognition. In regard to natural language processing, a natural language understanding module based on bidirectional encoder representations from transformers (BERT) has been developed. the module is capable of semantic parsing of instructions in the user's voice and converting them into executable operation commands. Semantic slot filling technology enables the system to automatically identify the key entities in a task and perform linkage operations with the backend WMS database. the experimental analysis demonstrates that the proposed system is effective in an actual warehouse scenario. Compared with the traditional method, the task completion speed and accuracy are significantly improved. © 2024 IEEE.

关键词： Semantics

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Metagenomic Face: Applying Machine Learning to Facial Microbiome Data for pattern recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

引用

4th international conference on intelligent Technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector Machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector Machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering intelligent Manufacturing with the Potential of Edge Computing with NVIDIA's Jetson Nano 4

Empowering Intelligent Manufacturing with the Potential of E...

引用

4th IEEE international conference on Computing, Communication, and intelligent systems, ICCCIS 2023

作者： Gawate, Esha Rane, Prachi RCOEM Department of Electronics Engineering Nagpur Maharashtra India

ISBN: (纸本)9798350306118

Manufacturing must use technology to meet demand since it is crucial to the supply chain. Particularly, artificial intelligence has been essential in helping to oversee and support the manufacturing process. this study puts forth the notion of how edge computing aids manufacturing. this article uses NVIDIA's Jetson Nano to address the demand for edge computing in preventing resource overstocking and understocking in the production unit. the concept of quality inspection is examined, as well as the quality inspection of the goods received in the production facility and its effective management through the use of image recognition. the paper primarily focuses on the implementation of deep learning and machine learning models on edge devices. By striking a balance between the computational capacity and memory limits, a domain-specific technique is applied for edge computation in the manufacturing industry employing artificial intelligence networks. © 2023 IEEE.

关键词： Edge computing

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Study on the Gas Diffusion pattern of Oil-impregnated Paper Bushing Under Typical Fault 4

Study on the Gas Diffusion Pattern of Oil-impregnated Paper ...

引用

4th international conference on intelligent Power and systems, ICIPS 2024

作者： Luo, Xiaoqing Liu, Taiwei Xie, Xiongjie Hu, Wei Ye, Qiming Yin, Pengbo State Key Laboratory of Power Grid Environmental Protection China Electric Power Research Institute Wuhan China

ISBN: (纸本)9798331532765

the oil impregnated paper bushing (OIP) technology is mature, with low technical difficulty and cost. However, due to its oil paper insulation structure, when internal discharge faults occur, characteristic gases such as H2 may be generated, leading to a sharp increase in the internal pressure of the bushing and causing the bushing to explode, potentially resulting in transformer explosion and combustion in severe cases. therefore, this paper constructs a simulation model of OIP to obtain the diffusion characteristics of H2 gas inside the OIP under the condition of constant release rate. Additionally, the insulation discharge faults of OIP are analyzed and simulated, and a test platform is established to measure the changes in the content of dissolved gases in the oil during the fault period. the overall trend of the characteristic gas content during the fault process is first sharply increasing and then gradually tending to equilibrium, verifying the accuracy of the simulation results. this provides new possibilities for establishing fault prediction and diagnostic models, which in turn contributes to improving the maintenance efficiency and reducing maintenance costs of transformer OIP. © 2024 IEEE.

关键词： Bushings

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intelligent Multimodal Form Assistance System 4

Intelligent Multimodal Form Assistance System

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Cholke, Puja Kolate, Gita Kolhe, Babusha Katkar, Maitrey Khandare, Rohan Khandare, Om Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798331523923

the intelligent Multimodal Form Assistance System is a combination of cutting-edge AI technologies which mainly aims to make form-filling easier for the elderly illiterate people. the system built on Speech-to-Text, Natural language processing (NLP). System can recognize English as well as regional languages. It translates given speech into text and validates input from user to error-free form filling. It also generates the pdf file of filled form, later on user can take printout of that filled form. It increases the efficiency of work. Sometimes even literate users have confusion that which information needs to go into which form of section, so overcome this problem the system also provides guidance on how to fill form and tells what are the personal details you required to fill specific form so that user can prepared in advanced with all documents and information, which simplifies the process of form filling and reduces the time of the users which spend on it. Existing form-filling systems have several problems, including poor speech recognition accuracy in noisy environments and poor multilingual support. Older and illiterate users may also have trouble understanding what belongs in each form section, leading to confusion and a longer completion time. the aim of the system is to overcome all these problems by providing the user to fill any agreements forms in all languages without making a single mistake with the real-time instructions where to put in what information for each section. this helps improve accuracy, reduce confusion, and ultimately leads to an improved user experience. © 2025 IEEE.

关键词： Speech recognition

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ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

引用

4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

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A Comprehensive Review on Object Detection in the Context of Autonomous Driving 4

A Comprehensive Review on Object Detection in the Context of...

引用

4th international conference on Ubiquitous Computing and intelligent Information systems, ICUIS 2024

作者： Islam, Md Moynul Chowdhury, Ismatul Jannat Mahboob, Tanvir Zobair Mazumder, Md Shah Jalal Hossain, Mohammad Jakaria Biswas, Md Siam Rone, Poncanon Datta Maharishi International University Department of Computer Science IA United States University of North Carolina Department of Electrical Engineering CharlotteNC United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529635

In autonomous driving, safety assessment is becoming an essential component, particularly in the perception and analysis of the environment around them. To make safe driving judgments, autonomous cars mostly rely on their capacity to observe and comprehend their environment. Detecting objects is a frequent and challenging problem in autonomous driving. Over the course of the last ten years, as deep learning has rapidly advanced, researchers have conducted numerous experiments and made significant contributions to the performance improvement of object recognition and related tasks including object categorization, localization, and segmentation. In this paper, an overview of state-of-the-art object detection algorithms is presented with in depth analysis. Region proposal-based approaches and single-shot approaches are two main frameworks of object detection which are discussed here. R-CNN, SPP-net, Fast R-CNN, and Faster R-CNN refer to region proposal-based methods while single-shot methods consist of YOLOv2, YOLOv3, YOLOv4 and SSD. the authors truly believe that this study would have a meaningful impact on the current advances in object detection algorithms. © 2024 IEEE.

关键词： Zero-shot learning

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2023 4th international conference on Computer Engineering and intelligent Control, ICCEIC 2023

2023 4th International Conference on Computer Engineering an...

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2023 4th international conference on Computer Engineering and intelligent Control, ICCEIC 2023

ISBN: (纸本)9798350308877

the proceedings contain 112 papers. the topics discussed include: multi-cycle deep recurrent learning for power consumption time series prediction;research on compressed sensing and transmission methods of video resources based on virtual reality;IoTAEG: automatic exploit generation of IoT devices;couple-group consensus for leader-following nonlinear multi-agent systems under network attacks and decay;research on improving the deep learning model of differential privacy using normalization method;target vessel motion model in an intelligent collision avoidance simulation system;automatic deflection detection of bridge and gantry cranes based on PLC control technology;construction of multi-scale target detection model for security signage recognition;an improved target detection algorithm applied to domestic edge computing module;and detection of traffic signs in complex weather conditions based on YOLOv5.

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Design of an intelligent Asset Recovery System Based on Deep Learning 4

Design of an Intelligent Asset Recovery System Based on Deep...

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4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yang, Fang Guizhou University Faculty of Public Administration Guizhou Guiyang550001 China

ISBN: (纸本)9798331528850

the increasing complexity of financial crimes poses significant challenges for traditional asset recovery methods, which often fail to effectively trace covert fund transfers and complex money laundering schemes. To address these limitations, this paper proposes and implements an intelligent asset recovery system based on deep learning, incorporating advanced computational techniques to enhance the detection and analysis of complex financial transactions. the system utilizes a hybrid CNN-LSTM model to extract both spatial and temporal features from transaction data, while a self-attention mechanism dynamically highlights critical transaction patterns, improving the model's interpretability and recognition capabilities. A multimodal data fusion approach integrates diverse sources, including bank transaction records, social network data, and public financial statements, enabling comprehensive analysis of heterogeneous data. Experimental results demonstrate that the proposed system significantly outperforms traditional methods in key metrics, achieving higher accuracy, precision, recall, and Fl scores. Moreover, it exhibits superior performance in identifying intricate fund transfer chains and adapting to diverse financial crime scenarios. the study not only provides a robust, automated tool for asset recovery but also establishes a foundation for the development of intelligent systems aimed at preventing and combating financial crimes. Future research will focus on enhancing system scalability, optimizing computational efficiency, and integrating additional data sources to further expand its application scope. © 2024 IEEE.

关键词： Decentralized finance

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