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共 7159 条记录，以下是341-350 订阅

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Next-Gen Parking Facilities: Deep learning for Face and License Plate based Access 18

Next-Gen Parking Facilities: Deep Learning for Face and Lice...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Kannan, N. Sivakami, S. Senthil, S. Immaculate Rexi Jenifer, P. Murali, N. E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Trichy India School of Computing SASTRA Department of Computer Science and Engineering India

ISBN: (纸本)9789380544519

Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. there is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. this shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues;they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. this system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking System in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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Designing an Advanced Situational Awareness Platform Using intelligent Multi-Drones for Smart Farming Towards Agriculture 5.0 11

Designing an Advanced Situational Awareness Platform Using I...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

With regard to Agriculture 5.0, the goal of this research is to create an intelligent platform for multi-drone collaboration that will improve situational awareness. this study examines the crucial phases in the Sensing, Understanding, and Action processes by fusing cutting-edge drone technology with creative coordination methods. Creating the architecture and foundation for drone collaboration, utilizing artificial intelligence for real-time data processing, and creating user interfaces that work are all part of the study technique. Area coverage, data quality, processing speed, and decision-making effectiveness are some of the performance evaluation measures. the goal of the research is to identify strategies for enhancing large-scale farming methods' resource management and operational effectiveness. the results will greatly enhance Agriculture 5.0 technologies and create avenues for the deployment of multi-drone collaboration platforms in a variety of high-situational awareness businesses. © 2024 IEEE.

关键词： data quality

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Application of Blockchain Technology in Student learning data Security and Privacy Protection 6

Application of Blockchain Technology in Student Learning Dat...

引用

6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Liu, Yan Office of Development Planning Shenyang University Shenyang110044 China

ISBN: (纸本)9781837242672

the rapid digital transformation across various sectors has intensified the need for secure, automated management systems to handle sensitive data with integrity and privacy. Traditional systems often struggle to securely manage and authenticate large volumes of data, leaving it vulnerable to unauthorized access and breaches. this paper presents an intelligent information management system based on blockchain technology, designed to enhance data security, integrity, and automated access control through decentralized storage, cryptographic algorithms, and smart contracts. the proposed system leverages blockchain’s decentralized ledger to offer a tamper-resistant, distributed storage solution, minimizing risks of data tampering and enhancing transparency. Smart contracts add automation to access control and data verification processes, reinforcing system robustness and reducing the potential for human error. this paper’s contributions include developing a blockchain-based intelligent data management framework, evaluating its efficacy in real-world scenarios, and demonstrating its advantages over traditional centralized approaches in terms of security, privacy, and operational automation. this study provides a foundational approach to enhance the security and autonomy of digital information systems, enabling more resilient data management infrastructures. © the Institution of engineering & Technology 2024.

关键词： data integrity

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Multi-Modal Knowledge Graph Representation Based on Counterfactual data Enhanced learning Link Prediction* 11

Multi-Modal Knowledge Graph Representation Based on Counterf...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Zhang, Hui Han, QiLong Sun, Hui Liu, Chao Harbin Engineering University Harbin China China

ISBN: (纸本)9798331531904

For most true multi-modal knowledge graphs, the missing links in the learning graph are very important. Most of the existing methods are based on the observed structure, text, and image information to determine whether a link exists. However, these methods can only learn to show correlations between observational data and the presence of links, and cannot capture potential causal associations between graph structure and the presence of links, text information and the presence of links, and image information and the presence of links. In this paper, we explore corresponding causality through several counterfactual questions: 'If the structure of the entity, the text, the image, and the observation are different, does the link exist?'. the multi-modal knowledge graph is enhanced by constructing counterfactual-links to answer counterfactual questions. In order to generate counterfactual links, a causal model is designed to observe the existence of data and links, and a neighborhood division method in multi-modal maps is designed. the model takes the multi-modal information of the given entity pair as the context, the graph structure information, text information and image information as the processing, and the existence of triples as the result. We propose to use observation data and counterfactual triples to represent nodes on multi-modal knowledge graph. Experiments on benchmark tests show that our approach is effective. © 2024 IEEE.

关键词： Knowledge graph

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Potential of Deep learning for Image Processing 11

Potential of Deep Learning for Image Processing

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Gill, Rupali Logeshwaran, J. Aggarwal, Priyanshi Srivastava, Durgesh Verma, Aditya Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Sri Eshwar College of Engineering Department of Ece Coimbatore641202 India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350350357

Deep learning has emerged as an effective tool for fixing a diffusion of image processing responsibilities such as object detection, category, segmentation, and reconstruction. this technique involves using synthetic neural networks to learn features and styles immediately from a big data set, rather than relying on predefined rules or handmade capabilities. It has resulted in enormous enhancements in performance for photo processing duties, surpassing traditional strategies in accuracy and performance. One key gain of deep getting to know for image processing is its capacity to deal with complicated and excessive-dimensional records, including photos, with many parameters. It permits the creation of extra complicated and accurate models that can deal with numerous records and produce better outcomes. Additionally, deep studying fashions can continuously enhance and adapt as they are uncovered to greater statistics, making them greater sturdy and flexible in processing picks. © 2024 IEEE.

关键词： Image enhancement

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Exploring Deep learning for Efficient data Management in Brain Tumor Classification: An Overview 11

Exploring Deep Learning for Efficient Data Management in Bra...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Verma, Vikram Aggarwal, Alankrita Chandigarh University Cse Department Mohali India Piet Cse Department Panipat India Department of Computer Science and Engineering Mohali India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350357

the effectiveness of brain tumor diagnosis with artificial intelligence (AI) based systems depends on effective data management (DM). the research on medical image analysis is increasingly focused on employing deep learning (DL) to detect and classify brain tumors. Presently, medical images are being evaluated and tumors are being classified using DL. Within DL a popular method, Convolutional Neural Networks (CNNs), is employed with great accuracy to detect brain tumors in medical images. the creation of these models involved utilizing extensive datasets of brain MRI scans, resulting in their exceptional accuracy in detecting the presence of tumors. the models have undergone testing and training utilizing several publicly accessible data, for example Kaggle, the Brain Tumor Segmentation (BraTS) dataset, etc dataset. Scientists have devised multiple techniques for data management (DM) involving machine learning (ML) to preprocess the data, augment it, and optimize the models in order to enhance their accuracy even further. In general, the topic of utilizing DM with the help of ML and DL in identifying brain tumors is quickly expanding. It holds great potential for improving patient outcomes. DL techniques have significantly improved the classification and identification of brain cancers using diverse datasets. © 2024 IEEE.

关键词： Magnetic resonance imaging

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On Multivariate Linear Regression Applications 11

On Multivariate Linear Regression Applications

引用

11th international conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Jovanović, Aleksa Krstić, Aleksandra Vujnović, Sanja Durović, Željko University of Belgrade School of Electrical Engineering The Department of Signals and Systems Belgrade Serbia

ISBN: (纸本)9798350386998

Linear regression is a very simple machine learning model that is supposed to find linear relations between input and output data. Its use is limited since real-world random variables are almost never linearly correlated, but when there is limited amount of data for training, using other larger and more sophisticated models might be impossible. Even though simple linear relations are hard to come by in nature, more complex mathematical dependencies can often be linearized i.e. approximated using linear models. Additionally, by preprocessing the input data using nonlinear mathematical operations linear regression can artificially account for nonlinear relations. this methodology was tested on real data representing measurements in thermal power plant while the goal was predicting the specific energy of the coal that was being burnt in the plant. Estimating coals caloric value is of tremendous value since using this information power plant can be configured to run more efficiently. © 2024 IEEE.

关键词： Multiple linear regression

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Enhancing efficiency in recurrent reinforcement learning for automated data-driven investment 28th

Enhancing efficiency in recurrent reinforcement learning for...

引用

28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Witkowski, Tomasz University of Economics Katowice Poland

In this study, we introduce a novel approach for training Recurrent Reinforcement learning (RRL) agents for automated, data-driven investment tasks. the primary goal of this paper is to provide a Proof of Concept for the approach. Our method has demonstrated the ability to achieve Sharpe Ratio (SR) values comparable to those of classical online RRL while accelerating the training process by a factor of 24.6. this training method is model-agnostic and input-agnostic, enhancing its flexibility with complex model architectures. However, a significant limitation is the assumption that the agent's actions influence rewards but do not affect the rest of the environment. this assumption may hinder generalisation to some non-financial tasks. this study advances the field of RRL by improving efficiency and facilitating more complex experimental schemes. © 2024 the Authors.

关键词： Deep reinforcement learning

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intelligent Sorting Method for Smartphones Based on Agents 6

Intelligent Sorting Method for Smartphones Based on Agents

引用

6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Hu, Xuejun Li, Jie Zheng, Hangbin Zhang, Gaohua School of Mechanical Engineering Donghua University Shanghai China School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331527426

automated disassembly of discarded smartphones is a critical step in electronic waste processing, yet existing automated systems often lack adaptability when handling various types of discarded smartphones. To address the issue of poor adaptability in sorting processes, this study proposes an agent-based sorting method for discarded smartphones. this method leverages large language model(LLM) for natural language understanding and generation, enhancing user-friendliness. It integrates information from knowledge graphs and historical sorting records to build a comprehensive knowledge base. By combining intelligent agents that track historical data and utilize external knowledge bases, the proposed method improves adaptability for sorting multiple phone types. this allows the system to dynamically formulate sorting strategies based on the specific characteristics of discarded smartphones, significantly enhancing the adaptability of disassembly lines. Furthermore, the mobile phone classification module draws inspiration from facial recognition model architectures. A deep learning-based classification method for discarded smartphones is proposed, achieving highly adaptable classification functionality for various phone types. Experimental results demonstrate that the proposed classification algorithm maintains high accuracy in recognizing phone models not included in the training set. © 2024 IEEE.

关键词： Smartphones

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Satellite System Anomaly Detection Method Based on intelligent data Mining Technology 11th

Satellite System Anomaly Detection Method Based on Intellige...

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11th China conference on Command and Control, C2 2023

作者： Du, Gang Hu, Pengju Li, Guang Liu, Songdan Zhang, Hongjiang Zhao, Xiaoning 2050 Lab China Academy of Aerospace Science and Innovation Beijing China

ISBN: (纸本)9789819990207

With the increasing complexity of advanced satellite functionality and composition, there is a greater variety of abnormal phenomena that can occur during satellite operations. To avoid a significant increase in modeling work for satellite anomaly detection, an intelligent-based method for satellite system anomaly detection is proposed. this method utilizes transfer learning to achieve efficient analysis of correlated parameters. A subsystem-based multi-parameter anomaly detection scheme based on semi-supervised learning is proposed to address challenges such as a lack of satellite data labels and non-linearity of data. Deep learning techniques are employed to fully utilize the characteristics of time series data and accomplish comprehensive system-wide anomaly detection. © Chinese Institute of Command and Control 2024.

关键词： Anomaly detection

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