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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20248 条记录，以下是4911-4920 订阅

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Multivariate Time Series Forecasting based on Time-Frequency Decomposition 3

Multivariate Time Series Forecasting based on Time-Frequency...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Li, Kehan Hao, Yingguang Wang, Hongyu Wang, Qiong Dalian University of Technology Department of Information and Communication Engineering Dalian China Force Research Unit 91550 42nd Liaoning Huludao China

ISBN: (纸本)9798350316674

Multivariate time series forecasting is a current research hot spot in academic and industrial fields;however, it is still challenging to effectively mine the underlying patterns in multivariate time series data and achieve accurate long-term predictions. To cope with this problem, a model based on timefrequency decomposition is proposed. First, the multivariate time-series data are decomposed into different frequency components by using frequency domain decomposition methods, combined by their contributions in energy, and then the combined different components are reduced back to the time domain, and the multi-scale time windows are used to extract the global and local attention of different components in the time series. Secondly, for the prediction task, a balance of prediction accuracy and stability is achieved by adaptively selecting two loss functions, mean square error (MSE) and mean absolute error (MAE), and adjusting the loss weights according to the prediction steps. There is an overall improvement in the performance of the model on six standard datasets and two real datasets. The results show that the model can effectively mine frequency patterns in multivariate time-series data and achieve accurate long-term forecasts. © 2023 IEEE.

关键词： Attention mechanism Deep learning Loss functions Multivariate time series forecasting Time-frequency decomposition

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Augmented Reality for Enhancing Linguistic Skills of international Students in Preschool Education 3rd

Augmented Reality for Enhancing Linguistic Skills of Interna...

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3rd international conference on Novel and intelligent Digital Systems, NiDS 2023

作者： Kapetanaki, Andrianthi Krouska, Akrivi Troussas, Christos Drosou, Stavroula Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031440960

The utilization of new technologies in education is essential for providing an effective and of high quality learning environment. The preschool education is considered quite demanding, especially with the increasing number of multilingual and multi-ethnic families in European countries. This fact leads to constantly find new teaching methods and ways of learning that deviate from traditional ones, in order to promote the active learning and innovative teaching. As such, this study presents the development of an Augmented Reality (AR) application, using the Quiver tool, to enrich vocabulary acquisition of international students who attend Greek Kindergartens and are taught Greek as a second / foreign language. For the needs of this study, qualitative research method – and specifically, case study and observation – was used to investigate the impact of the developed AR application in oral communication of foreign preschool students. The results show that the use of AR has a positive impact in vocabulary and linguistic skills acquisition, fluency in oral communication, motivation, commitment to the learning process and satisfaction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Linguistics

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Advancements in Diabetic Retinopathy: Prognostication, Classification, and Feature Extraction for Enhanced Diagnosis and Monitoring 3

Advancements in Diabetic Retinopathy: Prognostication, Class...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Vinodhini, R. Ramachandran, Vasukidevi Bharath Institute of Higher Education and Research Department of Biomedical Engineering Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Microbiology and Biotechnology Tamil Nadu Chennai India

ISBN: (纸本)9798350343632

Diabetic retinopathy (DR) is still an important cause of visual impairment in diabetics, and its treatment requires both early detection and continuous monitoring. Diabetes mellitus diagnostic and prognosis techniques have increased dramatically as a result of recent advances in imaging technology and machine learning algorithms. A thorough assessment of the most current developments in feature extraction, classification, and prognostication methods for improved diabetic retinopathy diagnosis and monitoring is provided in the work. The proposed study covers the analysis of several imaging modalities, such as fundus fluorescein angiography (FFA), optical coherence tomography (OCT), and fundus photography, as well as the developments in image analysis and feature extraction techniques for each modality. The research study also describes the development of machine learning methods, such as ensemble methods and deep learning models, which show promise in accurately classifying and risk-stratifying diabetic retinopathy. Additionally, it discusses the difficulties in acquiring large amounts of data, generalizing models, and making them interpretable. It emphasizes the requirement of strong validation processes and standardized datasets to guarantee the validity and clinical relevance of the proposed methodologies. Overall, the work shows how cutting-edge imaging technology and powerful machine-learning algorithms may be used to create tailored and timely treatments for the management of diabetic retinopathy. © 2023 IEEE.

关键词： Classification Diabetic Retinopathy Feature Extraction Imaging Technologies Machine learning Personalized Interventions. Prognostication

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Improved I-Log-SCMA Algorithm for Low Complexity Detection 3

Improved I-Log-SCMA Algorithm for Low Complexity Detection

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3rd international conference on Machine learning and Computer Application, ICMLCA 2022

作者： Liu, Fang Li, Chang Liu, Jian School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

SCMA (sparse code multiple access) is a kind of wireless Communication technology, which is used to process the uplink portion of wireless communication and ensure a high data transmission rate and massive data connection at the age of information technology. The urgent need for SCMA technology is to address the issue that the user capacity of SCMA technology couldn't be balanced with the complexity of the detection algorithm. As for solving this, an improved low-complexity detection algorithm I-log-MPA was analyzed, which is based on the modification of the traditional algorithm in multi-user detection. It made up for the solving of the bit error performance of MAX-log-MPA algorithm and the problem of high complexity. The simulation results revealed that the I-log-MPA detection algorithm with lower complexity and lower bit error rate could effectively improve the performance of SCMA system. © 2023 SPIE.

关键词： Bit error rate

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Waste Classification using Edge based Multilayer White Shark Dense Net-Spiking Neural Network from Urban Areas 3

Waste Classification using Edge based Multilayer White Shark...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kiran, Chitra N. Rawat, Ruchira Madhavi, T. Pothinathan, S.K.M. Rabadiya, Dhaval Kumar, Karthik Department of Electronics and Communication Engineering Alliance University Karnataka Bangalore562106 India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun248 002 India R.B.V.R.R. Women's College Telangana Hyderabad India Department of Civil Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Faculty of Management studies Parul University P.O.Limda Ta.Waghodia Gujarat Vadodara391760 India School of Civil Engineering REVA University Karnataka Bengaluru560064 India

ISBN: (纸本)9798331532420

Effective waste management is crucial for human health and a clean environment. Segregating waste based on types and classification is essential for recycling. Traditional methods, like manual sorting, are labor-intensive and error-prone, leading to environmental pollution and resource depletion and to classify the waste several deep learning algorithms used, but the accuracy is decreased, computational time, cost and complexity is increased. To overcome these issues, this work is proposed. This study proposes a Waste classification using Edge based Multilayer White Shark Dense Net-Spiking Neural Network (EBMW2SD-Net) from urban Areas. In this, for classifying the waste images urban areas are collected using Stanford TrashNet dataset. These images are collected from the various IoT sensors, so that, these images are full of noises, so, that these data are pre-processed and its features are extracted using the Orthonormal S-Transforms with Multiple Discrete (OSTMD). Then these data are classified using the Edge based Multilayer skill Dense Net-Spiking Neural Network method with seven categories (cardboard, glass, metal, organic, paper, plastic, and trash). Then, cardboard, organic, and paper class images are considered biodegradable waste, and other classes are considered non-biodegradable waste. The White Shark Optimization Algorithm (WSOA) further improves prediction accuracy and reduces error rates, with the entire model implemented on the Python platform. From the analysis the EMHS-ParCNN model attains 99.9% accuracy, 99.8% recall and attains better results compared with the existing methods. © 2024 IEEE.

关键词： Cardboard

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A Detailed Study on Preventing the Malicious URLs from Cyber Attacks 3

A Detailed Study on Preventing the Malicious URLs from Cyber...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Adnaan, Shaik Swathi, K. Tejasri, Gorli Praveen Krishna, Anne Venkata Sai Reddy, Mulagolla Gowtham Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9781665462167

Today, there are more malicious URLs and cyber-attacks than ever. Cybercrimes are developing along with the software system's progress. If we are able to stop one attack, then another one will follow in a different direction. These URLs were given via an unidentified website or email. The URLs were supplied in order to collect your personal information or to leak passwords and data content. You and your system are substantially impacted when you click on an unfamiliar URL. The difficulty is that browsers can only block well-known spam websites. At the moment, software incorporates several types of security measures, such as spam messages and call notifications, and our web browser implements these by employing blacklisting to protect from unknown URLs. As a result, not all cyberattacks could be alerted to. Our study is on identifying malicious URLs and phishing assaults from various websites, and we may apply cyber security by utilizing machine learning and deep learning. Many publishers have published on various attacks. These strategies allow us to stop URLs from spammers or hackers as well as cyberattacks like phishing. When we send an email or URL that appears to have come from a reliable source, the attacker can obtain our personal information if we click on it or access it. Therefore, we can protect our software utilizing machine learning and deep learning approaches from this kind of URLs. In order to prohibit or report assaults and cybercrimes, machine learning and deep learning algorithms are employed in cyber security to identify malicious and phishing URLs submitted by unidentified individuals. © 2023 IEEE.

关键词： Websites

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Analysis of passenger flow changes during urban rail transit emergencies based on big data 3

Analysis of passenger flow changes during urban rail transit...

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2023 3rd international conference on intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Shen, Sanchang Pu, Junbo Wang, Yubo Li, Wang Wei, Xuanyu School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Civil Engineering Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510672963

With the rapid development of urban rail transit systems in our country, the frequency of unexpected events has significantly increased. This poses a series of challenges for the networked operation and management of urban rail transit, especially the unpredictable and significant changes in passenger travel behavior during unexpected events. Therefore, studying the relevant issues of passenger flow distribution in urban rail transit during unexpected disruptions is of great theoretical and practical significance. In this paper, we analyze the passenger flow distribution based on the truthful AFC data of Beijing's rail transit system during a selected week in October 2017. We calculate the Origin-Destination (OD) passenger flow during working days and extract the OD passenger flow during the morning peak period in Beijing, which is used for the following two researches: (1) The normal situation passenger flow distribution. With the help of abundant historical data from the Automatic Fare Collection (AFC) system, the research establishes a passenger flow distribution model and algorithm based on the transformed OD matrix and the structural characteristics of the urban rail transit network, optimized through the MNL approach. This study serves as an experimental basis for passenger flow redistribution during emergency disruptions. (2) The interrupted passenger flow redistribution algorithm. Building on the basis of normal passenger flow distribution, the study analyzes and compares the passenger flow volume in disrupted segments and the transfer passenger flow before and after the interruption. By understanding the patterns of changes in passenger flow distribution before and after disruptions, it provides valuable guidance for post-disruption passenger evacuation. © 2024 SPIE. All rights reserved.

关键词： Big data

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Enhancing Energy Efficiency and Sustainability with Hybrid AC/DC Microgrids for Energy Transition 3

Enhancing Energy Efficiency and Sustainability with Hybrid A...

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3rd international conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus PV-Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350375923

The transition to a lower-carbon energy system across Europe brings numerous opportunities and challenges, necessitating advancements in sustainable and eco-friendly energy solutions. This study examines the design and operation of distribution networks and microgrids, particularly emphasizing the integration of hybrid AC/DC distribution systems. The primary goals of this integration are to enhance overall system performance and reduce operating expenses. Utilizing DIgSILENT Power Factory software, the research simulates a microgrid at the FOSS Research Center for Sustainable Energy at the University of Cyprus, incorporating real load and PV generation data. In order to reduce the amount of energy imported from the main grid, this approach evaluates various combinations of AC and DC loads, power production units, and energy storage solutions. The results show that the most optimal structure, with 65% DC loads and 35% AC loads, maximizes efficiency as well as minimizes running expenses. The analysis highlights a notable decrease in imported energy, which dropped from 60.3 kWh/day at full AC loads to 51.6 kWh/day at the optimal DC ratio. Finally, the design and implementation of effective, sustainable energy systems that are in line with changing European energy policy and technology breakthroughs will be significantly impacted by these outcomes. © 2024 IEEE.

关键词： Energy transition

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Research on Low Earth Orbit Satellite Orbit Prediction Based on a Two-stage Integrated Model 24

Research on Low Earth Orbit Satellite Orbit Prediction Based...

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3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Yuan, Shuai Zhang, Kun School of Cyberspace Security Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710834

With the number of low Earth orbit satellites and the expansion of application demands, satellite trajectory prediction is crucial for efficiently operating satellite systems. Traditional trajectory prediction methods rely on classical physical models such as Newtonian mechanics and celestial mechanics. Still, these methods have insufficient accuracy and real-time performance when facing low-orbit satellites’ complex in-orbit environments. Compared with traditional physical models, neural networks can better capture complex changes in trajectories and compensate for the shortcomings of traditional methods. This article analyzes the effects of atmospheric drag, solar radiation pressure, and other factors on the orbital dynamics of low Earth orbit satellites, proposes a trajectory prediction model based on deep neural networks, and uses Long Short-term Memory networks (LSTM) to improve prediction accuracy. To further improve prediction accuracy, this study adopted an ensemble learning strategy, integrating LSTM, GRU, and Transformer models to construct a two-stage ensemble model. The experimental results show that the fusion model significantly surpasses the single model in both short-term and long-term prediction tasks. In short-term forecasting, the combination of LSTM and GRU can efficiently capture rapid changes in trajectory data. In long-term forecasting, Transformer significantly reduces prediction errors through its powerful modeling capabilities. The two-stage integrated model performs excellently in satellite trajectory prediction, with RMSE and MAE reduced by 21.8% and 14.3%, respectively in short-term prediction compared to the LSTM model, and RMSE and MAE reduced by 4.5% and 10%, respectively in long-term prediction. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Multi-Agent-Based Emergency Supplies Dispatch 3

Multi-Agent-Based Emergency Supplies Dispatch

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3rd international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Yuan, Chi Yan, Shengkai Li, Chenglin Zhang, Hansheng Equipment Management and Unamnned Aerial Vehicle Engineering School Shanxi China

ISBN: (纸本)9781510671485

In urban assistance scenarios, the problem of resource allocation on the urban streets is essentially a specialized form of intelligent traffic control, where the logistics and security capabilities greatly influence efficiency of urban rescue operations. Currently, due to the lack of simulated training environments for urban assistance, solving such problems relies solely on theoretical deductions, providing limited quantitative basis for individual resource allocation and support. To simulate realistic city rescue logistics, this paper utilizes the NetLogo simulation environment to construct a two-dimensional traffic flow grid model, simplifying the urban traffic scenario. The emerging DQN reinforcement learning algorithm is employed in the software to enhance the transportation scheduling capabilities of multiple agents, addressing resource allocation challenges in various scenarios on the city rescue. This approach effectively tackles complex real-world problems and holds promising applications. © 2023 SPIE.

关键词： Resource allocation

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