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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13588 条记录，以下是881-890 订阅

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Ground unmanned vehicle cluster search method based on multi-agent reinforcement learning 4

Ground unmanned vehicle cluster search method based on multi...

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4th international conference on Telecommunications, Optics, and Computer Science, TOCS 2023

作者： Cheng, Yinjun Wei, Siwei Wang, Chunzhi School of Computer and Science Hubei University of Technology Wuhan430070 China CCCC Second Highway Consultants Co. Ltd. Wuhan430056 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

the achievements in modern unmanned systems technology are noteworthy, with Unmanned Ground Vehicles (UGVs) exemplifying exceptional payload capacity and endurance. the collaborative operation of UGV clusters has emerged as a pivotal direction for constructing future intelligent transportation systems and driving the development of smart cities. Existing algorithms predominantly focus on single-agent systems, leaving a research gap in the exploration of multiagent systems. To address this void, this paper concentrates on resolving the UGV cluster search problem in closed road sections. the approach involves the utilization of multi-agent reinforcement learning algorithms to handle task scheduling and collision avoidance, enabling UGV clusters to complete extensive area search tasks with minimal data transmission efficiently. this methodology ensures the decentralized and efficient operation of UGV clusters, showcasing robust search capabilities in closed road sections. the practical applications of this approach underscore its substantial potential. © 2024 SPIE.

关键词： Motion planning

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Deep learning Model for Chronic Obstructive Pulmonary Disease through Breathing Sound 4

Deep Learning Model for Chronic Obstructive Pulmonary Diseas...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Rukumani Khandhan, C. Kaviya, G.M. Kongu Engineering College Department of CT-PG Erode India

ISBN: (纸本)9798350300093

the lung condition known as Chronic Obstructive Pulmonary Disease (COPD) results in breathlessness, wheezing, or a persistent cough. Lack of oxygen causes the lungs to release extra carbon dioxide. the audio of the respiratory sounds is useful in the prediction of COPD since early detection is important for respiratory disorders. Initially, the diagnosis of COPD exacerbations has a lot of potential to benefit from deep learning approaches. Deep learning is an effective machine learning algorithm that combines supervised training with Convolutional Neural Network (CNN) and Long Short-Term Memory (LS TM) applications and unsupervised training to reduce optimization. the dataset of breathing sounds is from people with COPD and other six lung diseases, as well as those who are healthy. Feature extraction is done in order extract necessary features from the audio files. An artificial Convolutional Neural Network (CNN) was used to extract and classify features from the preprocessed dataset. Exacerbation forecasting could lessen their severe effects and the substantial expenditures related to COPD patients. Deep learning in breathing sounds is used to forecast whether an output will be COPD or non-COPD related. the findings demonstrate that the deep learning model was highly accurate in differentiating between people without COPD and those who have it. Also, this technique is a financially advantageous choice for long-term remote monitoring in medical applications. © 2023 IEEE.

关键词： Long short-term memory

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ICEENG 2024 - 14th IEEE international conference on Electrical Engineering

ICEENG 2024 - 14th IEEE International Conference on Electric...

引用

14th IEEE international conference on Electrical Engineering, ICEENG 2024

ISBN: (纸本)9798350343427

the proceedings contain 84 papers. the topics discussed include: reinforcement learning for precision navigation: DDQN-based trajectory tracking in unmanned ground vehicles;security enhancement of MIL-STD-1553 based on lightweight encryption algorithms;preparation and characterization of thermally reduced graphene oxide for electronic applications;machine learning-based detection of spoofing attacks in GNSS: a study using TEXBAT dataset;efficient lossy satellite image compression using hybrid autoencoder model;techno-economic analysis of hybrid power supply scenarios for a resort in Egypt;federated deep deterministic policy gradient for power control in 6G in-X subnetworks;detection and sub-classification of acute lymphoblastic leukemia cell types from the microscopic images based on the object detection model YOLOV5;deep learning-based strategies and optimization methods for SAR ATR;and optimization of direct current magnitude against braking time in a no load 3-phase induction motor using Matrix Laboratory Simulink.

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Energy Optimal Management of Microgrid With High Photovoltaic Penetration 4

Energy Optimal Management of Microgrid With High Photovoltai...

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4th IEEE international Electrical and Energy conference (CIEEC)

作者： Zhang, Xiaobin Huang, Chenxi Shen, Jia Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China

ISBN: (纸本)9781728171500

In order to further reduce carbon emissions, a large number of distributed photovoltaics (PVs) are connected to customer sider, which can form microgrids (MGs) with high PV penetration combined with energy storage system (ESS) adopting droop control. Due to the uncontrollability of PV output and frequent charging and discharging of ESS, the economic optimization of MG with high PV penetration is full of challenges, especially island state. Aiming at the lowest daily operating cost, the multi-factor collaborative energy optimization models are established for the grid-connected and islanded MG respectively. then using particle swarm optimization (PSO) with inertial weight factor to find the optimal solutions of the models under stable operating constraints, the day-ahead energy optimal management strategy (EOMS) for the MG is obtained. In order to reduce the influence of PV and load prediction errors on the energy management accuracy, model predictive control (MPC) is applied to improve the day-ahead EOMS, and intraday rolling horizon energy optimal management strategy (RHEOMS) is obtained. the RHEOMS corrects the forecast errors by feeding back the PV and load current operating value continuously and rolling updating the EOMS control value. the economy and effectiveness of the proposed strategies are verified on a typical MG with high PV penetration.

关键词： Costs optimization Uncertainty Biological system modeling Maintenance engineering Load modeling Renewable energy sources Energy optimal management high penetration particle swarm optimization photovoltaic microgrid rolling horizon

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Research on Efficiency and Security Balance of Federated learning Algorithm for Multi-node Privacy Computing 4

Research on Efficiency and Security Balance of Federated Lea...

引用

4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Mu, He Chen, Runying Bin, An Shanghai Fudata Technology Co. Ltd Shanghai China Inc. Beijing China

ISBN: (纸本)9798350368239

In order to solve the key problem of improving algorithm efficiency while ensuring data security, this paper introduces an innovative method, the cryptographic-Homomorphic Asynchronous (EHA) method, which combines homomorphic encryption (HE) with asynchronous federation learning (AFL). EHA method uses HE to realize the encrypted transmission and calculation of data, ensuring the confidentiality of data in the whole process and preventing information leakage. At the same time, AFL is used for asynchronous model update, allowing nodes to update model parameters at different time points according to their own conditions, effectively reducing the delay caused by synchronous waiting, and significantly improving the efficiency and response ability of federation learning. Finally, by comparing with the traditional federated learning algorithm, experiments and analysis of experimental results show that compared with the traditional methods, EHA has significant advantages in communication efficiency, confusion matrix optimization and system robustness enhancement. Especially in the multi-node environment, EHA method not only effectively balances the relationship between efficiency and security, but also significantly reduces the communication delay and computing cost, and significantly improves the overall performance of the system. these results fully verify the feasibility and effectiveness of EHA architecture in the federated learning domain of multi-node privacy protection. ©2024 IEEE.

关键词： Federated learning

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Comparative Analysis of Fake News Identification Using Machine learning Methods 4th

Comparative Analysis of Fake News Identification Using Machi...

引用

4th international conference on Data, Engineering, and applications, IDEA 2022

作者： Patel, Shivangi Singh, Dheeraj Kumar Parmar, Jayshree Parul University Vadodara India

ISBN: (纸本)9789819700363

the usage of social networks is growing exponentially day by day. Users can obtain a lot of information from social networks where some genuine information as well as some misinformation are there that mislead users and may face problematic situations. therefore, it is mandatory to identify fake content. thus, the main purpose of this study is to examine the state of the art in the detection of fake content and the contribution of machine learning algorithms such as Random Forest, Support Vector Machine, Passive Aggressive Classifier, and Logistic Regression with Term Frequency-Inverse Document Frequency vectorization technique applied for detecting fake content in English and Hindi language efficiently. Performed comparison analysis between algorithms on the FakeNewsNet dataset in English and another dataset in the Hindi language to indicate the effectiveness of classification outcome. the Support Vector Machine classifier generated the greatest results out of the four. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. the aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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Predicting Urban Traffic Flow Based on Deep Meta-learning 24

Predicting Urban Traffic Flow Based on Deep Meta-learning

引用

4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Zhu, Wenyan Kong, Hoiio Cai, Wenzheng Zhu, Wenhao Faculty of Data Science City University of Macau China Department of Academic Affairs Guangdong University of Science & Technology Dongguan Guangdong China Co. Ltd Dongguan Guangdong China

ISBN: (纸本)9798400710247

this paper aims to explore the application of deep meta-learning in urban traffic flow prediction, analyzing its advantages over traditional methods and the challenges it faces. Firstly, we introduce the background and importance of urban traffic flow prediction, along with the current application of deep learning technologies in this field. Subsequently, the paper elaborates on the theoretical foundations and key techniques of deep meta-learning, including the design of meta-models, training strategies, and feature integration mechanisms. Additionally, through experimental comparative analysis, we demonstrate the performance of deep meta-models in traffic flow prediction tasks and discuss their potential and limitations in practical applications. Finally, the paper summarizes research findings and proposes future research directions, aiming to provide more efficient and accurate prediction tools for urban traffic management and to promote the development of smart cities. through in-depth research, we seek to offer new perspectives and solutions to the field of urban traffic flow prediction, contributing to the advancement of intelligent transportation systems. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Deploying Hybrid Deep learning Methods In Conjunction With One-Class SVM For Anomaly Detection In Crowded Spaces 4

Deploying Hybrid Deep Learning Methods In Conjunction With O...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhingra, Lovish Geetha, B. Mishra, Abhinav Savitha, R. Chavhan, Gajanan H. Vigenesh, M. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India

ISBN: (纸本)9798331518981

the importance of public security and smart city initiatives like the proliferation of monitoring networks and the ability to automatically identify suspicious activity in surveillance footage is growing. Modern methods for recognizing moving objects rely on context modeling. Noise and background movement are introduced because current anomaly detection algorithms focus on geographical information while ignoring temporal information, either entirely or partially. there is a lot of visual and behavioral diversity among the many people in a crowded situation, making it challenging to keep track of individual *** get better and quicker tracking results, a strategy that combines strong segmentation with deep learning is suggested. this method involves taking an extracted video frame and then applying a two-dimensional variance plane and a GLCM to the highlighted pixels inside the video. A hybrid deep learning model is trained using one-class support vector machines using spatial data for robust abnormality detection, and Fuzzy C-Means is used to subsequent frames for segmentation. It assesses recall, precision, and accuracy. When compared to existing approaches, the suggested one achieves better results in experiments when measuring accuracy, precision, and recall. the technique is put into action in MATLAB using the PETS2009 and UCSD datasets. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Compact and Accurate Binary Neural Network for Real-Time ECG Classification on AIoT Devices 4

Compact and Accurate Binary Neural Network for Real-Time ECG...

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

作者： Zhang, Jieyi Wu, Zhongxing Pu, Ninghao Xu, Yifan Ding, Zhenyang Liu, Hao School of Electronic Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798331528850

Accurate detection of cardiac arrhythmias through electrocardiogram (ECG) signals is critical for timely medical intervention and improving patient outcomes. Despite the proliferation of deep learning-based ECG classification algorithms, many existing models tend to compromise on resource efficiency, resulting in high storage demands and excessive power consumption. this limits their deployment on wearable Artificial Intelligence-of-things (AIoT) devices, which are increasingly vital for continuous health monitoring. In contrast to conventional binarization approaches, we propose a novel Binary Neural Network (BNN) tailored for ECG signal classification, incorporating innovative basic blocks that enhance both performance and efficiency. Our approach transforms one-dimensional ECG signals into two-dimensional grayscale images, improving data augmentation for small sample sizes. the model achieves a classification accuracy of 97.85% with a remarkably compact memory footprint of just 14.07 KB, representing a 31.37-fold compression compared to traditional full-precision models. Furthermore, we introduce scaling factors for both weight and activation binarization, while replacing the ReLU function with the sign function during quantization-aware training. this approach effectively balances model complexity and classification performance. the simplified architecture of our BNN is particularly well-suited for real-time applications, paving the way for its implementation in wearable medical devices, particularly for enhancing patient monitoring and management of cardiac conditions. © 2024 IEEE.

关键词： Deep learning

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