检索结果-内蒙古大学图书馆

11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Guo, Kai Luo, Hanjiang Tao, Hang Ruby, Rukhsana Qin, Zhizun Liu, Kui Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350345346

Unmanned aerial vehicles (UAVs) have mobility in harsh environments and the flexibility to modify their flight altitude to acquire information in an adaptive manner, so these vehicles can be leveraged to solve the informative path planning (IPP) problem. As most of the research on IPP currently are on static targets and terrain detection tasks, in this paper, we focus on the search for dynamic targets on the sea surface, and study the issue of collaborative search with multiple granularities using a swarm of UAVs in marine environments. To capture the objects of interest (OOI), we design a binary classifier for UAVs to sense OOI based on a depth perception model of cameras. Afterwards, we propose a collaborative search scheme using the multi-agent deep deterministic policy gradient (MADDPG) approach to enable a swarm of UAVs to simultaneously maintain formation coordination while adapting to digital pheromone strength. The experimental results show that the proposed scheme can effectively detect targets on the sea surface in a multi-granular collaborative manner. © 2023 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

FastVGG network and its application in automatic identification of traffic signs

引用

International Journal of Information and Communication technology 2022年第2期20卷 192-203页

作者： Gui, Kaige Gao, Shangbing Li, Xinyuan Lu, Zhonghe College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Jiangsu Mobile Internet of Things Technology Engineering Laboratory Huaian223001 China

Traffic sign recognition is an important part of driver assistance systems. Because the types of traffic signs are complex and diverse, they are difficult to identify. Traditional recognition methods require manual extraction of features, which is difficult and inaccurate. This paper proposes a FastVGG network based on VGG neural network to extract the features of the target image to realise the recognition of traffic signs under different angles and illumination. In the method of this paper, the connection layer parameters and the number of network layers are moderately reduced, the merging step is increased, and the recognition speed is improved. When the parameter value is less than zero, the leaky ReLU is used to replace the activation function to solve the problem of neuron death. The experimental results of the German traffic sign recognition dataset (GTSRD) show that the algorithm can achieve accurate classification while increasing the speed. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

引用

4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

来源：评论

学校读者我要写书评

暂无评论

NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

引用

computers, Materials & Continua 2022年第11期73卷 2985-3001页

作者： Woo Hyun Park Isma Farah Siddiqui Dong Ryeol Shin Nawab Muhammad Faseeh Qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea

have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus’characteristics,and conducting epidemiological investigations through patient management and ***,researchers worldwide require funding to achieve these ***,there is a need for documentation to investigate and trace disease ***,it is time consuming and resource intensive to work with documents comprising many types of unstructured ***,in this study,natural language processing technology is used to automatically classify these *** used statistical methods include data cleansing,query modification,sentiment analysis,and ***,owing to limitations with respect to the data,it is necessary to understand how to perform data analysis suitable for medical *** solve this problem,this study proposes a robust in-depth mixed with subject and emotion model comprising three *** first is a subject and non-linear emotional module,which extracts topics from the data and supplements them with emotional *** second is a subject with singular value decomposition in the emotion model,which is a dimensional decomposition module that uses subject analysis and an emotion *** third involves embedding with singular value decomposition using an emotion module,which is a dimensional decomposition method that uses emotion *** accuracy and other model measurements,such as the F1,area under the curve,and recall are evaluated based on an article on Middle East respiratory syndrome.A high F1 score of approximately 91%is *** proposed joint analysis method is expected to provide a better synergistic effect in the dataset.

关键词： Computational linguistic AI epidemic healthcare classification

来源：评论

学校读者我要写书评

暂无评论

Cooperative and Dynamic UAV-Based Mobile Sink Deployment using Deep Reinforcement Learning 11

Cooperative and Dynamic UAV-Based Mobile Sink Deployment usi...

引用

11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Shao, Mingyue Luo, Hanjiang Ruby, Rukhsana Dong, Pengyan Tao, Hang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350345346

In maritime emergency response operations, autonomous underwater vehicles (AUVs) can perform underwater search and transmit emergency data in real-time. To collect the sensed data from AUVs across the water-air interface, the traditional method is to deploy surface relays. However, the deployment of surface relays has some disadvantages, such as poor and imbalanced communication coverage of real-time connections with AUVs. Since the deployment of static relays may limit the dynamic search area of AUVs, whereas the deployment of free surface drifting relays may disrupt the connections while the relays are drifting away with the ocean current. To address these challenges, in this paper, we propose a novel method by leveraging the flexibility of multiple unmanned aerial vehicles (UAVs) equipped with underwater hydrophones and design a dynamic deployment scheme based on a multi-agent deep deterministic policy gradient (MADDPG) approach. By this scheme, multiple UAVs are able to deploy in a cooperative manner to deal with the coverage imbalance issue and provide maximum coverage service. The numerical simulations demonstrate that the proposed scheme is effective in terms of communication coverage, fairness, and energy consumption. © 2023 IEEE.

关键词： Autonomous underwater vehicles

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Fusion and Decomposition Network for Single Image Deraining

引用

IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN) 7

Fingerprint generation and authentication though Adaptive co...

引用

7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammad Nabeel Zehra, Syeda Sundus Baber, Alina Siddiqui, Maria Andleeb Ned University of Engineering and Technology Dept. of Computer Science & Information Technology Karachi Pakistan Sir Syed University of Engineering and Technology Dept of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data. © 2023 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

引用

5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Healthcare Service Accessibility Path Planner: Unveiling a New Era of Intelligent Appointment Management Systems Based on Outpatient Prioritizing

Healthcare Service Accessibility Path Planner: Unveiling a N...

引用

2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Tokatli, Nazli Kocak, Muhammed Tayyip Kirtay, Seda Goztepeli, Gurkan Aktas, Ibrahim Serhat Altun, Halis Istanbul Health and Technology University Department of Computer Engineering Istanbul Turkey Istanbul Health and Technology University Department of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350306590

In light of increased constraints on healthcare systems, particularly as a result of the pandemic, the importance of directing patients to the appropriate healthcare departments for individualized treatment based on their health conditions has been emphasized. Numerous healthcare institutions currently employ an online booking system that enables patients to schedule appointments. However, because patient requests are the main driving force behind this process, appointments with inappropriate departments or the bypassing of primary care facilities like general practice clinics frequently occur. Many studies proposed the use of AI-based chatbots and machine learning algorithms in healthcare systems to improve clinic operations, reduce patient wait times, and predict outpatient appointment no-show rates. This paper describes the conception and implementation steps of an innovative (mhealth app) that uses open AI tools to prioritize and classify outpatients based on their symptoms. Our AI-based appointment scheduling app will decide for the outpatient either to schedule appointments with primary care facilities or direct them to the appropriate healthcare department in hospitals only when absolutely necessary, thereby nurturing a more efficient, patient-centered healthcare service. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：