检索结果-内蒙古大学图书馆

6th International conference on Communications Signal processing and their applications

作者： Lindsay, James Givigi, Sidney Royal Mil Coll Canada Dept Comp & Elect Engn Kingston ON Canada Queens Univ Sch Comp Kingston ON Canada

ISBN: (纸本)9798350384826;9798350384819

This paper explores the application of the Continuous Action Learning Automata (CALA) game optimizer to Convolutional neural networks (CNNs) for image classification tasks. The CALA game optimizer, initially developed for training artificial neural networks (ANNs), offers a non-gradient descent-based optimization approach that can adapt to different network architectures and activation functions. Leveraging the versatility of the CALA game optimizer, we investigate its performance on CNNs, specifically targeting image recognition within the MNIST dataset. The paper discusses the rationale behind using the CALA game optimizer for CNNs, including its ability to accommodate various activation functions and deeper network architectures. Experimental results demonstrate the efficacy of CALA in training CNNs, showcasing its flexibility and effectiveness in optimizing network parameters for image classification tasks.

关键词： Continuous Action Learning Automata CALA Convolutional neural networks CNNs image Classification Optimization Hyperparameter Tuning neural Network Architecture Game Theory

来源：评论

学校读者我要写书评

暂无评论

Application of deep reinforcement learning in various image processing tasks: a survey

引用

EVOLVING SYSTEMS 2025年第1期16卷 1-20页

作者： Tadesse, Daniel Moges Kebede, Samuel Rahimeto Debele, Taye Girma Waldamichae, Fraol Gelana Ethiopian Artificial Intelligence Inst Addis Ababa 40782 Ethiopia Addis Ababa Sci & Technol Univ Coll Elect & Mech Engn Addis Ababa 120611 Ethiopia Debreberhan Univ Coll Engn Debreberhan 222 Ethiopia

A subset of machine learning algorithm called Deep Reinforcement Learning (DRL) enables computers or agents to learn behavior by taking actions in a given environment through trial and error while observing the rewards. In this learning paradigm, the agent is given a set of actions to chose and is then rewarded or punished depending on the results of those actions. The agent gradually develops the ability to make the best decisions by maximizing its rewards. DRL blends the learning ability of deep neural networks into the decision making capability of reinforcement learning (RL) frameworks in order to seeks and identify the most favorable set of actions. This survey paper studies DRL applications for diverse image processing tasks. It starts by providing an overview of the latest model-free and model-based RL and DRL algorithms. Then, it looks at how DRL is being used for various image processing tasks including image segmentation and classification, object detection, image registration, image denoising, image restoration, and landmark detection. Lastly, the paper discusses the potential uses and challenges of DRL in the proposed area by addressing the research questions. Survey results have showed that DRL is a promising approach for image processing and that it has the potential to solve complex image processing tasks.

关键词： Deep reinforcement learning Deep neural networks image processing

来源：评论

学校读者我要写书评

暂无评论

Advancements in exponential synchronization and encryption techniques: Quaternion-Valued artificial neural networks with two-sided coefficients

引用

neural networks 2025年 183卷 106982页

作者： Li, Chenyang Kou, Kit Ian Zhang, Yanlin Liu, Yang Univ Macau Fac Sci & Technol Dept Math Taipa 999078 Macao Peoples R China Zhejiang Normal Univ Dept Math Zhejiang 321004 Peoples R China

This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued artificial neural networks (QVANNs) that incorporate two-sided coefficients. The study introduces a novel approach that harnesses the Cayley-Dickson representation method to simplify the complex equations inherent in QVANNs, thereby enhancing computational efficiency by exploiting complex number properties. The study employs the Lyapunov theorem to craft a resilient control system, showcasing its exponential synchronization by skillfully regulating the Lyapunov function and its derivatives. This management ensures the stability and synchronization of the network, which is crucial for reliable performance in various applications. Extensive numerical simulations are conducted to substantiate the theoretical framework, providing empirical evidence supporting the presented design and proofs. Furthermore, the paper explores the practical application of QVANNs in the encryption and decryption of color images, showcasing the network's capability to handle complex data processing tasks efficiently. The findings of this research not only contribute significantly to the development of complex artificial neural networks but pave the way for further exploration into systems with diverse delay types.

关键词： Quaternion-Valued artificial neural networks Exponential synchronization Controllers Encryption of image Two-sided coefficient

来源：评论

学校读者我要写书评

暂无评论

A review of hyperspectral image classification based on graph neural networks

引用

artificial INTELLIGENCE REVIEW 2025年第6期58卷 1-56页

作者： Zhao, Xiaofeng Ma, Junyi Wang, Lei Zhang, Zhili Ding, Yao Xiao, Xiongwu Xian Res Inst High Technol Key Lab Opt Engn Xian 710025 Shaanxi Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

Hyperspectral images provide rich spectral-spatial information but pose significant classification challenges due to high dimensionality, noise, mixed pixels, and limited labeled samples. Graph neural networks (GNNs) have emerged as a promising solution, offering a semi-supervised framework that can capture complex spatial-spectral relationships inherent in non-Euclidean hyperspectral image data. However, existing reviews often concentrate on specific aspects, thus limiting a comprehensive understanding of GNN-based hyperspectral image classification. This review systematically outlines the fundamental concepts of hyperspectral image classification and GNNs, and summarizes leading approaches from both traditional machine learning and deep learning. Then, it categorizes GNN-based methods into four paradigms: graph recurrent neural networks, graph convolutional networks, graph autoencoders, and hybrid graph neural networks, discussing their theoretical underpinnings, architectures, and representative applications. Finally, five key directions are further highlighted: adaptive graph construction, dynamic graph processing, deeper architectures, self-supervised strategies, and robustness enhancement. These insights aim to facilitate continued innovation in GNN-based hyperspectral imaging, guiding researchers toward more efficient and accurate classification frameworks.

关键词： Graph neural Hyperspectral image classification Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Nonlinear Noise-Resistant Zeroing neural Network Model for Solving Time-Varying Quaternion Generalized Lyapunov Equation and applications to Color image processing

引用

IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Xiao, Lin Yan, Xiangru He, Yongjun Luo, Biao Song, Qiya Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China Hunan Normal Univ MOE LCSM Changsha 410081 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

The time-varying Lyapunov equation (TVLE) plays a crucial role in control design and system stability. However, there has been limited research conducted on the time-varying generalized Lyapunov equation in the quaternion field. To tackle the time-varying quaternion generalized Lyapunov equation, a nonlinear noise-resistant zeroing neural network (NNR-ZNN) model with a novel power activation function (NPAF) is devised. The issue of non-commutativity within quaternion is circumvented by utilizing the real representation. The theoretical analyses provide a sufficient explanation for the global stability, fixed-time convergence, and robustness of the NNR-ZNN model. Under several different kinds of noises, the exceptional robustness of the NNR-ZNN model is highlighted by comparison with other existing models. In the end, the successful applications of the NNR-ZNN model to color image fusion and color image denoising confirm the practical value of the NNR-ZNN model.

关键词： Mathematical models Quaternions Color artificial neural networks Numerical models Noise Noise reduction Convergence Robustness Stability analysis Activation function (AF) color image denoising color image fusion noise resistance time-varying quaternion generalized Lyapunov equation (TVQGLE) zeroing neural network (ZNN)

来源：评论

学校读者我要写书评

暂无评论

Backpropagation-Based Learning Techniques for Deep Spiking neural networks: A Survey

引用

IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2024年第9期35卷 11906-11921页

作者： Dampfhoffer, Manon Mesquida, Thomas Valentian, Alexandre Anghel, Lorena Univ Grenoble Alpes Grenoble Inst Technol CEA CNRSINAC Spintec F-38000 Grenoble France CEA LIST F-38000 Grenoble France

With the adoption of smart systems, artificial neural networks (ANNs) have become ubiquitous. Conventional ANN implementations have high energy consumption, limiting their use in embedded and mobile applications. Spiking neural networks (SNNs) mimic the dynamics of biological neural networks by distributing information over time through binary spikes. Neuromorphic hardware has emerged to leverage the characteristics of SNNs, such as asynchronous processing and high activation sparsity. Therefore, SNNs have recently gained interest in the machine learning community as a brain-inspired alternative to ANNs for low-power applications. However, the discrete representation of the information makes the training of SNNs by backpropagation-based techniques challenging. In this survey, we review training strategies for deep SNNs targeting deep learning applications such as image processing. We start with methods based on the conversion from an ANN to an SNN and compare these with backpropagation-based techniques. We propose a new taxonomy of spiking backpropagation algorithms into three categories, namely, spatial, spatiotemporal, and single-spike approaches. In addition, we analyze different strategies to improve accuracy, latency, and sparsity, such as regularization methods, training hybridization, and tuning of the parameters specific to the SNN neuron model. We highlight the impact of input encoding, network architecture, and training strategy on the accuracy-latency tradeoff. Finally, in light of the remaining challenges for accurate and efficient SNN solutions, we emphasize the importance of joint hardware-software codevelopment.

关键词： Neurons Encoding Training Biological neural networks Synapses Neuromorphics Hardware Brain-inspired computing deep learning neuromorphic computing spiking neural networks (SNNs)

来源：评论

学校读者我要写书评

暂无评论

An improved pulse coupled neural networks model for semantic IoT

引用

Digital Communications and networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled neural networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network image segmentation

来源：评论

学校读者我要写书评

暂无评论

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

引用

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2024年第1期12卷 138-161页

作者： Wang, Haoyu Li, Xiaofeng Chinese Acad Sci Inst Oceanog Key Lab Ocean Circulat & Waves Qingdao 266071 Peoples R China NOAA Washington DC USA

In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to efficiently and accurately process and analyze ocean big data and solve practical problems based on ocean big data constitute a great challenge. artificial intelligence (AI) technology has developed rapidly in recent years. Numerous deep learning (DL) models have emerged, becoming prevalent in big data analysis and practical problem solving. Among these, convolutional neural networks (CNNs) stand as a representative class of DL models and have established themselves as one of the premier solutions in various research areas, including computer vision and remote sensing applications. In this study, we first discuss the model architectures of CNNs and some of their variants as well as how they can be applied to the processing and analysis of ocean remote sensing data. Then, we demonstrate that CNNs can fulfill most of the requirements for ocean remote sensing applications across the following six categories: reconstruction of the 3D ocean field, information extraction, image superresolution, ocean phenomena forecast, transfer learning method, and CNN model interpretability method. Finally, we discuss the technical challenges facing the application of CNN-based ocean remote sensing big data and summarize future research directions.

关键词： Oceans Remote sensing Convolutional neural networks Data models Biological system modeling Computational modeling Ocean temperature Feature extraction Predictive models Transfer learning Spatial resolution artificial intelligence Big Data Deep learning image processing Natural language processing Backpropagation algorithms

来源：评论

学校读者我要写书评

暂无评论

Assessment of Rain Attenuation in Satellite Telecommand Signals Using Numerical Weather Prediction Model and artificial neural networks 32

Assessment of Rain Attenuation in Satellite Telecommand Sign...

引用

32nd IEEE Signal processing and Communications applications conference (SIU)

作者： Gozutok, Arif Armagan TURKSAT AS Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

In this study, the signal fading prediction model was implemented with artificial neural networks by obtaining precipitation parameters and atmospheric conditions in order to evaluate the signal attenuation observed in the telecommand signals of the geosynchronous telecommunication satellite. For a real situation, the application of artificial deep neural networks (ANN) over the 24-hour high-resolution Weather Research & Forecast (WRF) model simulation outputs was performed on two high-resolution simulation areas and used to estimate and model satellite signal attenuation. Preliminary results obtained from error analysis (RMSE) on multiple-input single-output feedforward neural network (MISO FFNN) prediction model outputs tested with various artificial neural network algorithms agree with observations of signal attenuation observed in downlink telemetry (receiver automatic gain control TM) obtained from the satellite receiver equipment. showed quite high correlation accuracy.

关键词： numerical weather prediction signal fading artificial intelligence neural networks atmospheric attenuation

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

引用

5th International conference on Data Science, Machine Learning and applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. The rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional neural networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning image processing Backpropagation algorithm Convolutional neural networks (CNNs) Model structures

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：