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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2621 条记录，以下是1-10 订阅

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

A Comprehensive Analysis for Advancements and Challenges in ...

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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

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Heart Signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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3D Information in Robot Vision System Based on artificial neural Network 14th

3D Information in Robot Vision System Based on Artificial Ne...

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14th International conference on Frontier Computing, FC 2024

作者： Liu, Xiaoxiao Department of Mechanical and Electrical Engineering Jinan Engineering Vocational Technical College Shandong Jinan China

ISBN: (纸本)9789819627974

In the exploration of robot vision systems based on artificial neural networks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human visual system, this technology enables robots to more effectively interpret and understand the three-dimensional spatial information of their environment, which has a revolutionary role in robot navigation, object recognition, obstacle avoidance and the execution of complex tasks. And this technology shows great potential in many fields such as industrial automation, autonomous vehicles, drone monitoring, and service robots. Not only that, it also plays a very important role in providing in-depth information, accurate positioning and efficient decision-making applications. According to the experimental data, among the 800 experimental subjects, more than 754 people were satisfied with the recognition accuracy, recognition speed, machine efficiency improvement, image recognition clarity, and overall satisfaction with the system. These findings indicate that with the continuous advancement of technology, 3D vision systems based on artificial neural networks will show more significant performance and value in future applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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Computer System of Evaluation of the Mass Exam Results Based on Recognition of Handprinted Azerbaijani Characters 2nd

Computer System of Evaluation of the Mass Exam Results Based...

引用

2nd International conference on Information Technologies and Their applications

作者： Mustafayev, Elshan Azimov, Rustam Inst Control Syst Lab Recognit Identificat & Methods Optimal Solut Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

The utilization of pattern recognition is on the rise extensively in information systems. The convergence of progress in image processing and the accessibility of open-source libraries enables the implementation of innovative solutions for diverse practical problems. One notable challenge pertains to automatically processing responses in mass large-scale exams. This paper introduces a developed system tailored for recognizing such exam results, showcasing its capacity to deliver dependable, effective, and impartial assessments. This system can be configured on almost any type of form. Its use also allows you to abandon the expensive and difficult to use OMR scanners. To increase productivity of system we propose to use the multicore/multithreading property of modern processors to parallelize processes within a single workstation. As a result of experiments, itwas found that the transition to multi-threaded recognition can increase productivity up to 3.5 times in comparison with single-threaded. To reduce the physical size of exam cards, it is proposed to fill in the answers with handwritten symbols instead of filling in the circles. Multilayer and convolutional neural networks were used as a recognition module. A comparative evaluation of the dependence of recognition results on the architecture of neural networks and the feature extraction algorithm was carried out.

关键词： Evaluation of Exam Results Intelligent System Information processing Multithreading Recognition image processing OCR

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Speeding up the Multi-objective NAS Through Incremental Learning 23rd

Speeding up the Multi-objective NAS Through Incremental Lear...

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23rd Mexican International conference on artificial Intelligence

作者： Garcia-Garcia, Cosijopii Derbel, Bilel Morales-Reyes, Alicia Jair Escalante, Hugo Inst Nacl Astrofis Opt & Electr Luis Enrique Erro 1 Cholula 72840 Puebla Mexico Univ Lille CNRS Inria Cent LilleUMR 9189 CRIStAL F-59000 Lille France

ISBN: (纸本)9783031755422;9783031755439

Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in image processing and computer vision. neural architecture search (NAS) has emerged as a crucial field aiming to automate the design and configuration of CNN models. In this paper, we propose a novel strategy to speed up the performance estimation of neural architectures by gradually increasing the size of the training set used for evaluation as the search progresses. We evaluate this approach using the CGP-NASV2 model, a multi-objective NAS method, on the CIFAR-100 dataset. Experimental results demonstrate a notable acceleration in the search process, achieving a speedup of 4.6 times compared to the baseline. Despite using limited data in the early stages, our proposed method effectively guides the search towards competitive architectures. This study highlights the efficacy of leveraging lower-fidelity estimates in NAS and paves the way for further research into accelerating the design of efficient CNN architectures.

关键词： Evolutionary neural architecture search AutoML Lower fidelity estimates

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Enhanced Multimodal Conversational AI Using Speech and image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

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5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. The emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. The study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. This study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional neural networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deep Residual Learning for Fruits of Ceremonial Plants Recognition 2

Deep Residual Learning for Fruits of Ceremonial Plants Recog...

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2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Negara, Ida Bagus Kade Dwi Suta Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Udayana University Denpasar Indonesia

ISBN: (纸本)9781643685762

Fruit detection refers to a method within image processing and computer vision that focuses on automatically recognizing and distinguishing different types of fruits using advanced algorithms and techniques. Fruits are essential and widely used in ceremonial offerings (Banten). They are pivotal in ensuring the completeness of ceremonial offerings. The main goal of fruit detection is to recognize fruits in image form, making it applicable in various applications, including inventory management, automatic classification in the agricultural industry, and medical applications for monitoring dietary patterns. Commonly used methods include artificial neural networks (deep learning), digital image processing, and feature extraction techniques to distinguish between shapes, colours, textures, and other visual features of different types of fruits. In this research, the deep learning algorithm CNN (Convolutional neural Network) is employed. This technique, a deep learning algorithm, excels in accurate image recognition and classification. A total of 500 fruit images are used for five types of ceremonial fruits commonly used in religious activities, namely coconut, banana, candlenut, nutmeg, and areca nut. The residual learning method RESNET152 is used as the CNN algorithm architecture. According to the test findings, the highest accuracy rate achieved was 93% in correctly identifying ceremonial fruit images. © 2025 The Authors.

关键词： Convolutional neural networks

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Efficient neural Architecture Search: Computational Cost Reduction Mechanisms in DeepGA 17th

Efficient Neural Architecture Search: Computational Cost Red...

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent Learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican International conference on artificial Intelligence, MICAI 2024

作者： Barradas-Palmeros, Jesús-Arnulfo López-Herrera, Carlos-Alberto Acosta-Mesa, Héctor-Gabriel Mezura-Montes, Efrén Artificial Intelligence Research Institute University of Veracruz Xalapa Mexico

ISBN: (纸本)9783031838811

neural Architecture Search (NAS) aims to automate the design process of Deep neural networks (DNN) without requiring profound domain knowledge. The Deep Genetic Algorithm (DeepGA) was proposed to find the architectures of Convolutional neural networks (CNNs) for image processing, and its applications have covered a variety of data domains. Nonetheless, one of the main impediments of NAS is its computational cost, which is produced by evaluating candidate architectures. This work proposes using two cost-reduction mechanisms applied to DeepGA: using memory to avoid repeated evaluations and reducing the number of epochs for training in a low-fidelity estimation scheme for evaluations. The results indicate that the execution time of the algorithm was extensively reduced without diminishing the accuracy performance of the resulting architecture. The previous derives in a more efficient NAS procedure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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SVM-based malware classification through deep learning method 5

SVM-based malware classification through deep learning metho...

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5th International conference on Signal processing and Computer Science, SPCS 2024

作者： Shi, Jiawei Wang, Wancheng Zhang, Meng Yao, Qinbo Cheng, Shiyuan Zhan, Zhifei College of Artificial Intelligence and Automation Hohai University Nanjing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Various daily applications, including image categorization, natural language comprehension, and voice identification, heavily rely on fully connected and convolutional neural networks. When tackling classification problems, conventional deep learning architectures typically employ softmax activation functions to normalize outputs and minimize the model's cross-entropy loss. In this study, we present a novel malware classification model that integrates hybrid support vector machines with neural networks. Notably, this model substitutes the conventional softmax layer in neural networks with a support vector machine, thereby shifting the learning process towards minimizing margin losses instead of cross-entropy losses. This modification enhances the precision of malware classification for both standard machine learning algorithms and several prevalent deep learning models. To validate our proposed model, we employed the Malimg dataset, comprising malware images derived from binary malware samples. Subsequently, we trained an DL-SVM model to assign scores to each distinct malware family. Experimental outcomes revealed that the malware classification model achieved an accuracy of 96.31%, a precision rate of 97.00%, a recall rate of 97.35%, and an F1 score of 97.16%, respectively. © 2025 SPIE.

关键词： Convolutional neural networks

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

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3rd International conference on artificial Intelligence and Intelligent Information processing, AiiIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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