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检索条件"任意字段=3rd International Conference on Advanced Algorithms and Neural Networks, AANN 2023"

共 553 条记录，以下是1-10 订阅

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Human Motion Pose Recognition System Based on Convolutional neural networks 24

Human Motion Pose Recognition System Based on Convolutional ...

引用

3rd international conference on algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Song, Yaxun Wuhan Donghu University Hubei Wuhan China

ISBN: (纸本)9798400718120

This paper proposes a human motion pose recognition system based on convolutional neural networks (CNNs). The system consists of four modules: data acquisition, preprocessing, feature extraction, and pose recognition, capable of extracting high-dimensional features from various input forms (RGB, depth, skeletal data). The feature extraction module employs a novel network architecture, while the pose estimation module includes sub-networks for keypoint regression and pose classification. Experimental results demonstrate superior performance of the system in both keypoint regression and pose classification tasks on public datasets, highlighting its outstanding pose estimation and understanding capabilities. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Handwritten Digit Recognition Using Machine Learning Classifier 3rd

Handwritten Digit Recognition Using Machine Learning Classif...

引用

3rd international conference on Computing and Communication networks, ICCCN 2023

作者： Singh, Sakshi Yadav, Aditi Gupta, Sonam Gupta, Pradeep Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9789819726707

Handwritten digit recognition technology is a process of identifying digits automatically using a computer or another platform. It holds significant promise applications in Optical Character Recognition (OCR), biometric and signature verification, and suspect identification. This study utilizes the widely recognized MNIST handwritten digital database as the dataset and deliberates algorithms: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Backpropagation neural networks (BPNN), Convolutional neural networks (CNN), and deep learning for digital recognition. The research employs KNN, SVM, BPNN, and CNN with TensorFlow. The algorithm’s parameters are fine-tuned to attain optimal results for each method. This study aims to assess algorithm performance, comprehend their capabilities, and gain insights into real-world applications. Lastly, an analysis is conducted by evaluating the accuracy of recognition and duration of four algorithms, providing insights into the strengths and weaknesses of each method in the context of handwriting recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Three quantum neural network architectures for multi-class image classification 3

Three quantum neural network architectures for multi-class i...

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3rd international conference on algorithms, Network, and Communication Technology, ICANCT 2024

作者： Qu, Guangkai Yu, Xianglong Wang, Zhimin Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum neural networks (QNNs) integrate the advantages of quantum information with the formidable capabilities of classical neural networks, exhibiting notable potential in image classification tasks. However, the performance of current QNNs in processing multi-class image classification tasks still leaves room for improvement. In this paper, we propose three QNN architectures to enhance the accuracy of multi-class image classification by utilizing techniques of overparameterization, dense pooling, and local feature extraction. These networks are capable of mapping conventional data to the amplitudes of quantum states via amplitude encoding, eschewing the need for traditional dimensionality reduction techniques. Following processing with parameterized quantum circuits (PQCs), we conduct quantum measurements to extract feature information. We validate the proposed architectures using the MNIST dataset for eight-class image classification problems. The experimental results unequivocally demonstrate that the three techniques we employed significantly enhance the accuracy of QNNs in multi-class image classification tasks. © 2025 SPIE.

关键词： Quantum electronics

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3rd international conference on Emerging Electronics and Automation, E2A 2023

3rd International Conference on Emerging Electronics and Aut...

引用

3rd international conference on Emerging Electronics and Automation, E2A 2023

ISBN: (纸本)9789819730896

The proceedings contain 54 papers. The special focus in this conference is on Emerging Electronics and Automation. The topics include: P4 Language-Based Load-Aware Efficient Resource Utilization in Software Defined networks;implementation of Derived Quadratic Boost Dual Input Converter for Automotive Application;intelligent Solution for Voltage Profile Enhancement Using Dynamic Voltage Restorer;Optimizing Reliability of PV-Based Microgrid by Integrating Fuel Cell;secure Communication in Vehicle-to-Grid networks: A Study on Man in the Middle Attack Mitigation;Comparative Analysis of PID and LQR Control for Autonomous Vehicle Platoons;effect of Mass on Parameters of Electric Vehicle;Modeling and Analysis of an Underactuated Underwater Vehicle Manipulator System (UVMS) for Underwater Intervention;modified Artificial Potential Field algorithms for Mobile Robot Path Planning;Comparative Analysis of advanced Driver Assistance Systems (ADAS) in Automobiles;innovative Acoustic Wave Fire Suppression System for Portability;a Comparative Study on the Various Aging Laws Governing Lithium-Ion Capacitor Cycling Aging;Modified Carrier Based Phase Shift Modulation Method for Modular Multilevel Converter for the Improvement of THD and Switching Losses;design Analysis of Vertical Axis Wind Turbine in Jaipur & Thiruvananthapuram;structural and Numerical Analysis of Bladeless Wind Turbine;classification of Poikilocytosis Abnormality Using Ensemble Deep Learning Technique;person Re-identification Using Convolutional Fuzzy neural Network Classifier;demand Forecasting for Market Basket Analysis;framework to Evaluate Twitter Tweets Using K-Means Clustering;feature Selection Strategies for Accurate Crop Yield Prediction in Heterogeneous Datasets;deep Transfer Learning for Multiclass Dermatology Classification: A Comprehensive Study;depneumoNet: A Novel Model for Improved Pneumonia Diagnosis Through Chest X-Ray Imaging.

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3rd international conference on Emerging Electronics and Automation, E2A 2023

3rd International Conference on Emerging Electronics and Aut...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

ISBN: (纸本)9789819768011

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Quantum spiking neural networks for image classification 3

Quantum spiking neural networks for image classification

引用

3rd international conference on algorithms, Network, and Communication Technology, ICANCT 2024

作者： Liu, Shilong Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

As the field of artificial intelligence experiences the emergence of various large-scale models, the computing power requirements are increasing. Simultaneously, Moore’s Law is approaching its limits, which intensifies the need to address how to perform high-dimensional complex data computations efficiently and with minimal energy consumption. Quantum computing and neuromorphic computing, with their unique features, offer potential solutions to this contemporary challenge. In this study, we aim to integrate quantum computing with neuromorphic computing, and develop a quantum spiking neural networks architecture. We proposed three measurement strategy to reducing operational costs, making it more friendly to near-term quantum hardware. We evaluated the performance of these three models on the standard MNIST dataset for classification tasks and found that they maintained a high level of accuracy despite a reduced number of parameters and a substantial decrease in the number of quantum circuit shots. © 2025 SPIE.

关键词： Quantum computers

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Quantum neural network quantum state 3

Quantum neural network quantum state

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3rd international conference on algorithms, Network, and Communication Technology, ICANCT 2024

作者： Pang, Xin Li, Yanan Wang, Zhimin Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum many-body computation is a central challenge across multiple domains of scientific research, and Monte Carlo method has emerged as one of the most effective numerical techniques for investigating large-scale many-body systems. In recent years, the integration of Monte Carlo methods with machine learning has led to the development of neural quantum state approach. Quantum neural networks (QNNs) offer superior expressiveness compared to classical neural networks. In this paper, we propose a novel Quantum neural Network Quantum State (QNNQS) method, based on the variational Monte Carlo framework. This approach leverages a quantum neural network to represent the many-body quantum state, and optimizes it iteratively through gradient descent to determine the ground-state energy. To assess the performance of this method, we conduct simulations of the one-dimensional transverse-field Ising model, investigating the influence of quantum circuit architecture on circuit expressibility, entangling capability, and computational accuracy. The results demonstrate that the QNNQS method achieves comparable simulation accuracy while utilizing significantly fewer parameters and requiring less training resources than classical network quantum state methods. © 2025 SPIE.

关键词： Quantum computers

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Deep Learning-Based Automatic Identification System for Marine Plankton 24

Deep Learning-Based Automatic Identification System for Mari...

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3rd international conference on algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Xiang, Su Shandong University of Science and Technology Shandong Qingdao China

ISBN: (纸本)9798400718120

Due to their diverse species and complex morphology, the automatic identification of marine plankton has always been a challenging task. In response to this problem, this study proposes an innovative deep learning-based intelligent identification system. The system integrates advanced computer vision and artificial intelligence technologies, possessing the capability of automatic feature extraction and pattern recognition. We extensively collected marine sampling data and combined it with public datasets to construct a large-scale database containing 200 species of plankton and 500,000 images. Through the training and optimization of convolutional neural network models, the system demonstrates outstanding classification performance of 94.2% on the test set. Not only does the system outperform traditional methods in accuracy, but its inference speed has also been optimized through model compression and parallel computing, capable of processing 620 images per second. By deploying containerization and automatic scaling strategies, the system ensures high availability. This research provides strong technical support for marine ecological conservation, fisheries management, and other related fields. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Real-Time Detection of Rice Bacterial Blight using Remote Sensing and Deep Learning 3

Real-Time Detection of Rice Bacterial Blight using Remote Se...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rakesh, Nitin Thirumurugan, R. Mohankumar, N. Kasinathan, Prabakaran Prasad, S. N. Sheela Evangelin Srinivasan, C. Maharashtra Nagpur India Adhi College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Symbiosis Institute of Technology Nagpur Campus Pune India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Krishna Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research introduces an innovative method for the real-time identification of bacterial blight in rice crops via remote sensing and deep learning (DL) technologies. Bacterial blight represents a significant threat to rice production, requiring quick detection for efficient control. To address this challenge, remote sensing data from drones and satellites is analyzed to detect visual signs connected to the diseases. A convolutional neural network (CNN) model is used to classify and identify sick rice plants using high-resolution images. The results demonstrate that the model attained an accurate rate of 98.2%, significantly enhancing the speed and precision of illness identification relative to conventional approaches. The combination of advanced DL algorithms with remote sensing technologies enables quick responses and improves sustainable agriculture practices. It illustrates the capability of using technology for crop monitoring, enhancing food security, and optimizing resource management in agriculture. © 2025 IEEE.

关键词： Convolutional neural networks

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Detection of Tomato Plant Disease Using Convolutional neural networks 3rd

Detection of Tomato Plant Disease Using Convolutional Neural...

引用

3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Bhoi, Dhaval Odedra, Ranjit Makadia, Priya Bhatt, Nikita Thakkar, Amit Gujarat Anand India Gujarat Anand India

ISBN: (纸本)9789819743582

Global crop production has been severely impacted by tomato plant diseases, which hinder optimal yields. Efficiently curbing the spread of these diseases and minimizing their detrimental effects requires early detection. Recent advancements in deep learning algorithms have demonstrated promising outcomes in the research related to plant disease identification. This research paper presents a novel deep learning technique tailored specifically for detecting illnesses in tomato plants. By leveraging convolutional neural networks (CNNs) and training them on a comprehensive dataset of tomato plant photos, the proposed model effectively distinguishes between healthy and unhealthy plants. The results obtained from extensive experiments conclusively demonstrate the remarkable accuracy of this approach in diagnosing a wide range of tomato plant disorders. Furthermore, the suggested method holds significant potential to work as a real-time monitoring system for timely tomato plant disease diagnosis, thereby mitigating the prevalence of such diseases and promoting healthier crop production. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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