Graph neuralnetworks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph convolutional network (GCN),...
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On real-world applications like imageprocessing, speech reorganization, and signal processing, QNN have significantly outperformed real-valued neuralnetworks. This survey contains an overview of the most recent and ...
On real-world applications like imageprocessing, speech reorganization, and signal processing, QNN have significantly outperformed real-valued neuralnetworks. This survey contains an overview of the most recent and ongoing research on quaternion neuralnetworks and their applications in diverse disciplines. For each QVNN that has been proposed, the methods, algorithms, and applications described in the paper.
Deep image matting is a hot problem with applications in computer vision and imageprocessing. It has been widely used in image composition, film production and video editing etc. The current matting method based on i...
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Medicinal plants are a primary source of disease treatment in many countries. As most are edible however, consumption of the wrong herbal plants can have serious consequences and even lead to death. Automatic accurate...
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ISBN:
(纸本)9781665462198
Medicinal plants are a primary source of disease treatment in many countries. As most are edible however, consumption of the wrong herbal plants can have serious consequences and even lead to death. Automatic accurate recognition of plant species to help users who do not have specialist knowledge of herbal plants is thus a desirable aim. Several automatic medicinal plant identification systems have been proposed, though most are significantly constrained either in the small number of species or in requiring manual image segmentation of plant leaves. This means they are captured on a plain background rather than being readily identified in their natural surroundings, which often involve complex and noisy backgrounds. While deep learning (DL) based methods have made considerable strides in recent times, their potential has not always been maximised because they are trained with samples which are not always fully representative of the intra-class and interclass differences between the plant species concerned. This paper addresses this challenge by incorporating mutual information into a Convolutional neural Network (CNN) model to select samples for the training, validation, and testing sets based on a similarity measure. A critical comparative evaluation of this new CNN medicinal plant classification model incorporating a mutual information guided training (MIGT) algorithm for sample selection, corroborates the superior classification performance achieved for the VNPlant-200 dataset, with an average accuracy of more than 97%, while the precision and recall values are also consistently above 97%. This is significantly better than existing CNN classification methods for this dataset as it crucially means false positive rates are substantially lower thus affording improved identification reliability.
The proceedings contain 77 papers. The special focus in this conference is on neural Computing for Advanced applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Dia...
ISBN:
(纸本)9789811961410
The proceedings contain 77 papers. The special focus in this conference is on neural Computing for Advanced applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis;a Perception Method Based on Point Cloud processing in Autonomous Driving;many-Objective artificial Bee Colony Algorithm Based on Decomposition and Dimension Learning;LCSW: A Novel Indoor Localization System Based on CNN-SVM Model with WKNN in Wi-Fi Environments;large Parallax image Stitching via Structure Preservation and Multi-matching;traffic Congestion Event Mining Based on Trajectory Data;many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition;a New Unified Control Approach for Finite-/Fixed-Time Synchronisation of Multi-weighted Dynamical networks;aperiodic Sampling Based Event-Triggering for Synchronization of Nonlinear Systems;two-Stream 3D MobileNetV3 for Pedestrians Intent Prediction Based on Monocular Camera;Research on Non-intrusive Household Load Identification Method Applying LightGBM;design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm;an Ensemble Deep Learning Model Based on Transformers for Long Sequence Time-Series Forecasting;multi-layer Integrated Extreme Learning Machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker;wind Power Forecast Based on Multiple Echo States;UAV-Assisted Blind Area Pedestrian Detection via Terminal-Edge-Cloud Cooperation in VANETs;a Survey of Optimal Design of Antenna (Array) by Evolutionary Computing Methods;a Span-Based Joint Model for Measurable Quantitative Information Extraction;bayesian Optimization Based Seq2Seq Network Models for Real Estate Price Prediction in Hong Kong;ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects;Cage Mass Center Capture for Whirl Analysis Using an Improved MultiResUNet from the Multimodal Biomedical image Segmentation;master Multiple Real-Time Strategy Game
This paper presents the development and simulation of an analog artificialneural network (ANN) aimed at improving energy efficiency and processing speed for machine learning tasks. We detail the architectural design ...
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ISBN:
(数字)9798350350180
ISBN:
(纸本)9798350350197
This paper presents the development and simulation of an analog artificialneural network (ANN) aimed at improving energy efficiency and processing speed for machine learning tasks. We detail the architectural design of the ANN, encompassing input, hidden, and output layers, along with the incorporation of resistors and operational amplifiers as key components. Through hardware implementation, the ANN utilizes continuous voltage generators for input representation and resistance values for storing weights and biases. Our simulations demonstrate the model's accuracy and minimal error margins compared to expected outcomes. Additionally, we explore the implementation of activation functions such as ReLU and Sigmoid using specific components to further minimize energy consumption, showcasing the potential of analog ANNs in future machine learning hardware.
Integrating Graph neuralnetworks (GNNs) into hyperspectral (HS) image classification has led to impressive gains in accuracy. However, GNN models are complex, making it difficult to see exactly how they reach their c...
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ISBN:
(数字)9798350376876
ISBN:
(纸本)9798350376883
Integrating Graph neuralnetworks (GNNs) into hyperspectral (HS) image classification has led to impressive gains in accuracy. However, GNN models are complex, making it difficult to see exactly how they reach their conclusions. This study looks at how eXplainable artificial Intelligence (XAI) techniques can make GNN-based HS classification more transparent. Methods like Grad-CAM and Grad-CAM++ are applied to gain insight into the model’s decision-making process, while Saliency Maps and Integrated Gradients show the impact of individual HS bands on those choices. Testing on the popular Indian Pines HS dataset reveals that some spectral bands are particularly influential in guiding the model’s classifications. By improving model transparency, this work aims to increase trust in GNNs and support their broader use in remote sensing applications.
The proceedings contain 77 papers. The special focus in this conference is on neural Computing for Advanced applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Dia...
ISBN:
(纸本)9789811961342
The proceedings contain 77 papers. The special focus in this conference is on neural Computing for Advanced applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis;a Perception Method Based on Point Cloud processing in Autonomous Driving;many-Objective artificial Bee Colony Algorithm Based on Decomposition and Dimension Learning;LCSW: A Novel Indoor Localization System Based on CNN-SVM Model with WKNN in Wi-Fi Environments;large Parallax image Stitching via Structure Preservation and Multi-matching;traffic Congestion Event Mining Based on Trajectory Data;many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition;a New Unified Control Approach for Finite-/Fixed-Time Synchronisation of Multi-weighted Dynamical networks;aperiodic Sampling Based Event-Triggering for Synchronization of Nonlinear Systems;two-Stream 3D MobileNetV3 for Pedestrians Intent Prediction Based on Monocular Camera;Research on Non-intrusive Household Load Identification Method Applying LightGBM;design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm;an Ensemble Deep Learning Model Based on Transformers for Long Sequence Time-Series Forecasting;multi-layer Integrated Extreme Learning Machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker;wind Power Forecast Based on Multiple Echo States;UAV-Assisted Blind Area Pedestrian Detection via Terminal-Edge-Cloud Cooperation in VANETs;a Survey of Optimal Design of Antenna (Array) by Evolutionary Computing Methods;a Span-Based Joint Model for Measurable Quantitative Information Extraction;bayesian Optimization Based Seq2Seq Network Models for Real Estate Price Prediction in Hong Kong;ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects;Cage Mass Center Capture for Whirl Analysis Using an Improved MultiResUNet from the Multimodal Biomedical image Segmentation;master Multiple Real-Time Strategy Game
Convolutional neuralnetworks (CNN) have achieved state-of-the-art results in many Brain-Computer Interface (BCI) tasks, yet their applications in real-world scenarios and attempts at further optimizing them may be hi...
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This study suggests a novel approach for detecting and classifying plant leaf diseases using convolutional neuralnetworks (CNNs), which have shown promise in various image identification applications, including plant...
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