Deep learning is having a particularly revolutionary effect in the field of artificial intelligence, namely in computer vision, where machines are equipped with the capacity to comprehend and evaluate visual informati...
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In this paper reviews recent advancements in deep learning-based remote sensing imageprocessing, focusing on image registration, classification, and segmentation. Remote sensing faces challenges due to geometric, rad...
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image restoration, a critical task in computer vision and imageprocessing, focuses on recovering degraded or damaged images to their original, high-quality state. This paper introduces an innovative approach to image...
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Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of Fourier space (k-space). Existing deep learning-based CSMRI methods are commonly concerned with opti...
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ISBN:
(纸本)9798350349405;9798350349399
Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of Fourier space (k-space). Existing deep learning-based CSMRI methods are commonly concerned with optimizing datadriven network models with input undersampled data points and an efficient learning framework. Generative modelling is a learning framework employed in different applications for learning an abstract distribution of observed data and thereby generating new data points similar to the true features. In this regard, the current work proposes a Generative Adversarial Network (GAN) based Cross Domain Extrapolation Generative Adversarial Network (CdE-GAN) that incorporates an extrapolation mechanism through decoder-type architectural design to represent the fine details with a large set of pixels. The results obtained from the experiments show that the extrapolation network enables robust and accurate estimation of missing frequencies, alleviating the structural artifacts at higher acceleration/downsampling factors compared to state-of-the-art methods.
Convolutional neuralnetworks (CNNs) are important for many machinelearning tasks. They are built with different types of layers: convolutional layers that detect features, dropout layers that help to avoid over-reli...
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The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fau...
ISBN:
(纸本)9783031770777
The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and machinelearning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using machinelearning and imageprocessing;Leveraging the Power of MRMR in machinelearning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum machinelearning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through imageprocessing and Deep learning Approaches;deep learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.
Technological advancements have significantly contributed to the creation and processing of digital images through various image manipulation software available today. Consequently, the need for effective image forger...
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Prototypical part neuralnetworks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machinelearning. Their prototype learning scheme enables intuitive explanations o...
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ISBN:
(纸本)9798350318920;9798350318937
Prototypical part neuralnetworks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machinelearning. Their prototype learning scheme enables intuitive explanations of the form, this (prototype) looks like that (testing image patch). But, does this actually look like that? In this work, we delve into why object part localization and associated heat maps in past work are misleading. Rather than localizing to object parts, existing ProtoPartNNs localize to the entire image, contrary to generated explanatory visualizations. We argue that detraction from these underlying issues is due to the alluring nature of visualizations and an over-reliance on intuition. To alleviate these issues, we devise new receptive field-based architectural constraints for meaningful localization and a principled pixel space mapping for ProtoPartNNs. To improve interpretability, we propose additional architectural improvements, including a simplified classification head. We also make additional corrections to PROTOPNET and its derivatives, such as the use of a validation set, rather than a test set, to evaluate generalization during training. Our approach, PixPNET (Pixel-grounded Prototypical part Network), is the only ProtoPartNN that truly learns and localizes to prototypical object parts. We demonstrate that PixPNET achieves quantifiably improved interpretability without sacrificing accuracy(1).
The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fau...
ISBN:
(纸本)9783031770807
The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and machinelearning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using machinelearning and imageprocessing;Leveraging the Power of MRMR in machinelearning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum machinelearning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through imageprocessing and Deep learning Approaches;deep learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.
Food image classification is essential for numerous applications, like nutritional analysis, food recording, and dietary evaluation. The proliferation of social media and smartphones generated a significant increase i...
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ISBN:
(纸本)9798350373301;9798350373295
Food image classification is essential for numerous applications, like nutritional analysis, food recording, and dietary evaluation. The proliferation of social media and smartphones generated a significant increase in the sharing of food photographs online, necessitating automated systems capable of accurately classifying food items in images. Convolutional neuralnetworks (CNNs), particularly the MobileNetV2 architecture, have emerged as extremely strong machinery for identifying image tasks because of its capacity to extract unique characteristics directly from raw pixel data. This work focuses on utilizing CNNs and the MobileNetV2 architecture for deep learning-based automatic food image classification. MobileNetV2 strikes a balance between classification accuracy and processing efficiency, making it suitable for mobile and embedded vision applications. The study addresses important challenges in food image classification, including intra-class variances, cluttered backgrounds, and disparities in food appearance. The objective aims to set up a reliable and effective system which can determine food products in various visual scenarios by leveraging state-of-the-art deep learning techniques.
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