The main goals of this study are to identify the early stages of lung cancer and investigate the degree of accuracy of various machinelearning algorithms. A thorough review of the literature revealed that certain cla...
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This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least ge...
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ISBN:
(数字)9783030974541
ISBN:
(纸本)9783030974541;9783030974534
This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least generalization can extract a common pattern of input data. We introduce three different methods for feature extraction based on symbolic manipulation. We perform experiments using the MNIst datasets and show that the proposed methods successfully capture features from training data and classify test data in around 90% accuracies. The results of this paper show potentials of induction and symbolic reasoning to feature learning or patternrecognition from raw data.
The article is devoted to the synthesis of image encoding methods based on the images data themselves. The proposed approach is based on a previously developed special representation of images by samples of counts (sa...
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ISBN:
(纸本)9783031245374;9783031245381
The article is devoted to the synthesis of image encoding methods based on the images data themselves. The proposed approach is based on a previously developed special representation of images by samples of counts (sampling representations). Since the sampling representations are essentially random constructions, the synthesis of encoding methods is carried out strictly within the framework of the generative paradigm. In essence, the approach proposed treats the image coding within generative model as a special case of the classical statistical problem of probability distribution density estimation. In the paper we restrict ourselves to the class of parametric estimation procedures, which imply some parametric family of probability distributions. Namely, we propose to use the model of a parametric mixture of simple distribution components. Accordingly, a set of component weights estimates calculated from a sampling representation, considering as input data, is interpreted as an encoded image - output data. In this context, optimal coding is synthesized with the maximum likelihood method. For the algorithmic implementation of the coding procedure the mixture model is equipped with the structure of receptive fields, that is a well-known organizing principal for receptors in the human eye retina. On this basis, we synthesize a relatively simple recurrent coding algorithm, which turned out to be close to the popular in machinelearning EM algorithm. The paper presents interpretation of several features of the algorithm from the point of view of well-known facts about the imageprocessing in the periphery of the visual system, discusses options for the algorithm implementation, and presents the results of numerical simulation of its operation.
Food monitoring has become an indispensable practice for personal health management in increasingly growing populations. To facilitate this process, advanced imageprocessing and AI technology have empowered automated...
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ISBN:
(纸本)9783031133213;9783031133206
Food monitoring has become an indispensable practice for personal health management in increasingly growing populations. To facilitate this process, advanced imageprocessing and AI technology have empowered automated recognition of food items and nutrients using food images taken by smart mobile devices. However, precision is often compromised for convenience, which is also applicable in food logging. In this study, we have explored new solutions that can help improve food recognition accuracy with a particular focus on domestic cooking, by leveraging advanced machinelearning and natural language processing techniques, in conjunction with comprehensive food nutrient profiles in the knowledge base, as well as contextual ingredient information parsed from publicly available recipes. The optimized models were proved to be effective and have been integrated into an Android app named "FoodInsight" .
The use of active learning in supervised machinelearning is proposed in this study to reduce the expenses associated with labeling data. Active learning is a technique that includes iteratively selecting the most inf...
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A facial emotion recognition framework is proposed in this work. The convolutional neural network (CNN) has high ability in extraction of hierarchical spatial features from low level texture characteristics to high le...
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The rapid growth of Information Technology (IT) as well as Computer Science becomes an important evolution in marketable painting applications. The systematic authentication of paintings embraces the implication into ...
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The proceedings contain 41 papers. The special focus in this conference is on machinelearning for Astrophysics. The topics include: Event Reconstruction for Neutrino Telescopes;classification of Evolved stars with (U...
ISBN:
(纸本)9783031341663
The proceedings contain 41 papers. The special focus in this conference is on machinelearning for Astrophysics. The topics include: Event Reconstruction for Neutrino Telescopes;classification of Evolved stars with (Unsupervised) machinelearning;patterns in the Chaos: An Unsupervised View of Galactic Supernova Remnants;Clustering of Galaxy Spectra: An Unsupervised Approach with Fisher-EM;unsupervised Classification Reveals New Evolutionary Pathways;in Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe;classifying Gamma-Ray Burst X-Ray Afterglows with a Variational Autoencoder;reconstructing Blended Galaxies with machinelearning;time Domain Astroinformatics;stellar Dating Using Chemical Clocks and Bayesian Inference;a Convolutional Neural Network to Characterise the Internal structure of stars;finding stellar Flares with Recurrent Deep Neural Networks;planetary Markers in stellar Spectra: Jupiter-Hoststar Classification;using Convolutional Neural Networks to Detect and Confirm Exoplanets;machinelearning Applied to X-Ray Spectra: Separating stars from Active Galactic Nuclei;Classification of System Variability Using a CNN;deep learningprocessing and Analysis of Mock Astrophysical Observations;deep Neural Networks for Source Detection in Radio Astronomical Maps;radio image Segmentation with Autoencoders;citizen Science and machinelearning: Towards a Robust Large-Scale Automatic Classification in Astronomy;detection of Quasi-Periodic Oscillations in Time Series of a Cataclysmic Variable Using Support Vector machine;background Estimation in Fermi Gamma-Ray Burst Monitor Lightcurves Through a Neural Network;machinelearning Investigations for LSst: strong Lens Mass Modeling and Photometric Redshift Estimation;multi-Band Photometry and Photometric Redshifts from Astronomical images;deep learning 21 cm Lightcones in 3D.
Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air....
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The proceedings contain 17 papers. The special focus in this conference is on Pan-African Intelligence and Smart Systems. The topics include: Hybridised Loss Functions for Improved Neural Network Generalisation;d...
ISBN:
(纸本)9783030933135
The proceedings contain 17 papers. The special focus in this conference is on Pan-African Intelligence and Smart Systems. The topics include: Hybridised Loss Functions for Improved Neural Network Generalisation;diverging Hybrid and Deep learning Models into Predicting students’ Performance in Smart learning Environments – A Review;combining Multi-Layer Perceptron and Local Binary patterns for Thermite Weld Defects Classification;an Elliptic Curve Biometric Based User Authentication Protocol for Smart Homes Using Smartphone;Efficient Subchannel and Power Allocation in Multi-cell Indoor VLC Systems;autonomic IoT: Towards Smart System Components with Cognitive IoT;study of Customer Sentiment Towards Smart Lockers;A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation;facial recognition Through Localized Siamese Convolutional Neural Networks;face recognition in Databases of images with Hidden Markov’s Models;Brain MRI Segmentation Using Autoencoders;effective Feature Selection for Improved Prediction of Heart Disease;Convolutional Neural Network Feature Extraction for EEG Signal Classification;race recognition Using Enhanced Local Binary pattern;detection and Classification of Coffee Plant Diseases by imageprocessing and machinelearning.
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