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[Lecture Notes in Computer Science] Advances in Soft Computing Volume 10632 (16th Mexican International Conference on Artificial Intelligence, MICAI 2017, Enseneda, Mexico, October 23-28, 2017, Proceedings, Part I) || A Survey of Machine Learning Approaches for Age Related Macular Degeneration Diagnosis and Prediction
摘要： Age Related Macular Degeneration (AMD) is a complex disease caused by the interaction of multiple genes and environmental factors. AMD is the leading cause of visual dysfunction and blindness in developed countries, and a rising cause in underdeveloped countries. Currently, retinal images are studied in order to identify drusen in the retina. The classification of these images allows to support the medical diagnosis. Likewise, genetic variants and risk factors are studied in order to make predictive studies of the disease, which are carried out with the support of statistical tools and, recently, with Machine Learning (ML) methods. In this paper, we present a survey of studies performed in complex diseases under both approaches, especially for the case of AMD. We emphasize the approach based on the genetic variants of individuals, as it is a support tool for the prevention of AMD. According to the vision of personalized medicine, disease prevention is a priority to improve the quality of life of people and their families, as well as to avoid the inherent health burden.
关键词： Predictive diagnosis,Machine Learning,Classification,Automated diagnosis,Pattern recognition,AMD
更新于2025-09-23 15:23:52
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors
摘要： The detection of gravitational waves opened a new window on the cosmos. The Advanced LIGO and Advanced Virgo interferometers will probe a larger volume of Universe and discover new gravitational wave emitters. Characterizing these detectors is of primary importance in order to recognize the main sources of noise and optimize the sensitivity of the searches. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. In this paper we present a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm. Although the results show the accuracy is lower than that obtained with the use of deep learning, this method which extracts features while detecting signals in real time, can be configured as a fast classification system.
关键词： machine learning classification,signal processing,wavelet decomposition
更新于2025-09-23 15:22:29
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Effective Raman spectra identification with tree-based methods
摘要： Treatment of spectral information is an essential tool for the examination of various cultural heritage materials. Raman spectroscopy has become an everyday practice for compound identification due to its non-intrusive nature, but often it can be a complex operation. Spectral identification and analysis on artists’ materials is being done with the aid of already existing spectral databases and spectrum matching algorithms. We demonstrate that with a machine learning method called Extremely Randomised Trees, we can learn a model in a supervised learning fashion, able to accurately match an entire-spectrum range into its respective mineral. Our approach was tested and was found to outperform the state-of-the-art methods on the corrected RRUFF dataset, while maintaining low computational complexity and inherently supporting parallelisation.
关键词： Randomised trees,Random forest,Mineral identification,Raman spectroscopy,Machine learning,Classification,Raman spectra identification
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - Lviv, Ukraine (2018.9.11-2018.9.14)] 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - Machine-Learning Identification of Extragalactic Objects in the Optical-Infrared All-Sky Surveys
摘要： We present new fully-automatic classification model to select extragalactic objects within astronomical photometric catalogs. Construction of the our classification model is based on the three important procedures: 1) data representation to create feature space; 2) building hypersurface in feature space to limit range of features (outliers detection); 3) building hyperplane separating extragalactic objects from the galactic ones. We trained our model with 1.7 million objects (1.4 million galaxies and quasars, 0.3 million stars). The application of the model is presented as a photometric catalog of 38 million extragalactic objects, identified in the WISE and Pan-STARRS catalogs cross-matched with each other.
关键词： machine learning,classification,data mining,support vector machines,neural networks
更新于2025-09-09 09:28:46
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Feature-based Classification of Protein Networks using Confocal Microscopy Imaging and Machine Learning
摘要： Fluorescence imaging has become a powerful tool to investigate complex subcellular structures such as cytoskeletal filaments. Advanced microscopes generate 3D imaging data at high resolution, yet tools for quantification of the complex geometrical patterns are largely missing. Here we present a computational framework to classify protein network structures. We developed a machine-learning method that combines state-of-the-art morphological quantification with protein network classification through morphologically distinct structural features enabling live imaging–based screening. We demonstrate applicability in a confocal laser scanning microscopy (CLSM) study differentiating protein networks of the FtsZ (filamentous temperature sensitive Z) family inside plant organelles (Physcomitrella patens).
关键词： FtsZ,machine learning,classification,protein networks,confocal microscopy
更新于2025-09-04 15:30:14