Listar por tema "Classification accuracy"
Mostrando ítems 1-17 de 17
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Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective
(2019)Osteoarthritis is the most common form of arthritis in the knee that comes with a variation in symptoms’ intensity, frequency and pattern. Knee OA (KOA) is often diagnosed using invasive and expensive methods that can ... -
Artificial Neural Networks and Principal Components Analysis for Detection of Idiopathic Pulmonary Fibrosis in Microscopy Images
(2013)In this study we present a computer assisted image identification and recognition tool that aims to help the diagnosis of idiopathic pulmonary fibrosis in microscopy images. To this end, we use principal components analysis ... -
Bagged nonlinear Hebbian learning algorithm for fuzzy cognitive maps working on classification tasks
(2012)Learning of fuzzy cognitive maps (FCMs) is one of the most useful characteristics which have a high impact on modeling and inference capabilities of them. The learning approaches for FCMs are concentrated on learning the ... -
Bone fracture identification in x-ray images using fuzzy wavelet features
(2019)The fracture detection process is difficult and requires specialized knowledge of the anatomical structures of the area under consideration. X-ray imaging provides images of the body's internal structures. Despite the rapid ... -
Classification of Driving Behaviour using Short-term and Long-term Summaries of Sensor Data
(2020)The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for adaptive driving assistance and car insurance industry. Starting from raw measurements of acceleration ... -
Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm
(2020)A common task in a world of natural language processing is text classification useful for e.g.spam filters, documents sorting, science articles classification or plagiarism detection. This can still be done best and most ... -
Deep sensorimotor learning for RGB-D object recognition
(2020)Research findings in cognitive neuroscience establish that humans, early on, develop their understanding of real-world objects by observing others interact with them or by performing active exploration and physical ... -
Effective products categorization with importance scores and morphological analysis of the titles
(2018)During the past few years, the e-commerce platforms and marketplaces have enriched their services with new features to improve their user experience and increase their profitability. Such features include relevant products ... -
Efficient Learning Rate Adaptation for Convolutional Neural Network Training
(2019)Convolutional Neural Networks (CNNs) have been established as substantial supervised methods for classification problems in many research fields. However, a large number of parameters have to be tuned to achieve high ... -
Hybrid Time-series Representation for the Classification of Driving Behaviour
(2020)The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for providing a personalized view, or 'fingertip', of each driver, useful in driving assistance and car ... -
Intelligent identification of biomarkers for the study of obstructive nephropathy
(2013)Obstructive Nephropathy (ON) is a renal disease, which pathological profile is the result of various, tightly coupled and co-regulated, molecular processes, pervading various layers of molecular dissection. In this context, ... -
A Machine Learning workflow for Diagnosis of Knee Osteoarthritis with a focus on post-hoc explainability
(2020)Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute ... -
A novel adaptive learning rate algorithm for convolutional neural network training
(2017)In this work an adaptive learning rate algorithm for Convolutional Neural Networks is presented. Harvesting already computed first order information of the gradient vectors of three consecutive iterations during the training ... -
Recognition of urban sound events using deep context-aware feature extractors and handcrafted features
(2019)This paper proposes a method for recognizing audio events in urban environments that combines handcrafted audio features with a deep learning architectural scheme (Convolutional Neural Networks, CNNs), which has been trained ... -
RNNs for Classification of Driving Behaviour
(2019)Recurrent neural networks are an obvious choice for driving behavior analysis by means of time series of measurements, obtained either from telematics or mobile phone sensors. This work investigates such an application, ... -
Structural analysis and classification of search interfaces for the deep web
(2018)The Web has been identified to consist of a large portion of content that cannot be crawled by general-purpose search engines because it is only generated after a valid submission to a search interface. Accessing such ... -
Tactile identification of embossed lines and square areas in diverse dot heights by blind individuals
(2021)Most of the braille embossers incorporate a mode for the creation of braille embossed graphics (BEG). Some of them can produce dots in different elevations, but also various dot densities. Dot elevation and dot density ...