Πλοήγηση ανά Θέμα "machine learning"
Αποτελέσματα 1-20 από 44
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Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis
(2018)Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. ... -
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Artificial Intelligence in Cardiology—A Narrative Review of Current Status
(2022)Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical ... -
Big Data in Laboratory Medicine—FAIR Quality for AI?
(2022)Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as ... -
Biological properties of bee bread collected from apiaries located across Greece
(2021)Bee bread is the only fermented product of the beehive. It constitutes the main source of proteins, lipids, vitamins, and macro-and microelements in honeybee nutrition and it exerts antioxidant and antimicrobial properties, ... -
Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application
(2020)Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding ... -
Breast Cancer Classification on Multiparametric MRI – Increased Performance of Boosting Ensemble Methods
(2022)Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating ... -
Comparison of targeted and untargeted approaches in breath analysis for the discrimination of lung cancer from benign pulmonary diseases and healthy persons
(2021)The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases ... -
Data-driven machine-learning analysis of potential embolic sources in embolic stroke of undetermined source
(2021)Background and purpose: Hierarchical clustering, a common ‘unsupervised’ machine-learning algorithm, is advantageous for exploring potential underlying aetiology in particularly heterogeneous diseases. We investigated ... -
A deep learning approach for anthracnose infected trees classification in walnut orchards
(2021)This paper presents a novel approach for the detection of disease-infected leaves on trees with the use of deep learning. Focus of this study was to build an accurate and fast object detection system that can identify ... -
Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters
(2019)Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study ... -
DINOSARC: Color Features Based on Selective Aggregation of Chromatic Image Components for Wireless Capsule Endoscopy
(2018)Wireless Capsule Endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software ... -
Efficient and targeted COVID-19 border testing via reinforcement learning
(2021)Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all ... -
Evaluation of a Numerical, Real-Time Ultrasound Imaging Model for the Prediction of Litter Size in Pregnant Sows, with Machine Learning
(2022)The present study aimed to evaluate the accuracy of a numerical model, quantifying real-time ultrasonographic (RTU) images of pregnant sows, to predict litter size. The time of the test with the least error was also ... -
Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology
(2022)Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA’s multifactorial nature and the poor understanding of its pathophysiology, there is a ... -
An explainable machine learning model for material backorder prediction in inventory management
(2021)Global competition among businesses imposes a more effective and low-cost supply chain allowing firms to provide products at a desired quality, quantity, and time, with lower production costs. The latter include holding ... -
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
(2022)Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge ... -
Grapevine wood microbiome analysis identifies key fungal pathogens and potential interactions with the bacterial community implicated in grapevine trunk disease appearance
(2021)Background: Grapevine trunk diseases (GTDs) is a disease complex caused by wood pathogenic fungi belonging to genera like Phaeomoniella, Phaeoacremonium, Fomitiporia, Eutypa and members of the family Botryosphaeriaceae. ... -
Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients
(2021)Objective: Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed ... -
Interplay between oxidative damage, the redox status, and metabolic biomarkers during long-term fasting
(2020)Obesity and its related metabolic disorders, as well as infectious diseases like covid-19, are important health risks nowadays. It was recently documented that long-term fasting improves metabolic health and enhanced the ...