Konu "Machine learning" için Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed listeleme
Toplam kayıt 13, listelenen: 1-13
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Automated Temporal Lobe Epilepsy And Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel EEG Recordings Using DWT Based Analysis
(2022)Psychogenic nonepileptic seizure (PNES) and epileptic seizure resemble each other, behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus patients suffering from PNES may be treated with ... -
Characterization of Responder Profiles for Cardiac Resynchronization Therapy through Unsupervised Clustering of Clinical and Strain Data
(2021)Background: The mechanisms of improvement of left ventricular (LV) function with cardiac resynchronization therapy (CRT) are not yet elucidated. The aim of this study was to characterize CRT responder profiles through ... -
Comparison of Different Machine Learning Approaches to Detect Femoral Neck Fractures in X-Ray Images
(2021)Femoral neck fractures are a serious health problem, especially in the elderly population. Misdiagnosis leads to improper treatment and adversely affects the quality of life of the patients. On the other hand, when looking ... -
Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests
(2021)Objectives The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. Methods We ... -
Detection of multiple sclerosis from photic stimulation EEG signals
(2021)Background: Multiple Sclerosis (MS) is characterized as a chronic, autoimmune and inflammatory disease of the central nervous system. Early diagnosis of MS is of great importance for the treatment and course of the disease. ... -
Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features
(2020)The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event ... -
Feature selection and multiple classifier fusion using genetic algorithms in intrusion detection systems
(2018)With the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection ... -
Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach
(2021)Background: Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the ... -
Machine learning-enabled healthcare information systems in view of Industrial Information Integration Engineering
(2022)Recent studies on Machine learning (ML) and its industrial applications report that ML-enabled systems may be at a high risk of failure or they can easily fall short of business objectives. Cutting-edge developments in ... -
A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study
(2020)AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained ... -
A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection
(2021)Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment ... -
Saldırı tespit sistemlerinde genetik algoritma kullanarak nitelik seçimi ve çoklu sınıflandırıcı füzyonu
(2018)With the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection ... -
Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study
(2022)AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation ...