Browsing by Subject "Machine learning"
Now showing items 1-19 of 19
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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 ... -
Classification of Human Movements by Using Kinect Sensor
(2022)In recent years, studies have been carried out to classify human movements in many areas such as health and safety. To classify human movements, image processing methods have also started to be used in recent years. With ... -
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 ... -
Effect of Polynomial, Radial Basis, and Pearson VII Function Kernels in Support Vector Machine Algorithm for Classification of Crayfish
(2022)Freshwater crayfish are one of the most important aquatic organisms that play a pivotal role in the aquatic food chain as well as serving as bioindicators for the aquatic ecosystem health assessment. Hemocytes, the blood ... -
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 ... -
A Machine Learning Based Approach to Detect Survival of Heart Failure Patients
(2020)One of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart's ... -
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 ... -
Multiclass Classification of Brain Cancer with Machine Learning Algorithms
(2020)Brain cancer is one the most important disease to be treated all around the world. Classification of brain cancer using machine learning techniques has been widely studied by researchers. Microarray gene expression data ... -
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 ... -
Optımızed weıghted ensemble classıfıer for ıntrusıon detectıon applıcatıon
(Başkent Üniversitesi Fen Bilimleri Enstitüsü, 2017)Computer and communication systems become the foundations of modern life. With the advances in the Internet, usage of these systems increases and intrusions against these systems increases too. Therefore, finding and ... -
Prediction of Frailty Grade Using Machine Learning Models
(2022)Nowadays, frailty is becoming a major issue for the aging population. Frailty grading is important for patient quality of life because it is a geriatric syndrome of decreased physiological reserve that leads to increased ... -
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 ...
