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Toplam kayıt 16, listelenen: 1-16
A Deep LSTM Approach for Activity Recognition
(2019)
Since 1990s, activity recognition effectual field in machine learning literature. Most of studies that relevant activity recognition, use feature extraction method to achieve higher classification performance. Moreover, ...
Context-Sensitive Model Learning for Lung Nodule Detection
(2016)
Nodule detection in chest radiographs is a main component of current Computer Aided Diagnosis (CAD) systems. The problem is usually approached as a supervised classification task of candidate nodule segments. To this end, ...
Development of a MFCC-SVM Based Turkish Speech Recognition System
(2016)
In this study, a SVM-MFCC based Turkish Speech Recognition system is devoloped. In the structure, Mel Frequency Cepstral Coefficients (MFCC) are used for feature extraction and Support Vector Machines(SVM) are used for ...
Using Machine Learning Methods in Early Diagnosis of Breast Cancer
(2021)
Breast cancer is one of the most important health diseases to be treated in the world, and it is a subject that has a wide place in research subjects. In this study, results are provided by using seven different machine ...
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 ...
Classification of Different Objects with Artificial Neural Networks Using Electronic Nose
(2015)
In this paper; an e-nose with low cost which consisting of 8 different gas sensors was used and with this e-nose 9 different odors ((mint, lemon, egg, rotten egg, angelica root, nail polish, naphthalene, rose water, and ...
Wi-Fi Based Indoor Positioning System with Using Deep Neural Network
(2020)
Indoor positioning is one of the major challenges for the future large-scale technologies. Nowadays, it has become an attractive research subject due to growing demands on it. Several algorithms and techniques have been ...
Obstructive Sleep Apnea Classification with Artificial Neural Network Based On Two Synchronic Hrv Series
(2015)
In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) ...
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 ...
Evaluation of divided attention using different stimulation models in event-related potentials
(2019)
Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today's society. Default examinations for understanding attention are questionnaires or physiological ...
Deep neural network to differentiate brain activity between patients with euthymic bipolar disorders and healthy controls during verbal fluency performance: A multichannel near-infrared spectroscopy study
(2022)
In this study, we aimed to differentiate between euthymic bipolar disorder (BD) patients and healthy controls (HC) based on frontal activity measured by fNIRS that were converted to spectrograms with Convolutional Neural ...
Applications of Deep Learning Techniques to Wood Anomaly Detection
(2022)
Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. ...
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. ...
Obesity Level Estimation based on Machine Learning Methods and Artificial Neural Networks
(2021)
Obesity is a growing societal and public health problem starting from 1980 that needs more attention. For this reason, new studies are emerging day by day, including those looking for obesity in children, especially the ...
Investigation of some machine learning algorithms in fish age classification
(2021)
Marine and freshwater scientists use fish scales, vertebrae, otoliths and length-weights values to estimate fish age because reliable fish age estimation plays a very important role in fish stock management. The advances ...