Now showing items 31-40 of 76
Towards the classification of heart sounds based on convolutional deep neural network
Background and objective Heart sound contains various important quantities that help early detection of heart diseases. Many methods have been proposed so far where various signal-processing techniques have been used on ...
A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation
In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means ...
A Hybrid SCA Inspired BBO for Feature Selection Problems
(Hindawi Ltd, 2019)
Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method ...
Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques
It is extremely significant to identify sleep stages accurately in the diagnosis of obstructive sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a practical and applicable method. For this ...
KNCM: Kernel Neutrosophic c-Means Clustering
Data clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as 'neutrosophic c-means' ...
Freezing of Gait (FoG) Detection using Logistic Regression in Parkinson's disease from Acceleration signals
The detection and diagnosis of Parkinson disease (PD) are very important concerning the treatment of this disease. In this work, the freezing of gait (FoG) from subjects with Parkinson disease has been detected by the ...
A Hybrid Approach to Parkinson Disease Classification using speech signal: The combination of SMOTE and Random Forests
In this study, a novel method is proposed for the detection of Parkinson's disease with the features obtained from the speech signals. Detection and early diagnosis of Parkinson's disease are essential in terms of disease ...
A Novel ML Approach to Prediction of Breast Cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier<bold> </bold>
Breast cancer is the second most common cancer in our country and in the world. In this study, a breast cancer data set was formed from the findings obtained from experiments conducted in the city of Coimbra of Portugal. ...
Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models
Disc abnormalities cause a great number of complaints including lower back pain. Lower back pain is one of the most common types of pain in the world. The computer-assisted detection of this ailment will be of great use ...
Guest editorial: New trends in data pre-processing methods for signal and image classification
[No Abstract Available]