Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method.

Images and videos containing fake faces are the most common type of digital manipulation. Such content can lead to negative consequences by spreading false information. The use of machine learning algorithms to produce fake face images has made it challenging to distinguish between genuine and fake content.

A Novel Approach to Skin Lesion Segmentation: Multipath Fusion Model with Fusion Loss.

Segmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic systems using deep convolutional neural networks are gaining importance in order to cope with difficulties.

An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning.

Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur.

The adaptive ARMA analysis of EMG signals.

In this study, Adaptive auto regressive-moving average (A-ARMA) analysis of EMG signals recorded on the ulnar nerve region of the right hand in resting position was performed. A-ARMA method, especially in the calculation of the spectrums of stationary signals, is used for frequency analysis of signals, which give frequency response as sharp peaks and valleys. In this study, as the result of A-ARMA method analysis of EMG signals frequency-time domain, frequency spectrum curves (histogram curves) were obtained.

Prediction of minor head injured patients using logistic regression and MLP neural network.

In this study it is aimed to assess the posttraumatic cerebral hemodynamia in minor head injured patients. Eighty patients with minor head injury (Group 1) evaluated in the early 8 h of posttraumatic period between July 2003 and February 2004. The control group (Group 2) has composed of 32 healthy people.

Examination of the effects of degeneration on vertebral artery by using neural network in cases with cervical spondylosis.

The scope of this study is to diagnose vertebral arterial inefficiency by using Doppler measurements from both right and left vertebral arterials. Total of 96 patients' Doppler measurements, consisting of 42 of healthy, 30 of spondylosis, and 24 of clinically proven vertebrobasillary insufficiency (VBI), were examined. Patients' age and sex information as well as RPSN, RPSVN, LPSN, LPSVN, and TOTALVOL medical parameters obtained from vertebral arterials were classified by neural networks, and the performance of said classification reached up to 93.

The examination of the effects of obesity on a number of arteries and body mass index by using expert systems.

In this study, the areas affected from obesity were examined by classifying divergent arteries and body mass index (BMI) of 30 healthy persons and 52 obese persons by using expert systems, and the classifying performances of NEFCLASS and CANFIS, which are expert systems were compared. As a result of this comparison, it is observed that the classifying performance of NEFCLASS is better than that of CANFIS, and the causes of this are examined. Furthermore, it is observed that after these classifications, obesity affects the BMI rather than divergent arteries.

Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm.

Doppler signals, recorded from the output of tricuspid, mitral, and aorta valves of 60 patients, were transferred to a personal computer via 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at highly turbulent blood flows, it sometimes leads to wrong interpretation of cardiac Doppler signals.