Alzheimer's Dementia (AD), one of the age-related neurological disorders, causes loss of cognitive functions and seriously affects the daily life of patients. Electroencephalogram (EEG) is one of the most frequently used clinical tools to investigate the effects of AD on the brain. In the proposed study, a time-frequency representation and deep feature extraction based model is introduced to distinguish EEG segments of control subjects and AD patients. TF representations of EEG segments are obtained using high-resolution SynchroSqueezing Transform (SST), and conventional short-time Fourier transform (STFT) methods. The magnitudes of SST and STFT are used for deep feature extraction. Various classifiers are used to classify the extracted features to distinguish the EEG segments of control subjects and AD patients. STFT based deep feature extraction approach yielded better classification results than that of the SST method.
In this study, it was aimed to design smart walking stick in purpose of detect obtracles that cause danger for blind people. The selected sensors and electronic components were programmable with Arduino. Ultrasonic distance sensor was used for detect obtracles in determined closest distance. A buzzer was used to provide audible alert when obstracles were detected via ultrasonic distance sensor. Additionaly, experimential studies were applied on GPRS sensor which is used for send locational information via SMS to caretaker of blind person when she or he lose the way.
The Auto Train Brain mobile app improves dyslexia symptoms, according to a clinical trial. There was just one unique neurofeedback user interface in the original mobile app, which gave visually and vocally rewarding feedback to the subject via a colorful arrow on the screen. Later, new modules are added to the app in response to end-user requests, such that users can choose from a variety of “youtube” videos and begin neurofeedback sessions with both visual and vocal rewards, or they can choose from a “storyteller” and begin neurofeedback sessions with only vocal rewards. In this study, we looked into whether the multimodal neurofeedback method is more effective in improving sample entropy in the gamma band in the left temporal region for children with dyslexia.
The aim of this study is to determine the success of different parameter optimization methods in a classification done by using a new Convolutional Neural network (CNN) proposed in this study on a new original dataset consisting of lung CT images collected by the researchers from ill/healthy people in order to diagnose COVID-19. To achieve this purpose, the researcher prepared a new data set involving computerized tomography images collected from Erzurum City Hospital Emergency Department for the diagnosis of COVID-19 after the necessary permissions were taken. This new data set consists of 1081 lung CT images, 568 of which belong to 313 people infected with COVID-19. The results obtained for the proposed CNN model through different optimization methods were compared. Precision, recall and f1-score parameters were used while evaluating the results. In our dataset, the highest performance values for the proposed CNN model were achieved by AdaMax optimizer as follows: %89.86 accuracy, %86.51, precision, %95.61 recall and %87.33 f1-score. The classification accuracy values achieved by other optimizers were %89.40 (RMSProp), %87.10 (Adam), %81.11 (SGD).
Pain is widely acknowledged to be a complicated experience. Pain is also a symptom of joint inflammation in arthritis, such as rheumatoid arthritis and osteoarthritis. Machine learning (ML) for classification uses nonparametric supervised learning techniques called decision trees. By gaining knowledge from decision rules created from the characteristics of the supplied data, they may be utilized to forecast the target variable. We applied a machine learning decision model to anticipate j oint pain (target variable) while considering clinical biochemistry. A total of 650 patients were included who visited orthopedic OPD with joint swelling or myalgia. Decision Tree was trained tested, and cross-validated with supervised learning. The model was evaluated along with the selected features/attributes (age, gender, uric acid, & CRP). 44% of patients were diagnosed with joint pain. The decision tree model yielded an accuracy of94% and a validation accuracy of 96%. Uric acid was strongly correlated with joint pain. Early ML-based joint pain identification will help avert more significant orthopedic issues.
