Identifying of Quercus vulcanica and Q. frainetto growing in different environments through deep learning analysis.

Quercus is one of the important elements of forests worldwide. But the diagnosis of the species in this genus in particular using leaves is pretty challenging due to the presence of natural hybrids and phenotypically plastic trait expression. In this sense, this study aims to classify the leaves of Q. vulcanica and Q. frainetto using convolutional neural networks, VGG16 and VGG19, and Xception deep learning architectures to determine which method has the best performance in species assignment. For this purpose, leaf samples were collected from a total of 300 trees of 6 natural populations using a total of 1459 leaf images, 491 from Q. frainetto and 968 from Q. vulcanica. Before exporting images to the deep learning model, RGB/gray filters are applied and images are optimized with contrast limited adaptive histogram equalization to achieve maximum performance in the deep learning model. Accuracy rates of deep learning architectures varied from 79% (Xception) to 95% (VGG16). The VGG16 deep learning model provided superior performance compared to the others. Developing a mobile device using images from natural populations of many oak species will be beneficial not only for practitioners but also for scientists and local people.

A low-cost mobile adaptive tracking system for chronic pulmonary patients in home environment.

OBJECTIVE: The main objective of this study is presenting a real-time mobile adaptive tracking system for patients diagnosed with diseases such as asthma or chronic obstructive pulmonary disease and application results at home. The main role of the system is to support and track chronic pulmonary patients in real time who are comfortable in their home environment. It is not intended to replace the doctor, regular treatment, and diagnosis. MATERIALS AND METHODS: In this study, the Java 2 micro edition-based system is integrated with portable spirometry, smartphone, extensible markup language-based Web services, Web server, and Web pages for visualizing pulmonary function test results. The Bluetooth(®) (Bluetooth SIG, Kirkland, WA) virtual serial port protocol is used to obtain the test results from spirometry. General packet radio service, wireless local area network, or third-generation-based wireless networks are used to send the test results from a smartphone to the remote database. The system provides real-time classification of test results with the back propagation artificial neural network algorithm on a mobile smartphone. It also provides the generation of appropriate short message service-based notification and sending of all data to the Web server. In this study, the test results of 486 patients, obtained from Atatürk Chest Diseases and Thoracic Surgery Training and Research Hospital in Ankara, Turkey, are used as the training and test set in the algorithm. RESULTS: The algorithm has 98.7% accuracy, 97.83% specificity, 97.63% sensitivity, and 0.946 correlation values. The results show that the system is cheap (900 Euros) and reliable. CONCLUSIONS: The developed real-time system provides improvement in classification accuracy and facilitates tracking of chronic pulmonary patients.

Pulse oximeter based mobile biotelemetry application.

Quality and features of tele-homecare are improved by information and communication technologies. In this context, a pulse oximeter-based mobile biotelemetry application is developed. With this application, patients can measure own oxygen saturation and heart rate through Bluetooth pulse oximeter at home. Bluetooth virtual serial port protocol is used to send the test results from pulse oximeter to the smart phone. These data are converted into XML type and transmitted to remote web server database via smart phone. In transmission of data, GPRS, WLAN or 3G can be used. The rule based algorithm is used in the decision making process. By default, the threshold value of oxygen saturation is 80; the heart rate threshold values are 40 and 150 respectively. If the patient's heart rate is out of the threshold values or the oxygen saturation is below the threshold value, an emergency SMS is sent to the doctor. By this way, the directing of an ambulance to the patient can be performed by doctor. The doctor for different patients can change these threshold values. The conversion of the result of the evaluated data to SMS XML template is done on the web server. Another important component of the application is web-based monitoring of pulse oximeter data. The web page provides access to of all patient data, so the doctors can follow their patients and send e-mail related to the evaluation of the disease. In addition, patients can follow own data on this page. Eight patients have become part of the procedure. It is believed that developed application will facilitate pulse oximeter-based measurement from anywhere and at anytime.