vakkas dogan

Cerebrovascular disease (CVD) causes paralysis and even mortality in humans due to blockage or bleeding of brain vessels. The early diagnosis of the CVD type by the specialist can avoid these casualties with a correct course of treatment. However, it is not always possible to recruit enough specialists in hospitals or emergency services. Therefore, in this study, an artificial intelligence (AI)-based clinical decision support system for CVD detection from brain computed tomography (CT) images is proposed to improve the diagnostic results and relieve the burden of specialists. The deep learning model, a subset of AI, was implemented through a two-step process in which CVD is first detected and then classified as ischemic or hemorrhagic. Moreover, the developed system is integrated into our custom-designed desktop application that offers a user-friendly interface for CVD diagnosis. Experimental results prove that our system has great potential to improve early diagnosis and treatment ...

The objective of this study was to develop novel colorimetric films for food freshness monitoring. UV light irradiation (365 nm) and carbon dots (CDs) were tested as the potential crosslinkers in the fabrication of anthocyanins doped fish gelatin (FG) films. The effect of crosslinkers on the optical, surface, structural, barrier and mechanical properties of FG films was investigated. The incorporation of CD under UV irradiation improved the tested properties of FG films. The kinetic colorimetric responses of FG films against ammonia vaporwere studied to simulate the food spoilage and determine the ammonia sensitivity of the films. Among the tested films, UV-treated FG films containing 100 mg/l (FG-UV-CD100) indicated the best properties. Later, the color difference of FG-UV-CD100 films was observed to correlate well with microbial growth and TVB-N release in skinless chicken breast samples. At the same time, a custom-designed smartphone application (SmartFood) was also developed to be used with the FG-UV-CD100 film for quantitative estimation of food freshness in real-time. The proposed food freshness monitoring platform reveals a great potential to minimize global food waste and the outbreak of foodborne illness.

Diabetes is a chronic disease that requires lifelong treatment to keep blood sugar at a normal level. Hyperglycemia (high blood sugar) and hypoglycemia (low blood sugar) are critical blood glucose levels that should be monitored during the treatment. Alerting the patient when the blood glucose is at critical levels may minimize possible complications that may occur. Therefore, it was aimed to classify critical blood glucose levels with machine learning algorithms in this study. The performance of the classifiers has been tested with synthetic and real data. Synthetic data were created by adding noise to the sinusoidal wave while real data were obtained from diabetic patients. Features were extracted using the time series analysis method as the data is time-dependent. Machine learning algorithms were trained with these extracted features and blood glucose was classified in 5 levels (hypoglycemia, pre-hypoglycemia, normal, pre-hyperglycemia and hyperglycemia) with 95.12% accuracy.