Recently, with the widespread use of the internet, electronic communication tools have also been widely used. One of these tools is emails. Emails are easy to use and provide the opportunity to reach thousands of people at the same time. This advantage causes some bad uses. Email users are faced with dozens of unsolicited emails (spam) against their will. In this study, 1017 mails collected from about 20 different Gmail and Hotmail accounts were classified as spam or regular email using the algorithms in the Weka program, and the success of the algorithms was compared. In the study, 45 different algorithms were tested. The highest classification success was obtained with the Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms with 94.7886% correct classification. Among other classifier algorithms, Random Forest algorithm 93.6087%, Multi-Class Classifier and SGD 92.4287%, SMO 91.7404%, Random Committee 91.0521%, Naive Bayes and Naive Bayes Updateable 90.3638% classification success.

In recent years, studies on the elements used in producing electronic device components and the interaction of their different compounds with radiation have been emphasized. In developing this situation, giving importance to space studies and other searches in energy production have been very effective. In the light of these developments, the interaction of tin, which is widely used in producing electronic device components and different industrial areas, with radiation has been investigated. For this purpose, the variation of albedo factor values in some compounds of the tin element was analysed and presented. Am-241 radioactive sources were used as incident radiation in determining the albedo factor values mentioned in the study. The albedo factor values obtained by examining the Compton and coherent scattering peaks were used to determine the albedo factor values.

The Coronavirus (COVID-19) epidemic emerged in China and has caused many problems such as loss of life, and deterioration of the social and economic structure. Thus, understanding and predicting the course of the epidemic is very important. In this study, the SEIR model and machine learning methods LSTM and SVM were used to predict the values of Susceptible, Exposed, Infected, and Recovered for COVID-19. For this purpose, COVID-19 data from Egypt and South Korea provided by John Hopkins University were used. The results of the methods were compared by using MAPE. A total of 79% of MAPE were between 0 and 10. The comparisons show that although LSTM provided better results, the results of all three methods were successful in predicting the number of cases, the number of patients who died, and the peaks and dimensions of the epidemic.

This paper aims to investigate the performances of a semi-active tuned mass damper (STMD) used to reduce the vibrations of buildings under different seismic excitation by the real-time hybrid simulation (RTHS) method. In the STMD, the MR damper is used as a control element with a variable damping feature. The RTHS method is an alternative to experimentally studying the STMD system. MR damper is critically significant for the system and is experimentally installed. At the same time, the other parts are designed in numerical simulation and tested simultaneously. MR damper is a control element whose damping value can change according to the amount of voltage transmitted. Therefore, the groundhook control method determines the MR damper voltage variations. The results show that the control method applied to MR damper-controlled STMD effectively suppresses structural vibrations.

Due to the nature of hospital emergency services, density cannot be easily estimated. It is one of the important issues that should be planned for emergency service managers to have sufficient resources continuously in services that develop suddenly, and emergency interventions are made for human life. Effective and efficient management and planning of limited resources are important not only for hospital administrators but also for people who will receive service from emergency services. In this situation, estimating the number of people who will request service in the emergency service with the least error is of great importance in terms of resource management and the operations carried out in the emergency services. The density of patients coming to the emergency department may vary according to the season, special dates, and even time zones during the day. The aim of the study is to show that more successful results will be obtained because of processing the time series by considering the country and area-specific features instead of the traditional approach. In this paper, the patient admission dataset of the public hospital emergency service in Turkey was used. Data cleaning and arranging operations were carried out by considering the official and religious special days of Turkey and the time periods during the day. The data set is first handled holistically, and its performances are measured by making predictions with the LSTM (Long Short Term Memory) model. Then, to examine the effect of time zones, performance values were calculated separately by dividing each day into 3 equal time zones. Finally, to investigate the effect of triage areas on the total density, the model performance was measured by dividing the data forming each time zone into 3 different triage areas in 3 equal time periods. Three stages were applied both on the raw data set and on the data created by extracting the official, religious holidays, and weekend data specific to Turkey. According to the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) results, more successful results are obtained thanks to the cleaning and editing processes. Thanks to the study, it is thought that the data sets used for demand forecasting studies in the health sector will produce results closer to reality by determining and standardizing the purification criteria in this way.