TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4
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Article Attitudes of Nursing Senior Students Towards the Use of Computers in Healthcare and Related Factors(Association of Executive Nurses, 2022) Söylemez, B.A.; Özgül, E.; Akyol, M.A.; Küçükgüçlü, Ö.Aim: This study was conducted to determine the attitudes of nursing senior students towards the use of computers in healthcare and related factors. Method: The descriptive and cross-sectional study was conducted with 162 senior nursing students in a faculty of nursing at a university between June and July 2021. Data were collected with the “Participant Information Form” and “Attitudes toward Computers in Healthcare Assessment Scale.” The SPSS 25.0 package program was used to evaluate the data. Socio-demographic data were given as numbers, mean, percentages, and standard deviation. Number, mean, percentage distributions, independent groups t-test, Mann Whitney-U test, One-way ANOVA test, and Pearson correlation test were used to analyze the data. Results: In this research, 67.9% of the 162 students were females, and the mean age was 22.43±1.50 years. The mean score of the students on the scale was 15.65±8.91. Status of owning a computer (t=2.729, p<0.01), frequency of computer usage (u=637.500, p<0.01), level of knowledge in using a computer (F=13.410, p<0.001), and status of computer use in nursing practices (t=4.244, p<0.001) were found to affect attitudes of nursing students towards the use of computers in healthcare. Conclusion: Senior nursing students were found to have a moderate attitude towards using computers in healthcare. Adopting more positive attitudes towards this area will increase the quality of nursing care and provide easier access to clinical data and charts. © 2022 The Authors.Article Citation - Scopus: 1Performance Analysis and Feature Selection for Network-Based Intrusion Detection With Deep Learning(Scientific Technical Research Council Turkey-Tubitak, 2021) Caner, Serhat; Erdogmus, Nesli; Erten, Y. MuratAn intrusion detection system is an automated monitoring tool that analyzes network traffic and detects malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion detection and classification performances of different deep learning based systems are examined. For this purpose, 24 deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore, the best performing model is utilized to inspect raw network traffic features and rank them with respect to their contributions to success rates. By selecting features with respect to their ranks, sets of varying size from 3 to 77 are assessed in terms of classification accuracy and time efficiency. The results show that recurrent neural networks with a certain level of complexity can achieve comparable success rates with state-of-the-art systems using a small feature set of size 9; while the average time required to classify a test sample is halved compared to the complete set.