Factors Influencing Burnout Syndrome and Depression in Dentists across Various Institutions.

Background and Objectives: This study aimed to identify the occupational and personal factors influencing burnout syndrome (BS) and depression among dentists in academic faculties, oral and dental health centres (ODHCs), and private clinics. Materials and Methods: This prospective, cross-sectional study was carried out on dentists working in different regions of Turkey. Data were gathered through an online questionnaire hosted on Google Forms. The questionnaire consisted of demographic data and Maslach BS Inventory (MBI) and Beck Depression Inventory (BDI) sections. The demographic data collected included age, height, weight, marital status, blood type, gender, monthly income, income satisfaction, and whether the participant had enough free time. The dentists were divided into three groups, namely, faculty setting, private clinic, and ODHC, according to the institutions at which they worked. Results: The study was composed of 290 dentists, including 172 males and 118 females, with an average age of 36.98 ± 5.56 years. In total, 128 of the dentists worked in faculties, 72 worked in private clinics, and 90 worked in ODHCs. The study found that women exhibited higher EE scores than men (p < 0.05). The comparison of BS and depression scores showed no statistically significant differences between groups based on marital status or blood type (p > 0.05). There was no significant relationship between emotional exhaustion (EE), depersonalisation (DP), personal accomplishment (PA), and depression scores according to age, BMI, and work experience (p < 0.05). It was found that the EE scores of the dentists working in faculties and private clinics were lower than those of the dentists working in ODHCs (p < 0.05). Monthly income was associated with depression (r = -0.35). Conclusions: The findings reveal that dentists employed in ODHCs reported greater levels of EE. These results suggest a pressing need for enhancements in the work environments of dentists, especially in ODHCs.

Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities.

Background: Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That's why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Methods: Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Results: Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values.

Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers.

Background: Android is the most widely used operating system all over the world. Due to its open nature, the Android operating system has become the target of malicious coders. Ensuring privacy and security is of great importance to Android users. Methods: In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed architecture combines the feature extraction power of the convolutional neural network (CNN) architecture and the decision making capability of fuzzy logic. Our method extracts features from permission information with a small number of filters and convolutional layers, and also makes the feature size suitable for ANFIS input. In addition, it allows the permission information to affect the classification without being neglected. In the study, malware was obtained from two different sources and two different data sets were created. In the first dataset, Drebin was used for malware applications, and in the second dataset, CICMalDroid 2020 dataset was used for malware applications. For benign applications, the Google Play Store environment was used. Results: With the proposed method, 92% accuracy in the first data set and 92% F-score value in the weighted average was achieved. In the second data set, an accuracy of 94.6% and an F-score of 94.6% on the weighted average were achieved. The results obtained in the study show that the proposed method outperforms both classical machine learning algorithms and fuzzy logic-based studies.