In the present study, a desired reference trajectory was autonomously tracked by means of a quadrotor unmanned aerial vehicle with a self-tuning fuzzy proportional integral derivative controller. A proportional integral derivative controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize the quadrotor, to control the attitude and to track the trajectory. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of unmanned aerial vehicle and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which produce pitch, roll, yaw and height values. The fuzzy proportional integral derivative control algorithm is real-time applied to the quadrotor in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and self-tuning fuzzy proportional integral derivative controller have accomplished to track a defined trajectory with the aircraft, the self-tuning fuzzy proportional integral derivative controller has been able to control with less errors than the classical proportional integral derivative controller.

In this study, the factors influencing the cognitive load of computer programmers during the perception of different code tasks were investigated. The eye movement features of computer programmers were used to provide a significant relationship between the perceptual processes of the sample codes and cognitive load. Thanks to the relationship, the influence of various personal characteristics of programmers on cognitive load was examined. Various personal parameters such as programming experience, age, native language, and programming frequency were used in the study. The study was performed on the Eye Movements in Programming (EMIP) dataset containing 216 programmers with different characteristics. Eye movement information recorded during two different code comprehension tasks was decomposed into sub-information, such as pupil movement speed and diameter change. Rapid changes in eye movement signals were adaptively detected using the z-score peak detection algorithm. Regarding the cognitive load calculations, canonical correlation analysis was used to build a statistically significant and efficient mathematical model connecting the extracted eye movement features and the different parameters of the programmers, and the results were statistically significant. As a result of the analysis, the factors affecting the cognitive load of computer programmers for the related database were converted into percentages, and it was seen that linguistic distance is an essential factor in the cognitive load of programmers and the effect of gender on cognitive load is quite limited.

In honey production, high yields can be achieved with migratory beekeeping. Migratory beekeepers complete the honey production season by moving their colonies to areas with high nectar flow. Traditionally migratory beekeepers decide where to go based on their previous experience. Nowadays, given the rapidly changing climatic conditions, it is seen that the regions to be visited should be decided with more qualified information rather than experience. In this study, a web-based information system was developed which provides qualified information about the regions to be visited by migrant beekeepers. The information system takes into account the nectar flow and climatic conditions in the regions to be visited. These two important factors were evaluated using fuzzy cognitive maps (FCM) and an intelligent decision support system (IDSS) was developed. The cognitive map was analysed statistically in the conceptual modelling stage of FCM and it was found that it explained 82.3% of beekeeping potential according to multiple linear regression. Using IDSS, migratory beekeepers can learn the honey yield (good, average, and bad) they can obtain from the regions they will visit. The information system was also compared with the measurement results made with the wireless sensor network (WSN) and the migratory beekeeper information for 2017. As a result, it was found that IDSS operates with 79.8% accuracy.

In this study, studies conducted in the last 10 years in psychiatric disorders with virtual reality and augmented reality application were examined. Studies done so far; The psychiatric disorder studied, the equipment used, the number of people participating in the study, the database in which the study was scanned, and the results of the studies were evaluated according to the criteria. Studies using disease-specific questionnaires include 30% of all studies. In some studies, it is seen that physiological parameters are also used. Studies using physiological parameters correspond to only 10% of all studies. The practices carried out are to reduce the symptoms brought by the disease and to increase the quality of life by helping to eliminate the disease conditions of the people. Approximately 77% of the studies were carried out in the field of virtual reality and 23% in the field of augmented reality. Based on the articles examined within the scope of the study, some deficiencies in virtual reality and/or augmented reality applications in psychiatric diseases were identified. For example, whether the applications made have a disturbing effect on the users should be determined by using the SSQ questionnaire. In addition, the auditory parameters as well as the visual parameters that make up the virtual environment should be used effectively in this process. It has been observed that among the studies in the literature, the positive or negative effects of auditory parameters on users are not mentioned. It is considered that it will be beneficial for studies to examine the effect of auditory elements in the use of virtual reality environment.

Effective spam detection systems are essential in online social media networks (OSNs) and cybersecurity, and they directly influence the quality of decision-making pertaining to security. With today’s digital communications, unsolicited spam degrades user experiences and threatens platform security. Machine learning-based spam detection systems offer an automated defense. Despite their effectiveness, such methods are frequently hindered by the “black box” problem, an interpretability deficiency that constrains their deployment in security applications, which, in order to comprehend the rationale of classification processes, is crucial for efficient threat evaluation and response strategies. However, their effectiveness hinges on selecting an optimal feature subset. To address these issues, we propose a lightweight, explainable spam detection model that integrates a nature-inspired optimizer. The approach employs clean data with data preprocessing and feature selection using a swarm-based, nature-inspired meta-heuristic Rüppell’s Fox Optimization (RFO) algorithm. To the best of our knowledge, this is the first time the algorithm has been adapted to the field of cybersecurity. The resulting minimal feature set is used to train a supervised classifier that achieves high detection rates and accuracy with respect to spam accounts. For the interpretation of model predictions, Shapley values are computed and illustrated through swarm and summary charts. The proposed system was empirically assessed using two datasets, achieving accuracies of 99.10%, 98.77%, 96.57%, and 92.24% on Dataset 1 using RFO with DT, KNN, AdaBoost, and LR and 98.94%, 98.67%, 95.04%, and 94.52% on Dataset 2, respectively. The results validate the efficacy of the suggested approach, providing an accurate and understandable model for spam account identification. This study represents notable progress in the field, offering a thorough and dependable resolution for spam account detection issues.