The present study aimed to determine the effects of arduino-assisted robotics coding applications integrated into the 5E learning model used in science teaching on students' scientific creativity, robotics attitude and motivation toward science. For this aim, the study was planned according to the convergent parallel mixed research method and was conducted with the 6th grade students in a STEM (Science, Technology, Engineering and Mathematics) elective course in 2018–2019 academic year. Scientific creativity scale, robotics attitude scale and motivation scale toward science learning were used as quantitative data collection tools, and semi-structured interview form was used as a qualitative data collection tool. As a result of the study, it was found that the levels of students' creativity, attitude and motivation increased with the robotics coding activities integrated into 5E learning model applied in science subjects. In addition, it was determined that students produced many creative ideas in using robotics coding applications to solve various problems encountered in daily life, and that they were very eager for such applications being used in science classes. In this respect, it is suggested that arduino-assisted robotics coding applications should be implemented in the teaching of 6th grade science subjects.

In wireless sensor networks (WSN), flooding increases the reliability in terms of successful transmission of a packet with higher overhead. The flooding consumes the resources of the network quickly, especially in sensor networks, mobile ad-hoc networks, and vehicular ad-hoc networks in terms of the lifetime of the node, lifetime of the network, and battery lifetime, etc. This paper aims to develop an efficient and reliable protocol by using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Unicasting is used when the desired destination is at a minimum distance to avoid an extra overhead and increases the efficiency of the network in terms of overhead and energy because unicasting is favorable where the distance is minimum. Similarly, multicasting is used when the desired destination is at maximum distance and increases the network’s reliability in terms of throughput. The results are implemented in the Department of Computer Science, Bacha Khan University Charsadda (BKUC), Pakistan, as well as in the Network Simulator-2 (NS-2). The results are compared with benchmark schemes such as PUMA and ERASCA, and based on the results, the performance of the proposed approach is improved in terms of overhead, throughput, and packet delivery fraction by avoiding flooding.

Brain tumors are among the illnesses that, if not treated promptly, can lead to death. It is extremely difficult to detect tumor tissue using only eye examination methods. As a result, Magnetic Resonance (MR) imaging is used to diagnose brain tumors. T1, T1c, T2, and FLAIR MRI sequences provide detailed information about brain tumors. If the segmentation procedure is performed correctly, patients' chances of survival improve. This paper describes an automated brain tumor segmentation for FLAIR sequences in MR images using U-NeT method. The study has been carried out on the BraTS 2018 data set. The models' correctness has been assessed using the binary accuracy, dice coefficient, and IOU assessment criteria. The results of the comparison between the tumor regions identified by the expert physicians and the tumor regions calculated by the U-Net model are as follows: The model has been completed with 99.26% accuracy, and the dice coefficient value, which expresses the similarity on the basis of pixels for the test data, has been found to be 73.99%. Furthermore, the IOU value of 0.59 demonstrated that the model provided accurate estimates for the study.

The security of information resources is an extremely critical problem. The network infrastructure that enables internet access, in particular, may be targeted by attackers from a variety of national and international locations, resulting in losses for institutions that utilize it. Anomaly detection systems, sometimes called Intrusion Detection Systems (IDSs), are designed to identify abnormalities in such networks. The success of IDSs, however, is limited by the algorithms and learning capacity used in the background. Because of the complex behavior of malicious entities, it is critical to adopt effective techniques that assure high performance while being time efficient. The success rate of the boosting algorithms in identifying malicious network traffic was studied in this study. The boosting approach, one of the most used Ensemble Learning techniques, is accepted as a way to cope with this challenge. In this work, Google Colab has been used to model well-known boosting algorithms. The AdaBoost, CatBoost, GradientBoost, LightGBM, and XGBoost models have been applied to the CICID2017 dataset. The performance of the classifiers has been evaluated with accuracy, precision, recall, f1-score, kappa value, ROC curve and AUC. As a result of the investigation, it was discovered that the XGBoost algorithm produced the greatest results in terms of f1-score, with 99.89 percent, and the AUC values were extremely near to 1, with 0.9989. LightGBM and GradientBoost models, on the other hand, have been shown to be less effective in detecting attack types with little data.