Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers’ default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets.

In light of rapid technological developments, a marked rise in global internet usage has contributed to increased sensitive data flow across networks. This increase leads to the diversification of malicious attacks and makes cyber security requirements more evident. In order to ensure network security, intrusion detection systems stand out as an essential component. Intrusion detection systems detect suspicious and malicious activities over network traffic, allowing network administrators and experts to monitor current threats continuously. In anomaly-based systems, machine learning approaches are applied to identify abnormal attempts in network traffic. This study presents a feature selection framework for anomaly-based attack detection systems by combining machine learning and heuristic algorithms. This proposed study aims to improve the performance of IDSs in terms of both time and attack detection by selecting features with heuristic approaches. In the proposed approach, PSO, FPA, DE feature selection methods and LR, DT, RF, KNN, NB, GB, LDA, QDA, AdaBoost, and NN machine learning algorithms are used to perform anomaly-based comparative analyses on KDDCup99, NSL-KDD, UNSW-NB15, CSE-CIS-IDS2018 datasets. Analyses conducted on these datasets with various features demonstrated that models employing feature selection achieved an approximate two-hundred-percent improvement in time efficiency compared to models that did not utilize feature selection. It has been determined that DE, PSO, and FPA, which are used for feature selection, provide high-accuracy outputs when combined with different classifiers. When the analysis results are assessed according to the specified criteria, the highest F1-Score values achieved are as follows: 0.9972 for the DE method in GB, 0.9969 for the PSO method in GB, and 0.9948 for the FPA method in GB, on the KDD CUP 99 dataset. In the NSL-KDD, used as the second dataset, the DE method achieved a score of 0.9713 in GB, the PSO method reached 0.9112 in DT, and the FPA method obtained 0.9894 in RF, respectively. In the third dataset, UNSW-NB15, the DE method achieved a score of 0.9507 in DT, the PSO method reached 0.9068 in DT, and the FPA method obtained 0.8924 in NN. Finally, in the CSE-CIC-IDS2018 dataset, the highest scores achieved using the RF algorithm were 0.99986 for the DE method, 0.99989 for the PSO method, and 0.99987 for the FPA method, based on feature selection. The obtained results underscore the critical role of dataset generation processes and network traffic dynamics in enhancing the performance of intrusion detection systems. Additionally, the significance of feature selection was highlighted. These findings offer valuable insights and present opportunities for further advancements in future research.

This article presents a new benchmark MRI dataset called the Gazi Brains Dataset 2020, containing MRI images of 100 patients, and introduces initial experimental results performed on this dataset in comparison with available brain MRI datasets. Furthermore, the dataset is analyzed using eight different deep learning models for high-grade glioma tumor prediction, classification, and detection tasks. Additionally, this study demonstrates the results of an explainable Artificial Intelligence (XAI) approach applied to the trained models. To demonstrate the utility of the proposed dataset, different deep learning models were applied to the problem, and these models were tested on various data and models applied for various tasks such as region of interest extraction, whole tumor segmentation, prediction, detection, and classification with accuracy, precision, recall, and F1-score. The experimental results indicate that the dataset is highly effective for multiple purposes, and the models reached significant results with successful F1-scores ranging between 93.2% and 96.4%. ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. Moreover, this study explores the application of XAI to the trained models, providing interpretability and insights into the decision-making processes. The findings signify that this dataset holds significant potential for various future research directions, including age estimation, gender detection, causal inference with XAI, and disease-related survival analysis.

Automated emotion recognition (AEE) plays a crucial role in numerous industries that depend on understanding human emotional responses, such as advertising, technology, and human-robot interaction, particularly within the Information Technology (IT) field. However, current systems can often come up short in comprehensively understanding an individual's emotions, as prior research has mainly focused on assessing facial expressions and categorizing them into seven primary emotions, including neutrality. In this study, we present several Deep Convolutional Neural Network (CNN) models designed specifically for the task of facial emotion recognition, utilizing the FER2013 and RAF datasets. The baseline CNN model is established through a trial-and-error method, and its results are compared with more complex deep learning techniques, including ResNet18, VGGNet16, VGGNet19, and Efficient Net-B0 models. Among these models, the VGGNet19 model achieved the best results with a test accuracy of 71.02% on the FER2013 dataset. In comparison, the ResNet18 model outperformed all other models with an 86.02% test accuracy on the RAF-DB dataset. These results underscore the potential for advancing automated emotion recognition through complex deep-learning techniques.

Multi-task planning for diverse UAVs and missions can be approached as a Constraint Satisfaction Problem (CSP) where the Temporal CSP (TCSP) method adds time-based sequential task modeling. The Enhanced Temporal Constraint Satisfaction Problem (ETCSP) method innovatively merges dynamic domain features with a MIQP (Mixed Integer Quadratic Programming) based scoring system to optimally assign UAVs to tasks, moving beyond traditional greedy algorithms. This approach includes an enhanced forward checking method that evaluates task suitability and UAV compatibility in real-time using dynamic programming, thus refining search precision. The ETCSP model was tested in two phases, initially assigning various tasks and then employing CSP methods to monitor task changes over time. Results show that the generic TCSP method requires 61 UAVs to complete 70 tasks, while the Enhanced TCSP achieves the same with only 48 UAVs—which is roughly a 21% reduction in UAV usage. Similarly, the Enhanced method completes the task package in about 3800 min and with 1142 L of fuel, compared to 4855 min and 1615 L for the TCSP method, translating to approximately a 22% reduction in time and a 29% reduction in fuel consumption.