The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work.

Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion...

In speech signal, formant frequency is defined as the acoustic resonance of human vocal tract. Because it is changeable across genders, age, and languages, it has been studied for various purposes by many researchers. Analyzing formant frequency of speech signal in vowels is more feasible than analyzing it in consonants. In this study, formant frequency values (F1, F2 and F3) of eight Turkish vowels (/a/, /e/, /ı/, /i/, /o/, /ö/, /u/, /ü/) within CVC syllables have been extracted to identify most successful classification method. Totally 48 meaningful syllables in CVC context have been used for analysis and this speech corpus contains all 8 vowels existing in Turkish Language. The syllables were uttered by 10 male speakers. The results inferred that SVM (Support Vector Machines) operates best in distinguishing Turkish vowels by formant frequencies with %90 of classification success. In the future works, these findings may support studies on speech segmentation, vowel detection, speech signal enhancement and the deals that may require information about the best classification algorithm working on Turkish vowels.

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, “Early Diagnosis Saves Lives”. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis.

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential.

The purpose of this study is to learn how people who speak different languages interpret the same issues, and to compare the results obtained and show the difference between their perspectives. To learn this point of view, we must first turn to open source intelligence. In this execution, a sentiment analysis application was designed using the Python programming language and the Natural Language Processing algorithms in the texts, which were taken as a data set of comments in Azerbaijani, Turkish, Russian and English languages from social media. As the data set, the comments made on 4 subjects: the declaration of Hagia Sophia as a mosque, the objection events that started with the natural gas hike in Kazakhstan, the natural disasters in Turkey, the Ukraine crisis. After loading the texts in four languages from the network environment, after preprocessing, the text was divided into 8 different categories (neutral, fear, joy, anger, sadness, surprise, disgust, shame) by means of the application written in Python programming language based on Data Mining and Machine Learning topics. In the study, precision, sensitivity, accuracy and F1 score were obtained by using Random Decision Forests, K - Near Neighbor Algorithm, Decision Trees, Support Vector Machine, Naive Bayes Algorithm, Logistic Regression, which are machine learning methods. By comparing the results, it was determined that the Logistic Regression method obtained the highest result. A sentiment analysis model was created using the Logistic Regression method, and sentiment analysis was performed for each subject at separation and the results were compared.