Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Most of the criminal acts are performed using criminal tools. One of the most effective ways of preventing crime is to observe and detect offensive weapons by security camera systems. Deep learning techniques can show very high-performance in observing and perceiving objects. In the current study, the performances of the pre-trained AlexNet, VGG16, and VGG19 models based on convolutional neural networks, were tested for the detection and classification of criminal tools such as guns and knives. In the study, the training process was carried out using transfer learning approaches such as Fine-tuning and Training from scratch based on deep architectures. To test the deep architectures used in the proposed study, the gun and knife datasets frequently used in the literature were collected and combined with new datasets obtained originally from search engines and videos, and then their performances were tested. In the experimental results, the VGG16 model based on fine-tuning for the two and three classes achieved the highest accuracy in detecting criminal devices with a rate of 99.73% and 99.67%, respectively. As a result, the study has observed that offensive weapons could be detected with security cameras using learned weights of deep architectures

Ensuring worker safety in high-risk environments such as construction sites is of paramount importance. Personal protective equipment, particularly helmets, plays a critical role in preventing severe head injuries. This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time. The dataset used for the study consists of 16,867 images, with various data augmentation and preprocessing techniques applied to improve the model's robustness. The YOLOv8 model achieved a 96.9% mAP50 score, outperforming other deep learning models in similar studies. The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.

The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research.

Dijital dönüşüm, İnovasyon ve Ar-Ge süreçlerinin şekillenmesinde önemli bir güç olmuştur. Büyük veri, bulut bilişim ve endüstri 4.0 ile birlikte önde gelen yapay zeka uygulamaları bu sürecin itici güçleri olmuştur. Bu bağlamda geliştirilen yapay zekanın yeni nesil yöneticileri olan yapay zekâ ajanları ve çoklu ajan sistemleri, inovasyonun görünmeyen fakat giderek önem kazanan aktörleri olmaya başlamıştır. Ajanlar; literatür taramalarından, deneysel model tasarımlarına, model tasarımdan analiz işlemlerine ve sistem eğitimlerine, raporlama ve hatta Pazar araştırmasına kadar önemli bir rol oynamaya başlamıştır. Bu işlemler sonucunda ajanlar, inovasyon ve Ar-Ge faaliyet döngülerine hız kazandırmaya başlamıştır. Bu araştırmada öncelikle doğrusal, zincir bağlantılı, sistemik, açık ve kullanıcı inovasyonu gibi klasik inovasyon yaklaşımları özetlenmekte; bu yaklaşımlar ajanik yapay zekâ perspektifinden yeniden yorumlanmaktadır. Ardından yapay zekâ ajanlarının kavramsal çerçevesi çizilmekte, LLM tabanlı otonom ajanların temel bileşenleri ve çoklu ajan sistemleri ele alınmaktadır. Devamında ajanların Ar-Ge döngüsündeki konumları; fikir üretimi, literatür ve patent taraması, tasarım ve simülasyon, test ve validasyon, ticarileştirme ve pazar analizi bağlamlarında tartışılmaktadır. İnsan–ajan işbirliği modelleri, multi-ajan orkestrasyonu, etik ve yönetişim boyutları ile birlikte ele alınmakta; üniversite–sanayi işbirliği, KOBİ’ler, sağlık, enerji ve üretim sektörlerinden örnek uygulama senaryoları sunulmaktadır. Son olarak self-driving laboratories yaklaşımı ve ajanik YZ odaklı kurumsal dönüşüm trendleri çerçevesinde geleceğe dönük bir perspektif geliştirilmektedir. Bu araştıma çalışması, yapay zekâ ajanlarının insan aklının yerine geçen otoriteler değil, doğru tasarlandığında inovasyonu hızlandıran “akıllı ortaklar” olduğu hipotezini savunmaktadır.

Machine-learning models for sports injury risk are commonly evaluated within a single cohort, leaving uncertainty about transportability across sports and