In this study, a hybrid wheeled fire extinguisher robot has been created. The robot has a two-degrees-of-freedom (DoF) fire extinguisher gun turret. To control the disruptive effect of mechanical oscillations on the firing system during movement of the robot body, PID and SMC controllers are used. When closed on flat ground, the robot’s five-piece transformable wheel construction allows it to travel swiftly. The wheel mechanism opens on tough terrain, allowing the wheel to assume a star-shaped configuration and enabling the robot to ascend by grasping onto obstructions. The three-dimensional mechanical design of the firefighter robot was designed first, followed by the kinematic model of the turret system and the three-dimensional Simscape model in the Matlab Simmechanic environment. Simulations of throwing fire-extinguishing balls at fire locations positioned at 20 m to 80 m horizontal and 1–30 m vertical distances were carried out on this model for three different scenarios (the robot is stationary, moving at constant speed and rotating around itself). The simulations resulted in a shooting success rate of 85.71% with PID and 95.23% with SMC (for a total of 105 shots). When the mistake rates were investigated, it was discovered that the constructed fire robot was usable in firefighting.

With the developing technological process, it is expected that the usage of robots will increase in defense systems as in every field. One of the main objectives of the robotic studies for the defense industry is to capture the targeted success under all kinds of disruptive effects with robotic systems and to present this technology to the service of the army. A weapon system with a single degree of freedom was placed on a quadruped robot. System’s dynamic behavior, which has 12 degrees of freedom and planar movements, is modeled mathematically. Simulations of the shots made to the fixed targets were carried out during the walking of the quadruped robot. The gun barrel stabilization was realized to achieve accurate shots under disruptive effects. The sliding-mode control method was used to perform the barrel stabilisation. In this study, it is shown that a quadruped robot with a weapon system can perform successful shots against fixed targets. MATLAB is used for simulations and the results are shown with figures, graphics, and tables.

In this study, a deep learning-based multimodal framework is presented for forest fire detection using RGB images, which synthetically generates night-vision-like, white-hot, and green-hot pseudo-thermal representations. The synthetic modalities are derived directly from RGB data and integrated into a hardware-independent multimodal learning pipeline to increase visual diversity without relying on additional sensing hardware. Each modality is processed using an ImageNet-pretrained convolutional backbone, and modality-specific feature vectors are combined through feature-level concatenation before classification. The proposed framework was evaluated using multiple backbone architectures, including ResNet18, EfficientNet-B0, and DenseNet121, which were assessed independently under a unified experimental protocol. Experiments were conducted on two datasets with substantially different scales and characteristics: the FLAME dataset (39,375 images, binary classification) and the FireStage dataset (791 images, three-class classification). For both datasets, stratified 80–20% training–validation splits were employed, and online stochastic data augmentation was applied exclusively to the training sets. On the FLAME dataset, the proposed framework achieved consistently high performance across different backbone and modality configurations. The best-performing models reached an accuracy of 99.66%, precision of 99.80%, recall of 99.66%, F1-score of 99.73%, and ROC AUC value of 0.9998. On the more challenging FireStage dataset, the framework demonstrated stable performance despite limited data availability, achieving an accuracy of 93.71% for RGB-only configurations and up to 93.08% for selected multimodal combinations, while macro-averaged F1-scores exceeded 0.92, and ROC AUC values reached up to 0.9919. Per-class analysis further indicates that early-stage fire (Start Fire) patterns can be discriminated, achieving ROC AUC values above 0.96, depending on the backbone and modality combination. Overall, the results suggest that synthetic-modality-based multimodal learning can provide competitive performance for both large-scale and data-limited fire detection scenarios, offering a flexible and hardware-independent alternative for forest fire monitoring applications.

Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study. "Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.