Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms.

—Analyzing traffic accidents, human behavior represents the most relevant problem; especially drivers’ behavior. In this regard, driver education becomes essential. In our project, we present a simulator consisting of both software and hardware including, a steering wheel, a pedal system, a shift knob, a seat and a visual display. This system needs a virtual platform modeled using Google Sketchup-3D Modeling Program. We used a Unity Game Engine in order to enable the platform. Owing to this simulator, prospective drivers will be able to acquire experience in a virtual environment without the risks of the real world. In the simulator, the driver is penalized for every infraction according to the Turkish traffic rules. For instance, if the driver runs a red light, then penalty points are given. If the driver does not follow traffic signalization, again the simulator penalizes the driver with penalty points

Instant object detection is a critical capability in modern applications where timely decision-making is essential, such as in emergency medicine, autonomous systems, and intelligent surveillance. Efficiently handling high-throughput image streams with minimal delay presents significant challenges, particularly under demanding conditions. This study presents a scalable object detection pipeline that integrates YOLO models with Apache Kafka, a distributed streaming platform, to support just-in-time inference. The proposed architecture leverages Kafka’s partitioning and consumer group mechanisms to enable parallel processing, ensuring high throughput without requiring complex load-balancing logic. The system is deployed on a Virtual Private Server to demonstrate practical implementation. Two configurations are presented to illustrate Kafka’s native scalability: one with a single partition and a single consumer, and another with five partitions and five consumers. These setups visually demonstrate how Kafka efficiently distributes workloads across multiple consumers. Although specific latency or throughput metrics are not reported, the architecture effectively showcases how Kafka’s design enables prompt responses to high-volume input. This pipeline is well-suited for time-sensitive object detection tasks and can be extended to a wide range of instant analytics applications where rapid feedback is critical.

COVID-19 is an ongoing pandemic and according to the experts, using a face mask can reduce the spread of the disease. On the other hand, masks cause occlusion in faces and can create safety problems such as the recognition of the face and the estimation of its age. To prevent the spread of COVID-19, some countries have restrictions according to age groups. Also in different countries, people in some age groups have safety restrictions such as driving and consuming alcohol, etc. But these rules are difficult to follow due to occlusion in faces. Automated systems can assist to monitor these rules. In this study, a deep learning-based automated multi-task face detection and age group classification system is proposed for masked faces. The system first detects masked/no-masked-faces. Then, it classifies them according to age-groups. It works for multi-person regardless of indoor/outdoor environment. The system achieved 79.0% precision score for masked face detection using Faster R-CNN with resnet50 network. Also, 83.87% accuracy for classifying age groups with masked faces and 84.48% accuracy for no-masked faces using densenet201 network have been observed. It produced better results compared to the literature. The results are significant because they show that a reliable age classification for masked faces is possible.