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Number recognition is a critical skill in early education, laying the foundation for digital literacy and mathematical understanding. However, traditional methods of teaching number recognition often lack the interactivity and engagement necessary for effective learning, particularly for young learners. This study proposes the development of a deep learning-based Android application aimed at enhancing the number recognition process by providing an interactive and visual learning experience. The application utilizes a Convolutional Neural Network (CNN), a type of deep learning model, to recognize handwritten numbers, offering users an innovative and engaging way to learn and practice number recognition.In the proposed application, users can draw numbers on their devices, and the system will immediately provide feedback regarding the accuracy of the drawn numbers. The CNN model will be trained on a comprehensive dataset of handwritten digits to ensure high accuracy in recognition. This real-time feedback loop is designed to help users learn the correct form of numbers while also introducing the foundational concepts of deep learning. The main objective of this application is to provide an accessible, interactive platform for learning number recognition, especially for novice learners who may be unfamiliar with basic concepts in machine learning and digital literacy. By integrating deep learning technology, the application not only supports the learning of number recognition but also serves as an introduction to artificial intelligence (AI) concepts in a practical, easy-to-understand format. Furthermore, the application is designed to be user-friendly, ensuring that it is suitable for a wide range of learners, including children and beginners. The application aims to combine the fundamental principles of deep learning with a practical, hands-on learning experience, fostering a deeper understanding of both number concepts and the potential of AI in everyday life.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring.