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Browsing by Author "Ozdag, R."

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    Internet of Things and Arduino-Based Carbon Monoxide Detection System
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bulak, E.; Ozdag, R.
    Carbon monoxide (CO) is an inorganic molecule that is tasteless, colorless, and odorless, making it difficult for humans to detect and a highly toxic gas. It is produced during the combustion of certain carbon-containing fuels. The emission of CO gas is particularly critical in domestic stoves, the mining industry, livestock farming, and industrial factories, where it poses a significant threat to human life and requires immediate preventive measures. In this study, we have developed an Arduino-based hardware and software system utilizing the Internet of Things (IoT) to detect CO gas leaks. By monitoring the data collected from a CO gas sensor through an IoT-based cloud system, the system aims to prevent carbon monoxide poisoning by triggering an alarm when CO levels reach a harmful threshold. To achieve this goal, the sensor data will be transmitted to the IoT-based cloud system in real-time for analysis and alarm generation. Through this research, we plan to minimize CO gas emissions in the environment using the IoT-based CO gas leak detection system, while also ensuring that necessary precautions are taken to protect human health from its adverse effects. © 2024 IEEE.
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    Training Artificial Neural Network With Chaotic Cricket Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2018) Canayaz, M.; Ozdag, R.
    Artificial neural networks are decision-making mechanisms inspired by the human nervous system. Many areas are widely used such as medicine, economics, machine learning. These nets generate an output value by passing input values through the layers. The input values are firstly multiplied by some weight values and sent to activation functions, thus, training is realized. The purpose of this training is to ensure that appropriate weight values are found. Meta-heuristic methods are widely used to find these weight values. In this study, Chaotic Map Cricket Algorithm was used to find the weight values. The weights obtained during the algorithm's operation were sent to the network and the weight matrix providing the minimum error was obtained. Since it is the first study on artificial neural network training with the Cricket Algorithm, the performance of the algorithm has been tried to be shown on the appropriate data sets in comparison with the Particle Swarm Optimization. © 2018 IEEE.