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    Artificial intelligence–based COVID-19 detection using cough records
    (İstanbul Üniversitesi Cerrahpaşa, 2021) Gökcen, Alpaslan; Karadağ, Bulut; Riva, Cengiz; Boyacı, Ali
    In 2019, with the emergence of coronavirus disease 2019 (COVID-19) and its spread all over the world, many people were directly affected by the pandemic. As its spread increases, it is difficult to diagnose who is actually infected. In addition to continuing vaccination studies, some technological solutions are being used to try to control the virus. One of these technological solutions is presented in this study. The disease is detected using cough data through artificial intelligence (AI). To do this, an open source data set was used from the opensigma.mit.edu website. More than 20,000 cough records representing age, gender, geographic location, and COVID-19 status are available from this site. The AI model trained on cough detection achieved 79% COVID-19 accuracy with an F1 of 80%. With the designed AI-based mobile application, COVID-19 can be detected and monitored.
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    A Comparative Study on Energy Harvesting Battery-Free LoRaWAN Sensor Networks
    (Istanbul University, 2022) Riva, Cengiz; Zaim, Abdul Halim
    Sensor networks are emerging in many applications with the availability of the spreading IoT (Internet of Things) technologies. These applications having sensor networks generally do not require big data transfers, but instead, they need to send a small amount of data from time to time. Long-range wide area network (LoRaWAN) is a very good solution for this kind of communication since LoRaWAN offers long-range transmission with deep-sleep mechanism between the transmissions. However, these networks must be reliable, and from the point of energy supply for a long period of time, they should be sustainable. Since the nature of sensor-based IoT devices is being small and placable anywhere, they are generally battery-powered. With the usage of batteries, many disadvantages including limited power, cost of replacement of the dead battery, especially in rural areas, environmental concerns, etc. arise. Therefore, energy harvesting from different sources depending on the application and location of the sensor network may eliminate these problems. In this study, firstly, we focused on how the energy is consumed by the LoRAWAN communication systems by analyzing the power consumption during the transmit phase and providing necessary formulas. Secondly, by looking at the formulas, a possible optimization is recommended on the power consumption in LoRAWAN systems in order to have longer battery life. Thirdly, we have searched and analyzed energy harvesting methods and applications used by the researchers in recent years, as well as elaborating power consumptions, in typical microcontroller-controlled sensor nodes with LoRaWAN communication ability in order to provide comparative information on energy-harvesting battery-less LoRAWAN nodes.