Yazar "Bayraktar, Canan" seçeneğine göre listele

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  • Küçük Resim Yok
    Öğe
    Diagnosing diabetes using neural networks on small mobile devices
    (ELSEVIER, 2012) Karan, Oğuz; Bayraktar, Canan; Gümüskaya, Haluk; Karlık, Bekir
    Pervasive computing is often mentioned in the context of improving healthcare. This paper presents a novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies. The recent developments in small mobile devices and wireless communications provide a strong motivation to develop new software techniques and mobile services for pervasive healthcare computing. A distributed end-to-end pervasive healthcare system utilizing neural network computations for diagnosing illnesses was developed. This work presents the initial results for a simple client (patient’s PDA) and server (powerful desktop PC) two-tier pervasive healthcare architecture. The computations of neural network operations on both client and server sides and wireless network communications between them are optimized for real time use of pervasive healthcare services.
  • Küçük Resim Yok
    Öğe
    Diagnosing internal illnesses using pervasive healthcare computing and neural networks
    (Elsevier, 2025) Bayraktar, Canan; Karan, Oğuz; Gümüşkaya, Haluk
    This paper presents a novel distributed pervasive healthcare system for diagnosing internal illnesses using pervasive healthcare computing and artificial neural networks (ANNs) and reporting healthcare results to the patients. Mobile wireless communication and information technologies have been used in new healthcare systems. The new advances in wireless communications and small mobile devices such as personal digital assistants (PDAs) and new improvements in PDA’s CPU, memory and I/O components provide a particularly promising platform for pervasive healthcare applications due to PDA’s central role in people’s lives. Patients having internal diseases need to have many tests at hospitals, and a typical test report in some cases may have more than a hundred test results. It becomes a difficult, time consuming and error prone process for a doctor to diagnose the internal illnesses using such long reports. In this study we have been developing a distributed pervasive healthcare system which uses the hospital’s main database. An ANN classifier at the hospital’s server diagnoses internal illnesses and the simplified and understandable results are reported to the patients. The patients learn their analysis results anywhere and at any time using their PDA. Furthermore this system also provides the patients to learn their old and new analysis results. Our purpose in this study is not only to facilitate the work of doctors but also to provide freedom of movement for patients.