Yazar "Aydin A.A." seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    IoT Based Mobile Smart Home Surveillance Application
    (Institute of Electrical and Electronics Engineers Inc., 2020) Erzi H.M.; Aydin A.A.
    With widespread of internet and communication technologies, various smart devices have become reachable and controllable regardless of their geographic locations. The Internet of Things (IoT) technology enables communication of smart devices that can connect through the internet, and ranges from smart homes to smart cities. The IoT has been widely used in various domains such as business, healthcare, agriculture, government and education to accomplish variety of purposes. In this study, IoT based mobile smart home surveillance application has been developed for controlling smart home system with the purpose of reducing human intervention and increasing security, privacy and energy efficiency. In this application, smart sensors are controlled by our web service and our mobile application that utilizes react native technologies, and it allows data from the sensors in our smart home prototype to be recorded in the database periodically with quartz.net technology, interpreting this data and allowing users to be informed via the mobile application. This application is an example of monitoring smart homes that provides solutions for the problems arise in IoT applications and enables to process smart sensor data to get insight for future use of smart homes. © 2020 IEEE.
  • Küçük Resim Yok
    Öğe
    A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification
    (Institute of Electrical and Electronics Engineers Inc., 2023) Muoka G.W.; Yi D.; Ukwuoma C.C.; Martin M.D.; Aydin A.A.; Al-Antari M.A.
    Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.
  • Küçük Resim Yok
    Öğe
    An overview of quality attributes for data intensive systems in crisis informatics
    (Institute of Electrical and Electronics Engineers Inc., 2017) Aydin A.A.
    In today's digital world, we have been exposed by tremendous amounts of data generated by numerous sources and the developers of data intensive systems are confronted with the challenges of collecting, analyzing and storing large amounts of data. Dealing with those challenges and designing software systems provides demanded set of quality attributes require engaging with sophisticated approaches, developing clever techniques, and carefully making use of cutting edge technologies. In this paper, first, an outline of crisis informatics research and crisis management phases are introduced, next, an overview of quality attributes for data intensive systems is presented, last, a classification of frequently demanded quality attributes for crisis data intensive system is provided by taking into account crisis management phases and the type of data analytics performed. © 2017 IEEE.