Yazar "Seno, Mohammed E." seçeneğine göre listele

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    Detection of COVID-19 using classification of an x-ray image using a local binary pattern and k-nearest neighbors
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Ali Saadoon; Kurnaz, Sefer; Hamdi, Mustafa Maad; Khaleel, Arshad Mohammed; Jabbar, Amenah Nazar; Seno, Mohammed E.
    The recently identified coronavirus pneumonia, which was later given the name COVID-19, is a virus that can be fatal and has affected more than 300,000 individuals around the world. Because there is currently no antiviral therapy or vaccine that has been granted approval by the FDA to cure or prevent this sickness, an automatic method for disease identification is required because of the fast global distribution of this exceedingly contagious and lethal virus. A unique machine learning strategy for automatically detecting this ailment was discovered. Machine learning approaches should be applied in essential jobs in infectious illnesses. As a result, our major aim is to use computer vision algorithms to identify COVID-19 without the need for human interaction. This paper suggested using image processing to classify objects and make early detections using X-ray pictures. Features are extracted for this region using a variety of techniques, including (LBP), (HOG), and use K-Nearest Neighbor algorithm (KNN) for classification, with training percentages of 50%, 60%, 70%, 80%, and 90%. Experiments indicated that using the suggested approach to identify X-ray photos of corona patients, it is feasible to diagnose the disease using X-ray images by training the device on the image data set (about 2,400 photos). The results were tested on the average of the samples taken (random 2000 images) each time and the measurement of multiple training ratios (50%, 60%, 70%, 80%, and 90%). The experimental findings revealed remarkable prediction accuracy in all investigated scenarios, ranging from 85% to 99%.
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    Study for buildings with IoT system for energy management
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Ali Saadoon; Kurnaz, Sefer; Hamdi, Mustafa Maad; Khaleel, Arshad Mohammed; Khaleel, Amjed Mohammed; Seno, Mohammed E.
    A smart city makes use of sustainable information and communication technology to upgrade the effectiveness and quality of municipal services for inhabitants and government officials while conserving resources. Buildings with energy-efficient control are an essential part of this. The IoT may be able to help. Its goal is to link many heterogeneous devices over the internet, which necessitates a flexible layered architecture that combines things, people, and web services to simplify an application's work. Also, this Paper will offer a customizable Internet of things, hierarchical architectural model, with an overview of each important component enabling intelligent energy regulation in buildings of smart cities. This paper describes an advanced IoT-based energy management system for buildings. A semantics framework is proposed to unify and standardize the modelling of the things that make up the built environment. Appropriate rules are created with the goal of intelligent energy management and the overall Smart Building mode of operation.