Yazar "Jabbar, Amenah Nazar" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Deep learning and grey wolf optimization technique for plant disease detection: a novel methodology for improved agricultural health
    (International Information and Engineering Technology Association, 2023) Jabbar, Amenah Nazar; Koyuncu, Hakan
    Plant disease outbreaks have a profound impact on the agricultural sector, leading to substantial economic implications, compromised crop yields and quality, and potential food scarcity. Consequently, the development of effective disease prevention and management strategies is crucial. This study introduces a novel methodology employing deep learning for the identification and diagnosis of plant diseases, with a focus on mitigating the associated detrimental effects. In this investigation, Convolutional Neural Networks (CNNs) were utilized to devise a disease identification method applicable to three types of plant leaves - peppers (two classes), potato (three classes), and tomato (nine classes). Preprocessing techniques, including image resizing and data augmentation, were adopted to facilitate the analysis. Additionally, three distinct feature extraction methods - Haralick feature, Histogram of Gradient (HOG), and Local Binary Patterns (LBP) - were implemented. The Grey Wolf Optimization (GWO) technique was employed as a feature selection strategy to identify the most advantageous features. This approach diverges from traditional methodologies that solely rely on CNNs for feature extraction, instead extracting features from the dataset through multiple extractors and passing them to the GWO for selection, followed by CNN classification. The proposed method demonstrated high efficiency, with classification accuracies reaching up to 99.8% for pepper, 99.9% for potato, and 95.7% for tomato. This study thus provides a progressive shift in plant disease detection, offering promising potential for improving agricultural health management. In conclusion, the integration of deep learning and the Grey Wolf Optimization technique presents a compelling approach for plant disease detection, demonstrating high accuracy and efficiency. This research contributes a significant advancement in the field of agricultural health and disease management.
  • [ X ]
    Öğe
    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%.