Publications by authors named "Hasibul Islam Peyal"
Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans.
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- Rapid identification of COVID-19 can enhance treatment and prevention by utilizing deep learning (DL) models to analyze Chest X-ray (CXR) images, which allows for real-time detection of lung infections.
- A dataset of 18,564 CXR images was created to train various DL models, including a CNN model and popular architectures like VGG-16, VGG-19, and Inception-v3, to classify seven lung disease categories.
- The CNN model achieved the highest accuracy at 93.15%, outperforming other models and demonstrating the ability to quickly identify COVID-19 with reduced training and testing times.