The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths has exponentially increased on a daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly classify the COVID-19 disease from both chest X-ray (CXR) and CT images. Two AI-based deep learning models, namely ResNet50 and EfficientNetB0, are adopted and trained using both chest X-ray and CT images. The public datasets, consisting of 7863 and 2613 chest X-ray and CT images, are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNetB0 consistently performed a better classification result, achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively. For the hybrid AI-based model, the overall classification accuracy of 99.58% is achieved. The proposed hybrid deep learning system seems to be trustworthy and reliable for assisting health care systems, patients, and physicians.

Due to widespread usage of banana as a staple food crop and susceptibility to numerous illnesses. Bananas require sophisticated detection techniques to support sustainable agricultural practices. Bananas are particularly susceptible to various stem and leaf spot diseases, resulting in significant economic losses within the banana cultivation sector. In this paper, a new XAI framework for banana disease detection and classification is introduced. Our framework uses state-of-the-art AI methods to analyze photos of banana plants. With great precision, it can detect a variety of illnesses like Cordana, Black Sigatoka, Pestalotiopsis, and fusarium wilt. The outcomes show that the framework performs better than current techniques in precisely identifying and categorizing banana diseases. The research employed a Convolutional Neural Networks (CNNs) to detect diseases in banana plants using RGB images of banana leaves. We used pre-trained model called EfficientnetB0 model to evaluate using two datasets BLSD and BDT. For BLSD, the model achieved an accuracy of 99.22%. Next for BDT, on the other hand, demonstrated improved performance with an accuracy of 99.63%.