The rapid increase in fossil fuel depletion, environmental degradations, and industrialization have encouraged the need and production of sustainable fuel alternatives. This has led to the increase in interest in biofuels, especially third-generation biofuels produced from microalgae since they do not compete with food and land supplies. However, the global share for these biofuels has been inadequate recently, especially due to the ongoing global pandemic. Therefore, this paper offers a review of the state-of-the-art study of the production field of third-generation biofuel from microalgae. The current review aims to focus on the different aspects of algal biofuel production that requires further attention to produce it at a large scale. It was found that several strategies during the life cycle of algal biofuel production can significantly increase its quality and yield while reducing cost, energy, and other related attributes. This paper also focuses on the challenges for large-scale production of third-generation biofuels pre and post COVID-19 to better understand the barriers. The high cost of this fuel’s production and sale tends to be the major reason behind the lack of large-scale production, hence, inadequacy to meet the global need. Third-generation biofuel has so much to offer including many integrated applications and advanced uses in the future fuel industry. Therefore, it is important to cope with the ongoing circumstances and emphasize the future of algal biofuel as a sustainable source.
This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.
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