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The recent outbreak of SARS-CoV-2 across the globe and the absence of a specific cure against the disease lead the scientific community to investigate some alternative indigenous treatments. SARS-CoV-2 is the virus responsible for the coronavirus ailment 2019 (COVID-19). This virus has 4 auxiliary proteins namely the S (spike), E (envelope), M (membrane), and N (nucleocapsid) proteins. The main proteases and RNA dependent RNA polymerase are also essential structures by which the virus replicates and survives. Each of these proteins are structures of the virus that are potential targets for drugs which are leads in the drug discovery process of any drug for the virus. Currently available treatments are not specific to the disease and therefore carry unwanted adverse effects that can be highly dangerous and sometimes fatal. Many of these treatments are supplementary in nature or based on repurposed drugs from other viral outbreaks. Alternatives of conventional drugs are required to control the spread and severity of the disease. Allicin, curcumin and their derivatives have been researched for their antiviral property and shown to have good binding affinity towards SARS-CoV-2 structures essential in their survival, especially the main proteases and RNA dependent RNA polymerases. The structural bioinformatics tools have elicited methods to predict the bioactivity of the natural product-based compounds. Apart from the beneficial medication that they offer, natural products carry along other advantages for the current pandemic situation in terms of supply, logistics, and affordability.

Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require selecting healthy sperm. Hence, machine learning algorithms are presented as the subject of this research to effectively modernize and make accurate standards and decisions in classifying sperm. In this study, we developed a deep learning fusion architecture called SwinMobile that combines the Shifted Windows Vision Transformer (Swin) and MobileNetV3 into a unified feature space and classifies sperm from impurities in the SVIA Subset-C. Swin Transformer provides long-range feature extraction, while MobileNetV3 is responsible for extracting local features. We also explored incorporating an autoencoder into the architecture for an automatic noise-removing model. Our model was tested on SVIA, HuSHem, and SMIDS. Comparison to the state-of-the-art models was based on F1-score and accuracy. Our deep learning results accurately classified sperm and performed well in direct comparisons with previous approaches despite the datasets’ different characteristics. We compared the model from Xception on the SVIA dataset, the MC-HSH model on the HuSHem dataset, and Ilhan et al.’s model on the SMIDS dataset and the astonishing results given by our model. The proposed model, especially SwinMobile-AE, has strong classification capabilities that enable it to function with high classification results on three different datasets. We propose that our deep learning approach to sperm classification is suitable for modernizing the clinical world. Our work leverages the potential of artificial intelligence technologies to rival humans in terms of accuracy, reliability, and speed of analysis. The SwinMobile-AE method we provide can achieve better results than state-of-the-art, even for three different datasets. Our results were benchmarked by comparisons with three datasets, which included SVIA, HuSHem, and SMIDS, respectively (95.4% vs. 94.9%), (97.6% vs. 95.7%), and (91.7% vs. 90.9%). Thus, the proposed model can realize technological advances in classifying sperm morphology based on the evidential results with three different datasets, each having its characteristics related to data size, number of classes, and color space.