Comparison of Metaheuristic Techniques for Parcel Delivery Problem: Malaysian Case Study

Most people preferred e-commerce ensuing the Coronavirus Disease-2019 (COVID-19) outbreak, resulting in delivery companies receiving large quantities of parcels to be delivered to clients. Hurdle emerges when delivery person needs to convey items to a large number of households in a single journey as they never face this situation before. As a result, they seek the quickest way during the trip to reduce delivery costs and time. Since the delivery challenge has been classified as an NP-hard (non-deterministic polynomial-time hard)) problem, this study aims to search the shortest distance, including the runtime for the real case study located in Melaka, Malaysia. Hence, two metaheuristic approaches are compared in this study namely, Ant-Colony Optimization (ACO) and Genetic Algorithm (GA). The results show that the GA strategy outperforms the ACO technique in terms of distance, price, and runtime for moderate data sizes that is less than 90 locations. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans

Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia;lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.

In silico molecular docking and molecular dynamic simulation of potential inhibitors of 3C-like main proteinase (3CLpro) from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) using selected african medicinal plants

The Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) is an infectious virus that causes mild to severe life-threatening upper respiratory tract infection. The virus emerged in Wuhan, China in 2019, and later spread across the globe. Its genome has been completely sequenced and based on the genomic information, the virus possessed 3C-Like Main Protease (3CLpro), an essential multifunctional enzyme that plays a vital role in the replication and transcription of the virus by cleaving polyprotein at eleven various sites to produce different non-structural proteins. This makes the protein an important target for drug design and discovery. Herein, we analyzed the interaction between the 3CLpro and potential inhibitory compounds identified from the extracts of Zingiber offinale and Anacardium occidentale using in silico docking and Molecular Dynamics (MD) Simulation. The crystal structure of SARS-CoV-2 main protease in complex with 02J (5-Methylisoxazole-3-carboxylic acid) and PEJ (composite ligand) (PDB Code: 6LU7, 2.16 Å) retrieved from Protein Data Bank (PDB) and subject to structure optimization and energy minimization. A total of twenty-nine compounds were obtained from the extracts of Z. offinale and the leaves of A. occidentale. These compounds were screened for physicochemical (Lipinski rule of five, Veber rule, and Egan filter), Pan-Assay Interference Structure, and pharmacokinetic properties to determine the Pharmaceutical Active Ingredients. Of the 29 compounds, only nineteen (19) possessed drug-likeness properties with efficient oral bioavailability and less toxicity. These compounds subjected to molecular docking analysis to determine their binding energies with the 3CLpro. The result of the analysis indicated that the free binding energies of the compounds ranged between − 5.08 and − 10.24 kcal/mol, better than the binding energies of 02j (− 4.10 kcal/mol) and PJE (− 5.07 kcal/mol). Six compounds (CID_99615 = − 10.24 kcal/mol, CID_3981360 = 9.75 kcal/mol, CID_9910474 = − 9.14 kcal/mol, CID_11697907 = − 9.10 kcal/mol, CID_10503282 = − 9.09 kcal/mol and CID_620012 = − 8.53 kcal/mol) with good binding energies further selected and subjected to MD Simulation to determine the stability of the protein–ligand complex. The results of the analysis indicated that all the ligands form stable complexes with the protein, although, CID_9910474 and CID_10503282 had a better stability when compared to other selected phytochemicals (CID_99615, CID_3981360, CID_620012, and CID_11697907). © 2020, Institute of Korean Medicine, Kyung Hee University.