QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm.

One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.

Studies on Japanese encephalitis vector mosquitoes in Selangor, Malaysia.

Mosquito collections were carried out from May to June 1992 and from September to December 1992 in an area where a case of Japanese encephalitis was confirmed. A total of 40,072 mosquitoes belonging to 35 species and 8 genera were collected. The dominant species in that locality were Culex vishnui, Culex tritaeniorhynchus, Culex pseudovishnui, Culex gelidus, Aedes butleri, and Mansonia uniformis.