Invasion of Parthenocissus quinquefolia (L.) Planch in the forest-steppe of Ukraine

Research needs of adaptation mechanisms of invasive alien species arises in connection with the naturalization of species of the genus Parthenocissus Planch in forest ecosystems of Ukraine. The results showed that P. quinquefolia has a wide range of values of edaphic and climatic factors. The populations of P. quinquefolia differ according to the degree of anthropogenic transformation of the ecotope, the ecological conditions formed, and the allocation of coenotics. The biomorphological characteristics of the species variability are shown on the gradient of anthropogenic transformation. Diagnostic parameters of P. quinquefolia under the conditions of anthropogenic transformation are the number of crotches of the tendrils and the length of the tendrils. The number of flowers per plant is characterized by the highest level of variation and belongs to the V class of variability. The smallest plasticity is characterized by the diameter of the stem. Vitality analysis indicated that cenopopulations of P. quinquefolia belong to the equilibria or prosperous population types, regardless of the intensity of the anthropogenic factor

Invasive weed classification

Invasive weed optimization (IWO) is a recently published heuristic optimization technique that resembles other evolutionary optimization methods. This paper proposes a new classification technique based on the IWO algorithm, called the invasive weed classification (IWC), to face the problem of pattern classification for multi-class datasets. The aim of the IWC is to find the set of the positions of the class centers that minimize the multi-objective function, i.e., the optimal positions of the class centers. The classification performance is computed as the percentage of misclassified patterns in the testing dataset achieved by the best plants in terms of fitness performance. The performance of the IWC algorithm, both in terms of classification accuracy and training time, is compared with other commonly used classification algorithms