Host Plant Availability and Nest-Site Selection of the Social Spider Stegodyphus dumicola Pocock, 1898 (Eresidae)

An animals’ habitat defines the resources that are available for its use, such as host plants or food sources, and the use of these resources are critical for optimizing fitness. Spiders are abundant in all terrestrial habitats and are often associated with vegetation, which may provide structure for anchoring capture webs, attract insect prey, or provide protective function. Social spiders construct sedentary communal silk nests on host plants, but we know little about whether and how they make nest-site decisions. We examined host plant use in relation to host plant availability in the social spider Stegodyphus dumicola Pocock, 1898 (Eresidae) across different arid biomes in Namibia and analysed the role of host plant characteristics (height, spines, scent, sturdiness) on nest occurrence. Host plant communities and densities differed between locations. Spider nests were relatively more abundant on Acacia spp., Boscia foetida, Combretum spp., Dichrostachys cinerea, Parkinsonia africana, Tarchonanthus camphoratus, and Ziziphus mucronatus, and nests survived longer on preferred plant genera Acacia, Boscia and Combretum. Spider nests were relatively more abundant on plants higher than 2 m, and on plants with thorns and with a rigid structure. Our results suggest that spiders display differential use of host plant species, and that characteristics such as rigidity and thorns confer benefits such as protection from browsing animals.

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.