… Tn and/or In and associated resistance genes on an incoming plasmid may move into the
chromosome or other plasmid(s) in the recipient cell (E), as illustrated here for class 1 In/Tn, …

This paper introduces genetic algorithms (GA) as a complete entity, in which knowledge of
this emerging technology can be integrated together to form the framework of a design tool for …

Background In 2015, the second cycle of the CONCORD programme established global
surveillance of cancer survival as a metric of the effectiveness of health systems and to inform …

… schema “S” in the next generation. Let c(S,t) be the number of strings matched by schema “S”
… The probability of its selection (in a single string selection) is equal tof(S,t)/F(t). where F(t) is …

… a fitness value f(S, t) with schema "S", and the average fitness of the schema. ƒ(S, t) is then
… , we can estimate the number of matched strings of a schema S in the next generation. …

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired
optimization algorithm, which has been shown to be competitive with some conventional …

… To learn the mapping between an input image and its best-fitting curve parameter maps,
we propose a Deep Curve Estimation Network (DCE-Net). The input to the DCE-Net is a low-…

… 1) We present a systematic approach to generate widely spread weight vectors in a high-dimensional
objective space. Each weight vector not only defines a subproblem, but also …

… Our Water-Net can process an image with a size of 640 × 480 within 0.128s (8FPS). … Qian,
and S. Kwong, “Nested network with two-stream pyramid for salient object detection in optical …

Recently, the Generative Adversarial Networks (GANs) are fast becoming a key promising
research direction in computational intelligence. To improve the modeling ability of GANs, loss …