We implemented an automatic open-source processing pipeline for neonatal brain tissue segmentation. The framework makes use of N4 to correct bias field, the deformable registration SyN to warp a public neonatal template to the test image, and multivariate MRF-based segmentation Atropos to perform segmentation. The pipeline does not need training and runs efficiently. It can achieve satisfactory...

It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective ...

In this paper we study the performance of ant colony based routing algorithms in mobile ad hoc networks (MANETs) and present SAMP-DSR, a new algorithm to address the shortcomings of both ant colony based as well as the traditional DSR algorithms. Due to the unstable nature of MANETs memory based search algorithms such as ant based methods become inefficient and impractical as the network mobili...

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting (ANA). The inspired by Leptothorax ants and mimics the behavior of searching for positions to deposit grains while building new nest. Although swarming ants, it does not have any algorithmic similarity with colony optimization (ACO) algorithm. It worth mentioning that ANA considered continuous updates search agent...

An ant colony optimization is a technique which was introduced in 1990’s and which can be applied to a variety of discrete (combinatorial) optimization problem and to continuous optimization. The ACO algorithm is simulated with the foraging behavior of the real ants to find the incremental solution constructions and to realize a pheromone laying-and-following mechanism. This pheromone is the in...

We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony’s nest becomes unsuitable due to size constraints or damage, the colony relocates to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. They must additionally reach consensus on a final nest choice and trans...

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial optimization problems [6, 13, 17, 23, 34, 40, 49]. As the name suggests, ant algorithms have been inspired by the behavior of real ant colonies, in particular, by their foraging behavior. One of the main ideas of ant algorithms is the indirect comm...

We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve performance. Two modifications are proposed and tested. The first algorithm is a hybrid between ACS-TSP and a Genetic Algorithm that encodes experimental variables in ants. The algorithm does not yield improved results but offers concepts that can be used to improve the ACO algorithm. The second algorith...

Harvester ants (Messor galla Forel) defied various control strategies. Two field experiments were conducted to evaluate the effects of Spent Engine-Oil and other nature-based materials on their emergence in Maiduguri, Borno State of Nigeria. The treatments evaluated are spot application of spent engine-oil and some natural materials in experiment 1 and individual and equal mixture of Pure Neem ...

We consider the problem of rapidly searching for resources or files in a distributed, unstructured, peer-to-peer file sharing network. Unstructured p2p network protocols such as Gnutella use a flooding-based mechanism for resource searching that generates considerable traffic in the network for each search query. When the searching activity by users in a p2p network is high, the traffic generat...