The accountable care organization (ACO) model of health care delivery is rapidly being implemented under government and private-sector initiatives. The model requires that each ACO have a defined patient population for which the ACO will be held accountable for both total cost of care and quality performance. However, there is no empirical evidence about the best way to define how patients are ...

In cloud computing tasks scheduling problem is NP-hard, furthermore it does onerous for attaining an optimum resolution. Extremely quick optimization algorithms are used to proximate the optimum resolution, like ACO (ant colony optimization) algorithm. In cloud computing, in consideration to solve the problem of task scheduling, a period ACO (PACO)-based arranging algorithmic rule has been used...

This paper puts forward some rough approximations which are motivated from topology. Given a subset R ⊆ U × , we can use 8 types of id="M2"> E -neighborhoods to construct an arbitrary id="M3"> X on the one hand. On other hand, also relying topology is induced by id="M4"> -neig...

this work aims to investigate the reliability of software products as an important attribute of computer programs; it helps to decide the degree of trustworthiness a program has in accomplishing its specific functions. This is done using the Software Reliability Growth Models (SRGMs) through the estimation of their parameters. The parameters are estimated in this work based on the available fai...

Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant Colony Optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper proposes a novel document ...

nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

Ant colony optimisation (ACO) is a metaheuristic to solve optimisation problems inspired by the foraging behaviour of ant colonies. It has been successfully applied to several types of optimisation problems, such as scheduling and routing, and more recently for the discovery of classification rules. The classification task in data mining aims at predicting the value of a given goal attribute fo...

BACKGROUND Patients with symptoms of both asthma and chronic obstructive pulmonary disease (COPD) may be classified with the term asthma-COPD overlap (ACO). ACO is of considerable interest as it is currently poorly characterised and has been associated with worse health outcomes and higher healthcare costs compared with COPD or asthma alone. Patients with ACO in Asia remain poorly described, an...

The next generation tactical networks will be based on mobile ad hoc networks (MANETs). These networks require as well a stable clustered network structure as an efficient channel assignment optimization method. Efficient spatial channel reuse provides network scalability and high spectral efficiency. In this paper, a centralized clustering algorithm scheme based on ant colony optimization (ACO...