AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.     

An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.     

Implementation of call admission control scheme in next generation mobile communication networks using particle swarm optimization and fuzzy logic systems

In the present and next generation wireless networks, cellular system remains the major method of telecommunication infrastructure. Since the characteristic of the resource constraint, call admission control is required to address the limited resource problem in wireless network. The call dropping probability and call blocking probability are the major performance metrics for quality of service (QoS) in wireless network. Many call admission control mechanisms have been proposed in the literature to decrease connection dropping probability for handoffs and new call blocking probability in cellular communications. In this paper, we proposed an adaptive call admission control and bandwidth reservation scheme using fuzzy logic control concept to reduce the forced termination probability of multimedia handoffs. Meanwhile, we adopt particle swarm optimization (PSO) technique to adjust the parameters of the membership functions in the proposed fuzzy logic systems. The simulation results show that the proposed scheme can achieve satisfactory performance when performance metrics are measured in terms of the forced termination probability for the handoffs, the call blocking probability for the new connections and bandwidth utilization.     

高维进化策略调整神经网络结构和参数

提出一种基于佳点集原理的进化策略用于神经网络结构和参数的调整.为了克服正交设计法的一些不足来处理高维最优化问题,本文采用分步交叉框架,将佳点集技术引入实数域交叉算子增强高维空间的搜索能力.前馈神经网络的隐含节点与连接边数从小逐步递增直至学习效果足够好.通过调整能得到一个部分连接的前馈网络,减少了网络实现的耗费.最后,佳点集进化策略有效应用于生成预测太阳黑子的演化神经网络.实验结果证明了新方法的有效性.     

水声通信网络信道仿真研究

根据海洋环境下水声信道的特性,提出一个面向水声通信网络信道的仿真框架,其中包括传播延迟、接收功率、背景噪声、干扰噪声、信噪比与误码率等若干模型,并以一个典型的水声网络仿真场景为例,给出了部分仿真试验结果.     

一种高效的动态概率广播算法及其概率模型检测分析

在Ad Hoc网络中,广播有着相当广泛的应用,其算法的效率极大地影响着网络的性能.本文基于DP算法提出了BN-DP算法,考虑了节点分布、计数器值以及收发节点间距离对广播算法的影响,赋予处于接收边缘的节点更高的转发概率.然后使用概率模型检测工具PRISM,分析了计数器值和节点分布对BN-DP算法性能的影响.结果表明:在相同可达率的情况下,所提出的BN-DP算法与FP、DP算法相比,减少了转发分组的数量,提高了广播效率.     

基于多格的ad hoc网络密钥预配置方案

借鉴传感器网络中基于多项式的密钥对预配置方案,设计出一个应用于ad hoc网络的高效密钥对预配置方案:基于多格的密钥预配置方案.该方案提出了多格的概念,将密钥的预配置建立在多个由多项式构成的二维表格之上,从而增加了节点之间建立会话密钥的概率.第1次将门限机制应用到密钥的传输过程中,增强了密钥传输过程中的安全性.通过分析可以看出该方案有一些优良的特性,包括两节点间能以很高的概率成功建立密钥对,对入侵有较强的鲁棒性、低通信量.     

基于时间窗的随机时变交通网络信号相位协调

交通网络是随机时变网络,用周期性时间窗模拟各路口信号灯控制,建立交通网络中路口相位差协调控制模型。时间窗的设定使只有规定行驶方向的车辆可以通行路口,其他车辆不可通行。为得到车辆在路口前等待状况,定义时间窗函数,该函数采用协调交通网络路口信号相位差的方法求得随机时变网络的最短期望路径。结合改进的SDOT算法和穷举法及遗传算法设计一种混合算法。对一个四路口小型交通网络进行了仿真研究,结果验证了求解算法的有效性。     

基于图论聚合度的动态分层路由算法

讨论无线传感器网络簇的组成问题及其形式化描述,提出节点聚合度的概念并引入簇首的生成过程,给出一种新的优化路由算法。对算法的设计思想和工作过程,包括簇首的选举与簇建立、簇重组与自愈机制进行了分析与讨论。仿真结果显示,与传统的随机簇首生成算法和最小ID算法相比,该算法降低了簇内节点的重叠度,均衡了网络节点的能量消耗,延长了网络的生存时间。