Prediction of protein cellular localization sites using a hybrid method based on artificial immune system and fuzzy k-NN algorithm

The use of artificial intelligence methods in biological data analysis has been increased recent since performance of the classification and detection systems have improved considerably to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm with and without cross validation in a class of imbalanced problems in bioinformatics. Furthermore, we devise an unsupervised AIS algorithm in a supervised manner which contains a training stage for data reduction and a classification stage using fuzzy k-NN algorithm. The experiments show the efficacy of the proposed method with promising results. Using the Escherichia coli and yeast database, we compare the classification accuracy of the proposed method with those of other methods which have been proposed in the literature. The proposed hybrid system produced much more accurate results than the Horton and Nakai's method [P. Horton, K. Nakai, Better prediction of protein cellular localization sites with the k-nearest neighbors classifier, in: Proceedings of Intelligent Systems in Molecular Biology, Halkidiki, Greece, 1997, pp. 368–383]. Besides the improvement on the classification accuracy, one of the important aspects of the proposed method is the complexity. As the proposed AIS method incorporates data reduction in the training stage, the training complexity is considerably low comparing with the k-NN classifier.     

A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.     

基于混合算法的通信用户规模预测方法研究

准确地对通信用户规模进行预测对于通信运营商的决策具有十分重要的意义,而现有的常规预测方法存在预测误差较大、预测速率低等问题。研究一种基于RBF神经网络的通信用户规模预测模型。为了使得RBF神经网络算法预测性能更优,使用梯度下降算法与遗传算法混合对RBF神经网络进行参数优化,提高预测模型收敛效率。实例分析表明,使用本文研究的混合RBF神经网络预测模型的预测结果明显优于其他传统的预测模型。同时,在预测速度上也具有较大的优势。     

Dynamic route guidance algorithm based algorithm based on artificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.     

基于人工免疫系统的关联规则挖掘算法

给出了一个基于人工免疫系统的关联规则挖掘算法。将训练数据作为抗原,候选模式作为人工识别球(ARB),通过免疫学习生成频繁模式并以免疫记忆的形式加以保存,最终生成关联规则。所给的应用实例说明本算法是可行、有效的。     

Dynamic route guidance algorithm based on artificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems, this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the metaphor mechanism of vertebrate immune systems. This algorithm, applied to the urban traffic network model established by the node-expanding method, can expediently realize K-shortest paths search in the urban traffic guidance systems. Because of the immune memory and global parallel search ability from artificial immune systems, K-shortest paths can be found without any repeat, which indicates evidently the superiority of the algorithm to the conventional ones. Not only does it perform a better parallelism, the algorithm also prevents premature phenomenon that often occurs in genetic algorithms. Thus, it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications. A case study verifies the efficiency and the practicability of the algorithm aforementioned.     

Dynamic route guidance algorithm based on artificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems, this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the metaphor mechanism of vertebrate immune systems. This algorithm, applied to the urban traffic network model established by the node-expanding method, can expediently realize K-shortest paths search in the urban traffic guidance systems. Because of the immune memory and global parallel search ability from artificial immune systems, K-shortest paths can be found without any repeat, which indicates evidently the superiority of the algorithm to the conventional ones. Not only does it perform a better parallelism, the algorithm also prevents premature phenomenon that often occurs in genetic algorithms. Thus, it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications. A case study verifies the efficiency and the practicability of the algorithm aforementioned.     

一种基于人工免疫系统的聚类算法

根据数据分析中聚类判断所遵循的原则,模拟抗体捕获抗原的机制,提出了一种基于人工免疫系统的聚类算法,最终可以获得全局最优解,并且具有本质上的并行性、计算效率高和聚类能力强等优点。     

基于人工免疫系统的单类分类算法研究

为了增加检测器的覆盖率,提高算法性能,对V-detector检测器生成算法进行改进,提出了一种不限定检测器中心点位置的检测器生成算法。在检测器生成时,使大部分检测器中心点仍然在问题域内,覆盖问题域内的非我区域,少量检测器中心点位于问题域之外,覆盖问题域边缘。改进后的检测器生成算法中,检测器中心点分量服从正态分布,检测器覆盖问题域边缘的能力增强,检测器覆盖率更高。在基准数据集上的实验结果表明,改进后算法的性能有显著提高。     

基于Bagging的组合k-NN预测模型与方法

k-近邻方法基于单一k值预测,无法兼顾不同实例可能存在的特征差异,总体预测精度难以保证.针对该问题,提出了一种基于Bagging的组合k-NN预测模型,并在此基础上实现了具有属性选择的Bgk-NN预测方法.该方法通过训练建立个性化预测模型集合,各模型独立生成未知实例预测值,并以各预测值的中位数作为组合预测结果.Bgk-NN预测可适用于包含离散值属性及连续值属性的各种类型数据集.标准数据集上的实验表明,Bgk-NN预测精度较之传统k-NN方法有了明显提高.     

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Proper interpretation of the thyroid gland functional data is an important issue in the diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body’s metabolism. Thyroid hormone produced by the thyroid gland provides this. Production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism) defines the type of thyroid disease. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has shown an effective and intriguing performance on the problems it was applied. This study aims at diagnosing thyroid disease with a new hybrid machine learning method including this classification system. By hybridizing AIRS with a developed Fuzzy weighted pre-processing, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used thyroid disease dataset which is taken from UCI machine learning respiratory. We obtained a classification accuracy of 85%, which is the highest one reached so far. The classification accuracy was obtained via a 10-fold cross-validation.     

说话人识别中采用混合免疫算法的VQ码本设计

矢量量化(VQ)方法是文本无关说话人识别中广泛应用的建模方法之一,它的主要问题是码本设计问题。语音特征参数是高维数据,样本分布复杂,因此码本设计的难度也很大,传统的LBG算法只能获得局部最优的码本。提出一种VQ码本设计的新方法,将小生境技术与K-均值算法融入到免疫算法训练过程中,形成混合免疫算法,采用针对高维数据聚类的改进变异算子,降低了随机变异的盲目性,增强群体的全局及局部搜索能力,同时通过接种疫苗提高算法的收敛速度。说话人识别实验表明,与传统LBG和基于混合遗传算法的VQ码本设计方法相比,该方法可以得到更优的模型参数,使得系统的识别率进一步提高。     

基于免疫记忆人工鱼群算法的局部放电超声定位方法

针对人工鱼群算法(AFSA)在局部放电超声定位中存在定位精度较低,容易陷入局部最优解的问题,在AFSA中引入免疫调节机制和免疫记忆特性,提出一种基于免疫记忆AFSA的局部放电超声定位方法,并将其定位效果与基本AFSA、遗传算法(GA)和免疫算法进行对比.算例仿真表明:免疫记忆人工鱼群算法的定位精度更高、综合误差最小、收敛速度更快,定位效果都优于其它三种智能算法.     

A hybrid time-delay prediction method for networked control system

This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.     

基于多指纹联合匹配的混合定位算法

随着信息科技的迅猛发展,室内定位技术已经成为基于位置服务LBS的研究热点之一。基于接收信号强度RSS的位置指纹与步行者航位推算PDR相结合的定位算法能有效提高定位精度,但目前已有的算法难以同时满足较高的定位精度与较小的计算量,常见的卡尔曼滤波算法精度不够,而粒子滤波算法计算量较大。提出了一种基于多指纹联合匹配的混合定位算法,有效融合惯性信息与RSS指纹信息,在较低计算量的前提下实现了高精度定位。实验结果表明,该算法80%的定位精度低于1m,平均精度高达0.77m。     

A hybrid artificial immune system and Self Organising Map for network intrusion detection

Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. Two broad approaches exist to tackle this problem: anomaly detection and misuse detection. An anomaly detection system is trained only on examples of normal connections, and thus has the potential to detect novel attacks. However, many anomaly detection systems simply report the anomalous activity, rather than analysing it further in order to report higher-level information that is of more use to a security officer. On the other hand, misuse detection systems recognise known attack patterns, thereby allowing them to provide more detailed information about an intrusion. However, such systems cannot detect novel attacks.A hybrid system is presented in this paper with the aim of combining the advantages of both approaches. Specifically, anomalous network connections are initially detected using an artificial immune system. Connections that are flagged as anomalous are then categorised using a Kohonen Self Organising Map, allowing higher-level information, in the form of cluster membership, to be extracted. Experimental results on the KDD 1999 Cup dataset show a low false positive rate and a detection and classification rate for Denial-of-Service and User-to-Root attacks that is higher than those in a sample of other works.     

基于人工免疫的新型检测器生成模型

继承了人工免疫系统的思想,研究了KIM和BENTLEY的克隆选择算法,提出了一种适用于入侵检测的新的检测器生成模型。其核心在于两个新的算法：一是为了提高检测器的多样性及适应度水平,提出了基于相似性和适应度相结合的概率选择算法,并给出了此类概率选择的一般形式,理论分析了算法中的权重参数α。二是在产生子代检测器时,为了使得父代的优良基因能最大程度地遗传给子代,防止交叉变异中的退化现象,提出了检测器有效因子的概念和使用有效因子进行保优的策略。通过仿真实验证明合适选择α参数以及有效因子的长度阈值Neg,能使该模型具有很好的多样性和自适应性,呈现出较高的“非我”检测率和低的误检率。     

基于DV-Hop定位算法和RSSI测距技术的定位系统

针对 DV Hop算法在实验环境中存在的问题,加入接收信号强度指示器（RSSI）测距模块辅助定位,对算法进行改进。为了实现定位系统,首先,需要建立当前实验环境的RSSI模型;然后,应用该模型,从锚节点和非锚节点两方面分别控制DV Hop定位过程。实验证明：改进后的定位系统在增加少量计算复杂度的情况下,改善了系统的稳定性,提高了定位的精度,可以被应用到无线传感器网络中。