NEW HYBRID AI-SVM ALGORITHM: COMBINATION OF SUPPORT VECTOR MACHINES AND ARTIFICIAL IMMUNE NETWORKS

Support vector machines （SVMs） are combined with the artificial immune network （aiNet）, thus forming a new hybrid ai-SVM algorithm. The algorithm is used to reduce the number of samples and the training time of SVM on large datasets, aiNet is an artificial immune system （AIS） inspired method to perform the automatic data compression, extract the relevant information and retain the topology of the original sample distribution. The output of aiNet is a set of antibodies for representing the input dataset in a simplified way. Then the SVM model is built in the compressed antibody network instead of the original input data. Experimental results show that the ai-SVM algorithm is effective to reduce the computing time and simplify the SVM model, and the accuracy is not decreased.

Adaptive Immune Evolutionary Algorithms Based on Immune Network Regulatory Mechanism

Based on immune network regulatory mechanism, a new adaptive immune evolutionary algorithm (AIEA) is proposed to improve the performance of genetic algorithms (GA) in this paper. AIEA adopts novel selection operation according to the stimulation level of each antibody. A memory base for good antibodies is devised simultaneously to raise the convergent rapidity of the algorithm and adaptive adjusting strategy of antibody population is used for preventing the loss of the population adversity. The experiments show AIEA has better convergence performance than standard genetic algorithm and is capable of maintaining the adversity of the population and solving function optimization problems in an efficient and reliable way.

Parameter optimization of pharmacokinetics based on artificial immune network

A new method for parameter optimization of pharmacokinetics based on an artificial immune network named PKAIN is proposed. To improve local searching ability of the artificial immune network, a partition-based concurrent simplex mutation is developed. By means of evolution of network cells in the PKAIN artificial immune network, an optimal set of parameters of a given pharmacokinetic model is obtained. The Laplace transform is applied to the pharmacokinetic differential equations of remifentanil and its major metabolite, remifentanil acid. The PKAIN method is used to optimize parameters of the derived compartment models. Experimental results show that the twocompartment model is sufficient for the pharmacokinetic study of remifentanil acid for patients with mild degree of renal impairment.

A Multi-agent Artificial Immune Network Algorithm for the Tray Efficiency Estimation of Distillation Unit

Based on the immune mechanics and multi-agent technology, a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced. Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem, and combines the global and local search in the searching process. The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms. The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions. Furthermore, Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.

A Multi-Agent Immune Network Algorithm and Its Application to Murphree Efficiency Determination for the Distillation Column

Artificial Immune Network （aiNet） algorithms have become popular for global optimization in many modem industrial applications. However, high-dimensional systems using such models suffer from a potential premature convergence problem. In the existing aiNet algorithms, the premature convergence problem can be avoided by implementing various clonal selection methods, such as immune suppression and mutation approaches, both for single population and multi-population cases. This paper presents a new Multi-Agent Artificial Immune Network （Ma-aiNet） algorithm, which combines immune mechanics and multiagent technology, to overcome the premature convergence problem in high-dimensional systems and to efficiently use the agent ability of sensing and acting on the environment. Ma-aiNet integrates global and local search algorithms. The perform- ance of the proposed method is evaluated using 10 benchmark problems, and the results are compared with other well-known intelligent algorithms. The study demonstrates that Ma-aiNet outperforms other algorithms tested. Ma-aiNet is also used to determine the Murphree efficiency of a distillation column with satisfactory results.

Anomaly Detection with Artificial Immune Network

Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.

A decision hyper plane heuristic based artificial immune network classification algorithm

Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm （DHPA1NC） is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.

A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

Optimizing control of coal flotation by neuro-immune algorithm

Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles.

Multi-agent immune recognition of water mine model

It is necessary for mine countermeasure systems to recognise the model of a water mine before destroying because the destroying measures to be taken must be determined according to mine model. In this paper, an immune neural network (INN) along with water mine model recognition system based on multi-agent system is proposed. A modified clonal selection algorithm for constructing such an INN is presented based on clonal selection principle. The INN is a two-layer Boolean network whose number of outputs is adaptable according to the task and the affinity threshold. Adjusting the affinity threshold can easily control different recognition precision, and the affinity threshold also can control the capability of noise tolerance.

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge （SOC） trajectory in plug-in hybrid electric vehicles （PHEVs）. The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categoriz- ing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm （AIA）. The objective function of the optimizer is derived from a computationally efficient artificial neural network （ANN） which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomic software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

Rapid plant immune responses in the appropriate cells are needed for effective defense against pathogens.Although transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is challenging.We reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in Arabidopsis.Double-peak time-course patterns are prevalent among thousands of upregulated genes.We implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene.The decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated genes.We demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to AvrRpt2.Thus,the peak decomposition has extracted spatial information from the time-course data.The echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered immunity.Activation of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor types.We discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

Comparative Effects of Avoidance and Immunization on Epidemic Spreading in a Dynamic Small-World Network with Community Structure

Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.

Future View and Development of Immunology:Exploring the Immunology Based on Chinese Medicine and Culture

The immunologically mediated disease is a big family which affects many people in the world, since the cures are not yet available for most immune diseases, the patients face a lifetime of illness and treatment. Chinese medicine inspires us to develop new methods for the treatment of immune diseases. Previous researches of immune system have revealed that an immune network exists. The immune system is like a complex highway; if we travel on these highways, we must have a map to avoid travelling in the wrong direction. Drawing the map of immune network will provide new tools for us to look directly at the basis of the immune system.