Classification of Software Engineering Documents Based on Artificial Immune Systems

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

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

Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism

The research into intrusion detection system (IDS) based on immunology has shifted from the negative selection algorithm to a novel concept, the Danger Theory (DT). Dendritic Cells (DCs) are professional antigen presenting cells in innate immunity that perform the central role of sensing infection situation and coordinating T cells behavior. DCs play an integral part in incorporation of DT into responsive IDSs. To illustrate how the functionality and control mechanism of DCs can be harnessed to solve real network traffic problems, the DCs inspired Model (DCML) for intrusion detection is presented. Introducing interaction between DCs and memory T cells to sense danger is a highlight notion in DCML. Additonally, using multiplicity of signals for input into the final decision increases robustness and effectiveness of IDS. I d e x Terms-Danger Theory, Artificial Immune System, Intrusion Detection, Dendritic Cells

Artificial immune recognition system (AIRS) has been convincingly proved a highly effective classifier, which has been successfully applied to pattern recognition and etc. However, there are two shortcomings that limit its further applications, one is the huge size of evolved memory cells pool, and the other is low classification accuracy. In order to overcome these limitations, a supervised artificial immune classifier, UCAIS, is presented. The implementation of UCAIS includes: the first is to create a pool of memory cells. Then, B-cell population is evolved and the memory cells pool is updated until the stopping criterion is met. Finally, classification is accomplished by majority vote of the k nearest memory cells. Compared with AIRS, UCAIS not only reduces the huge size of evolved memory cells pool, but also improves the classification accuracy on the four famous datasets, the Iris dataset, the Ionosphere dataset, the Diabetes dataset, and the Sonar dataset.

Lysophosphatidic acid (LPA) is a biologically active lysophospholipid that can regulate immune activation. LPA can activate T cells and dendritic cells (DCs), but the effects of LPA on the ability of DCs to influence T cell polarization are not well understood. Human monocyte-derived DCs were differentiated in vitro in the presence of interleukin-4 (IL-4) and granulocyte-macrophage colonystimulating factor (GM-CSF), and matured with LPA and lipopolysaccharide (LPS) alone or in combination. DC activation was monitored by analyzing cell-surface expression of co-stimulatory receptors and cytokine production. The ability of DCs to influence T cell activation was determined using two models of DC:T cell co-culture. Maturation with LPS induced dose-dependent DC activation characterized by enhanced expression of co-stimulatory molecules (e.g., CD86) and production of cytokines including IL-6 and IL-10. Co-incubation with LPA attenuated the LPS-induced production of IL-6, without significantly affecting IL-10 secretion or the ability of DC to promote T cell proliferation. DCs matured in the presence of both LPA and LPS enhanced the production of interferon-gamma (IFN-gamma) when co-cultured with allogeneic T cells, compared with DC matured by LPS alone. Similar results were found using a model of allogeneic naïve T cell differentiation, where LPA- plus LPS-matured DC enhanced IFN-gamma as well as IL-4 secretion after restimulation. Lysophosphatidic acid fine-tunes the effects of LPS on human myeloid DC maturation, but does not exert a dominant effect on the ability of DC to influence Th cell polarization.

As the nanotechnology market expands and the prevalence of allergic diseases keeps increasing, the knowledge gap on the capacity of nanomaterials to cause or exacerbate allergic outcomes needs more than ever to be filled. Engineered nanoparticles (NP) could have an adjuvant effect on the immune system as previously demonstrated for particulate air pollution. This effect would be the consequence of the recognition of NP as immune danger signals by dendritic cells (DCs). The aim of this work was to set up an in vitro method to functionally assess this effect using amorphous silica NP as a prototype. Most studies in this field are restricted to the evaluation of DCs maturation, generally of murine origin, through a limited phenotypic analysis. As it is essential to also consider the functional consequences of NP-induced DC altered phenotype on T-cells biology, we developed an allogeneic co-culture model of human monocyte-derived DCs (MoDCs) and CD4+ T-cells. We demonstrated that DC: T-cell ratios were a critical parameter to correctly measure the influence of NP danger signals through allogeneic co-culture. Moreover, to better visualize the effect of NP while minimizing the basal proliferation inherent to the model, we recommend testing three different ratios, preferably after five days of co-culture.

Description Dendritic cells are professional and potent antigen presenting cells that screen all tissues for antigens, infection agents, and tumor cells to prime and activate T cells during immune responses. Dendritic cells are required for efficient recognition and elimination of infected and tumor cells, but they also can amplify pathological inflammation in autoimmunity and allergic responses leading to tissue damage. How dendritic cells balance inflammatory and anti-inflammatory programs remains largely unknown. We and others have shown that dendritic cells metabolism has a profound impact on antigen presentation and inflammation. We showed that excessive fatty acid metabolism increases chronic inflammation driven by dendritic cells in models of skin inflammation in obesity. This metabolic reprogramming of dendritic cells initiated the unfolded protein stress response and targeted stress-activated transcription factors to amplify the expression of pro-inflammatory cytokines. We also found that glutamine metabolism of dendritic cells directly regulates antigen presentation and cytokine secretion through the mechanism involving the conversion of glutamine to glutamate by mitochondrial glutaminase. Glutaminase-dependent metabolism of dendritic cells enhanced autophagy and autophagy-dependent antigen presentation to CD4 T cells, thus linking metabolic sensing to immunity. These results show that metabolism is an integral regulator of immune functions of dendritic cells. Funding Sources Supported by NIH/NIAMS R21AR080233; Glenn Foundation for Medical Research and AFAR Grant for Junior Faculty; Discovery Grant from The National Psoriasis Foundation (USA) Topic Categories Antigen and Dendritic Cell Processing, Presentation, and Biology (AGDC)

Danger Theory is presented with particular predominance on analogies in the Artificial Immune Systems world. Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). The idea is that the artificial cells release signals describing their status, e.g., safe signals and danger signals. The various artificial cells use the signals in order to adapt their behavior. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e., linking it directly to the attacker. In this paper, we look at Danger Theory from the perspective of AIS practitioners and an overview of the Danger Theory is presented with particular emphasis on analogies in the Artificial Immune Systems world.

Intrusion detection systems play increasingly essential roles in modern society. With all various approaches used to Anomaly Detection, we investigate Artificial Immune Systems (AIS) to model anomaly detection. One new field of AIS is called Danger Theory. Since traditional Anomaly Detection Methods produce a large number of false alarms, AIS methods based on Danger Theory are suitable options for intrusion detection that can handle the above problem. So in this paper, we propose a novel method in danger theory field for anomaly detection and decrease the amount of system false alarms by using danger signals which are produced through a KNN classifier. Our experimental results show that this proposed method has low false alarm, by using the danger signals. Use of this danger signal that created by danger theory can improved our Artificial Immune Network and help us to achieve low false alarm in our results.

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

A comprehensive analysis of an automated system for epileptic seizure detection is explained in this work. When a seizure occurs, it is quite difficult to differentiate the non-stationary patterns from the discharges occurring in a rhythmic manner. The proposed approach deals with it efficiently by clustering it initially for the sake of feature extraction by using six different techniques categorized under two different methods, e.g., bio-inspired clustering and learning-based clustering. Learning-based clustering includes K-means clusters and Fuzzy C-means (FCM) clusters, while bio-inspired clusters include Cuckoo search clusters, Dragonfly clusters, Firefly clusters, and Modified Firefly clusters. Clustered values were then classified with 10 suitable classifiers, and after the performance comparison analysis of the EEG time series, the results proved that this methodology flow achieved a good performance index and a high classification accuracy. A comparatively higher classification accuracy of 99.48% was achieved when Cuckoo search clusters were utilized with linear support vector machines (SVM) for epilepsy detection. A high classification accuracy of 98.96% was obtained when K-means clusters were classified with a naive Bayesian classifier (NBC) and Linear SVM, and similar results were obtained when FCM clusters were classified with Decision Trees yielding the same values. The comparatively lowest classification accuracy, at 75.5%, was obtained when Dragonfly clusters were classified with the K-nearest neighbor (KNN) classifier, and the second lowest classification accuracy of 75.75% was obtained when Firefly clusters were classified with NBC.

Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER + DC + HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p = 0.02069) and 80% (16/20, P = 0.005909), respectively. Multi-cell computational models became ‘personalized’ when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.

Recent developments show that naive Bayesian classifier (NBC) performs significantly better in applications, although it is based on the assumption that all attributes are independent of each other. However, in the NBC each variable has a finite number of values, which means that in large data sets NBC may not be so effective in classifications. For example, variables may take continuous values. To overcome this issue, many researchers used fuzzy naive Bayesian classification for partitioning the continuous values. On the other hand, the choice of the distance function is an important subject that should be taken into consideration in fuzzy partitioning or clustering. In this study, a new fuzzy Bayes classifier is proposed for numerical attributes without the independency assumption. To get high accuracy in classification, membership functions are constructed by using the fuzzy C-means clustering (FCM). The main objective of using FCM is to obtain membership functions directly from the data set instead of consulting to an expert. The proposed method is demonstrated on the basis of two well-known data sets from the literature, which consist of numerical attributes only. The results show that the proposed the fuzzy Bayes classification is at least comparable to other methods.