Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Immune cell profiling in COVID-19 recovered patients using mass cytometry

Coronavirus disease 2019 (COVID-19), caused by the zoologic virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is among the most impactful pandemics in modern history. Infection impact has variating tendency within patients that experience mild disease compared to severely affected patients, such as respiratory failure and death. Currently, treatment strategies aim to alleviating the symptoms, but a healing cure except vaccination is not resolved yet, due to lack of knowledge about the virus and how it affects the immune system. Duration of immunological memory after experiencing COVID-19 is unclear and unknown, along with limited knowledge about the disease grade influence on the immune system recovery. Firstly, this thesis aimed to study the peripheral blood immune system in SARS-Cov-2 infected patients 6 months post-infection. Using a mass cytometric approach, fixed whole blood in SARS-Cov-2 infected patients and healthy controls were analyzed. We compared immune cell frequencies among moderate and severe disease patients compared to healthy controls. A SARS-CoV-2 specific heterogeneity was observed which indicated recovery based on other factors, such as genetics and medical history. Secondly, we aimed to identify cell-affecting concentrations of randomly selected phytochemicals (Ellagic acid, rumic acid, Dinatin 7-glucuoronide, and plantainoside D) that can potentially be used in COVID-19 treatment. MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay was exploited to find concentrations that affected the cell proliferation on human embryonic kidney cells (HEK293), human embryonic kidney cells variant that express a temperature- sensitive allele of SV40 T antigen (HEK-293t) and colorectal adenocarcinoma cells 2 (CACO-2).Masteroppgave i FarmasiFARM399/05HMATF-FAR

1999 Eleventh Annual IMSA Presentation Day

Abstracts can be found attached in alphabetical order under the first presenter.https://digitalcommons.imsa.edu/archives_sir/1025/thumbnail.jp

The suitability of the dendritic cell algorithm for robotic security applications

The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction

05. 1999 Eleventh Annual IMSA Presentation Day

https://digitalcommons.imsa.edu/class_of_2000/1004/thumbnail.jp