Immune system

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An immune system is a system of biological structures and processes within an organism that protects against disease by identifying and killing pathogens and tumor cells. (Wikipedia.org)






Conferences related to Immune system

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2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

The goal of the 14th ASME/IEEE MESA2018 is to bring together experts from the fields of mechatronic and embedded systems, disseminate the recent advances in the area, discuss future research directions, and exchange application experience. The main achievement of MESA2018 is to bring out and highlight the latest research results and developments in the IoT (Internet of Things) era in the field of mechatronics and embedded systems.


2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

The RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics - BioRob 2018 - is a joint effort of the two IEEE Societies of Robotics and Automation - RAS - and Engineering in Medicine and Biology - EMBS.BioRob covers both theoretical and experimental challenges posed by the application of robotics and mechatronics in medicine and biology. The primary focus of Biorobotics is to analyze biological systems from a "biomechatronic" point of view, trying to understand the scientific and engineering principles underlying their extraordinary performance. This profound understanding of how biological systems work, behave and interact can be used for two main objectives: to guide the design and fabrication of novel, high performance bio-inspired machines and systems for many different applications; and to develop novel nano, micro-, macro- devices that can act upon, substitute parts of, and assist human beings in prevention, diagnosis, surgery, prosthetics, rehabilitation.


2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)

The SAM Workshop is an biennial IEEE Signal Processing Society event dedicated to sensor array and multichannel signal processing. It is managed by the Sensor Array and Multichannel Signal Processing Technical Committee of the IEEE Signal Processing Society.


2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

The conference will deal will all aspects of power electronics, motor drives and Power electronics applications to energy systems.


2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

Computational analysis and interpretation of images and video


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Periodicals related to Immune system

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Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


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Most published Xplore authors for Immune system

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Xplore Articles related to Immune system

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Synthesis and learning of fuzzy neural networks for solving forecasting problems

Serhii Okrenets; Andrii Fefelov; Volodymyr Lytvynenko; Volodymyr Osypenko; Maksym Korobchynskyi; Maria Voronenko 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017

Two variations of the network are described i.e. Larsen model and Tsukamoto model. In each case an artificial immune network is proposed as a means of building fuzzy network structure and performing parameters inference.


A Stroke-Based Textual Password Authentication Scheme

Ziran Zheng; Xiyu Liu; Lizi Yin; Zhaocheng Liu 2009 First International Workshop on Education Technology and Computer Science, 2009

Textual-based password authentication scheme tend to more vulnerable to attacks such as shoulder-surfing and hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based scheme. Because simply adopting graphical password authentication also has some drawbacks, some hybrid schemes based on graphic and text were developed. In this ...


Intrusion Detection Model Based on Hierarchical Structure in Wireless Sensor Networks

Lei Li; Yan-hui Li; Dong-yang Fu; Wan Ming 2010 International Conference on Electrical and Control Engineering, 2010

In this paper, presents a intrusion detection model based on hierarchical structure in wireless sensor network. The model structure is simple and improves the security of wireless sensor networks; this model uses a multi- node with the idea of joint collaboration, intrusion detection nodes with anomaly detection algorithm. It shows through experiments with real data that the algorithm can lower ...


A hybrid heuristic design technique for real-time matching optimization for wearable near-field ambient RF energy harvesters

Jo Bito; Manos M. Tentzeris; Apostolos Georgiadis 2016 IEEE MTT-S International Microwave Symposium (IMS), 2016

In this paper, a novel real-time active matching circuit design process based on preliminary measurements and a hybridization of a genetic algorithm and a data mining method is discussed. As a result, our proposed matching circuit can potentially have higher dc output power at 92.0 % and 69.6 %of potential load combinations with a maximum matching performance improvement of 21.4 ...


Immune RBF neural network algorithm for DSTATCOM

G. Arthy; C. N. Marimuthu 2016 International Conference on Computer Communication and Informatics (ICCCI), 2016

A Distribution STATic COMpensator (DSTATCOM) controller with an immune RBF neural network algorithm is used for harmonic elimination, Power Factor correction by compensating reactive power, load balancing with nonlinear loads is discussed in this paper. The immune algorithm incorporated with Radial Basis Function(RBF) neural network control algorithm combines the pros of immune optimization algorithm with the Radial Basis Function neural ...


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Educational Resources on Immune system

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eLearning

Synthesis and learning of fuzzy neural networks for solving forecasting problems

Serhii Okrenets; Andrii Fefelov; Volodymyr Lytvynenko; Volodymyr Osypenko; Maksym Korobchynskyi; Maria Voronenko 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017

Two variations of the network are described i.e. Larsen model and Tsukamoto model. In each case an artificial immune network is proposed as a means of building fuzzy network structure and performing parameters inference.


A Stroke-Based Textual Password Authentication Scheme

Ziran Zheng; Xiyu Liu; Lizi Yin; Zhaocheng Liu 2009 First International Workshop on Education Technology and Computer Science, 2009

Textual-based password authentication scheme tend to more vulnerable to attacks such as shoulder-surfing and hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based scheme. Because simply adopting graphical password authentication also has some drawbacks, some hybrid schemes based on graphic and text were developed. In this ...


Intrusion Detection Model Based on Hierarchical Structure in Wireless Sensor Networks

Lei Li; Yan-hui Li; Dong-yang Fu; Wan Ming 2010 International Conference on Electrical and Control Engineering, 2010

In this paper, presents a intrusion detection model based on hierarchical structure in wireless sensor network. The model structure is simple and improves the security of wireless sensor networks; this model uses a multi- node with the idea of joint collaboration, intrusion detection nodes with anomaly detection algorithm. It shows through experiments with real data that the algorithm can lower ...


A hybrid heuristic design technique for real-time matching optimization for wearable near-field ambient RF energy harvesters

Jo Bito; Manos M. Tentzeris; Apostolos Georgiadis 2016 IEEE MTT-S International Microwave Symposium (IMS), 2016

In this paper, a novel real-time active matching circuit design process based on preliminary measurements and a hybridization of a genetic algorithm and a data mining method is discussed. As a result, our proposed matching circuit can potentially have higher dc output power at 92.0 % and 69.6 %of potential load combinations with a maximum matching performance improvement of 21.4 ...


Immune RBF neural network algorithm for DSTATCOM

G. Arthy; C. N. Marimuthu 2016 International Conference on Computer Communication and Informatics (ICCCI), 2016

A Distribution STATic COMpensator (DSTATCOM) controller with an immune RBF neural network algorithm is used for harmonic elimination, Power Factor correction by compensating reactive power, load balancing with nonlinear loads is discussed in this paper. The immune algorithm incorporated with Radial Basis Function(RBF) neural network control algorithm combines the pros of immune optimization algorithm with the Radial Basis Function neural ...


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IEEE-USA E-Books

  • Enact Organizational Change

    Change is hard for almost all of us. Depending upon how entrenched the slide design practices are in an organization, a presenter may find varying reactions from the audience of resistance, acceptance, and excitement upon deploying the alternative model of design outlined in this chapter. Engineers and other technical experts become quite excited about the possibilities of reinvigorating their presentations. They begin to process how the new techniques will actually be received at work or in the classroom. The chapter explains the four phases of this process: inspired, incredulous, assessed, and accepting, maybe to the point of being evangelical. Being able to tailor content, design, purpose, and knowledge makes it all the more possible for the presenter to enact change where he works. The authors suggest that change is good, especially when it is enacted by people who are good problem solvers.

  • Australia's HealthConnect

    This chapter contains sections titled: * Introduction * Overview * Architecture * Discussion * Conclusion * Bibliography

  • Immune Principle and Neural Networks-Based Malware Detection

    Detection of unknown malware is one of most important tasks in Computer Immune System (CIS) studies. By using nonself detection, diversity of anti-body (Ab) and artificial neural networks (ANN), this chapter proposes an NN-based malware detection algorithm. A number of experiments illustrate that this algorithm has high detection rate with a very low false positive rate. Aiming at automation detection malicious executables, the chapter proposes a novel malware detection algorithm (MDA) based on the immune principle and ANN. Extensive experiments show that the algorithm has a better detection performance than Schultz's method. The first goal is to verify the detection ability of the malware detection algorithm for malicious executables. The experimental results are all scaled with false positive rate (FPR) and detection rate (DR). The second goal is to calculate the probability of the reducing detection hole with the diversity of detectors.

  • Artificial Immune System

    Artificial immune system (AIS) is a computational intelligence system inspired by the working mechanism and principle of biological immune system (BIS). BIS makes use of innate immunity and adaptive immunity systems to generate accurate immune response against the invading antigens. The two systems mutually cooperate to resist the invasion of external antigens. The key to designing the AIS is to take full advantage of the immunology principles and to replicate the effectiveness and capability of the BIS in computer systems. Most of the AISs and malware detection methods have some deficiencies and shortcomings, which stimulates researchers to explore more efficient models and algorithms, including negative selection algorithm, clonal selection algorithm, immune network model, Danger theory, and immune concentration. At present, AIS has been widely used in many fields such as pattern recognition, function optimization, computer security, robot control, and data analysis.

  • Hong Kong's eHR Sharing System

    This chapter contains sections titled: * Introduction * Overview * Architecture * Discussion * Conclusion * Bibliography

  • Immune Cooperation Mechanism-Based Learning Framework

    Inspired from the immune cooperation (IC) mechanism in biological immune systems (BIS), this chapter presents an IC mechanism-based learning (ICL) framework. In this framework, a sample is expressed as an antigen-specific feature vector and an antigen-nonspecific feature vector, simulating the antigenic determinant and danger features in the BIS. The ICL framework simulates the BIS in the view of immune signals and takes full advantage of the cooperation effect of the immune signals, which improves the performance of the ICL framework. The ICL-MD model involves two modules, feature extraction and classification. In the malware detection problem, malware are taken as antigens, while benign programs are non-antigens. In order to ensure that the experimental results are reliable and the proposed ICL-MD model outperforms the GC-MD and LC-MD approaches statistically, an analysis of variance (ANOVA) was done followed by two t hypothesis tests (t-test). Comprehensive experimental results demonstrate that the ICL framework is an effective learning framework.

  • Standard for Patient Health Summary

    This chapter contains sections titled: * Introduction * Continuity of Care Record (CCR) * Discussion * Conclusion * Bibliography

  • Multiple-Point Bit Mutation Method of Detector Generation

    In self and nonself discrimination (SNSD) model, it is very important to generate a desirable detector set since it decides the performance and scale of the SNSD model based task. This chapter proposed a novel detector generating algorithm on the basis of the negative selection principle in natural immune systems. It utilizes random multiple-point mutation to look for nonself detectors in a large range of the space of detectors, such that one can obtain a required detector set in a reasonable computing time. The chapter describes the work procedure of the proposed detector generating algorithm. Then, it tests the algorithm on many datasets and compared it with the Exhaustive Detector Generating Algorithm (EDGA) in detail. The experimental results show that the proposed algorithm outperforms the EDGA in both detection performance and computational complexity. The major difference between growth algorithm and negative selection algorithm (NSA) is the generating method of candidate detectors.

  • Malware Detection

    This chapter introduces the classic malware detection approaches and immune- based malware detection approaches. These classic malware detection approaches are static and dynamic techniques, and heuristics. Many researchers have proposed various kinds of heuristics to detect the malwares with some success. Artificial Immune System (AIS) is an immune-based malware detection approach that have been applied to many complex problem domains, such as fault and anomaly diagnosis, network intrusion detection, and virus detection. There are three typical algorithms in AIS: negative selection algorithm (NSA), clonal selection algorithm, and immune network model. Immune-based malware detection techniques have the ability to detect new variations and unknown malwares and paved a new way for anti-malware research. Aiming at building a light-weight, limited computer resources and early virus warning system, an immune-based virus detection system using affinity vectors (IVDS) was proposed. Finally, the chapter presents hierarchical artificial immune model for virus detection (HAIM).

  • An Artificial Intelligence Perspective on Ensuring Cyber¿¿¿Assurance for the Internet of Things

    The technology challenge facing the Internet of Things (IoT) requires a multi¿¿¿disciplined approach to artificial intelligence (AI) systems engineering and it is critical that the IoT effort address cyber¿¿¿assurance (e.g., embedded, automatic security processing) for rapid responses to significant IoT activities. This chapter briefly provides the possibility of the usage of AI for cyber¿¿¿assurance for the IoT. The IoT offers an interactive connection between the physical world and computer systems in order to obtain improved efficiency, accuracy, and economic advantages. An AI approach does not demand the discarding of current IoT systems to start anew. On the contrary, it enables the utilization of existing technology assets by integrating select functionality into the IoT. Although it is highly debated that the technology influence in our daily lives may make us ever more dependent on technology, the growth of AI within the IoT will also continue.



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