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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2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)

The world's premiere conference in MEMS sensors, actuators and integrated micro and nano systems welcomes you to attend this four-day event showcasing major technological, scientific and commercial breakthroughs in mechanical, optical, chemical and biological devices and systems using micro and nanotechnology.The major areas of activity in the development of Transducers solicited and expected at this conference include but are not limited to: Bio, Medical, Chemical, and Micro Total Analysis Systems Fabrication and Packaging Mechanical and Physical Sensors Materials and Characterization Design, Simulation and Theory Actuators Optical MEMS RF MEMS Nanotechnology Energy and Power


2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

Industrial information technologies


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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 Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


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.


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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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A new type of gallium arsenide field-effect phototransistor

[{u'author_order': 1, u'affiliation': u'RCA Labs., Princeton, NJ, USA', u'full_name': u'G. Swartz'}, {u'author_order': 2, u'full_name': u'A. Gonzalez'}, {u'author_order': 3, u'full_name': u'A. Dreeben'}] 1971 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, 1971

A gallium-arsenide field-effect phototransistor, which responds to infrared radiation at a wavelength of 1.5 μ will be covered. The device is fabricated from chromium-doped semi-insulating GaAs with a thin epitaxial N-type surface layer.


Immune Principle and Neural Networks-Based Malware Detection

[{u'author_order': 1, u'full_name': u'Ying Tan'}] Artificial Immune System: Applications in Computer Security, None

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 ...


Discussion on “electric heating as applied to marine service” (Mcdowell and Mahood), Detroit, Mich., June 23, 1914. (see proceedings for June, 1914)

[] Proceedings of the American Institute of Electrical Engineers, 1914

W. S. Hadaway, Jr.: The paper by Messrs. McDowell and Mahood is of value in showing that while different types of heaters develop the same amount of heat with the same input, their effective value may vary according to conditions of service.


Ideal students

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/37271992900', u'full_name': u'J.L. Schmalzel', u'id': 37271992900}] IEEE Instrumentation & Measurement Magazine, 2002

None


The application of class B insulation to auxiliary-type D-C motors in severe duty service

[] Electrical Engineering, 1940

P. L. Alger (General Electric Company, Schenectady, N. Y.) and R. E. Hellmund (Westinghouse Electric and Manufacturing Company, East Pittsburgh, Pa.): An important feature of this paper is the information given on the relative values of winding temperature-rise by the resistance and by the thermometer methods. Figure 5 indicates that, due to the time lag of the thermometer temperature, the ...


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eLearning

No eLearning Articles are currently tagged "Immune system"

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IEEE-USA E-Books

  • 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.

  • Hierarchical Artificial Immune Model

    As viruses become more complex, current anti-virus methods are inefficient to detect various forms of viruses, especially new variants and unknown viruses. This chapter proposes a hierarchical artificial immune model (HAIM) for virus detection to overcome three specific shortcomings in traditional artificial immune system (AIS) models: randomly generating the detectors leading to the bad efficiency; poor generalization and poor performance with a big dataset; and ignoring the relevance between different extracted signatures in one virus. The virus gene library generating module works on the training set consisting of legal and virus programs. The model can obtain the frequency information of deoxyribonucleotides (ODN) appearing in the legal and virus programs. Finally, classification decision is an overall behavior that greatly reduces the information loss. The model can effectively and efficiently recognize obfuscated virus, detect new variants of known virus and some unknown viruses.

  • 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.

  • Malware Detection System Using Affinity Vectors

    This chapter proposes an immune-based virus detection system using affinity vectors (IVDS) based on the negative selection and clonal selection algorithms in artificial immune system (AIS). AVDS first generates the detector set from virus files in dataset, negative selection is used to eliminate autoimmunity detectors for the detector set, while clonal selection is exploited to increase the diversity of the detector set in the non-self space. The affinity vectors of the training set and the testing set are used to train and test classifiers, respectively. Finally, based on the affinity vectors, three classic classifiers, that is, Support Vector Machine (SVM), radial basis kernel function (RBF) network andk-nearest neighbor (KNN), are used to verify the performance of the model. Experimental results showed that the IVDS with the rbf-SVM classifier has a strong generalization ability with a low false positive rate in detecting unknown viruses.

  • 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.

  • Negative Selection Algorithm with Penalty Factor

    A malware detection model based on a negative selection algorithm with a penalty factor is proposed to overcome the drawback of traditional negative selection algorithms in defining the harmfulness of self and nonself. The effectiveness of the proposed model is improved greatly by using the dangerous signatures that would have been discarded in the traditional negative selection algorithm. This chapter presents a malware detection model based on a negative selection algorithm with penalty factor (NSAPF). The proposed NSAPF model consists of a malware signature extraction module (MSEM) and a suspicious program detection module (SPDM). In the SPDM, signatures of suspicious programs are extracted using the malware instruction library (MIL). The chapter considers the malware candidate signature library (MCSL) as nonself and the BPMSL as self and generates a Malware Detection Signature Library (MDSL) using NSAPF. Comprehensive experimental results demonstrated that the proposed model is effective in detecting unknown malware with a lower FPRs.

  • Danger Feature-Based Negative Selection Algorithm

    This chapter presents a danger feature-based negative selection algorithm (DFNSA). In the DFNSA, the danger feature space is divided into four parts, and the information of danger features is reserved as much as possible, laying a good foundation for measuring the danger of a sample. A danger feature is a feature with dangerous properties, that is able to identify its corresponding dangerous operations. It is the basic element for an immune system to decide whether an immune response should be produced. In order to incorporate the DFNSA into the procedure of malware detection, a DFNSA-based malware detection (DFNSA-MD) model is proposed. The danger of a sample is measured precisely in this way and used to classify the sample. Comprehensive experimental results suggest that the DFNSA is able to reserve as much information about the danger features as possible, and the DFNSA-MD model is effective to detect unseen malware.

  • Spaces of Many Functions

    This chapter contains sections titled: Form and Flux, A Protean Architecture for a Protean People

  • Index

    None

  • 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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