Signal analysis on strings for immune‐type pattern recognition

Atreas, Nikolaos; Karanikas, C.; Polychronidou, Persefoni

doi:10.1002/cfg.367

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Then the reduced set of points after apoptosis and immunization can represent the feature extraction by FIN and the quality of such FIN can be estimated by the index of inseparability and thus can be compared with other FINs (e.g., those obtained by a preprocessing of the signal). On the other hand, let us note once again that the SVD can model the binding energy between proteins [3], whereas the dyadic DTT can model an immune-type antigen processing [29,31].…”

Section: Resultsmentioning

confidence: 99%

“…According to [29][30][31], the proposed approach to signal processing is inspired by a mode of biomolecular computing [15] when immune cells chop unknown antigen to its local singularities and expose them to the immune system. Analogously, the IC approach represents unknown signal as a tree of data, and chop the branches of the tree at the level l to detect local singularities of the signal.…”

Section: Discrete Tree Transformmentioning

confidence: 99%

“…A connection of IC with cellular automata (CA) [23] looks encouraging for threedimensional (3D) computer graphics [24] and identification of CA [25]. The IC is also connected with advances in applied self-organizing systems [26] including intelligent reconstruction and simulation of hydro-physical fields [27,28], signal processing [29][30][31] and intrusion detection [32][33][34].…”

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confidence: 99%

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Immunocomputing for intelligent signal processing

Tarakanov

2010

Neural Comput & Applic

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Based on immunocomputing (IC), this paper describes an approach to intelligent signal processing. The approach includes both low-level feature extraction and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN), which includes apoptosis (programmed cell death) and immunization and controls them by cytokines (messenger proteins). Such FIN can be formed from the signal or combination of signals using discrete tree transform, singular value decomposition and the proposed index of inseparability as a measure of quality of FIN. Several comparisons with neural computing and support vector machines suggest that IC may outperform state of the art approaches to intelligent signal processing.

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Section: Resultsmentioning

confidence: 99%

Section: Discrete Tree Transformmentioning

confidence: 99%

mentioning

confidence: 99%

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Immunocomputing for intelligent signal processing

Tarakanov

2010

Neural Comput & Applic

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“…On the other hand, let us note once again that the SVD (Section 2.2) can model the binding energy between two proteins [3], whereas the dyadic DTT (Section 2.3) can model an immune-type antigen processing [14], [27].…”

Section: Resultsmentioning

confidence: 99%

“…For example, our comparison of image recognition in [25] shows that the IC works 10 time faster and more accurate than conventional statistics. No wonder that more and more of modern approaches to signal processing are based on wavelet analysis, where the dyadic DTT (Section 2.3) is also a wavelet-type transform for signal analysis [26], [27].…”

Section: Discussionmentioning

confidence: 99%

Mathematical Models of Intrusion Detection by an Intelligent Immunochip

Tarakanov

Communications in Computer and Information Science

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Based on mathematical models of immunocomputing, this paper proposes an approach to intrusion detection that allows both low-level signal processing (feature extraction) and high-level ("intelligent") pattern recognition.The key model is the formal immune network (FIN) including apoptosis (programmed cell death) and immunization both controlled by cytokines (messenger proteins). FIN can be formed from the raw signal using discrete tree transform, singular value decomposition, and the proposed index of inseparability in comparison with the Renyi entropy. The speed and the accuracy of the approach probably mean a further step toward placing more of the intelligent functions on the chip.

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Current awareness on comparative and functional genomics

2004

Comp Funct Genom

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In order to keep subscribers up‐to‐date with the latest developments in their field, this current awareness service is provided by John Wiley & Sons and contains newly‐published material on comparative and functional genomics. Each bibliography is divided into 16 sections. 1 Reviews & symposia; 2 General; 3 Large‐scale sequencing and mapping; 4 Genome evolution; 5 Comparative genomics; 6 Gene families and regulons; 7 Pharmacogenomics; 8 Large‐scale mutagenesis programmes; 9 Functional complementation; 10 Transcriptomics; 11 Proteomics; 12 Protein structural genomics; 13 Metabolomics; 14 Genomic approaches to development; 15 Technological advances; 16 Bioinformatics. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted

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Signal analysis on strings for immune‐type pattern recognition

Abstract: We use wavelet-type discrete transforms for signal analysis on strings of finite length. We apply these transforms for edge and hidden Markov process detection. We also present new approaches for string matching and for measures of the diversity of chaotic strings.

Cited by 7 publications

References 11 publications

Immunocomputing for intelligent signal processing

Immunocomputing for intelligent signal processing

Mathematical Models of Intrusion Detection by an Intelligent Immunochip

Current awareness on comparative and functional genomics

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