scholarly journals A new method for constructing kernel vectors in morphological associative memories of binary patterns

2011 ◽  
Vol 8 (1) ◽  
pp. 141-166 ◽  
Author(s):  
Yiannis Boutalis
Keyword(s):  
Data Analysis ◽  
Intelligent Control ◽  
Image Understanding ◽  
Input Pattern ◽  
New Method ◽  
Associative Memories ◽  
Theoretical Justification ◽  
High Recall ◽  
Scientific Disciplines

Kernel vectors represent an elegant representation for the retrieval of pattern associations, where the input patterns are corrupted by both erosive and dilative noise. However, their action completely fails when a particular kind of erosive noise, even of very low percentage, corrupts the input pattern. In this paper, a theoretical justification of this fact is given and a new method is proposed for the construction of kernel vectors for binary patterns associations. The new kernels are not binary but ?gray?, because they contain elements with values in the interval [0, 1]. It is shown, both theoretically and experimentally that the new kernel vectors carry the good properties of conventional kernel vectors and, at the same time, they can be easily computed. Moreover, they do not suffer from the particular noise deficiency of the conventional kernel vectors. The recalling result is in general a gray pattern, which in the sequel undergoes a simple thresholding action and passes through a simple Hamming network to produce high recall rates, even in heavily corrupted patterns. Retrieval of pattern associations is very significant for a variety of scientific disciplines including data analysis, signal and image understanding and intelligent control.

Estimation of Failure Probability and its Applications in Reliability Data Analysis

2010 ◽  
Vol 118-120 ◽  
pp. 601-605
Author(s):  
Han Ming
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Bayesian Estimation ◽  
Failure Probability ◽  
Evaluation Method ◽  
Estimation Method ◽  
New Method ◽  
Reliability Parameter ◽  
Hierarchical Bayesian Estimation ◽  
Definition Of

Evaluation method of reliability parameter estimation needs to be improved effectively with the advance of science and technology. This paper develops a new method of parameter estimation, which is named E-Bayesian estimation method. In the case one hyper-parameter, the definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation, and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is feasible and easy to perform.

scholarly journals A Novel Concept Acquisition Approach Based on Formal Contexts

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ting Qian ◽  
Ling Wei
Keyword(s):  
Data Analysis ◽  
Formal Concept Analysis ◽  
Real Life ◽  
Experimental Results ◽  
New Method ◽  
Concept Acquisition ◽  
Formal Concept ◽  
Knowledge Processing ◽  
Formal Concepts ◽  
Novel Concept

As an important tool for data analysis and knowledge processing, formal concept analysis (FCA) has been applied to many fields. In this paper, we introduce a new method to find all formal concepts based on formal contexts. The amount of intents calculation is reduced by the method. And the corresponding algorithm of our approach is proposed. The main theorems and the corresponding algorithm are examined by examples, respectively. At last, several real-life databases are analyzed to demonstrate the application of the proposed approach. Experimental results show that the proposed approach is simple and effective.

Mapping Forensic Odontology Literature Using Open Source Bibliographies and Software

2015 ◽  
pp. 170-189 ◽  
Author(s):  
J. John Jeyasekar ◽  
P. Saravanan
Keyword(s):  
Data Analysis ◽  
Domain Structure ◽  
Forensic Odontology ◽  
Bibliographic Databases ◽  
The Internet ◽  
Bibliometric Indicators ◽  
Computer Algorithms ◽  
Publish Or Perish ◽  
Growing Domain ◽  
Scientific Disciplines

Domain visualization, an emerging field of study is used to map the growing domain structure of scientific disciplines. Scientometrics is a distinct discipline that has emerged from citation based domain visualization. Visualization with the aid of science maps enables visual comprehension. Science maps can be effectively created with the help of computer algorithms. Bibliographic databases are also available freely over the internet. The various computer algorithms and bibliographic databases are discussed. Some of the different bibliometric indicators are also briefly explained. A mapping study of forensic odontology literature for a five year period of 2009 to 2013 is done using two bibliometric databases, viz., PubMed and Google Scholar, which are freely available. MS-Excel spreadsheets and Publish or Perish (PoP) software are used for data analysis. Co-word maps are also created using VOSviewer to visualize the sub-fields of forensic odontology.

Overlapping Clustering: A New Method for Product Positioning

1981 ◽  
Vol 18 (3) ◽  
pp. 310-317 ◽  
Author(s):  
Phipps Arabie ◽  
J. Douglas Carroll ◽  
Wayne DeSarbo ◽  
Jerry Wind
Keyword(s):  
Market Segmentation ◽  
Equivalence Classes ◽  
New Method ◽  
Product Positioning ◽  
Theoretical Justification ◽  
Clustering Techniques ◽  
Overlapping Clustering ◽  
Clustering Model

Most clustering techniques used in product positioning and market segmentation studies render mutually exclusive equivalence classes of the relevant products or subjects space. Such classificatory techniques are thus restricted to the extent that they preclude overlap between subsets or equivalence classes. An overlapping clustering model, ADCLUS, is described which can be used in marketing studies involving products/subjects that can belong to more than one group or cluster simultaneously. The authors provide theoretical justification for and an application of the approach, using the MAPCLUS algorithm for fitting the ADCLUS model.

scholarly journals Iterative Thematic Inquiry: A New Method for Analyzing Qualitative Data

2020 ◽  
Vol 19 ◽  
pp. 160940692095511
Author(s):  
David L. Morgan ◽  
Andreea Nica
Keyword(s):  
Qualitative Research ◽  
Content Analysis ◽  
Data Analysis ◽  
Thematic Analysis ◽  
Theoretical Basis ◽  
Qualitative Data ◽  
Qualitative Content Analysis ◽  
New Method ◽  
Data Collection And Analysis ◽  
Primary Procedure

Because themes play such a central role in the presentation of qualitative research results, we propose a new method, Iterative Thematic Inquiry (ITI), that is guided by the development of themes. We begin by describing how ITI uses pragmatism as a theoretical basis for linking beliefs, in the form of preconceptions, to actions, in the form of data collection and analysis. Next, we present the four basic phases that ITI relies on: assessing beliefs; building new beliefs through encounters with data; listing tentative themes; and, evaluating themes through coding. We also review several notable differences between ITI and existing methods for qualitative data analysis, such as thematic analysis, grounded theory, and qualitative content analysis. The use of ITI is then illustrated through its application in a study of exiters from fundamentalist religions. Overall, the two most notable features of ITI are that it begins the development of themes as early as possible, through an assessment of initial preconceptions, and that it relies on writing rather than coding, by using a continual revision of tentative results as the primary procedure for generating a final set of themes.

Functional principal component data analysis: A new method for analysing microbial community fingerprints

2009 ◽  
Vol 79 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Janine B. Illian ◽  
James I. Prosser ◽  
Kate L. Baker ◽  
J. Ignacio Rangel-Castro
Keyword(s):  
Data Analysis ◽  
Microbial Community ◽  
Principal Component ◽  
New Method ◽  
Functional Principal Component

scholarly journals A New Method of Subjective Evaluation Using Visual Analog Scale for Small Sample Data Analysis

2021 ◽  
Vol 29 (0) ◽  
pp. 424-433
Author(s):  
Naruki Shirahama ◽  
Satoshi Watanabe ◽  
Kenji Moriya ◽  
Kazuhiro Koshi ◽  
Keiji Matsumoto
Keyword(s):  
Data Analysis ◽  
Visual Analog Scale ◽  
Subjective Evaluation ◽  
Small Sample ◽  
New Method ◽  
Analog Scale ◽  
Sample Data

scholarly journals Data Analysis for microRNA and Related Diagnoses

2022 ◽  
Vol 16 ◽  
pp. 134-139
Author(s):  
Eugenia Namiot ◽  
Maxim Khakhin
Keyword(s):  
Data Analysis ◽  
Cardiovascular Diseases ◽  
Significant Role ◽  
New Method ◽  
Huge Number ◽  
Large Numbers ◽  
Proposed Model ◽  
The Future ◽  
The Creation

MicroRNAs are non-coding molecules that play a significant role in the development of the disease. MicroRNAs can act as biomarkers or independently lead to the development of a disease. Due to the large numbers of microRNAs, most of the current works focus on the creation of a new way of microRNA clustering or grouping. Today, there are a huge number of different databases that distribute open microRNAs into groups. The problem is that there is no way to evaluate such databases and created clusters. In this work, we propose a new method for assessing the distribution of microRNAs in a cluster, which in the future can be used to predict new sequential ones capable of causing disease. The proposed method can also be used for a better understanding of the mechanisms of various diseases. Since cardiovascular diseases rank first in terms of the number of deaths, they were chosen as the analyzed ones. The Human microRNA Disease Database was used as an analyzed database in this work. The obtained results show that the proposed method can analyze the created databases and can be used in further practice. The proposed model makes it possible to predict new microRNAs for given diagnoses.

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