Method for Fine Pattern Recognition of Space Targets Using the Entropy Weight Fuzzy-Rough Nearest Neighbor Algorithm

The multiple-query nearest-neighbor (MQNN) problem is stated as follows: given a set S of n points in plane and a set Q of m(1≤m≤n) query points, determine for every point in Q its closest neighbor in S. Besides the pure theoretical interest, this problem has many practical applications in various areas such as: computer graphics, pattern recognition and image processing. First, this paper proposes a new time-optimal algorithm to solve the all nearest-neighbor (ANN) problem in [Formula: see text] time on a mesh-connected computer of size [Formula: see text]. Next, using this result in conjunction with the generalized multiple search (GMS) paradigm of Bokka et al.3,5 we devise a time-optimal algorithm that solves the MQNN problem in [Formula: see text] time on a mesh with multiple broadcasting (MMB) of size [Formula: see text].

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On Some Fuzzy Classification Algorithms and the AEC Model

WSEAS TRANSACTIONS ON SYSTEMS ◽

10.37394/23202.2020.19.22 ◽

2020 ◽

Vol 19 ◽

Keyword(s):

Pattern Recognition ◽

Decision Rule ◽

Nearest Neighbor ◽

Fuzzy Classification ◽

Classification Algorithms ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

Recognition Algorithms ◽

K Nearest Neighbor Algorithm

In the paper some fuzzy classification algorithms based upon a nearest neighbor decision rule areconsidered in terms of the pattern recognition algorithms which are based on the computation of estimates (theso-called AEC model). It is shown that the fuzzy K nearest neighbor algorithm can be assigned to the AECclass. In turn, it is found that some standard AEC algorithms, which depend on a number of numericalparameters, can be used as fuzzy classification algorithms. Yet among them there exist algorithms extremalwith respect to these parameters. Such algorithms provide maximum values of the associated performancemeasures.

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A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems

2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) ◽

10.23919/mipro48935.2020.9245195 ◽

2020 ◽

Author(s):

A. Sagdic ◽

C. Tekinbas ◽

E. Arslan ◽

T. Kucukyilmaz

Keyword(s):

Recommendation System ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm

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Handwritten Balinesse Character Recognition using K-Nearest Neighbor

10.31227/osf.io/z6m8u ◽

2018 ◽

Author(s):

I Wayan Agus Surya Darma

Keyword(s):

Feature Extraction ◽

Success Rate ◽

Character Recognition ◽

Nearest Neighbor ◽

Recognition System ◽

Extraction Process ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm ◽

Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

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