scholarly journals Pengenalan Motif Karawo Menggunakan Ekstraksi Fitur SIFT Dan Approximate Nearest Neighbor

2019 ◽  
Author(s):  
Syahrial
Keyword(s):  
Nearest Neighbor ◽  
Feature Matching ◽  
Recognition Accuracy ◽  
Sift Algorithm ◽  
Approximate Nearest Neighbor ◽  
Single Pattern ◽  
Tree Data ◽  
Threshold Testing ◽  
Root Word ◽  
Tree Data Structure

An art culture from Gorontalo became iconic handcraft is kerawang or karawo. The word “karawo” came from root word of “mokarawo” which means slicing or making holes. It’s created with precision, carefulness, and patience in work using handmade masterpiece. Pattern of karawo itself held four kinds which is flora, fauna, geometric, and nature. From those kinds born vary pattern which come difficult to identify both its names and its kind. Karawo patterns can be form as a single pattern or a pattern that it parts came from several or many pattern combined. Those patterns had its own characteristic from shape and scale perspective. Identifying single pattern on combined pattern are particularly a problem because it’s combined involve scaling and rotation. This research is recognizing single pattern on combined pattern using feature extraction SIFT algorithm which is capable extract feature that invariant from scale and rotation. Feature matching using approximate nearest neighbor (aNN) for similarity of features labor best bin first strategy on kd-tree data structure. Those methods can be a reference to recognize single pattern on combined pattern using from range 5 to 20 match features as a threshold. Testing result indicated recognition accuracy is good which range form 76.36% to 85.45% on recognize the kind of karawo pattern and 76.36% on its name.

Research on Query Mechanism of Moving Object Database

2014 ◽  
Vol 530-531 ◽  
pp. 823-826 ◽  
Author(s):  
Chun Ping Wang
Keyword(s):  
Nearest Neighbor ◽  
Frequency Dispersion ◽  
Radio Communication ◽  
Mobile Environment ◽  
Moving Object Database ◽  
Time Domain Signal ◽  
Tree Data ◽  
Historical Trajectory ◽  
Tree Data Structure ◽  
Nearest Neighbor Queries

Since the mobile radio communication in a wireless mobile environment, which inevitably subject to fading and interference effects of the various signals, multipath and time-domain signal to bring the mobile domain and frequency dispersion problems, bandwidth resources restricted and transmission delay increases and so on. In this paper, the use of Class R-Tree data structure to store the data on the basis of historical trajectory, the update query results by the beach line iterative way to solve the problem of nearest neighbor queries efficiently update existing intermediate results.

scholarly journals Tree-Based Algorithm for Stable and Efficient Data Clustering

Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 38
Author(s):  
Hasan Aljabbouli ◽  
Abdullah Albizri ◽  
Antoine Harfouche
Keyword(s):  
Data Structure ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Convergence Properties ◽  
Insertion Technique ◽  
Efficient Data ◽  
Tree Data ◽  
Tree Data Structure ◽  
Nearest Neighbor Searches

The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions.

scholarly journals Secure and Practical Group Nearest Neighbor Query for Location-Based Services in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jingjing Guo ◽  
Jiacong Sun
Keyword(s):  
Data Structure ◽  
Data Privacy ◽  
High Efficiency ◽  
Nearest Neighbor ◽  
Resource Constraints ◽  
Location Based Services ◽  
Nearest Neighbor Query ◽  
Tree Data ◽  
Random Database ◽  
Tree Data Structure

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from k − anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with n n ≥ 3 LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec GNN + ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec GNN + scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec GNN + schemes. The experimental results show the high efficiency of our proposed schemes.

Logical Structure for User Friendly dynamic Web Page Visualization for Small Screen Terminals Promoting E-Business

2020 ◽  
Vol 14 ◽  
Author(s):  
Shefali Singhal ◽  
Poonam Tanwar
Keyword(s):  
Logical Structure ◽  
Vital Role ◽  
Web Pages ◽  
Main Concern ◽  
Web Page ◽  
Large Screen ◽  
Tree Data ◽  
Tree Data Structure ◽  
User Friendly ◽  
Small Screen

Abstract:: Now-a-days when everything is going digitalized, internet and web plays a vital role in everyone’s life. When one has to ask something or has any online task to perform, one has to use internet to access relevant web-pages throughout. These web-pages are mainly designed for large screen terminals. But due to mobility, handy and economic reasons most of the persons are using small screen terminals (SST) like mobile phone, palmtop, pagers, tablet computers and many more. Reading a web page which is actually designed for large screen terminal on a small screen is time consuming and cumbersome task because there are many irrelevant content parts which are to be scrolled or there are advertisements, etc. Here main concern is e-business users. To overcome such issues the source code of a web page is organized in tree data-structure. In this paper we are arranging each and every main heading as a root node and all the content of this heading as a child node of the logical structure. Using this structure, we regenerate a web-page automatically according to SST size. Background:: DOM and VIPS algorithms are the main background techniques which are supporting the current research. Objective:: To restructure a web page in a more user friendly and content presenting format. Method Backtracking:: Method Backtracking: Results:: web page heading queue generation. Conclusion:: Concept of logical structure supports every SST.

TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

2011 ◽  
Vol 10 (02) ◽  
pp. 373-406 ◽  
Author(s):  
ABDEL-RAHMAN HEDAR ◽  
EMAD MABROUK ◽  
MASAO FUKUSHIMA
Keyword(s):  
Search Algorithm ◽  
Benchmark Problems ◽  
Problem Solver ◽  
Learning Tools ◽  
Mutation Operators ◽  
Tree Data ◽  
Tree Data Structure ◽  
Comprehensive Framework ◽  
Rate Of Success ◽  
Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

Sign in / Sign up

Export Citation Format

Share Document