A MapReduce-Based Approach for Mining Embedded Patterns from Large Tree Data

2018 ◽  
pp. 455-462
Author(s):  
Wen Zhao ◽  
Xiaoying Wu
Keyword(s):  
Large Tree ◽  
Tree Data

From Homomorphisms to Embeddings: A Novel Approach for Mining Embedded Patterns from Large Tree Data

2018 ◽  
Vol 14 ◽  
pp. 37-53
Author(s):  
Xiaoying Wu ◽  
Dimitri Theodoratos ◽  
Timos Sellis
Keyword(s):  
Large Tree ◽  
Novel Approach ◽  
Tree Data

scholarly journals Discovering closed and maximal embedded patterns from large tree data

2021 ◽  
pp. 101890
Author(s):  
Xiaoying Wu ◽  
Dimitri Theodoratos ◽  
Nikos Mamoulis
Keyword(s):  
Large Tree ◽  
Tree Data

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.

scholarly journals Airborne laser scanning reveals large tree trunks on forest floor

2021 ◽  
Vol 491 ◽  
pp. 119225
Author(s):  
Einari Heinaro ◽  
Topi Tanhuanpää ◽  
Tuomas Yrttimaa ◽  
Markus Holopainen ◽  
Mikko Vastaranta
Keyword(s):  
Forest Floor ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Large Tree ◽  
Airborne Laser

Gamma-ray computed tomography for scanning of agarwood tree: Data analysis and comparison

2020 ◽  
Author(s):  
Mohd Fitri Abdul Rahman ◽  
Lahasen Dahing ◽  
Muhamad Noor Izwan Ishak ◽  
Hearie Hassan ◽  
Nur Liyana Abdullah ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Data Analysis ◽  
Gamma Ray ◽  
Tree Data ◽  
Gamma Ray Computed Tomography

scholarly journals A novel method for large tree visualization

2009 ◽  
Vol 25 (4) ◽  
pp. 557-558 ◽  
Author(s):  
J. Heard ◽  
W. Kaufmann ◽  
X. Guan
Keyword(s):  
Large Tree ◽  
Novel Method ◽  
Tree Visualization

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.

scholarly journals Predicting Increased Blood Pressure Using Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hudson Fernandes Golino ◽  
Liliany Souza de Brito Amaral ◽  
Stenio Fernando Pimentel Duarte ◽  
Cristiano Mauro Assis Gomes ◽  
Telma de Jesus Soares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Predictive Power ◽  
Classification Tree ◽  
Training Group ◽  
Test Sample ◽  
Hip Circumference ◽  
Training Set ◽  
Learning Technique ◽  
Tree Data

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudoR2(.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudoR2(.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

Sine-skewed axial distributions with an application for fallen tree data

2012 ◽  
Vol 19 (3) ◽  
pp. 295-307
Author(s):  
Toshihiro Abe ◽  
Kunio Shimizu ◽  
Timo Kuuluvainen ◽  
Tuomas Aakala
Keyword(s):  
Tree Data
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