Evaluating PageRank methods for structural sense ranking in labeled tree data

2012 ◽  
Author(s):  
Andrea Tagarelli ◽  
Francesco Gullo
Keyword(s):  
Tree Data ◽  
Labeled Tree

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.

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

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

scholarly journals Modeling interactions between fire and atmosphere in discrete element fuel beds

2005 ◽  
Vol 14 (1) ◽  
pp. 37 ◽  
Author(s):  
Rodman Linn ◽  
Judith Winterkamp ◽  
Jonah J. Colman ◽  
Carleton Edminster ◽  
John D. Bailey
Keyword(s):  
Ponderosa Pine ◽  
Fire Behavior ◽  
Canopy Structure ◽  
Treatment Strategies ◽  
Convective Heating ◽  
Initial Attempt ◽  
Fuel Density ◽  
Porous Element ◽  
Tree Data ◽  
Fuel Elements

In this text we describe an initial attempt to incorporate discrete porous element fuel beds into the coupled atmosphere–wildfire behavior model HIGRAD/FIRETEC. First we develop conceptual models for use in translating measured tree data (in this case a ponderosa pine forest) into discrete fuel elements. Then data collected at experimental sites near Flagstaff, Arizona are used to create a discontinuous canopy fuel representation in HIGRAD/FIRETEC. Four simulations are presented with different canopy and understory configurations as described in the text. The results are discussed in terms of the same two discrete locations within the canopy for each simulation. The canopy structure had significant effects on the balance between radiative and convective heating in driving the fire and indeed sometimes determined whether a specific tree burned or not. In our simulations the ground fuel density was the determining factor in the overall spread rate of the fire, even when the overstory was involved in the fire. This behavior is well known in the fire meteorology community. In the future, simulations of this type could help land managers to better understand the role of canopy and understory structure in determining fire behavior, and thus help them decide between the different thinning and fuel treatment strategies available to them.

On Two Novel Applications of Ranked Set Sampling

2003 ◽  
Vol 54 (1-2) ◽  
pp. 105-114
Author(s):  
Sukuman Sarikavanij ◽  
Montip Tiensuw
Keyword(s):  
Case Studies ◽  
Random Sample ◽  
Ranked Set Sampling ◽  
Simple Random Sample ◽  
Single Family ◽  
Ranked Set Sample ◽  
Population Mean ◽  
Sales Data ◽  
Tree Data ◽  
Novel Applications

In this paper we discuss two case studies which clearly indicate the advantages of using a ranked set sample (RSS) over those of a simple random sample (SRS). The applications of RSS considered here cover single family homes sales data, and tree data. It is demonstrated that in each case RSS is much more efficient than SRS for estimation of population mean.

scholarly journals Molecular Characterization of a Poly-β-Hydroxybutyrate-Producing Microbacterium Isolate

2015 ◽  
Vol 3 (2) ◽  
pp. 143-150 ◽  
Author(s):  
Yehia A. Osman ◽  
Ahmed Abd Elrazak ◽  
Wesam Khater ◽  
EL-Shahat Nashy ◽  
Attia Mohamadeen
Keyword(s):  
Biosynthetic Pathway ◽  
Nitrogen Deficiency ◽  
Rrna Gene ◽  
Excess Carbon ◽  
Controlling Elements ◽  
Tree Data ◽  
Microbacterium Paraoxydans ◽  
Coa Reductase ◽  
Beta Hydroxybutyrate

Bacterial poly-β-hydroxybutyrate (PHB) is a natural, biodegradable polymer, which is accumulated in the cells as an energy reserve materialdue to depletion of nitrogen or phosphorous in the presence of excess carbon source. This polymer is foreseen to possess high industrialpotentiality and excellent alternative to the non-degradable petroleum-based plastics. In this study, we isolated and characterized a localbacterial strain WA81 which accumulated 18mg/L PHB after 72 h growth in mineral salt medium under nitrogen deficiency. The PHB granuleswere detected in the cells using TEM and the genes encode for this polymer were detected by oligonucleotide primers using PCR technology.The 16S rRNA gene nucleotide sequence for this isolate was used to construct a phylogentic tree against all available sequences in the GenBank.The phylogenetic tree data suggested that the closest type strain to the local bacterium is the Microbacterium paraoxydans CF36T and hencewe named it Microbacterium sp. strain WA81. Moreover, the set of enzymes responsible for the PHB biosynthetic pathway and their controllingelements were detected in this local isolate using PCR. The genes encode for the biosynthesis enzymes are phbA (β-ketothiolase), phbB(acetoacetly CoA reductase), phbC (PHB polymerase), while the genes encode for the controlling elements are phbP (phasin), phbZ (PHBdepolymerase). The novelty of this local bacterium lies in its ability to accumulate huge amounts of PHB in its cytoplasm and the presence ofa whole set of genes encode for the PHB biosynthetic and catabolic pathways of this polymer.Int J Appl Sci Biotechnol, Vol 3(2): 143-150 DOI: http://dx.doi.org/10.3126/ijasbt.v3i2.12277 

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