Hierarchical cluster for scalable web servers

2001 ◽  
Author(s):  
Jonghyuck Hong ◽  
Dongseung Kim
Keyword(s):  
Hierarchical Cluster ◽  
Web Servers

New Type of Computational Devices: The Embedded Controlling Built-in Micro Web-Servers

2004 ◽  
Vol 62 (1-6) ◽  
pp. 485-496
Author(s):  
A. V. Agranovskiy ◽  
N. Yu. Polushkin ◽  
S. V. Khristich
Keyword(s):  
Web Servers ◽  
New Type

Mitigation of Application Layer DDoS Flood Attack Against Web Servers

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ahamed Aljuhani ◽  
Talal Alharbi ◽  
Bradley Taylor
Keyword(s):  
Application Layer ◽  
Web Servers

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

Flash evaporation-gas chromatographic fingerprints with hierarchical cluster analysis for the determination of cigarette flavors

2011 ◽  
Vol 29 (6) ◽  
pp. 549-553
Author(s):  
Jian JIANG ◽  
Jun YANG ◽  
Fangfang HUANG ◽  
Shiqiang XU ◽  
Xiaoqing WANG ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Flash Evaporation ◽  
Chromatographic Fingerprints

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

scholarly journals Correlation between Elderly Migrants’ Needs and Environmental Adaptability: A Discussion Based on Human Urbanization Features

2021 ◽  
Vol 18 (10) ◽  
pp. 5068
Author(s):  
Yi Hua ◽  
Zhi Qiu ◽  
Wenjing Luo ◽  
Yue Wang ◽  
Zhu Wang
Keyword(s):  
Urban Community ◽  
Hierarchical Cluster ◽  
Small Towns ◽  
Living Environment ◽  
Rural Settlements ◽  
Structured Interviews ◽  
Elderly Migrants ◽  
Environmental Adaptability ◽  
Community Environments ◽  
Rural Group

Building concentrated resettlement community in small towns is mostly used to deal with resettlement construction for rural migrants in economically developed regions in China, which leads to migrants’ living environment changing from rural settlements where production and living are intertwined to an urban community that only supports living functions. However, the urbanized environment is contrary to elderly migrants’ behavior, resulting in contradictions or conflicts between migrants and resettlement communities, reflecting a lack of urbanization synchronization between migrants and resettlement community environments. Further, elderly migrants are also equipped with different degrees and types of urbanization characteristics, thus reflecting different abilities to adapt to the urban community environment. Based on the corresponding relationship between people’s different production and living needs and urbanization, this research starts by investigating the production and living needs of elderly migrants, and further clarifies the environmental adaptability of elderly migrants by sorting the types and characteristics of urbanization of elderly migrants to provide a reference basis for the planning and construction of future resettlement areas. The research uses questionnaires and semi-structured interviews to investigate the population attributes and characteristics of elderly migrants, as well as their different needs for production and living. The research uses hierarchical cluster analysis, the one-way ANOVA test and Chi-square test to constructed a four-quadrant model on human urbanization features: an Urban Group with both living and production urbanized (Group H-H); a Half-urban-half-rural Group with only living needs urbanized (Group H-L); a Half-urban-Half-rural Group with only production needs urbanized (Group L-H); and a Rural group with both living and production needs not urbanized (Group L-L). Finally, based on the results, this research proposed three elderly environment construction orientations of “Promote the Supply Level of Urban Public Services”, “Create a Place That Embodies the Spirit of Immigrants’ Homeland”, and “Moderate Consideration of Agricultural Production Needs” for residential planning.

Revisiting Poststroke Upper Limb Stratification: Resilience in a Larger Cohort

2021 ◽  
pp. 154596832199204
Author(s):  
Benjamin J. Varley ◽  
Christine T. Shiner ◽  
Liam Johnson ◽  
Penelope A. McNulty ◽  
Angelica G. Thompson-Butel
Keyword(s):  
Clinical Practice ◽  
Motor Function ◽  
Upper Limb ◽  
Hierarchical Cluster ◽  
Community Dwelling ◽  
Stratification Method ◽  
Wolf Motor Function Test ◽  
Block Test ◽  
Time Restriction ◽  
Post Hoc

Background Upper limb (UL) impairment in stroke survivors is both multifactorial and heterogeneous. Stratification of motor function helps identify the most sensitive and appropriate assessments, which in turn aids the design of effective and individualized rehabilitation strategies. We previously developed a stratification method combining the Grooved Pegboard Test (GPT) and Box and Block Test (BBT) to stratify poststroke UL motor function. Objective To investigate the resilience of the stratification method in a larger cohort and establish its appropriateness for clinical practice by investigating limitations of the GPT completion time. Methods Post hoc analysis of motor function for 96 community-dwelling participants with stroke (n = 68 male, 28 female, age 60.8 ± 14 years, 24.4 ± 36.6 months poststroke) was performed using the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment (F-M), BBT, and GPT. Hypothesis-free and hypothesis-based hierarchical cluster analyses were conducted to determine the resilience of the stratification method. Results The hypothesis-based analysis identified the same functional groupings as the hypothesis-free analysis: low (n = 32), moderate (n = 26), and high motor function (n = 38), with 3 exceptions. Thirty-three of the 38 participants with fine manual dexterity completed the GPT in ≤5 minutes. The remaining 5 participants took 6 to 25 minutes to place all 25 pegs but used alternative movement strategies to complete the test. The GPT time restriction changed the functional profile of the moderate and high motor function groups leading to more misclassifications. Conclusion The stratification method unambiguously classifies participants by UL motor function. While the inclusion of a 5-minute cutoff time for the GPT is preferred for clinical practice, it is not recommended for stratification purposes.

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