An integrated principal component and reduced multivariate data analysis technique for detecting DDoS attacks in big data federated clouds

2021 ◽  
Vol 10 (4) ◽  
pp. 339
Author(s):  
Sengathir Janakiraman
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Ddos Attacks ◽  
Data Analysis Technique ◽  
Analysis Technique

scholarly journals Bioleaching of Complex Sulfides Concentrate and Correlation of Leaching Parameters Using Multivariate Data Analysis Technique

2009 ◽  
Vol 50 (9) ◽  
pp. 2318-2322 ◽  
Author(s):  
Dong-Jin Kim ◽  
Debabrata Pradhan ◽  
Gautam Roy Chaudhury ◽  
Jong-Gwan Ahn ◽  
Seoung-Won Lee
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Data Analysis Technique ◽  
Analysis Technique ◽  
Leaching Parameters

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

scholarly journals Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube

Nanomaterials ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 891 ◽  
Author(s):  
Constantin Apetrei ◽  
Catalina Iticescu ◽  
Lucian Puiu Georgescu
Keyword(s):  
Data Analysis ◽  
Natural Water ◽  
Water Samples ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Cyclic Voltammograms ◽  
Physico Chemical ◽  
Multisensory System ◽  
Collection Point

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola’s Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.

Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 680-687 ◽  
Author(s):  
Karin Fackler ◽  
Manfred Schwanninger ◽  
Cornelia Gradinger ◽  
Ewald Srebotnik ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Lignin Content ◽  
Beech Wood ◽  
Multivariate Data ◽  
Principal Component ◽  
Brown Rot ◽  
White Rot ◽  
Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

Entwicklung, Beobachtung und Steuerung integrierter, quasi-kontinuierlicher pharmazeutischer Produktionsprozesse mit Methoden der Multivariaten Datenverarbeitung

2016 ◽  
Author(s):  
Sven-Oliver Borchert
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Process Analytical Technology ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Analytical Technology

Die vorliegende Arbeit befasst sich mit Aspekten einer modernen Bioverfahrenstechnik am ­Beispiel von Prozessen zur Herstellung rekombinanter potentieller Malariavakzine. Dabei ­wurden zwei quasi-kontinuierliche Prozesse aus herkömmlichen Batch-Unit Operationen auf­gebaut, in denen die Anwendung von Process Analytical Technology im Vordergrund steht. Das Hauptaugenmerk dieser Arbeit lag dabei auf einer Implementierung der Multivariate Data ­Analysis zum Monitoring und zur Evaluierung des zyklischen Prozessablaufes und seiner Reproduzierbarkeit. Im Bereich der Principal Component Analysis wurde die Methode der Prozessüberwachung mit dem Golden Batch-Tunnel angewendet. Mit dem Golden Batch-Ansatz ­wurden Methoden zur Prozessprädiktion implementiert und mit einer Model Predictive Multi­variate Control auch zur Steuerung von realen Prozesses erprobt. Darüber hinaus wurde die MVDA zur Prädiktion von Medienkomponenten sowie deren zellspezifische Reaktionsraten aus klassischen Onli...

scholarly journals Analisis Data Multivariat Dengan Program R

2018 ◽  
Vol 2 (2) ◽  
pp. 83-86
Author(s):  
Dhoriva Urwatul Wustqa ◽  
Endang Listyani ◽  
Retno Subekti ◽  
Rosita Kusumawati ◽  
Mathilda Susanti ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Univariate Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Training Methods ◽  
Factors Analysis ◽  
R Program ◽  
Questions And Answers ◽  
Statistical Program

Analisis multivariat adalah salah satu teknik dalam statistika yang digunakan untuk menganalisis secara simultan variabel lebih dari satu. Perhitungan dalam analisis data multivariat lebih kompleks dibandingkan dengan analisis univariat, sehingga penggunaan program statistika akan mempermudah dalam analisis.  Salah satu program statistika yang dapat diperoleh secara gratis (tanpa lisensi) adalah program R. Workshop program R untuk analisis data multivariat bagi para lulusan S1 Pendidikan Matematika/Matematika dan mahasiswa program pasca sarjana Pendidikan Matematika secara umum bertujuan untuk memberikan pengetahuan dan ketrampilan dasar penggunaan program R pada analisis data multivariat. Metode yang digunakan dalam pelatihan meliputi tutorial dan praktek secara langsung. Sebagian peserta belum pernah menggunakan program R, dan terlihat bahwa mereka antusias dalam mengikuti pelatihan. Berdasarkan pengamatan dan tanya jawab dengan peserta pelatihan, tampak bahwa peserta bersemangat mengikuti kegiatan pelatihan. Dengan pelatihan ini para peserta mendapat pengetahuan secara teoritis tentang analisis komponen utama, analisis faktor dan secara praktek meliputi ketrampilan tentang bagaimana menganalisis data multivariat dengan program R, dan menginterpretasikan hasil analisis dengan kedua metode tersebut. Kata kunci: analisis multivariat, program statistika R. Multivariate Data Analysis Using R Program Abstract           Multivariate analysis is a technique in statistics that is used to simultaneously analyze more than one variable. Dealing with multivariate data analysis calculations are more complex than the univariate analysis, so the use of statistical program will make it easier. One of the free statistical programs (free license) is R program. Workshop R program on the multivariate data analysis for people who had mathematics or mathematics education degree or graduate students in general aims to provide multivariate data analysis skills using statistics R program. The training methods were tutorial and practices in class. Some participants had never used the R program prior to the training, and they were enthusiastic during training. According to the observations and questions and answers session, the participants appeared to have passions on learning the usage of  the statistical R program on analyzing multivariate data. From the training, the participants gained theoretical knowledge about the principal component analysis, factors analysis, and practices about the skills on how to analyze mulivariate data, and interpret the results of the analysis with both methods using the  R program. Keywords: multivariat analysis, R statistical program

scholarly journals Romanian Wines Quality and Authenticity Using FT-MIR Spectroscopy Coupled with Multivariate Data Analysis

2014 ◽  
Vol 42 (2) ◽  
pp. 556-564 ◽  
Author(s):  
Roxana BANC ◽  
Felicia LOGHIN ◽  
Doina MIERE ◽  
Florinela FETEA ◽  
Carmen SOCACIU
Keyword(s):  
Amino Acids ◽  
Data Analysis ◽  
Organic Acids ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Mir Spectroscopy ◽  
Mir Spectra ◽  
Main Components

Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) combined with multivariate data analysis have been applied for the discrimination of 15 different Romanian wines (white, rosé and red wines), obtained from different origin-denominated cultivars. Principal component analysis and hierarchical cluster analysis was performed using different regions of FT-MIR spectra for all wines. The general fingerprint of wines was splitted in four characteristic regions, corresponding to phenolic derivatives, carbohydrates, amino acids and organic acids, which confer the wines quality and authenticity. By qualitative and quantitative evaluation of each component category, it was possible to discriminate each wine category, from red, to rosé and white colours, to dry, half-dry and half-sweet flavours. The multivariate data analysis based on absorption peaks from FT-MIR spectra demonstrated a very good, significant clustering of samples, based on the four main components: phenolics, carbohydrates, amino acids and organic acids. Therefore, the ATR-FT-MIR analysis proved to be a very fast, cheap and efficient tool to evaluate the quality and authenticity of wines, and to discriminate each wine category, based on their colour and sweetness, as consequence of their biological (cultivar) specificity.

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