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6.2.3 Web technologies and Scripting languages Over the past years, the World Wide Web (WWW or W3) (Berners-Lee, 1996; Berners-Lee et al., 2001; Berners-Lee and Fischetti, 2008) has profoundly changed the rapidity of acquisition and storage of knowledge, as well as promoting the development of hitherto unimaginably intricate methods of dealing with the vast enormity of newly acquired knowledge and/or data. Web development and design is indeed an immense and complex field. With the exponential development of the informatics field over the past few years, nowadays there can be traced literally thousands upon thousands of different web technologies, methodologies, disciplines and standards, programming languages, and software packages. Some of the core and commonly used web technologies are depicted in Figure 6-4. The Web as we know it nowadays is actually the second phase in the Web’s evolution; namely, Web 2.0 (DiNucci, 1999; O’Reilly, 2005; O’Reilly, 2009). Web 2.0 is a collection of technologies, business strategies, and social trends; compared to its predecessor (Web 1.0), it is more dynamic, interactive, customised and media-intensive. Whereas with Web 1.0 the end-users remained passive simply viewing the contents of a web page, Web 2.0 allows its users to view, interact but also contribute to the content of a web page. Currently, a transition from Web 2.0 towards Web 3.0 is already emerging; Web 3.0 will include personalised and user-behaviour features, thus providing “a portable personal semantic web”. Web and grid technologies have developed at a dazzling rate, offering a multitude of advantages to the life sciences as a bonus from the computational sciences. These technologies have moved from their classical and somewhat static architectures to more dynamic and service-oriented ones as illustrated in Figure 6-4. Rich Internet Applications (RIAs) (Fraternali et al., 2010) became increasingly important and popular during the past decade, and currently play a prominent role towards the evolution of Web 2.0. RIAs resemble to a vast degree the characteristics and functionality of desktop applications, even though they are web-based, since they feature responsive user interfaces and interactive capabilities. Thus, web-based programs become easier to use and more functional to use compared to the static and limited web pages of Web 1.0. Clients in the RIA model handle user-interface-related 177
activity, while simultaneously the server processes and stores data in addition to returning the results back to the user. A RIA runs on a web browser, usually without the necessity of installing any software on the client side. In addition, RIAs are typically based on, or feature, asynchronous communication such as Asynchronous JavaScript and XML (AJAX). Therefore, clients can interact with the webpage without having to wait for results from the server. Figure 6-4 The progress of web technologies and programming languages over time The figure illustrates the progress of web technologies, from the PC era to current times where Web 2.0 is the dominant web philosphy, all the way to proposed future web technologies such as Web 3.0 and Web 4.0. Along with the advance of web technologies, various different programming languages are displayed based upon each era. The shift of interest from static technologies such as HTML to dynamic, interactive and content-rich languages such as AJAX is depicted. The figure has been extracted from http://thepaisano.files.wordpress.com/2008/03/webtimeline.jpg Asynchronous JavaScript and XML (AJAX) (Garrett, 2005) was introduced as a means of web application development. AJAX is not a novel language or a single technology; rather, it is “a powerful combination of several different vigorous technologies, each flourishing in its own right” (Garrett, 2005; Lin et al., 2008; Wang et al., 2008). AJAX incorporates: 178
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CRANFIELD UNIVERSITY Eleni Anthippi
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ABSTRACT Muscle foods such as meat,
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TABLE OF CONTENTS ABSTRACT ........
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6.2.4 The iWebPlots package .......
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Figure 3-12 Speedup produced by the
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Figure 6-9 Sweave example for the d
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TABLE OF EQUATIONS Equation 1 Mean-
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RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
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Figure 1-1 Evolution from molecular
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1.1.2.1 Fourier Transform Infrared
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1.1.3 Microbial Spoilage in Meat Sy
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The multivariate techniques applied
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1.4 Multivariate Analysis: Unsuperv
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1.4.2 Cluster Analysis Cluster anal
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1.5 Multivariate Analysis: Supervis
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In any linearly separable binary da
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Where ( ) are the Lagrange multipli
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Every kernel is characterised by a
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The “one-against-all” approach
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Furthermore, metrics such as the bi
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1.6.3 Leave-One-Out Cross-Validatio
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In such cases, the model becomes qu
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1.8 Aims and objectives The overall
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2 Development of the multivariate a
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Sensory panel scores were of the hi
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DF (Hz) 2.2.1.5 Electronic Nose (e-
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Figure 2-3 Data intersection The fi
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3. Validation Techniques In the cas
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Figure 2-4 The process of construct
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2.3 Results and Discussion 2.3.1 Pr
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(b) HPLC data (c) e-nose data Figur
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In the case of FTIR, linear classif
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2.3.2.2 Class Prediction Accuracies
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Figure 2-7 Class prediction rates o
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The topology of the three-dimension
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Figure 2-9 Three-dimensional error
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Furthermore, a thorough comparison
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In the master/slave architecture, a
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precision of the classification acc
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Figure 3-3 The steps of the Nelder-
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3.3 Results and Discussion 3.3.1 Li
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3.3.2 Nonlinear Models In order to
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9) 10) 11) 12) 13) 14) 15) 16) Figu
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As an additional visual aid, these
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a) Number of iterations b) Number o
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Figure 3-10 Comparison of the execu
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Figure 3-12 Speedup produced by the
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4 Integration of Heterogeneous Data
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4.2.2 Procrustes Analysis Procruste
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symmetric method since the ordering
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Figure 4-3 Steps of CPCA for the da
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4.2.5 Data Integration and Analysis
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Figure 4-4 Data integration workflo
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a) GPA b) CPCA Figure 4-6 The conse
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accuracies as the linear models; po
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Figure 4-8 Classification Results f
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Similar to FTIR, the PLS-DA ensembl
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Figure 4-9 Class prediction rates o
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4.3.3 Permutation Tests Even though
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Even though the interpretation of t
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Figure 4-12 Distribution plots of t
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RBF SVMs Datasets Original %CC Mean
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Figure 4-14 Boxplots representing t
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5 Application of the multivariate a
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Figure 5-1 Mean FTIR spectra for ca
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5.2.2.2 Fourier Transform Infrared
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5.2.2.4 Data Overview For each expe
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5.2.3.3 High Throughput Liquid Chro
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Figure 5-5 displays the PCA scores
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(a) GPA (b) CPCA Figure 5-6 The con
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Page 143 and 144: Figure 5-8 Class prediction rates o
Page 145 and 146: (a) RBF SVMs (b) PLS-DA Figure 5-9
Page 147 and 148: RBF SVMs Datasets Original %CC Mean
Page 149 and 150: Figure 5-12 Execution times of the
Page 151 and 152: (a) FTIR data (b) Raman data Figure
Page 153 and 154: The classification results for the
Page 155 and 156: Figure 5-16 Class prediction rates
Page 157 and 158: (a) RBF SVMs (b) PLS-DA Figure 5-17
Page 161 and 162: Figure 5-20 Execution times of the
Page 163 and 164: (a) FTIR data (b) HPLC data 148
Page 165 and 166: (a) GPA (b) CPCA Figure 5-22 The co
Page 167 and 168: The classification results of the f
Page 169 and 170: Figure 5-24 Classification Results
Page 171 and 172: Figure 5-25 Class prediction rates
Page 173 and 174: The permutation results for the dat
Page 175 and 176: Figure 5-28 Distribution plots of t
Page 179 and 180: Figure 5-30 Boxplots representing t
Page 181 and 182: 5.4 Comparison of the individual ca
Page 183 and 184: Figure 5-32 Investigating the commo
Page 185 and 186: 6 Development of improved visualisa
Page 187 and 188: 6.2.2 Generating static graphs As d
Page 189 and 190: Figure 6-2 Construction process of
Page 191: 6.2.2.2 Generating dynamic reports
Page 195 and 196: or disappeared (Wang et al., 2008).
Page 197 and 198: ectangles, circles and/or irregular
Page 199 and 200: In addition to the static figures g
Page 201 and 202: c) graphics package d) ggplot2 pack
Page 203 and 204: In addition to aesthetics, faceting
Page 205 and 206: Datasets FTIR HPLC e-nose PLS-DA (L
Page 207 and 208: Figure 6-10 Interactive dendrogram
Page 209 and 210: 6.4 Conclusion The work reported in
Page 211 and 212: large number of individual models i
Page 213 and 214: The generated suite of tools, as pr
Page 215 and 216: In addition, the difficulty to corr
Page 217 and 218: REFERENCES Almeida, J. A. S., Barbo
Page 219 and 220: Processing., Proceedings of the 12t
Page 221 and 222: Clarke, B., Fokoué, E. and Zhang,
Page 223 and 224: spectroscopy and machine learning",
Page 225 and 226: Gower, J. C. (1975), "Generalized p
Page 227 and 228: Jardon, M. (2006), Systems Biology:
Page 229 and 230: Nicolaou, N., Xu, Y. and Goodacre,
Page 231 and 232: Shah, A. A., Barthel, D., Lukasiak,
Page 233 and 234: Vera, G., Jansen, R. and Suppi, R.
Page 235: Xu, Y. and Goodacre, R. (2012), "Mu
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