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COMPUTER SCIENCE (1196 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 27)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 12)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 4)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 29)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 44)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 5)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 142)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 294)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 37)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 47)
British Journal of Educational Technology     Hybrid Journal   (Followers: 140)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 21)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 16)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 96)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 19)
Computer Physics Communications     Hybrid Journal   (Followers: 7)

        1 2 3 4 5 6 | Last

Journal Cover
Chemometrics and Intelligent Laboratory Systems
Journal Prestige (SJR): 0.672
Citation Impact (citeScore): 3
Number of Followers: 14  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0169-7439
Published by Elsevier Homepage  [3163 journals]
  • The Sequential Multi-block PLS algorithm (SMB-PLS): Comparison of
           performance and interpretability
    • Abstract: Publication date: Available online 18 July 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Julien Lauzon-Gauthier, Petre Manolescu, Carl Duchesne The Sequential Multi-block PLS algorithm, called SMB-PLS, was recently proposed to improve interpretability of large multi-block data structures. It combines the strengths of Multi-block PLS (MB-PLS) and those of the Sequential Orthogonal PLS (SO-PLS) methods. It uses the two-level hierarchical structure of the first (i.e., block and super levels) providing two levels of scrutiny for the analysis of large datasets, and the sequential orthogonalization scheme of SO-PLS, while keeping between block correlated information in the model. This enables the exploration and interpretation of the full data structure without loss of information. SMB-PLS also allows the selection of a different number of latent variables for each regressor block. The modeling performance and interpretation of SMB-PLS were illustrated using two datasets, covering different types of structural relationships between the regressor blocks. SMB-PLS leads to similar predictive performance of response data as MB-PLS and SO-PLS. However, it was shown that SMB-PLS clearly reveals the correlation structure between the regressor blocks, while MB-PLS leads to more ambiguous results. The correlated information between the blocks extracted with SMB-PLS also improves interpretability, for example, by identifying control actions made to attenuate disturbances, such as raw materials variations. Such information cannot be obtained with SO-PLS since it removes between block correlated variations.
  • Prediction of bacteriophage proteins located in the host cell using hybrid
    • Abstract: Publication date: Available online 17 July 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Jing-Hui Cheng, Hui Yang, Meng-Lu Liu, Wei Su, Peng-Mian Feng, Hui Ding, Wei Chen, Hao Lin The identification of bacteriophage proteins in the host subcellular localization could provide important clues for understanding the interaction between phage and host bacteria as well as antibacterial drug design. To date, computational methods have been reported to identify bacteriophage proteins located in the host cell. However, there is still space for improving the prediction accuracy. The existing methods considering the sequence order correlation and the physicochemical property of protein provide us insights to construct an integrated descriptor based on sequence for phage proteins. Meanwhile, we proposed a feature selection technique to obtain the optimal features. In the jackknife test, the prediction accuracies are 86.7% and 97.9%, respectively for discrimination between PH proteins and non-PH proteins as well as PHM proteins and PHC proteins. Based on our model, we updated the web server PHPred to version 2.0 which can be freely accessed from
  • Professor Yi-Zeng Liang; Great Global Scientist with Strong Enthusiastic
           and Friendship
    • Abstract: Publication date: Available online 17 July 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Yukihiro Ozaki
  • Active learning based semi-supervised exponential discriminant analysis
           and its application for fault classification in industrial processes
    • Abstract: Publication date: 15 September 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 180Author(s): Jun Liu, Chunyue Song, Jun Zhao For the industrial fault classification, exponential discriminant analysis (EDA) requires that all the training samples should be labeled; however, only a minority of the training samples are randomly labeled in real industrial processes. This motivates the formulation of the active learning based semi-supervised exponential discriminant analysis in this paper. Firstly, to make EDA applicable to the semi-supervised industrial scenario, scatter matrices are transformed into their regularization variants through combining unlabeled training samples. Moreover, to reduce the adverse effect of random labeling of training samples, the best versus second-best rule is employed to select more informative training samples in an active way to upgrade the model classification performance. And the obvious performance improvement of the proposed method is demonstrated with extensive experiments on synthesized data, the TE process and a real industrial process.
  • Image-based manufacturing analytics: Improving the accuracy of an
           industrial pellet classification system using deep neural networks
    • Abstract: Publication date: 15 September 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 180Author(s): Ricardo Rendall, Ivan Castillo, Bo Lu, Brenda Colegrove, Michael Broadway, Leo H. Chiang, Marco S. Reis Manufacturing analytics is of paramount importance in many plants today, and its relevance increases in the current big data context of Industry 4.0. The fields of statistics, chemometrics, and machine learning are expected to provide tools that effectively handle many of the characteristics of industrial data. In this paper, the task of image-based product classification is considered. This is a supervised learning problem where the input is an image and the output is a unique label attributed to the image from a finite set of labels corresponding to the available product classes. This is a prevalent and highly relevant industrial challenge and recent developments in deep learning have proven to be successful in increasing the image classification accuracy, providing state-of-the-art results. Thus, in this work, we leverage deep neural networks' (DNN) ability to automatically learn features from images and test their performance in a real industrial context for predicting the pellet shape. In order to accelerate the training of DNN, transfer learning is employed and a network previously developed for one task is adapted to predict pellet shape. Furthermore, other less complex techniques such as partial least squares discriminant analysis (PLS-DA) and random forests (RF) are also explored in order to assess the benefits of adopting DNN as opposed to current classifiers.An industrial image classification case study was utilized to compare PLS-DA, RF, and DNN models. Compared to the in situ classification system currently in use, increasingly complex models (PLS-DA and RF) were able to better utilize the same pre-defined features and improve prediction accuracy significantly. DNN obtained the highest accuracy on the independent test set, with the advantages of not requiring the a priori computation of image features since they are directly extracted from the raw images. Moreover, by visualizing the output of some layers of the DNN, it is possible to verify that activations occurred in regions that are indeed meaningful for the classification tasks, further supporting that DNN were effectively modelling the relevant features of the pellet.
  • Evaluation and assessment of homogeneity in images. Part 2: Homogeneity
           assessment on single channel non-binary images. Blending end-point
           detection as example
    • Abstract: Publication date: 15 September 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 180Author(s): Neirivaldo Cavalcante da Silva, Leandro de Moura França, José Manuel Amigo, Manel Bautista, Maria Fernanda Pimentel This paper demonstrates that the use of the homogeneity curve approach introduced in the first part of this work for assessing the unique homogeneity percentage (%H) can also be applied in single channel non-binary (SCNB) images (a.k.a grayscale images) analysis. In order to address a more general homogeneity index approach for SCNB images, more realistic assumptions such as the presence of divisible objects or the mixture of different objects in the same pixel, are considered. The proposed parameter, %H, is an absolute homogeneity percentage that uses only the self-contained information of one image, with no need for additional modelling steps, as described in most of the works addressing the homogeneity assessment. To demonstrate it, two cases have been evaluated. The first case validates the %H by assessing the homogeneity in images with different textures (distribution of objects). In the second case, the reliability of assessing the evolution of homogeneity and providing an end-point detection in blending process trials done at small scale has been evaluated. The results obtained in the current work show the high potential when using the %H for industrial applications where the distributional homogeneity represents a critical process parameter to ensure the quality of the final product.Graphical abstractImage 1
  • Variable selection optimization for multivariate models with Polar
           Qualification System
    • Abstract: Publication date: 15 September 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 180Author(s): Shikhar Mohan, Bruce R. Buchanan, Glen D. Wollenberg, Benoît Igne, James K. Drennen, Carl A. Anderson Multivariate models are used in many fields to predict a response from a set of variables having an undetermined covariate structure. Variable selection often improves multivariate model performance by removing information not related to the response of interest. Many variable selection methods exist for this purpose. This study investigates Polar Qualification System (PQS) as a tool for variables selection. A Raman transmission dataset of tablets containing Niacinamide (active pharmaceutical ingredient) and Niacin (degradant) was modeled for degradant weight concentration using Partial Least Squares (PLS) regression. Three variable selection techniques were compared for the development of a stability indicating method: specific peak selection (manual selection), genetic algorithms (GA-PLS), and a newly developed PQS-Hadamard method. The model performance of these techniques was compared to a model developed with the whole spectrum. All models built with selected variables showed reduced prediction error compared to model created with the full variable range. However, the PQS-Hadamard method was demonstrated to be more computationally efficient compared to GA-PLS. Further, it is a potentially automatable process, unlike the specific peak selection, which requires expert selection of variables.
  • Just-in-time semi-supervised soft sensor for quality prediction in
           industrial rubber mixers
    • Abstract: Publication date: 15 September 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 180Author(s): Wenjian Zheng, Yi Liu, Zengliang Gao, Jianguo Yang Increasing data-driven soft sensors have been adopted to online predict the quality indices in polymerization processes to improve the availability of measurements and efficiency. However, in industrial rubber mixing processes, most existing soft sensors for online prediction of the Mooney viscosity only utilized the limited labeled data. By exploring the unlabeled data, a novel soft sensor, namely just-in-time semi-supervised extreme learning machine (JSELM), is proposed to online predict the Mooney viscosity with multiple recipes. It integrates the just-in-time learning, extreme learning machine (ELM), and the graph Laplacian regularization into a unified online modeling framework. When a test sample is inquired online, the useful information in both of similar labeled and unlabeled data is absorbed into its prediction model. Unlike traditional just-in-time learning models only utilizing labeled data (e.g., just-in-time ELM and just-in-time support vector regression), the prediction performance of JSELM can be enhanced by taking advantage of the information in lots of unlabeled data. Moreover, an efficient model selection strategy is formulated for online construction of the JSELM prediction model. Compared with traditional soft sensor methods, the superiority of JSELM is validated via the Mooney viscosity prediction in an industrial rubber mixer.
  • Bioengineering for multiple PAHs degradation using process centric and
           data centric approaches
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Haren B. Gosai, Bhumi K. Sachaniya, Dushyant R. Dudhagara, Haresh Z. Panseriya, Bharti P. Dave The study aims sequential bioengineering for multiple PAHs degradation using a process centric approach – response surface methodology (RSM) and a data centric approach – artificial neural networks (ANN). The study involves stepwise media optimization protocol for multiple PAHs degradation using newly isolated Stenotrophomonas maltophilia. RSM, a non linear model has predicted 94.70% degradation on 5th day with R2 value of 0.97. The analysis of desirability revealed the optimum value of the process conditions: NaCl – 27.55 g/L; NH4Cl- 0.28 g/L; Na2HPO4- 0.08 g/L, TAPSO- 1.74 g/L. Feed forward ANN, a linear model has predicted 95.94% degradation with R2 value of 0.99. The change in the magnitude of error functions viz. root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) were low during ANN prediction as compared to RSM. Moreover, sensitivity analysis of both models also proves efficiency of the prediction capability and generalization of the data. Thus, for the very first time chemometrics study of medium components for multiple PAHs degradation offers constructive and powerful alternative to scientific community to design microcosm and mesocosm experiments.
  • Constructing 3-level foldover screening designs using cyclic generators
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Nam-Ky Nguyen, Tung-Dinh Pham, Mai Phuong Vuong Most factors in chemical science and engineering are quantitative. Therefore, chemists and engineers are more familiar with the notion that factors should necessarily have three levels. Ref. [5] introduced a new class of 3-level screening designs which allows the assessment of curvature of the factor-response relationship. They called these designs definitive screening designs (DSDs). These DSDs are (i) saturated for estimating the intercept, the m main effects and the m quadratic effects; (ii) unlike resolution III designs, all main effects are orthogonal to 2-factor interactions; (iii) unlike resolution IV designs, 2-factor interactions are not fully aliased with one another; and (iv) unlike resolution III and IV designs, the quadratic effects can be estimated and are orthogonal to main effects and not fully aliased with 2-factor interactions; (v) when the design is sufficiently large, it allows efficient estimation of the full quadratic model in any three factors. This paper introduces a new class of DSD-like designs generated by cyclic generators. This new class can be used to study the presence of the second-order effects more efficiently.
  • MCR-ALS of hyperspectral images with spatio-spectral fuzzy clustering
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Patrizia Firmani, Siewert Hugelier, Federico Marini, Cyril Ruckebusch In recent years, in the context of the application of Multivariate Curve Resolution (MCR) to hyperspectral image analysis, attention has been more and more put onto the possibility of exploiting not only the spectral but also the spatial information for constraining the algorithmic solution. Examples involve the introduction of different spatial constraints during the iterative Alternating Least Squares (ALS) calculation of the MCR solution or the post-processing of the score images using conventional image processing techniques. In this framework, this work proposes an approach for constraining concentration distribution maps within MCR-ALS analysis of hyperspectral images, based on the use of spatio-spectral fuzzy clustering in order to obtain smoother, more contrasted, and better interpretable chemical images. We show the relevance of the proposed approach and investigate the effect of the application of a spectral-spatial fuzzy clustering constraint on samples of different nature.
  • Definitive Screening Designs and latent variable modelling for the
           optimization of solid phase microextraction (SPME): Case study -
           Quantification of volatile fatty acids in wines
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Ana C. Pereira, Marco S. Reis, João M. Leça, Pedro M. Rodrigues, José C. Marques In the present study, we apply the recently proposed Definitive Screening Designs (DSD) to optimize HS-SPME extraction in order to analyze volatile fatty acids (VFA) present in wine samples. This is the first attempt to apply this new class of designs to one of the most well-known and widely applied extraction techniques. The latent structure of the responses is also explored for defining the optimal extraction conditions. DSD is a new screening design with the potential to significantly reduce the number of experiments required to estimate the model parameters and to establish the optimum operation conditions. Therefore, there is an obvious interest in assessing the benefits of DSD in practice. In this work, this design framework is applied to the simultaneous optimization of seven extraction parameters (responses). Both qualitative and quantitative extraction parameters are considered, in order to test the flexibility of DSD designs: a two-level qualitative variable, the fiber coating, and six quantitative variables, namely the pre-incubation time, the extraction time and temperature, the headspace/sample volume, the effect of agitation during extraction and the influence of the ethanol content (sample dilution). Optimization of analytes' chromatographic responses was carried out both individually (response by response) and altogether, by modelling the responses in the latent variable space (i.e., explicitly considering their underlying correlation structure). In the end, a consensus analysis of all perspectives was considered in the definition of the overall optimal extraction conditions for the quantification of VFA in fortified wines. The solution found was to use a DVB/Car/PDMS fiber, 10 mL of samples in 20 mL vial, 40 min of extraction at 40 °C. The analysis also revealed that the factors incubation time, agitation and sample dilution do not play a significant role in explaining the variability of extraction parameters. Therefore, they were set to the most convenient levels. The methodology followed was thoroughly validated and the following figures of merit were obtained: good linearity (R2 > 0.999, for all compounds), high sensitivity (LOD and LOQ are close or below the values found in literature), recoveries of approximately 100% and suitable precision (repeatability and reproducibility lower than 7.21% and 8.61%, respectively). Finally, the optimized methodology was tested in practice. Several wine samples were analyzed and the odor activity value calculated to facilitate the identification of their importance as odor active compounds in different aged fortified wines. This work demonstrates the benefits of using DSD and latent variable modelling for the optimization of analytical techniques, contributing to the implementation of rigorous, systematic and more efficient optimization protocols.
  • A novel intelligent modeling framework integrating convolutional neural
           network with an adaptive time-series window and its application to
           industrial process operational optimization
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Yongjian Wang, Hongguang Li With the increasing complexity of industrial processes, it becomes more and more difficult to set up process operational optimization models. Recently, the convolutional neural network (CNN) has been widely and successfully applied to extracting useful information due to its deep learning capability. Aiming at extracting useful information concerning operational optimization from complex industrial process data, this paper proposes a novel framework integrating CNN with an adaptive time-series window (ATSW-CNN). The proposed ATSW-CNN method is composed of four kinds of network layers, i.e. the proposed adaptive layer, convolutional layers, pooling layers and fully connected layers. The proposed adaptive layer provides CNN paradigms with a capability of adaptively selecting appropriate time-series windows for different steady-state operational optimization data. As a result, the proposed ATSW-CNN method can effectively extract steady-state optimal operating conditions from process time-series data. In order to validate the performance of the proposed ATSW-CNN, simulations on an industrial furnace are carried out. Simulation results verify the effectiveness of the proposed method, which demonstrates the proposed ATSW-CNN method is applicable for searching steady state operating strategies.
  • Sparse N-way partial least squares with R package sNPLS
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): D. Hervás, J.M. Prats-Montalbán, A. Lahoz, A. Ferrer We introduce the R package sNPLS that performs N-way partial least squares (N-PLS) regression and Sparse (L1-penalized) N-PLS regression in three-way arrays. N-PLS regression is superior to other methods for three-way data based in unfolding, thanks to a better stabilization of the decomposition. This provides better interpretability and improves predictions. The sparse version also adds variable selection through L1 penalization. The sparse version of N-PLS is able to provide lower prediction errors and to further improve interpretability and usability of the N-PLS results. After a short introduction to both methods, the different functions of the package are presented by displaying their use in simulated and a real dataset.
  • Near infrared spectroscopy for classification of bacterial pathogen
           strains based on spectral transforms and machine learning
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Ke-Xin Mu, Yao-Ze Feng, Wei Chen, Wei Yu The potential of near-infrared spectroscopy in classifying individual bacterial strains from different species was investigated in this study. Bacterial samples in liquid nutrient culture were collected periodically (0, 6 and 12 h) during incubation and their spectra were acquired in the near-infrared (NIR) range of 1000–2500 nm. Spectral transforms, including absorbance (A), transmittance (T) and Kubelka-Munk (KM) units were explored in order to enhance classification performance. Partial least squares discriminant analysis (PLS-DA), radial basis function neural network (RBF) and support vector machine (SVM) were used in classification model development. The results illustrated that nonlinear methods such as SVM and RBF neural network outperformed PLS-DA, where the overall correct classification rates (OCCRs) were both above 96%. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and random forest (RF) were employed to reduce spectral redundancy and to identify important wavelengths for simplifying models. The RF model yielded the best predictions as indicated by the shortest modeling time and the excellent OCCRs (100%) for both calibration and prediction. The overall results demonstrated the suitability of NIR spectroscopy with RF for the simultaneous classification of water-borne pathogenic strains from different species.
  • An advanced model for the detection of short DNA sequences by mass
           spectrometry based on exonuclease III assisted recycling amplification
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Cai-Xia Shi, Qing Liu, Zeng-Ping Chen, Ru-Qin Yu Exonuclease III assisted target recycling amplification strategy can be used to enhance the sensitivity of mass spectrometry for the detection of short DNA sequences. However, the distribution pattern of DNA fragments produced by exonuclease III assisted target recycling amplification is generally different for samples with different concentrations of the target DNA sequence, which hinders the extraction of both qualitative and quantitative information of the target DNA from mass spectral measurements using traditional univariate or multivariate models. In this contribution, an advanced model was derived based on a reasonable assumption for the qualitative and quantitative analysis of the mass spectral measurements of DNA fragments produced by exonuclease III-assisted target recycling amplification. Experimental results demonstrated that the integration of exonuclease III assisted target recycling amplification, mass spectrometry and the advanced model could achieve sensitive and accurate quantitative results for a target short DNA sequence in complex biological medium with a detection limit of 50 pM and a mean recovery rate within the range of 89.5%–106.7%. More interestingly, the proposed model could unambiguously identify single nucleotide polymorphisms based on the distribution patterns of residual DNA fragments. Therefore, with the aid of the proposed model, mass spectrometry based on exonuclease III assisted recycling amplification has great potential for the reliable, sensitive, selective, and relatively low-cost detection and quantification of short DNA sequences in clinical diagnosis and biomedical research.
  • Predicting protein lysine methylation sites by incorporating
           single-residue structural features into Chou's pseudo components
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Hao Qiu, Yanzhi Guo, Lezheng Yu, Xuemei Pu, Menglong Li Identification of the methylated residues is helpful for us to understand the molecular mechanism of many biological processes. Currently, almost all existing computational methods for methylation site prediction are based on the protein sequences. However, the 3-D structures of proteins are more directly correlated with their biological properties than the sequences. Therefore, in view of few similar works have been done before, a novel method for predicting protein lysine methylation sites were firstly proposed based on single-residue structural features. Different from previous works extracting fragments with the methylated site in the center which contain several neighboring residues as samples, only the single methylated lysine site is considered as a sample in this paper. Then, on basis of the 3-D structures of methylated proteins, we gave a comprehensive feature representation for each methylated lysine by combing accessible surface area (ASA), protrusion index (CX) and depth index (DPX), secondary structure (SS), residue interaction network (RIN) and electrostatics potential (EP). All of these features can well characterize the environmental information of each methylated lysine, in other words, the structural information of the neighboring residues has been integrated into the features of it. According to our analysis, we suggest that it’s more efficient to establish the model focusing on single sites than adding adjacent residues. The prediction model was assessed by the testing set and yielded a good performance with the sensitivity of 95.1% and specificity of 89.0%. Moreover, a common independent dataset was collected for further evaluating our model and other five existing sequence-based methods. The prediction results indicate that our method outperforms them and all experimentally confirmed methylated sites are successfully identified by our model. Finally, we conducted predictions on a proteomic scale in order to provide guidance for further experiments. All results indicate that our method can be a useful implement in identifying methylated lysine sites.
  • Comparison of integration rules in the case of very narrow chromatographic
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Yuri Kalambet, Yuri Kozmin, Andrey Samokhin Theory of peak integration is revised for very narrow peaks. It is shown, that Trapezoidal rule area is efficient estimate of full peak area with extraordinary low error. Simpson's rule is less efficient in full area integration. Theoretical conclusions are illustrated by digital simulation and processing of experimental data. It was shown that for Gaussian peak Trapezoidal rule requires 0.62 points per standard deviation (2.5 points per peak width at baseline) to achieve integration error of only 0.1%, while Simpson's rule requires 1.8 times higher data rates. Asymmetric peaks require higher data rates as well. Reasons of poor behavior of Simpson's rule are discussed; averaged Simpson's rules are constructed, these rules coincide with those based on Euler-Maclaurin formula. Euler-Maclaurin rules can reduce error in the case of partial peak integration. Higher peak moments (average retention time, dispersion, skewness, etc.) also exhibit extraordinary low errors and can potentially be used for evaluation of peak shape.
  • Survival forest with partial least squares for high dimensional censored
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Lifeng Zhou, Hong Wang, Qingsong Xu Random forest and partial least squares have proved wide applicability in numerous contexts. However, the combination of these versatile tools has seldom been studied. Inspired by a relatively new decision tree ensemble called rotation forest, we introduce a new survival ensemble algorithm using partial least squares regression and the Buckley-James estimator within the framework of random forest. First, the approach taken to cope with the high dimensionality is to reduce the dimension by a random subspace method. Then, censored survival times are imputed by the Buckley-James estimator. After dimension reduction and time imputation, partial least squares regression is applied to extract the features. Similar to rotation forest, all extracted components are used as covariates in a bagged survival tree to predict the survival probabilities. Experimental results on a variety of simulation and real datasets demonstrate that the proposed approach is a strong competitor to other popular survival prediction models under high or ultra-high dimensional setting.
  • Generalization of Powered–Partial-Least-Squares
    • Abstract: Publication date: 15 August 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 179Author(s): Francis B. Lavoie, Koji Muteki, Ryan Gosselin Indahl originally proposed a variant to Wold's PLS1 algorithm in which weight coefficients were all modified by an exponent coefficient. This led to Powered-PLS (P-PLS). The aim of this paper is to revisit Indahl's P-PLS algorithm in order to make a robust and fast regression methodology calculating easy to interpret models. We first demonstrate that P-PLS is in fact a regression based on correlation maximization, but constrained by weight coefficients originally calculated in standard PLS1. From that, we propose a generalization of P-PLS by replacing the power transformation function by β Cumulative Density Functions (β-CDFs), leading to our proposed regression methodology called β-PLS. With two public datasets, we demonstrate that P-PLS and even more β-PLS regressions outperform standard PLS1 in terms of cross-validation performances in the case where the number of calibration observations is largely lower than the number of variables in X.
  • A simple algorithm for despiking Raman spectra
    • Abstract: Publication date: Available online 28 June 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Darren A. Whitaker, Kevin Hayes Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required. The majority of Raman instruments use charge-coupled device (ccd) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically, our algorithm uses modified Z scores calculated from the once-differenced detrended spectrum to locate the offending spikes, followed by a simple moving average to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike.
  • Comparison of different image analysis algorithms on MRI to predict
           physico-chemical and sensory attributes of loin
    • Abstract: Publication date: Available online 9 April 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Daniel Caballero, Andrés Caro, Anders B. Dahl, Bjarne K. ErsbØll, José Manuel Amigo, Trinidad Pérez-Palacios, Teresa Antequera Computer vision algorithms on MRI have been presented as an alternative to destructive methods to determine the quality traits of meat products. Since, MRI is non-destructive, non-ionizing and innocuous methods. The use of fractals to analyze MRI could be another possibility for this purpose. In this paper, a new fractal algorithm is developed, to obtain features from MRI based on fractal characteristics. This algorithm is called OPFTA (One Point Fractal Texture Algorithm). Three fractal algorithms (Classical Fractal Algorithm –CFA-, Fractal Texture Algorithm –FTA- and OPFTA) and three classical texture algorithms (Grey level co-occurrence matrix –GLCM-, Grey level run length matrix –GLRLM- and Neighbouring grey level dependence matrix –NGLDM-) were tested in this study. The results obtained by means of these computer vision algorithms were correlated to the results obtained by means of physico-chemical and sensory analysis. CFA reached low relationship for the quality parameters of loins, the remaining algorithms achieved correlation coefficients higher than 0.5 noting OPFTA that reached the highest correlation coefficients in all cases except for the L* coordinate color that GLCM obtained the highest correlation coefficient. These high correlation coefficients confirm the new algorithm as an alternative to the other computer vision approaches in order to compute the physico-chemical and sensory parameters of meat products in a non-destructive and efficient way.
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