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  Subjects -> COMPUTER SCIENCE (Total: 2072 journals)
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COMPUTER SCIENCE (1202 journals)                  1 2 3 4 5 6 7 | 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: 28)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 14)
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: 9)
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: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 5)
ACM Transactions on Economics and Computation     Hybrid Journal   (Followers: 1)
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
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: 31)
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: 4)
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 Computer Science : an International Journal     Open Access   (Followers: 14)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 54)
Advances in Engineering Software     Hybrid Journal   (Followers: 28)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 14)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Science     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: 49)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 6)
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: 12)
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: 3)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 8)
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: 12)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 34)
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: 146)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 8)
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   (Followers: 1)
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: 5)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 12)
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: 53)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 304)
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: 48)
British Journal of Educational Technology     Hybrid Journal   (Followers: 144)
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: 22)
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 Information Science and Management Engineering     Open Access   (Followers: 4)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 51)
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: 17)
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: 98)
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: 24)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)

        1 2 3 4 5 6 7 | 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  [3161 journals]
  • Tutorial and spreadsheets for Bayesian evaluation of risks of false
           decisions on conformity of a multicomponent material or object due to
           measurement uncertainty
    • Abstract: Publication date: Available online 15 September 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): RicardoJ.N.B. da Silva, FrancescaR. Pennecchi, D. Brynn Hibbert, Ilya KuselmanAbstractA tutorial and a user-friendly program for evaluating risks of false decisions in conformity assessment of a multicomponent material or object due to measurement uncertainty, based on a Bayesian approach, are presented. The developed program consists of two separate MS-Excel spreadsheets. It allows calculation of the consumer's and producer's risks concerning each component of the material whose concentration was tested (‘particular risks’) as well as concerning the material as a whole (‘total risks’). According to the Bayesian framework, probability density functions of the actual/‘true’ component concentrations (prior pdfs) and likelihood functions (likelihoods) of the corresponding test results are used to model the knowledge about the material or object. Both cases of independent and correlated variables (the actual concentrations and the test results) are treated in the present work. Spreadsheets provide an estimate of the joint posterior pdf for the actual component concentrations as the normalized product of the multivariate prior pdf and the likelihood, starting from normal or log-normal prior pdfs and normal likelihoods, using Markov Chain Monte Carlo (MCMC) simulations by the Metropolis-Hastings algorithm. The principles of Bayesian inference and MCMC are described for users with basic knowledge in statistics, necessary for correct formulation of a task and interpretation of the calculation results. The spreadsheet program was validated by comparison of the obtained results with analytical results calculated in the R programming environment. The developed program allows estimation of risks greater than 0.003% with standard deviations of such estimates spreading from 0.001% to 1.5%, depending on the risk value. Such estimation characteristics are satisfactory, taking into account known variability in measurement uncertainty associated with the test results of multicomponent materials.
       
  • Professor Yi-Zeng Liang; great global scientist with strong enthusiastic
           and friendship
    • Abstract: Publication date: 15 November 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 182Author(s): Yukihiro Ozaki
       
  • A combination strategy of random forest and back propagation network for
           variable selection in spectral calibration
    • Abstract: Publication date: Available online 11 September 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Huazhou Chen, Xiaoke Liu, Zhen Jia, Zhenyao Liu, Kai Shi, Ken CaiRandom forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and attractive analytical properties. When developing a RF regression model for spectral analysis, some informative wavelengths are supposed to be selected so as to reduce dimension effectively and improve interpretability. Whereas a neural network has the merit of restoring high signals in data. A chemometric strategy was proposed in this paper, implemented through the combined use of the RF algorithm and back propagation (BP) network. The RF-selected informative wavelengths were further refined by a moderate 3-layer BP network, where the number of hidden nodes was tunable and finally determined by searching the minimum output error. The BP network was trained with the combined running of RF to generate a new comprehensive variable, so that a renewal informative-plus-net variable group could be produced. This renewed group of variables (or this selected group of variables) was used in a multiple linear regression model to predict the spectral analytical ability in quantitatively determining the content of the target analyte. The application case was based on the Fourier transform near infrared dataset of soil samples, aiming to chemometrically determine the content of the nutritional organic carbon. The prediction results indicated that the proposed strategy of combining RF and BP network can improve prediction accuracy and enhance model interpretability in comparison with the general RF method and the conventional benchmark partial least squares regression. The methodology presented here is of practical significance and has wide application in rapid nutrition determination in the development of precise agriculture.Graphical abstractImage 1
       
  • Illustration of merits of semi-supervised learning in regression analysis
    • Abstract: Publication date: 15 November 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 182Author(s): Hiromasa KanekoAbstractSemi-supervised learning (SSL) is a method for learning the relationship between X and y, and the essential structure of the corresponding dataset, using both labeled and unlabeled data. In this paper, an approach to use a combination of labeled and unlabeled samples to reduce the dimension, then perform regression analysis using the labeled samples in a low-dimensional space is focused in SSL methods. While various SSL methods for regression have been developed, there has been insufficient discussion as to why SSL is effective in regression analysis. Therefore, in this study, the merits of SSL in regression analysis are discussed in terms of the stability or the robustness and applicability domains of regression models and prior distribution of X-variables. The superiorities of SSL methods over fully supervised methods in regression are demonstrated using data from numerical simulations, quantitative structure–activity relationships and quantitative structure–property relationships.
       
  • A computational approach to partial least squares model inversion in the
           framework of the process analytical technology and quality by design
           initiatives
    • Abstract: Publication date: Available online 1 September 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): S. Ruiz, M.C. Ortiz, L.A. Sarabia, M.S. SánchezAbstractIn the context of the paradigms founding the Quality by Design and Process Analytical Technology initiatives, the work herein presents a computational approach to support the decision-making process, in particular, about the feasibility of a product defined for some a priori given quality characteristics.The approach is based on the computation of the pareto-optimal front when simultaneously minimizing the expected differences between the predicted and the desired characteristics. Thus, the feasibility is tackle as an optimization problem with the novelty of doing so simultaneously for all the characteristics, preserving the correlation structure, but by handling each individual characteristic separately.With data from a low-density polyethylene production process, with fourteen process variables and five measured characteristics of the final polyethylene, solutions are found to define the Design Space for targeted quality characteristics on the product, and without the need of explicitly inverting the PLS (Partial Least Squares) prediction model fitted to the process.
       
  • Feature selection using particle swarm optimization-based logistic
           regression model
    • Abstract: Publication date: Available online 1 September 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Omar Saber Qasim, Zakariya Yahya AlgamalAbstractIn any classification problem, the dataset typically has a large number of features. However, not all features are necessary to obtain a good classification performance because some of them are irrelevant and redundant. Therefore, classifiers with less number of features but with better classification accuracy are favored for ease of interpretation. In this work, particle swarm optimization algorithm along with logistic regression model is proposed. Additionally, the Bayesian information criterion (BIC) as a fitness function is proposed. The performance of different fitness functions is investigated and compared with BIC. The performance of the proposed method is evaluated based on a large number of different types of datasets. Experimental results using different types of datasets demonstrate the usefulness of our proposed method in significantly obtaining an improved classification performance with few features. Further, the results show that the proposed methods have a competitive performance comparing with other existing fitness functions.
       
  • Descriptor selection evaluation of binary gravitational search algorithm
           in quantitative structure-activity relationship studies of benzyl phenyl
           ether diamidine's antiprotozoal activity and Chalcone's anticancer potency
           
    • Abstract: Publication date: Available online 30 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Tayebeh Baghgoli, Mehdi Mousavi, Behnam Mohseni BababdaniAbstractIn this work, the effect of various gravitational constant functions, G(t), were evaluated on the performance of the newly proposed binary gravitational search algorithm (BGSA) as a feature selection method for chemical systems. To fulfill this aim, linear, exponential, logarithmic and square root functions were studied. These functions were implemented in the binary gravitational search algorithm computer code sequentially and their performances were evaluated in a quantitative structure-activity relationship (QSAR) study for selection of the most informative descriptors. In the QSAR study, sixty cationic benzyl phenyl ether diamidine derivatives, which had been synthesized and evaluated against acute infection of Trypanosoma brucei rhodesiense (T.b. rhodesiense), were examined. The number and the kind of descriptors, which were selected by the BGSA, were highly dependent on the applied G(t) function. The results of internal and external validation tests indicate that the exponential function was superior to the other gravitational constant functions for applying in the binary gravitational search algorithm. A general model was established using seven descriptors for the ten training and validation sets. Regardless of subsetting, the selected descriptors and generated model can successfully describe experimental variation of antiprotozoal activity of benzyl phenyl ether diamidine derivatives. In addition, in another QSAR study, anticancer potency of a series of 87 Chalcone derivatives was satisfactorily modeled by using the BGSA-BRANN method. Comparison of BGSA results with those obtained by genetic algorithm (GA) indicates superiority of the BGSA.
       
  • DBPPred-PDSD: Machine Learning Approach for Prediction of DNA-binding
           Proteins using Discrete Wavelet Transform and Optimized Integrated
           Features Space
    • Abstract: Publication date: Available online 23 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Farman Ali, Muhammad Kabir, Muhammad Arif, Zar Nawab Khan Swati, Zaheer Ullah Khan, Matee Ullah, Dong-Jun YuAbstractDNA-binding proteins play a crucial role in various biological processes such as regulation of DNA modification, repair, replication, and transcription. These proteins widely participate in the production of drugs, antibiotics, and steroids. Many computational approaches have been developed to identify DNA-binding proteins, but some methods are time-consuming and expensive while some are laborious. Still, it is a challenging task for the researchers to develop highly promising computational methods to identify DNA-binding proteins with high precision. In our work, we developed a new computational approach named as DBPPred-PDSD which has more promising prediction power for DNA-binding proteins. We employed two datasets, extracted features via Split Amino Acid Composition (SAAC) and Position Specific Scoring Matrix (PSSM). Further, we applied the Discrete Wavelet Transform (DWT) on PSSM to extract dominant features. From these features space, optimal features are generated by Maximum Relevance and Minimum Redundancy (mRMR) and fused. To obtain highly informative features, we used Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and provided to well-known classifiers namely Support Vector Machine (SVM) and Random Forest (RF). Our model with the SVM classifier on three tests i.e. Jackknife cross-validation, 10-fold cross-validation and Independent tests achieved the highest success rate than other existing methods in the literature.
       
  • Calibration transfer of near infrared spectrometers for the assessment of
           plasma ethanol precipitation process
    • Abstract: Publication date: Available online 23 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Zhongyu Sun, Jiayue Wang, Lei Nie, Lian Li, Dawei Cao, Jiajin Fan, Haiyan Wang, Ruichen Liu, Yinran Zhang, Hengchang ZangPlasma ethanol precipitation process is an important step in the biopharmacy production process, and near infrared spectroscopy (NIRS) is an effective process analytical technology (PAT) tool in detecting the key quality attribute: the total protein (TP) content. But establishing a robust model is pretty complex. The aim of this study was to explore the calibration transfer strategy between master and slave spectrometers with the biopharmacy. Firstly, 8 batches of ethanol precipitation process were experimented. The changing spectra were collected by two spectrometers, reference values were measured by biuret method. Then, calibration transfer based on the PRS algorithm was carried out. Finally, before establishment of the optimal TP content partial least squares regression (PLSR) model of calibration transfer, rank sample set partitioning based on joint x-y distance (Rank-SPXY) and spectral pretreatment methods were selected after comparison. The determination coefficients of prediction (Rp2) and root mean squares error of prediction (RMSEP) were 0.934 and 0.8395 g/L, respectively. The results showed that Rank-SPXY-PRS was an effective calibration transfer strategy with different NIRS spectrometers. To some extent, the predictive capability of PLSR model was improved after calibration transfer, and it could provide reference for the further research in biopharmacy production process.Graphical abstractImage 1
       
  • Collaborative representation based classifier with partial least squares
           regression for the classification of spectral data
    • Abstract: Publication date: Available online 21 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Weiran Song, Hui Wang, Paul Maguire, Omar NiboucheABSTRACTThe need to classify high-dimensional spectral data is an increasingly common occurrence in rapid and non-destructive detection of object features and chemical species using spectroscopy. Partial least squares discriminant analysis (PLS-DA) is an effective method for spectral data classification, which is based on a multivariate regression model. Although powerful, PLS-DA suffers from performance degradation under complex conditions such as nonlinearity, imbalance and multiclass, which are common in real applications. Collaborative representation-based classifier (CRC) is a new machine learning algorithm which represents a query by a linear combination of training samples and classifies the query based on the representation. It offers the possibility of classifying even under nonlinearity, imbalance and multiclass conditions. In this paper, we present a novel method for spectral data classification, namely CRC-WPLS, which reaps the benefits of both PLS regression and CRC. This method searches for a weighted, linear combination of all training samples to represent the query by using PLS regression, and then assigns the query to the class which yields the least approximation error. CRC-WPLS is compared to PLS-DA, kernel PLS-DA and representation-based classifiers on fourteen general machine learning datasets and three spectral datasets. Experimental results show the proposed method can outperform 5 baseline methods in most cases, and achieve a high classification accuracy (> 92%) for low grade spectra obtained from portable instrumentation.
       
  • Optimizing manipulated trajectory based on principal time-segmented
           variables for batch processes
    • Abstract: Publication date: Available online 18 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Xiaofeng Liu, Xiaoli Luan, Fei LiuAbstractNon-unitized loading cosine similarity A recursive optimization method that updates fewer parameters of the manipulated trajectory determined by principal time-segmented variables (PTVs) for batch processes with multi-stage characteristics is presented. First, the correlation analysis between time-segmented variables and the controlled product quality index variable is carried out in a non-unitized orthogonal latent variable space. Next, the parameters of the manipulated trajectory and the PTVs of each stage are determined according to the correlation and trend characteristics of the trajectory. Then, the parameters of the manipulated trajectory are recursively updated according to the cosine similarity between PTVs and the controlled quality index variable. Finally, performance of the proposed optimization technique is evaluated using the Bisphenol A (BPA) crystallization process to verify the effectiveness and advantages of the methods.
       
  • Which regression method to use' Making informed decisions in
           “data-rich/knowledge poor” scenarios – The Predictive Analytics
           Comparison framework (PAC)
    • Abstract: Publication date: Available online 17 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Ricardo Rendall, Marco S. ReisAbstractIn the big data and Manufacturing 4.0 era, there is a growing interest in using advanced analytical platforms to develop predictive modeling approaches that take advantage of the wealthy of data available. Typically, practitioners have their own favorite methods to address the modeling task, as a result of their technical background, past experience or software available, among other possible reasons. However, the importance of this task in the future justifies and requires more informed decisions about the predictive solution to adopt. Therefore, a wider variety of methods should be considered and assessed before taking the final decision. Having passed through this process many times and in different application scenarios (chemical industry, biofuels, drink and food, shipping industry, etc.), the authors developed a software framework that is able to speed up the selection process, while securing a rigorous and robust assessment: the Predictive Analytics Comparison framework (PAC). PAC is a systematic and robust framework for model screening and development that was developed in Matlab, but its implementation can be carried out on other software platforms. It comprises four essential blocks: i) Analytics Domain; ii) Data Domain; iii) Comparison Engine; iv) Results Report. PAC was developed for the case of a single response variable, but can be extended to multiple responses by considering each one separately. Some case studies will be presented in this article in order to illustrate PAC's efficiency and robustness for problem-specific methods screening, in the absence of prior knowledge. For instance, the analysis of a real world dataset reveals that, even when addressing the same predictive problem and using the same response variable, the best modeling approach may not be the one foreseen a priori and may not even be always the same when different predictor sets are used. With an increasing frequency, situations like these raise considerable challenges to practitioners, underlining the importance of having a tool such as PAC to assist them in making more informed decisions and to benefit from the availability of data in Manufacturing 4.0 environments.
       
  • Comparison of dimensionality assessment methods in Principal Component
           Analysis based on permutation tests
    • Abstract: Publication date: Available online 17 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Raffaele Vitale, Edoardo SaccentiAbstractWe compare the performance of several data permutation methods for assessing dimensionality in Principal Component Analysis. We consider the classical Horn's Parallel Analysis, Dray's approach based on the similarity between the data matrix under study and its lower rank approximation and Vitale et al.’s method based on sequential deflation and rank reduction. Their potential is assessed on a large array of simulated data sets accounting for different data correlation structures, data distributions and homo- and heteroscedastic noise, and on 15 experimental data sets from different disciplines, such as metabolomics, proteomics, chemometrics and sensory analysis. In both the simulated and real life case-studies we report differential behaviours of the concerned techniques for which we propose theoretical explanations. The paper also discusses their limits of applicability and some guidelines are offered to the practitioners.
       
  • Multiway principal polynomial analysis for semiconductor manufacturing
           process fault detection
    • Abstract: Publication date: Available online 16 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Xinmin Zhang, Yuan LiAbstractIn semiconductor industry, the etching process is a highly sophisticated nonlinear process, which significantly affects the wafer quality. Fault detection technique has been investigated as a promising tool to reduce the fault wafers and increase overall equipment effectiveness. However, traditional fault detection models are not adequate enough to describe the etching process due to the high complexity and non-linearity of wafer processing process. In this study, a novel fault detection technique called multiway principal polynomial analysis (MPPA) is proposed. MPPA is a nonlinear modeling technique which learns a low-dimensional representation from process data based on a sequence of principal polynomials. Compared to linear methods, MPPA is more flexible and efficient in tackling process nonlinearity. Furthermore, MPPA has the desirable properties of invertibility, volume-preservation, and straightforward out-of-sample extension, thus making it interpretable and easier to implement in real application. To verify the effectiveness of the proposed MPPA, it was applied to a nonlinear numerical example and the real-world operation data of a semiconductor manufacturing process. The application results demonstrated that the proposed MPPA method outperforms the conventional MPCA, FD-kNN, and PC-kNN in the fault detection performance.
       
  • The Use Of Equant Grain Particles To Validate Analytical Sample Size In
           Gold Deposits – a Case Study
    • Abstract: Publication date: Available online 15 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Fernando Lucas dos Santos Peixoto de Villanova, Ana Carolina ChieregatiAbstractThe quest to sample and quantify a gold deposit depends on the ability to collect a representative sample and then maintain the lot´s constitution throughout all sampling steps. Differences in ore type, grade, gold size, distribution, liberation and association are some variables which implies differences in procedures from one deposit to another. In a low-grade nugget environment, the final analysis depends more on the chance occurrence of a particle in the analytical aliquot rather than the actual concentration in the ore. A commonly used 30 g fire assay could result in bad exploration decisions and create a highly skewed database. The concept of equant grains simplifies particle size and distribution, and works with a uniform particle that represents the total content divided by the number of grains necessary to attain a certain precision. In this paper, we test this hypothesis in which 20 and 10 equant grains are used to simulate the grade values of six different analytical samples sizes, representing the smoky quartz of Lamego Mine. The results confirm that a 30 g final aliquot does not represent the rock and a 500 or 1000 g analytical sample is required to be assayed.
       
  • Direct calibration transfer to principal components via canonical
           correlation analysis
    • Abstract: Publication date: Available online 13 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Xiaqiong Fan, Hongmei Lu, Zhimin ZhangAbstractIn order to solve the calibration transfer problem in near-infrared (NIR) spectroscopy, a fast and efficient method named principal components canonical correlation analysis (PC-CCA) has been proposed. PC-CCA can extract principal components of spectra from slave instrument with principal component analysis (PCA), and then transfer the slave principal components toward latent variables of partial least squares (PLS) model with canonical correlation analysis directly. Two NIR datasets of corn and tobacco samples measured with three and two spectrometers, respectively, were used to test the reliability of this method. The piecewise direct standardization (PDS), spectral space transformation (SST) and calibration transfer method based on canonical correlation analysis (CTCCA) methods were performed for comparative study of the proposed model transfer technique. The results of both datasets reveal that PC-CCA can drastically reduce time of transfer and lead to dozens-fold speedup. It can also reduce prediction errors and achieve the smallest root mean square errors of prediction (RMSEPs). For example, the spectra transfer from M5 to MP5, the comparative experiment results show that RMSEP with the proposed method PC-CCA is reduced from 2.5641 (without transfer) to 0.0934, and much smaller than that with PDS (0.1999), CTCCA (0.1377) and SST (0.1296) at N=50. These advantages make PC-CCA a promising calibration transfer method in NIR application.
       
  • Fault detection based on time series modeling and multivariate statistical
           process control
    • Abstract: Publication date: Available online 11 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): A. Sánchez-Fernández, F.J. Baldán, G.I. Sainz-Palmero, J.M. Benítez, M.J. FuenteAbstractMonitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on time series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto- and cross-correlations for every variable. After that, a time-series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.
       
  • An improved method based on a new wavelet transform for overlapped peak
           detection on spectrum obtained by portable Raman system
    • Abstract: Publication date: Available online 8 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Minghui Liu, Zuoren Dong, Guofeng Xin, Yanguang Sun, Ronghui QuAbstractPeak detection is a particularly important pre-processing step in chemical identification using Raman spectra. At present, most peak detection methods have limited applicability when there are overlapping peaks, especially the spectrum measured by portable spectrometers with low resolution. In this paper, an improved method is proposed based on the application of continuous wavelet transform (CWT) peak detection using a new wavelet to the deconvolved Raman spectrum. The new wavelet has a smaller linewidth and is more similar to the intrinsic Lorentz line profile of the Raman spectrum. It, therefore, has several advantages with regard to the detection of overlapping peaks. The proposed method was evaluated using the Raman spectrum of solid amino acid mixtures, and the results show that it is better at detecting overlapping peaks than the two other investigated wavelets. The receiver operating characteristic curves show that this method can detect more true peaks while maintaining a low false discovery rate. Moreover, the maximum of the true positive rate is the largest in the new approach, which indicates better performance for overlapping peak detection.
       
  • Quantitative analysis modeling of infrared spectroscopy based on ensemble
           convolutional neural networks
    • Abstract: Publication date: 15 October 2018Source: Chemometrics and Intelligent Laboratory Systems, Volume 181Author(s): Chen Yuanyuan, Wang ZhibinAbstractIn infrared spectroscopy analysis area, quantitative modeling is often an essential and inevitable procedure to obtain the relationship between collected spectral information and target components. Over the past decades, there are many linear or nonlinear methods have been proposed, such as multi-regression, partial least squares, artificial neural network, support vector machine etc. However, these traditional methods commonly need some preprocessing steps including denoising, baseline correction, wavelength selection and so on. Hence, it requires the users to master so many skilled knowledges before they can establish a good performance quantitative model. Additionally, the stabilities of the above-mentioned methods are often not well enough because there are many random parameters which will result in the model's output are always changing every time. To solve these problems, this paper proposed an end-to-end modeling method based on convolutional neural network and ensemble learning. The experimental results on three infrared spectral datasets (corn, gasoline and mixed gases) showed that the generalized performance of proposed ECNN method outperforms traditional methods like PLS, BP neural network and single CNN method with whole spectral range raw data instead of selected wavelengths. Hence, the proposed method can obviously reduce the modeling knowledge for users and easier to use.
       
  • ILS: An R package for statistical analysis in Interlaboratory Studies
    • Abstract: Publication date: Available online 3 August 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Miguel Flores, Rubén Fernández-Casal, Salvador Naya, Javier Tarrío-Saavedra, Roberto BossanoAbstractIn this paper we present an R package with routines to perform Interlaboratory Studies (ILS). The aim of the ILS package is to detect laboratories that provide not consistent results, working simultaneously with different test materials, from the perspective of the Univariate Data Analysis and the Functional Data Analysis (FDA).The ILS package estimates the Mandel's h and k scalar statistics, based on the ASTM E691 and ISO 5725-2 standards, to identify laboratories that provide significantly different results. Cochran and Grubbs tests to evaluate the presence of outliers are also available. In addition, Analysis of Variance (ANOVA) techniques are provided, both for the cases of fixed and random effects, including confidence intervals for the parameters.One of the novelties of this package is the incorporation of tools to perform an ILS from a functional data analysis approach. Accordingly, the functional nature of the data obtained by experimental techniques corresponding to analytical chemistry, applied physics and engineering applications (spectra, thermograms, and sensor signals, among others) is taking into account by implementing the functional extensions of Mandel's h and k statistics. For this purpose, the ILS package also estimates the functional statistics H(t) and K(t), as well as the dH and dK test statistic, which are used to evaluate the repeatability and reproducibility hypotheses where the critical ch and ck values are estimated by using a bootstrap algorithm.
       
  • Representation Learning based Adaptive Multimode Process Monitoring
    • Abstract: Publication date: Available online 31 July 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Feiya Lv, Chenglin Wen, Meiqin LiuThis paper focus on developing representations that are formed by the composition of multiple non-linear transformations, with the goal of yielding more abstract and ultimately more useful representations in multimode process monitoring. To enhance the sensitivity of the learned higher-order correlations, an adaptive thresholding scheme is developed. Thus a representation learning based adaptive monitoring method is proposed in this paper. A geometry interpretation of AE net is presented to explore its expressive powers. Moreover, the proposed framework can do real-time mode identification and fault detection online collectively under a global model than describing variations in each isolated mode separately. Experiment results show that the proposed method not only improves the divisibility between multimodes, but also exhibits superior performance of fault detection on an industrial benchmark of chemical process, Tennessee Eastman process (TEP).
       
  • Modeling of simultaneous adsorption of dye and metal ion by sawdust from
           aqueous solution using of ANN and ANFIS
    • Abstract: Publication date: Available online 30 July 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Marjan Mehrabpour, Morteza Esfandyari, Hosein Alidadi, Mojtaba Davoudi, Maryam DolatabadiAbstractThe current work deals with the investigation of Simultaneous of Basic Red46 (BR46) and Cu (dye and heavy metal) removal efficiency from aqueous solution through adsorption process using a laboratory scale reactor. In this research, a feed forward artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) have been utilized to the prediction of adsorption potential of sawdust in simultaneous removal of a cationic dye and heavy metal ion from aqueous solution. Five Operational variables, concluding initial dye, initial Cu (II), pH, contact time, and adsorbent dosage were selected to investigate their effects on the adsorption study. The application of (ANN) and (ANFIS) models for experiments were employed to optimize, create and develop prediction models for dye and Cu (II) adsorption by using sawdust of Melia Azedarach wood. The result reveals that ANN and ANFIS models as a promising predicting technique would be effectively used for simulation of dye and metal ion adsorption. According to this result, in training dataset determination coefficient were obtained 0.99 and 0.98 for dye and metal ion, respectively. Also, in ANFIS model R2 were calculated 0.99 for both of pollutants.
       
  • Modern practical convolutional neural networks for multivariate
           regression: Applications to NIR calibration
    • Abstract: Publication date: Available online 20 July 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Chenhao Cui, Tom FearnAbstractIn this study, we investigate the use of convolutional neural networks (CNN) for near infrared (NIR) calibration. We propose a unified CNN structure that can be used for general multivariate regression purpose. The comparison between the CNN method and the partial least squares regression (PLSR) method was done on three different NIR datasets of spectra and lab reference values. Datasets are from different sources and contain 6998, 1000 and 415 training and 618, 597 and 108 validation samples, respectively. Results indicated that compared to the PLSR models, the CNN models are more accurate and less noisy. The convolutional layer in the CNN model can automatically find the suitable spectral preprocessing filter on the dataset, which significantly saves efforts in training the model.
       
 
 
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