for Journals by Title or ISSN
for Articles by Keywords
help
  Subjects -> COMPUTER SCIENCE (Total: 2122 journals)
    - ANIMATION AND SIMULATION (31 journals)
    - ARTIFICIAL INTELLIGENCE (105 journals)
    - AUTOMATION AND ROBOTICS (105 journals)
    - CLOUD COMPUTING AND NETWORKS (67 journals)
    - COMPUTER ARCHITECTURE (10 journals)
    - COMPUTER ENGINEERING (11 journals)
    - COMPUTER GAMES (21 journals)
    - COMPUTER PROGRAMMING (26 journals)
    - COMPUTER SCIENCE (1231 journals)
    - COMPUTER SECURITY (50 journals)
    - DATA BASE MANAGEMENT (14 journals)
    - DATA MINING (38 journals)
    - E-BUSINESS (22 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (22 journals)
    - IMAGE AND VIDEO PROCESSING (40 journals)
    - INFORMATION SYSTEMS (107 journals)
    - INTERNET (96 journals)
    - SOCIAL WEB (53 journals)
    - SOFTWARE (34 journals)
    - THEORY OF COMPUTING (9 journals)

COMPUTER SCIENCE (1231 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: 24)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 31)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 17)
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: 16)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 7)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 6)
ACM Transactions on Economics and Computation     Hybrid Journal   (Followers: 2)
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 5)
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: 35)
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: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 29)
Advanced Science Letters     Full-text available via subscription   (Followers: 11)
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: 6)
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: 59)
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: 21)
Advances in Materials Science     Open Access   (Followers: 15)
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: 51)
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   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 14)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 7)
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: 6)
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)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access  
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: 5)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
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: 11)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 17)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 7)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 153)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 8)
Artifact     Open Access   (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 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: 6)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 12)
Automation in Construction     Hybrid Journal   (Followers: 7)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Balkan Journal of Electrical and Computer Engineering     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 51)
Big Data and Cognitive Computing     Open Access   (Followers: 3)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 327)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 51)
British Journal of Educational Technology     Hybrid Journal   (Followers: 160)
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: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Hybrid Journal   (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)
Clinical eHealth     Open Access  
Cluster Computing     Hybrid Journal   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
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: 13)
Communication Theory     Hybrid Journal   (Followers: 24)
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: 4)
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: 3)
Computational and Mathematical Biophysics     Open Access   (Followers: 1)
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: 1)
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   (Followers: 1)
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: 24)
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: 8)
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: 35)
Computer     Full-text available via subscription   (Followers: 105)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)

        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: 15  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0169-7439
Published by Elsevier Homepage  [3161 journals]
  • Data-driven supervised fault diagnosis methods based on latent variable
           models: A comparative study
    • Abstract: Publication date: Available online 16 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Santiago Vidal-Puig, Raffaele Vitale, Alberto Ferrer A comparison among widely used multivariate latent variable-based techniques for supervised process fault diagnosis was carried out.In order to assess their overall performance several diagnosis criteria were proposed (C1: most suspected fault assignment; C2: threshold-based fault assignment). Additionally, it was evaluated i) how the size of the training set used to build the latent variable models affected the diagnosis ability of the methods under study, ii) how they behaved under new types of failures not included in the original list of fault candidates and iii) which of them were more suitable for either early or late diagnosis.To accomplish all these objectives, the approaches were tested in different scenarios. Two datasets were analysed: the first was generated by a Simulink-based model of a binary distillation column, while the second relates to a pasteurisation process performed in a laboratory-scale plant.
       
  • An improved signal-conservative approach to cope with Rayleigh and Raman
           signals in fluorescence landscapes
    • Abstract: Publication date: Available online 15 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Fabricio A. Chiappini, Mirta R. Alcaraz, Héctor C. Goicoechea Fluorescence excitation-emission matrix spectroscopy coupled to multi-way analysis has proved to be a powerful tool for the study of complex systems with analytical purposes. However, scattering phenomena that are usually present in fluorescence landscapes can significantly affect the performance of the chemometric modelling or lead to misinterpretations of the spectral information. In this work, an improved algorithm that collects the strengths of the reported approaches and enhances their performances was developed. The proposed algorithm, which is based on the signal-conservative principle, enables the fluorescence landscapes correction by preserving the inherent particularities of the original. Moreover, corrected second-order data were subjected to trilinear decomposition analysis to assess the performance of the scatter correction in terms of trilinearity and prediction ability. In the light of the obtained results, this new strategy showed to be adequate for scattering correction in several experimental situations.Graphical abstractImage 10471
       
  • Application of Multivariate Image Analysis for on-line Monitoring of a
           Freeze-Drying Process for pharmaceutical products in vials
    • Abstract: Publication date: Available online 13 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Domenico Colucci, José M. Prats-Montalbán, Davide Fissore, Alberto Ferrer A new Process Analytical Technology (PAT) has been developed and tested for on-line process monitoring of a vacuum freeze-drying process. The sensor uses an infrared camera to obtain thermal images of the ongoing process and multivariate image analysis (MIA) to extract the information. A reference model was built and different kind of anomalous events were simulated to test the capacity of the system to promptly identify them. Two different data structures and two different algorithms for the imputation of the missing information have been tested and compared. Results show that the MIA-based PAT system is able to efficiently detect on-line undesired events occurring during the vacuum freeze-drying process.
       
  • Determine the significant digit of spectral data and reduce its redundant
           digits to eliminate the chance correlation problem based on the “salami
           slicing” method
    • Abstract: Publication date: Available online 13 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Qiuyue Xiao, Gang Li, Ling Han, Wenjuan Yan, Guoquan He, Ling Lin In recent years, complex solution composition analysis based on spectroscopy has been a research hotspot for researchers and has broad application prospects. Improving the ability of complex solutions component analysis based on spectroscopy, eliminating spectral data redundancy and the chance correlation problem it brings has become an urgent issue. In order to solve these problems, this paper takes the dynamic spectrum (DS) as the research object, and uses the “salami slicing” method to process the DS data. Firstly, the significant digit of the decimal DS data is processed, and then the partial least-squares (PLS) method is applied to model and analyze the processed DS data. The turning point of the modeling accuracy's change is found, and the significant digit number of DS data is determined roughly. On this basis, the weight of the binary number is used skillfully to process the significant digit of DS. The processed DS data is modeled, and the significant digit number of the DS data is accurately analyzed to establish the efficacy of the proposed method. This method does not only improve the signal-to-noise ratio (SNR) of DS data, but also avoids the chance correlation problem due to the data redundancy. This method also provides a good means for SNR estimation of other spectral data and avoiding the chance correlation problem in modeling, and has a high application value.
       
  • Optimal Design of the Synthetic Control Chart for Monitoring the
           Multivariate Coefficient of Variation
    • Abstract: Publication date: Available online 6 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Khai Wah Khaw, Xinying Chew, Wai Chung Yeong, Sok Li Lim Control charting techniques have been applied to a wide variety of areas. In certain areas, such as in finance and healthcare, the process mean and variance are not independent of one another. Hence, adopting traditional control charts monitoring the process mean and variance are dubious. In such situations, the coefficient of variation (CV), which is the ratio of the standard deviation to the mean should be monitored instead. In real situations, there are many processes monitoring at least two or more quality variables simultaneously. In these processes, the use of univariate control charts will cause erroneous conclusions. This paper proposes a synthetic chart (Syn MCV) to monitor the multivariate CV. Formulae and algorithms to optimize the various performance measures are explained. The proposed chart is numerically compared with its counterparts, in terms of average run length (ARL), standard deviation of the run length (SDRL) and expected average run length (EARL) criteria. The results indicate that the proposed chart has the best performance among its counterparts for all or certain ranges of shifts in the multivariate CV. The application of the proposed Syn MCV chart is illustrated using real dataset from a manufacturing process.
       
  • Multiresolution Interval Partial Least Squares: A Framework for Waveband
           Selection and Resolution Optimization
    • Abstract: Publication date: Available online 6 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Tiago J. Rato, Marco S. Reis Spectroscopic data generated by several PAT technologies is routinely used for the rapid assessment of quality properties in several industrial sectors, such as agrofood, beverages, pharmaceutics, chemicals, pulp & paper, etc. While spectra can easily provide hundreds of measurements across several wavelengths, only a fraction of the collected spectrum conveys relevant information to predict the property of interest. Therefore, the performance of current models is highly related with the ability to select key wavebands, for which the existence of prior knowledge cannot be always secured. Therefore, several feature selection procedures consisting of variants of interval partial least squares (iPLS) have been proposed. These methodologies are however solely focused on determining the most relevant wavebands and do not attempt to further enhance the prediction capabilities within each interval. On the other hand, standard full-spectrum models are often improved by reducing the spectral resolution, but this operation has not been yet synergistically integrated together with waveband selection. As spectral aggregation can effectively improve modelling performance, a multiresolution selection algorithm that simultaneously selects the most relevant wavebands and their optimal resolution is here proposed. By design, this methodology leads to prediction models that are at least as good as the full-spectrum models. The performance comparison made on simulated data and real NIR spectra of gasoline samples also shows that the proposed methodology outperforms iPLS and its variants based on forward and backward selection of intervals, in a statistically significant way.
       
  • A multi-scale prediction model based on empirical mode decomposition and
           chaos theory for industrial melt index prediction
    • Abstract: Publication date: Available online 2 February 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Miao Zhang, Le Zhou, Jing Jie, Xinggao Liu Melt index (MI) is one of the most important variables determining the product quality in the industrial propylene polymerization process. In this paper, a multi-scale prediction model is proposed for MI prediction by combining the empirical mode decomposition (EMD), chaos theory and optimized relevance vector machine (RVM) model. First, the EMD method is used to decompose the MI time series into intrinsic mode functions (IMFs) and the residual. Then the chaotic characteristics of each component are identified with chaos theory. For the components with chaotic characteristics, relevance vector machine (RVM) chaotic prediction model is developed as the predictive model. For the components without chaotic characteristics, least squares support vector machine (LSSVM) is used as the predictive model. At the same time, an improved ant colony optimization (IACO) algorithm is used to optimize the parameters of RVM and LSSVM. In the end, the final prediction results of MI are obtained by summing the predicted results of all components. Research on the proposed multi-scale model is carried out on a real propylene polymerization plant and the results are compared among the RVM-chaos, IACO-RVM-chaos and multi-scale models. The research results show that the model developed achieves a good performance in the industrial MI prediction process.
       
  • Probabilistic artificial neural network and E-nose based classification of
           Rhyzopertha dominica infestation in stored rice grains
    • Abstract: Publication date: Available online 24 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Shubhangi Srivastava, Gayatri Mishra, Hari Niwas Mishra The versatility of artificial neural network with back propagation multilayer perceptron approach could entitle an easy and methodical interpretation of results corresponding to multiple metal oxides sensor in an electronic nose. Three algorithms discriminant factorial analysis (DFA), soft independent modeling by class analogy (SIMCA), probabilistic artificial neural network (PANN) with back propagation multilayer perceptron (BPNN) were used for the classification of R. dominica infested rice stored for 225 days via 18 metal oxide sensors in E-nose. The coefficient of correlation for the three approaches were 88 (DFA), 96 (SIMCA), 98.96 (BPNN) %, respectively. The percentage discrimination index was more distinct between 0 to 225 days R. dominica infested rice (98%) than 0 to 180 days (93%), and 0 to 135 days (88%). The residual errors of validation and cross validation for SIMCA were 1.04 x 10-3 and 1.26 x 10-3 respectively. Major metal oxide sensors responsible for the production of volatiles were P30/1, T 30/1, PA/2, P30/2, T70/2, P40/1, and P40/2. The overall relative errors during artificial neural network training and testing were 0.092 and 0.286 respectively. The artificial neural network relative error for scale dependents in response to metal oxide sensors for mean, SD, % RSD were 0.033, 0.162, 0.081, respectively. The applicability of E-nose with neural network could help in securing the data analysis time without loss of information and can also work well for noxious odors which might not be able to be categorized by human olfactory.
       
  • Basil leaves disease classification and identification by incorporating
           survival of fittest approach
    • Abstract: Publication date: Available online 21 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Gittaly Dhingra, Vinay Kumar, Hem Dutt Joshi Basil leaves are the major ingredients in traditional medicinal drugs with significant health benefiting phytonutrients. The quality of leaves describe the measure of excellence from deficits, substantial variations and defects. Also, the disease on leaves poses threats to the economic and production status in the agricultural industry worldwide. So, a new classification model using survival of fittest approach with successive generation of best results is proposed in this study. A three level hierarchical approach is developed to recognize basil leave diseases. Healthy and diseased leave samples with downy mildew and cercospora leave spot are taken as experimental objects. For enhancing contrast, a Contrast Limited Adaptive Histogram Equalization algorithm is performed by adjusting intensities of the image in order to highlights the target area for the segmentation of the disease from their background. Experiments are performed by utilising combination of texture and color features. Afterwards Random Forest, feature selection technique is used to attain the high informative features. Finally, a new approach of classification is employed to characterize the leave diseases. Our new classification model effectively detects, classified basil leaves diseases with 95.73% accuracy. Proposed model attain the maximum success rate than other existing methods.
       
  • An improved weighted multiplicative scatter correction algorithm with the
           use of variable selection: Application to near-infrared spectra
    • Abstract: Publication date: Available online 14 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Yifan Wu, Silong Peng, Qiong Xie, Quanjie Han, Genwei Zhang, Haigang Sun Multiplicative light scattering has posed great challenge in near-infrared (NIR) quantitative analysis. When estimating the scattering parameters, uninformative variables for scattering effects may bias the estimation. Weighted least squares (WLS) can be used to avoid the influence of the uninformative variables. In this work, we proposed an improved weighted multiplicative scatter correction algorithm with the use of variable selection (WMSCVS). Baseline is removed first and then variable selection is used to obtain the optimal weights of WLS in estimating multiplicative parameters. The variable selection algorithm, which is designed based on model population analysis (MPA), implements an iterative optimization process. In each iteration, weighted bootstrap sampling (WBS) is used to generate variable subsets and exponentially decreasing function (EDF) is used to control the number of sampled variables. The interpretability and stability of the variable selection results as well as the predictive performance of the corrected spectra were investigated by using two NIR datasets. The experimental results showed that the proposed WMSCVS could give better predictive performance than the state-of-art correction methods.
       
  • Supervised feature selection via matrix factorization based on singular
           value decomposition
    • Abstract: Publication date: Available online 14 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Masoumeh Zare, Mahdi Eftekhari, Gholamreza Aghamollaei Feature selection is a main research issue in gene expression data. It tries to determine the best subset of features (genes) for a machine learning model or statistical analysis. In this paper, a novel supervised feature selection algorithm is proposed by using matrix factorization and Singular Value Decomposition (SVD) for microarray datasets. First, an unsupervised matrix factorization based existed method in the literature is used for feature selection as the main framework of our proposed method. Then SVD is incorporated into the first matrix factorization approach for increasing its efficiency. In SVD, a matrix decomposes into three matrices such that one of these factors contains an orthonormal basis for original matrix. Thus, a submatrix of the original matrix is located instead of this factor. The replaced submatrix produces the same space as original matrix, therefore without any complex measure we find a submatrix that has the least distance with data matrix. By considering linear independence as a redundancy criterion, selected features have the least redundancy between themselves. To improve prediction accuracy, the sum of Information Gain (IG) values for selected features is formulated in the objective function as the relevancy measure and makes our algorithm to be a supervised approach. Finally, the proposed algorithm is compared with eleven common feature selection methods and two state-of-the-art approaches using nine publicly available microarray datasets. In terms of accuracy, the experimental results indicate that the proposed method is comparable with the state-of-the-art methods and it has the better performance than the others.
       
  • Sparse N-way Partial Least Squares by L1-penalization
    • Abstract: Publication date: Available online 12 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): D. Hervás, J.M. Prats-Montalbán, J.C. García-Cañaveras, A. Lahoz, A. Ferrer N-PLS, as the natural extension of PLS to N-way structures, tries to maximize the covariance between an X and a YN-way data arrays. It provides a useful framework for fitting prediction models to N-way data. However, N-PLS by itself does not perform variable selection, which indeed can facilitate interpretation in different situations (e.g. the so-called “–omics” data). In this work, we propose a method for variable selection within N-PLS by introducing sparsity in the weights matrices WJ and WK by means of L1-penalization. The sparse version of N-PLS is able to provide lower prediction errors by filtering all the noise variables and to further improve interpretability and usability of the N-PLS results. To test Sparse N-PLS performance two different simulated data sets were used, whereas to show its utility in a biological context a real time course metabolomics data set was used.
       
  • BiMM forest: A random forest method for modeling clustered and
           longitudinal binary outcomes
    • Abstract: Publication date: Available online 11 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Jaime Lynn Speiser, Bethany J. Wolf, Dongjun Chung, Constantine J. Karvellas, David G. Koch, Valerie L. Durkalski Clustered binary outcomes and datasets with many predictor variables are frequently encountered in clinical research (e.g. longitudinal studies). Generalized linear mixed models (GLMMs) typically employed for clustered endpoints have challenges for some scenarios, particularly for complex datasets which contain many interactions among predictors and nonlinear predictors of outcome. We propose a new method called Binary Mixed Model (BiMM) forest, which combines random forest and GLMM methodology. BiMM forest offers a flexible and stable method which naturally models interactions among predictors and can be employed in the setting of clustered data. Simulation studies show that BiMM forest achieves similar or superior prediction accuracy compared to standard random forest, GLMMs and its tree counterpart (BiMM tree) for clustered binary outcomes. The method is applied to a real dataset from the Acute Liver Failure Study Group. BiMM forest offers an alternative method for modeling clustered binary outcomes which may be applied in myriad research settings.
       
  • A novel quadrilinear decomposition method for four-way data arrays
           analysis based on algorithms combination strategy: Comparison and
           application
    • Abstract: Publication date: Available online 11 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Tong Wang, Hai-Long Wu, Li-Xia Xie, Wan-Jun Long, Li Cheng, Ru-Qin Yu Recently, there has been growing interest in the decomposition of multilinear component models in the field of multi-way calibration. In this work, a novel quadrilinear decomposition method, i.e. four-way algorithm combination method (FACM), is proposed for four-way calibration. The FACM skillfully integrates the alternating quadrilinear decomposition (AQLD) algorithm with the four-way parallel factor analysis (FPARAFAC) algorithm and gives full play to their advantages. The performance of the FACM and the existing five algorithms are compared by using two simulated data sets. Moreover, a published four-way data array and a new four-way data array, which record a series of fluorescence spectra information of biomarkers in biological samples, are investigated by the proposed method respectively. The results demonstrate that the novel method can accurately and effectively extract the qualitative and quantitative information of analytes of interest even in the presence of unknown interferents and varying background. Besides, the FACM has attractive properties, such as very fast convergence, insensitive to initial values and excess number of components, and suitable for high noise level as well as severely collinear data. In addition, the proposed method can also be extended to combine other iterative algorithms, providing a feasible idea for the exploration and development of new algorithms.Graphical abstractImage 1
       
  • Multistep virtual screening for rapid identification of G Protein-Coupled
           Receptors Kinase 2 inhibitors for heart failure treatment
    • Abstract: Publication date: 15 February 2019Source: Chemometrics and Intelligent Laboratory Systems, Volume 185Author(s): Wen-Ling Ye, Su-Qing Yang, Liu-Xia Zhang, Zhen-Ke Deng, Wen-Qun Li, Jin-Wei Zhang, Lin Zhang, Yong-Huan Yun, Alex F. Chen, Dong-Sheng Cao Heart failure (HF) has become an important social problem that seriously threatens human health due to its high morbidity and mortality. G Protein-Coupled Receptors Kinase 2 (GRK2), as a novel target of heart failure, is overexpressed in the pathogenesis and progression of HF. In this study, we conducted a multistep virtual screening process from the ZINC database to discover potential GRK2 inhibitors. First, by using 6 known GRK2 inhibitors with high bioactivity and diverse structures as the template molecules, 7499 compounds were obtained from ligand-based similarity screening by 4 types of fingerprints. Subsequently, these hits were further screened by three quantitative structure-activity relationship (QSAR) models, which were built by 197 known GRK2 compounds and represented by two-dimensional MOE descriptors (MOE2D), molecular fragments (MACCS) and 2- dimensional pharmacophore (CATS) based on random forest algorithm, respectively. These hit compounds were then filtered by three molecular docking packages (MOE, GOLD and GLIDE) to improve the docking accuracy for avoid losing candidate compounds. The compounds with high docking scores were selected for the evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADMET). Finally, 17 compounds were identified the binding affinity to GRK2 by using SPR assay, three of which were considered as novel GRK2 inhibitors, and their binding modes with GRK2 active sites were analyzed. As new potential GRK2 inhibitors, COM5(KD = 0.172 μM) and COM17(KD = 0.595 μM) with the KD value less than 1 μM could be directly used in the next study, while the remaining hit COM4 (KD = 4.72 μM) whose KD over 1 μM can be optimized into the required low nanomole range for further study.
       
  • CMSENN: Computational Modification Sites with Ensemble Neural Network
    • Abstract: Publication date: Available online 3 January 2019Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Wenzheng Bao, Bin Yang, Dan Li, Zhengwei Li, Yong Zhou, Rong Bao With the rapid development of high-through technology, vast amounts of protein molecular data has been generated, which is crucial to advance our understanding of biological organisms. An increasing number of protein post translation modification sites identification approaches have been designed and developed to detect such modification sites among the protein sequences. Nevertheless, these methods are merely suitable for one type of modification site, their performance deteriorate rapidly when applied to other types of modification sites’ prediction. In this paper, with the method of different types of neural network algorithm ensemble, a novel method, named CMSENN (http://121.250.173.184/) Computational Modification Sites with Ensemble Neural Network, was proposed to detect protein modification. The algorithm mainly consists of several steps: First, the predicted peptide sequences translate to the feature vectors. Second, the three types of employed amino acid residues properties should be normalized. Finally, various combination of features and classification model have been compared the performances with several current typical algorithms. The results demonstrate that the proposed model have well performance at the sensitivity, specificity, F1 score and Matthews correlation coefficient (MCC) value in the identification modification with the approach of the selected features and algorithm combination.
       
  • Small sample robust approach to outliers and correlation of atmospheric
           pollution and health effects in Santiago de Chile
    • Abstract: Publication date: Available online 31 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Ludy Núñez Soza, Pavlina Jordanova, Orietta Nicolis, Luboš Střelec, Milan Stehlík Adverse effects of air pollution on health are a global problem. Chile is no exception due to the increase of urban population and increasing pollution sources. For several years in the winter months in Santiago de Chile, environmental pre-emergency is decreed, which is due to the increase of measurements of contaminants and the risk that this means to health. In order to model the effects of pollution on health we consider a hierarchical Bayesian generalized linear mixed autoregressive model proposed by Ref. [18]. In particular, we apply the model to the number of children with respiratory diseases in the town of Santiago for the period June–August 2011, using the PM2.5 data as covariate obtained by a spatiotemporal pollution model. In order to detect anomalous data, we apply to residuals both robust normality tests together with novel method of probabilities for mild or extreme outliers. We detected significant heterogeneity between stations which offer us better monitoring planning for the future.AMS 2010 subject classifications. Primary 60K10; Secondary 60E15, 60E05, 62F10.
       
  • Rapid determination of lumbrokinase potency in the earthworm extract
           intermediate by near-infrared spectroscopy
    • Abstract: Publication date: Available online 29 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Lingling Gao, Tongtong Li, Lian Li, Zhen Li, Lei Nie, Haiyang Zhou, Zhongyu Sun, Suyan Ye, Ruichen Liu, Hengchang Zang In traditional Chinese medicine, an extract intermediate is an extracted and purified intermediary of the production process, and its quality is crucial to the efficacy of the final product. Lumbrokinase (LK), which is found in earthworm extract intermediate, has good fibrinolysis, is an anticoagulant, provides hemorheological improvement and has other pharmacological effects, and it shows significant advantages in the treatment of thromboses. The traditional method for determining lumbrokinase potency is an agarose-fibrin plate assay, but it takes more than a dozen hours to perform and requires expensive culture medium. To develop a new method for rapidly and accurately determining the potency of lumbrokinase, near-infrared (NIR) spectroscopy combined with chemometrics is presented in this study. In addition, the variable selection combination method was investigated to optimize the modeling variables and improve the predictive abilities of the NIR model. After optimization, the parameter values of the correlation coefficient, R2c, R2p, RMSEC and RMSEP, of the partial least squares regression (PLSR) quantitative model were 0.9398, 0.9340, 289.618 U, and 538.313 U, respectively. This study demonstrates that NIR analysis techniques are a potential tool for rapidly determining lumbrokinase potency in earthworm extract intermediate.
       
  • 1H NMR adulteration study of hempseed oil with full chemometric approach
           on large variable data
    • Abstract: Publication date: Available online 28 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Ozren Jović, Katarina Pičuljan, Tomica Hrenar, Tomislav Smolić, Ines Primožič Adulteration of hempseed (H) oil, a well known health beneficial nutrient, is studied in this work by means of 1H NMR spectroscopy. H oil samples were mixed with cheap and widely used oils such as rapeseed (R) oil and sezame (Se) and sunflower (Su) oil. Many samples of different geographic origins were taken into account. Binary mixture sets of hempseed oil with these three oils (HR, HSe and HSu) were considered. 1H NMR spectra of pure oils and their mixtures were recorded and quantitative analyses were performed using Partial Least Squares Regression (PLS), First-Break Forward Interval PLS methods (FB-FiPLS) and Interval Ridge Regression (iRR). The obtained results show that each particular oil can be very successfully quantified (RMSEP 1.4–3.0%), and that NMR coupled with iRR has a great potential in studying signals of low intensity belonging to oil microconstituents. This means that 1H NMR spectroscopy coupled with multivariate methods can rapidly and effectively determine both the fatty acid profile and the level of adulteration in the adulterated hempseed oil for these studied and frequently used adulterant oils.
       
  • Hybrid independent component analysis (H-ICA) with simultaneous analysis
           of high-order and second-order statistics for industrial process
           monitoring
    • Abstract: Publication date: Available online 28 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Shumei Zhang, Chunhui Zhao Both the Gaussian and non-Gaussian information exist simultaneously in many real industrial processes. However, the standard independent component analysis (ICA) cannot effectively explore process information when there is more than one independent component (IC) following Gaussian distribution. Besides, process may present an obvious temporal structure due to wide applications of controllers, which has not been investigated by standard ICA. To solve the above problem, the paper proposed a hybrid ICA (H-ICA) algorithm for process monitoring by concurrent analysis of both high-order and second-order statistics. The time-delayed correlation matrices are first diagonalized for robust whitening to remove the effect of the additive white noise. Then, non-Gaussian information is explored by FastICA with the utilization of higher-order statistics, and Gaussian information is extracted by analyzing the time structure information contained in second-order nonzero-delayed covariance matrices. By the hybrid analysis of high-order and second-order statistics, both non-Gaussian-distributed and Gaussian-distributed ICs are extracted. In this way, more comprehensive process information is fully investigated and analyzed for the development of process monitoring strategy. The proposed H-ICA process monitoring algorithm is verified by both a numerical example and a real thermal power plant process which illustrates its feasibility and efficacy.
       
  • Image processing for three defects of topography images by SPM
    • Abstract: Publication date: Available online 26 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Fan Xian-guang, Wang Xiao-dong, Chen Yu-xin, Wang Xin Image processing plays an important role in the topography imaging by SPM. Due to the imperfect hardware and the environmental interference, the image defects can be easily found in the topography images. In order to deal with these defects, image processing technology is the most effective and convenient way, so image processing functions are integrated in most kinds of SPM software. In this study, we present image processing methods for three common defects of topography images: background, damage and fringe. According to the characteristics of the topography images and the defects, some algorithms are adopted in the proposed methods, such as B-spline, TV, Criminisi, Flourier transform and so on. The principles, processes and application scopes of the methods were described in detail, and the topography images with typical defects were selected to verify them. The processing results showed the feasibility of the methods, which offer an effective approach to acquire high-quality topography images in a fast, simple and cheap way.
       
  • Recognition and sensing of organic compounds using analytical methods,
           chemical sensors, and pattern recognition approaches
    • Abstract: Publication date: Available online 21 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Sunil Kr Jha, R.D.S. Yadava, Kenshi Hayashi, Nilesh Patel Currently, the development of smart systems for recognition and sensing of volatile organic compounds (VOCs) in medical, agricultural, biometric, security and safety, applications is an emerging research area. This review presents an introduction to the field of VOC recognition by analytical methods and sensing by chemical sensors. The role of pattern recognition methods in the analysis of sensor array response is briefly discussed. Besides, the electronic nose (E-Nose) system (a bio-inspired prototype of the natural olfaction system by combining a chemical sensor array and pattern recognition methods) and its significance for VOCs recognition and sensing in different applications is explained. The study concludes current constraint and future prospects of VOC recognition and sensing in real-time applications.
       
  • Investigating native state fluorescence emission of Immunoglobulin G using
           polarized Excitation Emission Matrix (pEEM) spectroscopy and PARAFAC
    • Abstract: Publication date: Available online 20 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Marina Steiner-Browne, Saioa Elcoroaristizabal, Yannick Casamayou-Boucau, Alan G. Ryder Intrinsic fluorescence spectroscopy (IFS) measurements for protein structural analysis can be enhanced by the use of anisotropy resolved multidimensional emission spectroscopy (ARMES). ARMES attempts to overcome the tryptophan (Trp) and tyrosine (Tyr) spectral overlap problem and resolve emitting components by combining anisotropy measurements with chemometric analysis. Here we investigate for the first time the application of polarized excitation-emission matrix (pEEM) measurements and Parallel Factor (PARAFAC) analysis to study IFS from an Immunoglobulin G (IgG) type protein, rabbit IgG (rIgG), in its native state. Protein IFS is a non-trilinear system primarily because of Förster resonance energy transfer (FRET). Non-trilinearity is also caused by inner filter effects, and Rayleigh/Raman scattering, both of which can be corrected by data pre-processing. However, IFS FRET cannot be corrected for, and thus here we carefully evaluated the impact of various different data pre-processing methods on IFS data which was evaluated using PARAFAC. Care must be taken with data pre-processing and interpolation, as both had an impact on PARAFAC modelling and the recovered anisotropy values, with residual shot noise from the Rayleigh scatter which overlapped the emission blue edge being the root cause.pEEM spectra from thawed rIgG solutions (15–35 °C temperature range) were collected with an expectation being that this should cause sufficient emission variation to facilitate component resolution but without major structural changes. The only significant changes observed were of the overall intensity due to thermal motion induced quenching and this was confirmed by the PARAFAC scores. PARAFAC resolved one major component (>99%) for the emission data (polarized and unpolarized) which mostly represented the large Tyr-to-Trp hetero-FRET process, with a second, very weak component (
       
  • A graphical user interface for PCA-based MSPC: A benchmark software for
           multivariate statistical process control in MATLAB
    • Abstract: Publication date: Available online 17 December 2018Source: Chemometrics and Intelligent Laboratory SystemsAuthor(s): Pedro Villalba, Javier Sanchis, Alberto Ferrer A Graphical User Interface (GUI) is developed in MATLAB as a tutorial for understanding the PCA-based MSPC strategy. The software allows users to analyze both simulated and external data sets. Simulated data are obtained from a nonlinear model of a binary distillation column implemented in Simulink. The nonlinear model has four manipulated variables, four controlled variables and three input measured disturbances, plus 41 M fractions corresponding to every column stage. The methodology for PCA-based MSPC is implemented in two phases. During Phase I, the user can simulate the distillation column under normal operating conditions at three different operating points. When the simulation is finished, the GUI obtains the corresponding PCA model automatically. In Phase II, the user can simulate several scenarios with different combinations of disturbances and failures and monitor them using Squared Prediction Error (SPE) and T2 control charts. Contribution plots are used to diagnose the original variables responsible of such abnormal situations. The software also incorporates the possibility to analyze external multivariate process datasets.
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 34.229.175.129
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-