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  Subjects -> COMPUTER SCIENCE (Total: 2050 journals)
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COMPUTER SCIENCE (1196 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 22)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 11)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 17)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 14)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 3)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 7)
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: 7)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 8)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 27)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
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: 9)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 18)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 6)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 43)
Advances in Science and Research (ASR)     Open Access   (Followers: 4)
Advances in Technology Innovation     Open Access   (Followers: 5)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 5)
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: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
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: 130)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 53)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 281)
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: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 43)
British Journal of Educational Technology     Hybrid Journal   (Followers: 142)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
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: 2)
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: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access  
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: 11)
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: 15)
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: 94)
Computer Aided Surgery     Hybrid Journal   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 19)

        1 2 3 4 5 6 | Last

Journal Cover Applied Soft Computing
  [SJR: 1.763]   [H-I: 75]   [16 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1568-4946
   Published by Elsevier Homepage  [3175 journals]
  • One-class synthesis of constraints for Mixed-Integer Linear Programming
           with C4.5 decision trees
    • Authors: Patryk Kudła; Tomasz P. Pawlak
      Pages: 1 - 12
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Patryk Kudła, Tomasz P. Pawlak
      We propose Constraint Synthesis with C4.5 (CSC4.5), a novel method for automated construction of constraints for Mixed-Integer Linear Programming (MILP) models from data. Given a sample of feasible states of a modeled entity, e.g., a business process or a system, CSC4.5 synthesizes a well-formed MILP model of that entity, suitable for simulation and optimization using an off-the-shelf solver. CSC4.5 operates by estimating the distribution of the feasible states, bounding that distribution with C4.5 decision tree and transforming that tree into a MILP model. We verify CSC4.5 experimentally using parameterized synthetic benchmarks, and conclude considerable fidelity of the synthesized constraints to the actual constraints in the benchmarks. Next, we apply CSC4.5 to synthesize from past observations two MILP models of a real-world business process of wine production, optimize the MILP models using an external solver and validate the optimal solutions with use of a competing modeling method.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.025
      Issue No: Vol. 68 (2018)
       
  • Multi-sensor information fusion for remaining useful life prediction of
           machining tools by adaptive network based fuzzy inference system
    • Authors: Jun Wu; Yongheng Su; Yiwei Cheng; Xinyu Shao; Chao Deng; Cheng Liu
      Pages: 13 - 23
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Jun Wu, Yongheng Su, Yiwei Cheng, Xinyu Shao, Chao Deng, Cheng Liu
      Remaining useful life (RUL) prediction of machining tools is a typical multi-sensor information fusion problem. It involves the use of the monitoring information acquired from different types of sensors installed on computer numerical control machine to realize the RUL prediction of the machining tools in cutting process. Owing to the nonlinear and stochastic nature between the extracted features and tool wear level, the promptness and precision of online RUL prediction of machining tools are still difficult to be obtained. In this paper, a multi-sensor information fusion system for online RUL prediction of machining tools is proposed. The system includes sensor signal preprocessing based on ensemble empirical mode decomposition method, statistics feature extraction based on time domain and frequency domain analysis, optimum feature selection based on Pearson correlation coefficient, monotonicity and autocorrelation, feature fusion based on adaptive network based fuzzy inference system and RUL prediction model based on polynomial curve fitting method. We report a practical application of this multi-sensor information system and estimate its prediction performance. The proposed system may be applied to the industrial field. Meanwhile, the comparison between the proposed method and other standard methods is carried out using several statistical indices.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.043
      Issue No: Vol. 68 (2018)
       
  • Terrain classification based on spatial multi-attribute graph using
           Polarimetric SAR data
    • Authors: Hongying Liu; Shuyuan Yang; Shuiping Gou; Shuai Liu; Licheng Jiao
      Pages: 24 - 38
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Hongying Liu, Shuyuan Yang, Shuiping Gou, Shuai Liu, Licheng Jiao
      Recently the graph-based semi-supervised classification arouses vast amount of interest in remote sensing as it can utilize only a few labeled samples and large numbers of unlabeled samples to enhance the classification accuracy on various types of terrains. However in most of the conventional methods, multiple features (e.g. the scattering components, texture, color, etc) are concatenate together into a long vector for graph construction and classification. This not only ignores the physical attribute of features, but also causes so-called curse of dimensionality and limits the performances of classification. Inspired by the multi-view machine learning, we propose a spatial multi-attribute graph model and rank the attributes of polarimetric synthetic aperture radar (PolSAR) data for terrain classification in this paper. It firstly constructs multiple graphs according to the physical attributes of the groups of features based on different similarity metrics, then automatically optimizes a balanced weight for each graph and combines the spatial information between pixels for label propagation and classification. Since it takes into account of the physical properties from PolSAR data for feature fusion and graph construction, the mechanically feature stacking and curse of dimensionality can be avoided. Experimental results on synthesized PolSAR data and real ones show enhanced classification accuracy of the proposed method compared with state-of-the-art graph-based methods when only a small number of labeled samples are available. Our empirical studies also indicate that the covariance matrix play predominant roles for PolSAR classification.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.029
      Issue No: Vol. 68 (2018)
       
  • A high accurate localization algorithm with DV-Hop and differential
           evolution for wireless sensor network
    • Authors: Laizhong Cui; Chong Xu; Genghui Li; Zhong Ming; Yuhong Feng; Nan Lu
      Pages: 39 - 52
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Laizhong Cui, Chong Xu, Genghui Li, Zhong Ming, Yuhong Feng, Nan Lu
      Localization technology has been a core component for Internet of Things (IoT), especially for Wireless Sensor Network (WSN). Among all localization technologies, Distance Vector-Hop (DV-Hop) algorithm is a very frequently used algorithm for WSN. DV-Hop estimates the distance through the hop-count between nodes in which the value of hop-count is discrete, and thus there is a serious consequence that some nodes have the same estimated distance when their hop-count with respect to identical node is equal. In this paper, we ameliorate the value of hop-count by the number of common one-hop nodes between adjacent nodes. The discrete values of hop-count will be converted to more accurate continuous values by our proposed method. Therefore, the error caused by the estimated distance can be effectively reduced. Furthermore, we formulate the location estimation process to be a minimizing optimization problem based on the weighted squared errors of estimated distance. We apply Differential Evolution (DE) algorithm to acquire the global optimum solution which corresponds to the estimated location of unknown nodes. The proposed localization algorithm based on improved DV-Hop and DE is called DECHDV-Hop. We conduct substantial experiments to evaluate the effectiveness of DECHDV-Hop including the comparison with DV-Hop, GADV-Hop and PSODV-Hop in four different network simulation situations. Experimental results demonstrate that DECHDV-Hop can achieve much higher localization accuracy than other algorithms in these network situations.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.036
      Issue No: Vol. 68 (2018)
       
  • Semi-supervised deep rule-based approach for image classification
    • Authors: Xiaowei Gu; Plamen P. Angelov
      Pages: 53 - 68
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Xiaowei Gu, Plamen P. Angelov
      In this paper, a semi-supervised learning approach based on a deep rule-based (DRB) classifier is introduced. With its unique prototype-based nature, the semi-supervised DRB (SSDRB) classifier is able to generate human interpretable IF...THEN...rules through the semi-supervised learning process in a self-organising and highly transparent manner. It supports online learning on a sample-by-sample basis or on a chunk-by-chunk basis. It is also able to perform classification on out-of-sample images. Moreover, the SSDRB classifier can learn new classes from unlabelled images in an active way becoming dynamically self-evolving. Numerical examples based on large-scale benchmark image sets demonstrate the strong performance of the proposed SSDRB classifier as well as its distinctive features compared with the “state-of-the-art” approaches.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.032
      Issue No: Vol. 68 (2018)
       
  • Physics-aware Gaussian processes in remote sensing
    • Authors: Gustau Camps-Valls; Luca Martino; Daniel H. Svendsen; Manuel Campos-Taberner; Jordi Muñoz-Marí; Valero Laparra; David Luengo; Francisco Javier García-Haro
      Pages: 69 - 82
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Gustau Camps-Valls, Luca Martino, Daniel H. Svendsen, Manuel Campos-Taberner, Jordi Muñoz-Marí, Valero Laparra, David Luengo, Francisco Javier García-Haro
      Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics, and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve predictions and understanding. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of both forward and inverse modeling. After reviewing the traditional application of GPs for parameter retrieval, we introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Then, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical constraints of the system governing equations. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model using concepts from Bayesian optimization and at the same time builds an optimally compact look-up-table for inversion. We give empirical evidence of the performance of these models through illustrative examples of vegetation monitoring and atmospheric modeling.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.021
      Issue No: Vol. 68 (2018)
       
  • Improving variable neighborhood search to solve the traveling salesman
           problem
    • Authors: Samrat Hore; Aditya Chatterjee; Anup Dewanji
      Pages: 83 - 91
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Samrat Hore, Aditya Chatterjee, Anup Dewanji
      The traveling salesman problem (TSP) is one of the classical combinatorial optimization problems and has wide application in various fields of science and technology. In the present paper, we propose a new algorithm for solving the TSP that uses the variable neighborhood search (VNS) algorithm coupled with a stochastic approach for finding the optimal solution. Such neighborhood search with various other local search algorithms, named as VNS-1 and VNS-2, has been reported in the literature. The proposed algorithm is compared in detail with these algorithms, in the light of two benchmark TSP problems (one being symmetric while the other is asymmetric) suggested in the TSPLIB dataset in programming language R, along with two asymmetric problems obtained through simulation experiment. The present algorithm has been found to perform better than the conventional algorithms implemented in R for solving TSP's, and also, on an average, found to be more effective than the VNS-1 and the VNS-2 algorithms. The performance of the proposed algorithm has also been tested on 60 benchmark symmetric TSPs from the TSPLIB dataset. Apart from solving the TSP, the flexibility of the proposed hybrid algorithm to solve some other optimization problems related to other disciplines has also been discussed.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.048
      Issue No: Vol. 68 (2018)
       
  • Integration of efficient multi-objective ant-colony and a heuristic method
           to solve a novel multi-objective mixed load school bus routing model
    • Authors: Naz-afarin Mokhtari; Vahidreza Ghezavati
      Pages: 92 - 109
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Naz-afarin Mokhtari, Vahidreza Ghezavati
      In this paper, a novel mixed-load school bus routing problem (MLSBRP) is introduced. MLSBRP assumes that several students from different schools can simultaneously take a ride on the same bus. A bi-objective mixed integer linear programming (BO-MILP) formulation is proposed to model MLSBRP. The objectives are: a) minimizing the number of the buses; and b) minimizing the average riding time of the students. A hybrid multi-objective ant colony optimization (h-MOACO) algorithm, incorporating a novel routing heuristic algorithm, is developed to solve the associated BO-MILP. Performance of the proposed h-MOACO is compared with those achieved by commercial operation research software called CPLEX, and a customized NSGAII algorithm through multi-objective diversity and accuracy metrics over several small-size and large-size test problems, respectively. Sensitivity analysis are conducted on the main parameters of the MLSBRP. The computational results indicate the capability of the new proposed MLSBRP and suitability of the proposed h-MOACO algorithm.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.049
      Issue No: Vol. 68 (2018)
       
  • Assessment of skills and adaptive learning for parametric exercises
           combining knowledge spaces and item response theory
    • Authors: Pedro J. Muñoz-Merino; Ruth González Novillo; Carlos Delgado Kloos
      Pages: 110 - 124
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Pedro J. Muñoz-Merino, Ruth González Novillo, Carlos Delgado Kloos
      Many computer systems implement different methods for the estimation of students’ skills and adapt the generated exercises depending on such skills. Knowledge Spaces (KS) is a method for curriculum sequencing but fine-grained decisions for selecting next exercises among the candidates are not taken into account, which can be obtained with the application of techniques such as Item Response Theory (IRT). The combination of KS and IRT can bring advantages since the semantics of both models are included but some issues such as the required local independence of IRT should be considered. In addition, an open issue is how to handle with parametric exercises for skill modelling, i.e. exercises which are not static content but that can change from instance to instance depending on some parameters and a student can try to solve them again with different parameters after correct resolution. The correct inclusion of several instances of the parametric exercises on the adaptive decisions is important since the adaptation process can improve. This work describes two new algorithms for skill modelling and for adaptation of exercises that integrate IRT and KS to have a more powerful approach with more knowledge in the models and at the same time provides a solution for taking into account parametric exercises where a student should solve an exercise correctly several times to get proficiency. We have evaluated the different skill modelling algorithms using real data of students from their interactions in an Intelligent Tutoring System, and the correspondent adaptation algorithms using a simulator. Results show that the accuracy of the prediction is good with values of RMSE under 0.35. Both proposed algorithms got similar results on the accuracy of the prediction but one of them is better regarding performance. Changes of the buffer size for the MLE in IRT did not have a significant effect on the accuracy and on the performance. There is a trade-off for selecting one of the two proposed algorithms: while the first algorithm has better performance time for the calculation of the ability (because there is no need of calculation of local abilities), the second algorithm has better performance time for the selection of the next exercise and better accuracy and depending on the scenario one or another should be selected.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.045
      Issue No: Vol. 68 (2018)
       
  • A particle swarm approach for tuning washout algorithms in vehicle
           simulators
    • Authors: Sergio Casas; Cristina Portalés; Pedro Morillo; Marcos Fernández
      Pages: 125 - 135
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Sergio Casas, Cristina Portalés, Pedro Morillo, Marcos Fernández
      The MCA tuning problem involves finding the most appropriate values for the parameters (or coefficients) of Motion Cueing Algorithms (MCA), also known as washout algorithms. These algorithms are designed to control the movements of the robotic mechanisms, referred to as motion platforms, employed to generate inertial cues in vehicle simulators. This problem can be approached in several different ways. The traditional approach is to perform a manual pilot-in-the-loop subjective tuning, using the opinion of several pilots/drivers to guide the process. A more systematic approach is to use optimization techniques to explore the vast parameter space of the MCA, using objective motion fidelity indicators, so that the process can be automated. A genetic algorithm (GA) has been recently proposed to perform this process, with promising results. Following this approach, this paper proposes applying Particle Swarm Optimization (PSO) to solve the MCA tuning problem. The PSO-based proposed solution is assessed using the classical washout MCA, comparing its performance, convergence and correctness against the GA-based solution. Results show that a PSO-based tuning of MCA can provide better results and converges faster than a GA-based one. In addition, PSO is easier to set-up than GA, since only one parameter of the optimization algorithm itself (the number of particles) needs to be set-up, instead of a minimum of four in the case of the GA.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.044
      Issue No: Vol. 68 (2018)
       
  • Fuzzy QoS requirement-aware dynamic service discovery and adaptation
    • Authors: Ajaya K. Tripathy; Pradyumna K. Tripathy
      Pages: 136 - 146
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Ajaya K. Tripathy, Pradyumna K. Tripathy
      The integration of coherent services plays a potential role in the field of Service Oriented Applications. Achieving this potential standard crucially depends on the ability to recognize and exploit the available services based on user requirements. In general, the user preferences on Quality of Service (QoS) requirements are fuzzy in nature. In addition to that, the QoS requirements are user dependent even if the functional requirements are the same. With a large number of available services, service selection for dynamic composition at run time is a challenge. Functional and non-functional assumptions made at design time may violate at run-time. These violations require run time reaction, by adopting a run-time process. Therefore, dynamic and fuzzy QoS-aware service discovery for run-time composition and continuous adaptation is a strong requirement in service oriented computing. Considering that different users follow different fuzzy reasoning in various contexts at different times, a fuzzy inference based service selection approach has been proposed in this paper. Continuous adaptation is done, as and when a design time assumption violation is reported by a run-time monitoring system. We have implemented and tested the proposed approach and the results show its effectiveness.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.038
      Issue No: Vol. 68 (2018)
       
  • Improving the prediction of ground motion parameters based on an efficient
           bagging ensemble model of M5′ and CART algorithms
    • Authors: S.M. Hamze-Ziabari; T. Bakhshpoori
      Pages: 147 - 161
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): S.M. Hamze-Ziabari, T. Bakhshpoori
      In the present study, an efficient bagging ensemble model based on two well-known decision tree algorithms, namely, M5′ and Classification and Regression Trees (CART) is utilized so as to estimate the peak time-domain strong ground motion parameters. Four different predictive models, namely, CART, Ensemble M5′, Ensemble CART, and Ensemble M5′ + CART are developed to evaluate Peak Ground Acceleration, Peak Ground Velocity, and Peak Ground Displacement. A big database from the Pacific Earthquake Engineering Research Center is employed so as to develop the proposed models. Earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms are considered as the predictive parameters. The superior performances of the developed models are observed in the validation against the most recent soft computing based models available in the specialized literature. Parametric as well as sensitivity analyses are carried out to ensure the robustness of the predictive models in discovering the physical concept latent in the nature of the problem.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.052
      Issue No: Vol. 68 (2018)
       
  • Gaussian process regression for automated signal tracking in step-wise
           perturbed Nuclear Magnetic Resonance spectra
    • Authors: Maciej Zieba; Piotr Klukowski; Adam Gonczarek; Yaroslav Nikolaev; Michal J. Walczak
      Pages: 162 - 171
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Maciej Zieba, Piotr Klukowski, Adam Gonczarek, Yaroslav Nikolaev, Michal J. Walczak
      Tracking of signals in Nuclear Magnetic Resonance (NMR) spectra is a basic technique used in drug discovery, systems and structural biology. Current experimental setups allow to measure hundreds of spectra, which require analysis, ideally in an automated and reproducible manner. In this study, we present a novel approach to the automate tracking of signals in NMR spectroscopy. We model perturbation of the signal position with Gaussian process regression, which allows for scalability and robustness. The proposed method outperforms the other existing tracking routines, and is applicable for complex data featuring common hindrances such as: missing signals, noise and crossing trajectories.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.046
      Issue No: Vol. 68 (2018)
       
  • Hierarchical Skin-AdaBoost-Neural Network (H-SKANN) for multi-face
           detection
    • Authors: Zulhadi Zakaria; Shahrel Azmin Suandi; Junita Mohamad-Saleh
      Pages: 172 - 190
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Zulhadi Zakaria, Shahrel Azmin Suandi, Junita Mohamad-Saleh
      Face is one of the important parts of the human body that can be used in video surveillance security (VSS) system for identity recognition purposes. However, systems that work under uncontrolled environment such as VSS system suffer from illumination changes, unpredictability of face appearance due to the presence of accessories such as sunglasses and scarf, connected face and multiple face sizes. In this paper, a novel algorithm known as Hierarchical Skin-AdaBoost-Neural Network (H-SKANN) is introduced to overcome these problems. Skin is used to roughly locate face candidates. Then, AdaBoost is used to filter out non-face candidates. Subsequently, an artificial neural network is utilized as the main filter to finally detect the face. In order to handle multiple face sizes, all these algorithms are arranged in hierarchical manner. On top of this, face skin merging (FSM) is also introduced to connect blobs of skin regions to form a face. Experiments conducted on six single-face databases (AR, FERET, IMM, Georgia, Caltech, and Talking-PIE) and one multi-face benchmark database (ChokePoint) demonstrated that 98.07% and 95.48% of averaged accuracy have been achieved for single- and multi-face detection, respectively, using the proposed method.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.030
      Issue No: Vol. 68 (2018)
       
  • A group decision making support system for the Web: How to work in
           environments with a high number of participants and alternatives
    • Authors: J.A. Morente-Molinera; G. Kou; I.J. Pérez; K. Samuylov; A. Selamat; E. Herrera-Viedma
      Pages: 191 - 201
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): J.A. Morente-Molinera, G. Kou, I.J. Pérez, K. Samuylov, A. Selamat, E. Herrera-Viedma
      One of the main challenges that the appearance of Web 2.0 and the overall spreading of the Internet have generated is how to tackle with the high number of users and information available. This problem is also inherited by the group decision making problems that can be carried out over the Web. In this article, to solve this issue, a group decision making support system that allows the use of a high number of participants and alternatives is presented. This method allows any number of participants to join the decision making process at any time. Furthermore, they let them provide information only about a certain subset of alternatives. The high participation rate can provide enough information for the decision process to be carried out even if the participants do not provide information about all the high number of available alternatives.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.047
      Issue No: Vol. 68 (2018)
       
  • Multi-objective ensemble forecasting with an application to power
           transformers
    • Authors: Abdolrahman Peimankar; Stephen John Weddell; Thahirah Jalal; Andrew Craig Lapthorn
      Pages: 233 - 248
      Abstract: Publication date: July 2018
      Source:Applied Soft Computing, Volume 68
      Author(s): Abdolrahman Peimankar, Stephen John Weddell, Thahirah Jalal, Andrew Craig Lapthorn
      In this paper we present an ensemble time series forecasting algorithm using evolutionary multi-objective optimization algorithms to predict dissolved gas contents in power transformers. In this method, the correlation between each individual dissolved gas and other transformers’ features such as temperature characteristics and loading history is first determined. Then, a non-linear principal component analysis (NLPCA) technique is applied to extract the most effective time series from the highly correlated features. Afterwards, the forecasting algorithms are trained using a cross validation technique. In addition, evolutionary multi-objective optimization algorithms are used to select the most accurate and diverse group of forecasting algorithms to construct an ensemble. Finally, the selected ensemble is examined to predict the value of the dissolved gases on the testing set. The results of one day, two day, three day, and four day ahead forecasting are presented which show higher accuracy and reliability of the proposed method compared with other statistical methods.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.042
      Issue No: Vol. 68 (2018)
       
  • Elastic memory learning for fuzzy inference models
    • Authors: Marta Režnáková; Lukas Tencer; Mohamed Cheriet
      Pages: 1 - 7
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Marta Režnáková, Lukas Tencer, Mohamed Cheriet
      In this paper we present a novel approach for solving the consequent part of neuro-fuzzy modeling with an emphasis on the forgetting factor in the multi-class learning problem. Our solution is based on recursive least squares (RLS) for online and incremental learning applications, where the data stream is not necessarily uniformly distributed over time. Such a setup can lead to forgetting of specific classes that have not been used for a period of time. In this work we present a reasoned and detailed description of elastic memory learning (EML) and EML with the use of confidence interval (EML+) to avoid unnecessary treatment of the forgetting factor. We present the experimental results and evaluation of our methods in order to show their usefulness not only against forgetting of unused classes, but also for dealing with the lowered recognition rate after all classes have been learned. We note that by using EML, forgetting is significantly eliminated and the recognition rate is slightly affected as well, while EML+ puts more emphasis on keeping the recognition rate higher than the forgetting. Thus, this paper presents two methods that significantly eliminate the forgetting factor for incremental learning with a different focus on its importance, i.e. high recognition rate vs high immunity to forgetting; both of these methods perform significantly better than RLS for these aspects.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.022
      Issue No: Vol. 67 (2018)
       
  • Intelligent computing to analyze the dynamics of Magnetohydrodynamic flow
           over stretchable rotating disk model
    • Authors: Ammara Mehmood; Aneela Zameer; Muhammad Asif Zahoor Raja
      Pages: 8 - 28
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja
      In this study a novel application of neurocomputing technique is presented for nonlinear fluid mechanics problem arising in the model of the flow over stretchable rotating disk in the presence of strong magnetic field. The scheme comprises of the power of effective modelling of neural networks supported with integrated optimization strength of genetic algorithm and interior-point method. The governing partial differential equation of the system is converted to nonlinear systems of simultaneous ordinary differential equations by incorporating the similarity variables. Neural network based approximate differential equation models are formulated for the transformed system that are used to construct the merit function in mean squared error sense. The networks are trained initially by genetic algorithm for the global search and rapid local refinements is attained through efficient interior point method. The given scheme is applied for dynamical analysis of the system model in terms of radial, tangential, axial velocities and heat effects by varying magnetic interaction parameters, unsteadiness factors, disk stretchable magnitudes, and Prandtl numbers. The statistical performance indices based on error from standard numerical solutions are used to validate the correctness, consistency, robustness and stability of the proposed stochastic solver.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.024
      Issue No: Vol. 67 (2018)
       
  • A quasi-virtual online analyser based on an artificial neural networks and
           offline measurements to predict purities of raffinate/extract in simulated
           moving bed processes
    • Authors: Idelfonso B.R. Nogueira; Ana M. Ribeiro; Reiner Requião; Karen V. Pontes; Hannu Koivisto; Alírio E. Rodrigues; José M. Loureiro
      Pages: 29 - 47
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Idelfonso B.R. Nogueira, Ana M. Ribeiro, Reiner Requião, Karen V. Pontes, Hannu Koivisto, Alírio E. Rodrigues, José M. Loureiro
      The quality control and optimization of Simulated Moving Bed processes are still a challenge. Among the main reasons for that, the real time measurement of its main properties can be highlighted. Further developments in this field are necessary in order to allow the development of better control and optimization systems of these units. In the present work, a system composed by two Artificial Neural Networks working concomitantly with an offline measurement system is proposed, named Quasi-Virtual Analyser (Q-VOA) system. The development of the Q-VOA is presented and the system is simulated in order to evaluate its efficiency. The methodology used to select the input variables for the Q-VOA is another contribution of this work. The results show that the Q-VOA is capable of reducing the system errors and keep the prediction closer to the process true responses, when compared with the simple VOA system, which is based solely on model predictions. Furthermore, the results show the efficiency of the measurement system even under the presence of non-measured perturbations.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.001
      Issue No: Vol. 67 (2018)
       
  • Modified cuckoo search algorithm for the optimal placement of actuators
           problem
    • Authors: Bo Yang; Jun Miao; Zichen Fan; Jun Long; Xuhui Liu
      Pages: 48 - 60
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Bo Yang, Jun Miao, Zichen Fan, Jun Long, Xuhui Liu
      This paper proposes a novel modified cuckoo search algorithm (NMCSA) to solve optimal placement of actuators (OPA) for active vibration control. The purpose of OPA is to minimize control spillover effect and maximize the control force applied to the desired modes. To achieve this objective, NMCSA first employs speed factor (SFR) and aggregation factor (AFR) for recording and analyzing the current and history information of nests. Secondly, SFR and AFR are mapped to suitable space by scale conversion factors (SCF). Thus, the NMCSA based on SCF can give adaptively actions on the step size α and discovery probability pa to balance exploration and exploitation. The performance of NMCSA is confirmed by some well-known benchmark functions. Subsequently, the NMCSA is applied to solve OPA and compared with several state-of-the-art algorithms in the literature, the statistical results demonstrate that the proposed algorithm has a higher convergence speed and better search ability.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.004
      Issue No: Vol. 67 (2018)
       
  • Iris localization using rough entropy and CSA: A soft computing approach
    • Authors: Mousumi Sardar; Sushmita Mitra; B. Uma Shankar
      Pages: 61 - 69
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Mousumi Sardar, Sushmita Mitra, B. Uma Shankar
      Identification of a person depends on the proper extraction of the iris region. Segmentation, being the first step in iris analysis, constitutes the most important phase in iris localization. Images are often captured in non-ideal conditions, and are incomplete with different kinds of associated uncertainties. Therefore, iris segmentation assumes paramount importance towards its subsequent localization and analysis. A novel soft-computing approach is proposed for the segmentation of iris based on rough entropy, with localization using circular sector analysis (CSA); thereby minimizing uncertainties. We compare the performance of this algorithm with that by the circular Hough transform, which is state-of-the-art in approximating the iris region although being computationally intensive. The proposed rough entropy based segmentation, followed by CSA for localization of iris, is found to perform more efficiently and accurately in comparison to the state-of-the-art methodologies.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.047
      Issue No: Vol. 67 (2018)
       
  • Meta-Lamarckian learning in multi-objective optimization for mobile social
           network search
    • Authors: Andreas Konstantinidis; Savvas Pericleous; Christoforos Charalambous
      Pages: 70 - 93
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Andreas Konstantinidis, Savvas Pericleous, Christoforos Charalambous
      Mobile Social Networks (MSNs) have recently brought a revolution in socially-oriented applications and services for mobile phones. In this paper, we consider the search problem in a MSN that aims at simultaneously maximizing the user's search outcome (recall) and mobile phone performance (battery usage). Because of the conflicting nature of these two objectives, the problem is dealt within the context of Multi-Objective Optimization (MOO). Our proposed approach hybridizes a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) with a Meta-Lamarckian (ML) learning strategy that learns from the problem's properties and objective functions. The ML strategy is devised for adaptively select the best performing local search heuristic for each case, from a pool of general-purpose heuristics, so as to locally optimize the solutions during the evolution. We evaluated our propositions on a realistic multi-objective MSN search problem using trace-driven experiments with real mobility and social patterns. Extensive experimental studies reveal that the proposed method successfully learns the behaviour of individual local search heuristics during the evolution, adaptively follows the pattern of the best performing heuristics at different areas of the objective space and offers better performance in terms of both convergence and diversity than its competitors. The proposed Meta-Lamarckian based MOEA does not utilize any problem-specific heuristics, as most cases in the literature do, facilitating its applicability to other combinatorial MOO problems. To test its generalizability the proposed method is also evaluated on various test instances of the well-studied multi-objective Permutation Flow Shop Scheduling Problem.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.026
      Issue No: Vol. 67 (2018)
       
  • Dealing with high-dimensional class-imbalanced datasets: Embedded feature
           selection for SVM classification
    • Authors: Sebastián Maldonado; Julio López
      Pages: 94 - 105
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Sebastián Maldonado, Julio López
      In this work, we propose a novel feature selection approach designed to deal with two major issues in machine learning, namely class-imbalance and high dimensionality. The proposed embedded strategy penalizes the cardinality of the feature set via the scaling factors technique, and is used with two support vector machine (SVM) formulations designed to deal with the class-imbalanced problem, namely Cost Sensitive SVM, and Support Vector Data Description. The proposed concave formulations are solved via a Quasi-Newton update and Armijo line search. We performed experiments on 12 highly imbalanced microarray datasets using linear and Gaussian kernel, achieving the highest average predictive performance with our approach compared with the most well-known feature selection strategies.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.051
      Issue No: Vol. 67 (2018)
       
  • A stock market risk forecasting model through integration of switching
           regime, ANFIS and GARCH techniques
    • Authors: Werner Kristjanpoller R.; Kevin Michell V.
      Pages: 106 - 116
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Werner Kristjanpoller R., Kevin Michell V.
      Stock market volatility forecasting is an important and interesting topic of research due to its impact on trading decisions. This behavior is particularly important in emerging economies in Latin America, and moreover, in the larger stock markets of this region (Brazil, Mexico, and Chile). The Latin American region is highly influenced by macroeconomic factors; therefore, it is relevant to discover ways in which the market index forecast accuracy can be improved. Thus, in this study, we present a novel methodology: first, we forecast the volatility of each market using different GARCH models. Then, we use Markov Switching to determine the states of external factors. Subsequently, these states are combined in a ANFIS model to determine individual impact on each index, and finally, we use an ANN algorithm to improve the forecast accuracy of the best GARCH model forecast with the combined effects of all the external factors. The results indicate that this methodology manages to improve prediction in terms of MAPE and RMSE, thus providing a more accurate volatility estimation.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.055
      Issue No: Vol. 67 (2018)
       
  • Multi-modal kernel ridge regression for social image classification
    • Authors: Xiaoming Zhang; Wenhan Chao; Zhoujun Li; Chunyang Liu; Rui Li
      Pages: 117 - 125
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Xiaoming Zhang, Wenhan Chao, Zhoujun Li, Chunyang Liu, Rui Li
      There is growing interest in social image classification because of its importance in web-based image application. Though there are many approaches on image classification, it is still a great problem to integrate multi-modal contents of social images simultaneously for classification, since the textual content and visual content are represented in two heterogeneous feature spaces. In this study, a multi-modal learning algorithm is proposed to fuse the multiple features through their correlation seamlessly. Specifically, two classification modules based on the kernel ridge regression (KRR) are learned for the two types of features, and they are integrated via a joint model. With the joint model, the classification based on visual features can be reinforced by the classification based on textual features, and vice verse. Then, an efficient optimization method is proposed to resolving the object function. The query image can be classified based on both of the textual features and visual features by combing the results of the two classifiers. Two methods are proposed to combine the classification results to obtain the final result. To evaluate the approach, extensive experiments are conducted on the real-world datasets, and the result demonstrates the superiority of our approach.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.030
      Issue No: Vol. 67 (2018)
       
  • Integration of interval rough AHP and interval rough MABAC methods for
           evaluating university web pages
    • Authors: Dragan Pamučar; Željko Stević; Edmundas Kazimieras Zavadskas
      Pages: 141 - 163
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Dragan Pamučar, Željko Stević, Edmundas Kazimieras Zavadskas
      Websites are one of the most widely distributed information resources. Educational institutions use this resource to ensure that the best quality of information transmission is achieved. As such, academic sites have become a very important aspect of academic institutions, one that affects their overall quality. Bearing in mind the importance of university websites’ quality, the authors of this paper presented a multi-criteria model for evaluating the quality of university websites. This paper presents the hybrid IR-AHP-MABAC (Interval Rough Analytic Hierarchy Process - MultiAttributive Border Approximation Area Comparison) model. The model is adapted to group decision making as based on the application of a new approach to treating uncertainties through the use of interval rough numbers (IRN). The modified IR-AHP method was used to determine the weight coefficients of the criteria in the group decision-making process. The results of the IR-AHP model are compared with results provided by the traditional AHP method and the fuzzy AHP approach. The IR-MABAC model was used for the evaluation of university websites. In order to verify the results of the IRN based approach, the IR-MABAC model was compared to the F-TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution), F-VIKOR (Fuzzy MultiCriterion Optimization and Compromise Solution), F-COPRAS (Fuzzy COmpressed PRoportional ASsessment), F-MAIRCA (Fuzzy MultiAtributive Ideal-Real Comparative Analysis), and F-TODIM (an acronym of Interactive and Multi Criteria Decision Making in Portuguese) models. The credibility of the IR-AHP-MABAC model was demonstrated by comparing the results of different multi-criteria techniques and analyzing viability. The results of the IRN approach and fuzzy comparison have shown that the new approach to dealing with imprecision yields credible, reputable ranks.
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      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.057
      Issue No: Vol. 67 (2018)
       
  • Combining sparse representation and singular value decomposition for plant
           recognition
    • Authors: Shanwen Zhang; Chuanlei Zhang; Zhen Wang; Weiwei Kong
      Pages: 164 - 171
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Shanwen Zhang, Chuanlei Zhang, Zhen Wang, Weiwei Kong
      Plant recognition is one of important research areas of pattern recognition. As plant leaves are extremely irregular, complex and diverse, many existing plant classification and recognition methods cannot meet the requirements of the automatic plant recognition system. A plant recognition approach is proposed by combining singular value decomposition (SVD) and sparse representation (SR) in this paper. The difference from the traditional plant classification methods is that, instead of establishing a classification model by extracting the classification features, the proposed method directly reduces the image dimensionality and recognizes the test samples based on the sparse coefficients, and uses the class-specific dictionary learning for sparse modeling to reduce the recognition time. The proposed method is verified on two plant leaf datasets and is compared with other four existing plant recognition methods The overall recognition accuracy of the proposed approach for the 6 kinds of plant leaves is over 96%, which is the best classification rate. The experimental results show the feasibility and effectiveness of the proposed method.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.052
      Issue No: Vol. 67 (2018)
       
  • An efficient hybrid clustering method based on improved cuckoo
           optimization and modified particle swarm optimization algorithms
    • Authors: Asgarali Bouyer; Abdolreza Hatamlou
      Pages: 172 - 182
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Asgarali Bouyer, Abdolreza Hatamlou
      Partitional data clustering with K-means algorithm is the dividing of objects into smaller and disjoint groups that has the most similarity with objects in a group and most dissimilarity from the objects of other groups. Several techniques have been proposed to avoid the major limitations of K-Means such as sensitive to initialization and easily convergence to local optima. An alternative to solve the drawback of the sensitive to centroids’ initialization in K-Means is the K-Harmonic Means (KHM) clustering algorithm. However, KHM is sensitive to the noise and easily runs into local optima. Over the past decade, many algorithms are developed for solving this problems based on evolutionary method. However, each algorithm has its own advantages, limitations and shortcomings. In this paper, we combined K-Harmonic Means (KHM) clustering algorithm with an improved Cuckoo Search (ICS) and particle swarm optimization (PSO). ICS is intended to global optimum solution using Lévy flight method through changing radius in a dynamic and shrewd manner. Therefore, it is faster than standard cuckoo search. ICS is effected with PSO to avoid falling into local optima. The proposed algorithm, called ICMPKHM, solves the local optima problem of KHM with significant improvement on efficacy and stability. Experiments with benchmark datasets show that the proposed algorithm is quite insensitive to the centroids’ initialization. Comparative studies with other algorithms reveal that the proposed algorithm produce high quality and stable clustering results.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.011
      Issue No: Vol. 67 (2018)
       
  • Softly combining an ensemble of classifiers learned from a single
           convolutional neural network for scene categorization
    • Authors: Shuang Bai; Huadong Tang
      Pages: 183 - 196
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Shuang Bai, Huadong Tang
      In this paper we propose to train an ensemble of classifiers from a single convolutional neural network (CNN) and softly combine these classifiers for scene categorization. Specifically, we explore the hierarchical structure of a CNN to extract multiple types of features from images, and train a multi-class classifier corresponding to each type of features. To combine these classifiers effectively, a soft combination strategy is introduced. Considering the fact that different images may need to be discriminated by using different types of features, we train a set of auxiliary binary-class classifiers to estimate the quality of categorizing an image by using the corresponding multi-class classifiers, so that a dynamic weight can be assigned to each of the multi-class classifiers for combination. On the other hand, because features extracted from different layers of a CNN differ largely in their levels of abstraction, classifiers trained based on these features have quite different capabilities for scene categorization. To address this issue, in the soft combination strategy we adopt the genetic algorithm to learn another set of static weights for the multi-class classifiers for combination. The static weights are to adapt the multi-class classifiers to given datasets. Finally, to categorize an image, the multi-class classifiers are combined by using both dynamic and static weights. We conduct experiments on two challenging benchmark datasets, MIT-indoor scene 67 and SUN397. Experiment results show that the proposed method is effective for scene categorization and can give superior results to state-of-the-art approaches.
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      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.007
      Issue No: Vol. 67 (2018)
       
  • Performance analysis of a higher order neural network with an improved
           shuffled frog leaping algorithm for currency exchange rate prediction
    • Authors: Rajashree Dash
      Pages: 215 - 231
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Rajashree Dash
      Accurate and unbiased prediction of future currency exchange rate is always a potential field of research in domain of financial time series analysis. In this paper, an attempt is urged to examine the predictability of a higher order neural network called Pi-Sigma network for forecasting the highly non linear and dynamic currency exchange rates. An Improved Shuffled Frog Leaping (ISFL) algorithm is set forth to estimate the unrevealed parameters of the network. The network is also examined with few other meta-heuristic learning techniques and compared with other state of art models. Empirically the model validation is realized over three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. Practical analysis of results suggests that the Pi-Sigma network learned with ISFL is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.043
      Issue No: Vol. 67 (2018)
       
  • Evaluating spiking neural models in the classification of motor imagery
           EEG signals using short calibration sessions
    • Authors: R. Salazar-Varas; Roberto A. Vazquez
      Pages: 232 - 244
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): R. Salazar-Varas, Roberto A. Vazquez
      Since the emergence of brain computer interface (BCI), several methods have been applied to associate an electroencephalographic (EEG) recording with a specific mental task. Particularly, in the classification stage, several techniques such as linear Fisher discriminant (LD), feed-forward artificial neural networks (FNN) and radial basis function neural networks (RBF) have been applied successfully with BCI applications. However, in BCI applications, there is a challenge related to avoid long and tedious calibration session for users, this implies that the classification techniques used during the classification stage, have to be trained with a reduced number of EEG recordings. However, most of the classification techniques require several samples to learn accurately an association with a particular mental task. Since the spiking neural models (SNM) have shown their robustness in pattern recognition problems, this paper is focused on demonstrating that they are potential alternatives to classify EEG recordings when they are trained with a reduced number of data samples. To do that, we computed the coherence from a subset of three electrodes to obtain the feature vector of each EEG recording. Then, this information was classified using the SNM. In order to evaluate the robustness, the SNM was trained varying the number of samples. Furthermore, based on the performance and the confidence interval achieved in the classification, we developed two indexes to evaluate and compare the SNM against LD, FNN and RBF. The experimental results over the IIIa, IVa and V data sets from BCI International Competition III, suggest that the SNM are the best option to avoid long calibration sessions.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.054
      Issue No: Vol. 67 (2018)
       
  • A two-stage R2 indicator based evolutionary algorithm for many-objective
           optimization
    • Authors: Fei Li; Ran Cheng; Jianchang Liu; Yaochu Jin
      Pages: 245 - 260
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Fei Li, Ran Cheng, Jianchang Liu, Yaochu Jin
      R2 indicator based multi-objective evolutionary algorithms (R2-MOEAs) have achieved promising performance on traditional multi-objective optimization problems (MOPs) with two and three objectives, but still cannot well handle many-objective optimization problems (MaOPs) with more than three objectives. To address this issue, this paper proposes a two-stage R2 indicator based evolutionary algorithm (TS-R2EA) for many-objective optimization. In the proposed TS-R2EA, we first adopt an R2 indicator based achievement scalarizing function for the primary selection. In addition, by taking advantage of the reference vector guided objective space partition approach in diversity management for many-objective optimization, the secondary selection strategy is further applied. Such a two-stage selection strategy is expected to achieve a balance between convergence and diversity. Extensive experiments are conducted on a variety of benchmark test problems, and the experimental results demonstrate that the proposed algorithm has competitive performance in comparison with several tailored algorithms for many-objective optimization.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.048
      Issue No: Vol. 67 (2018)
       
  • p-Median based formulations with backbone facility locations
    • Authors: Pablo Adasme
      Pages: 261 - 275
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Pablo Adasme
      In this paper, we propose new mixed integer linear programming (MILP) models for the p-Median problem subject to ring, tree and star backbone topology constraints on the facility locations. More precisely, we minimize simultaneously the total connection cost distances between customers and facilities, and cost distances among facilities when connected under a ring, tree or a star network topology. In principle, all our proposed models can be used in any application related with the classical p-Median problem. Application examples include wired and wireless network design, computer networks, transportation, water supply and electrical networks, just to name a few. The proposed models arise as a combination of the classical p-Median problem with the traveling salesman, spanning tree, and star network problems, respectively. We prove the correctness of each proposed model. Then, we further propose variable neighborhood search (VNS) meta-heuristic algorithms, one for each topology. Our numerical results indicate that the ring models are harder to solve with CPLEX than the tree and star ones. Whilst VNS algorithms proved to be highly efficient when compared to the optimal solutions of the problem for small, medium and large size instances. Moreover, we obtain better feasible solutions than CPLEX for the large instances and in significantly less computational cost.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.008
      Issue No: Vol. 67 (2018)
       
  • Identifying central and peripheral nerve fibres with an artificial
           intelligence approach
    • Authors: David Gil; Jose Luis Girela; Jorge Azorín; Alba De Juan; Joaquin De Juan
      Pages: 276 - 285
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): David Gil, Jose Luis Girela, Jorge Azorín, Alba De Juan, Joaquin De Juan
      Distinguishing axons from central or peripheral nervous systems (CNS or PNS, respectively) is often a complicated task. The main objective of this work was to facilitate and support the process of automatically distinguishing the different types of nerve fibres by analysing their morphological characteristics. Our approach was based on a multi-level hierarchical classifier architecture that can handle the complexity of directly identifying nerve-fibre groups belonging to either the CNS or the PNS. The approach adopted comprises supervised methods (multilayer perceptron and decision trees), which are responsible for distinguishing the origin of the axons (CNS or PNS), whereas the unsupervised method (K-means clustering) performs nerve fibre clustering based on similar characteristics for both the CNS and PNS. Our experiments produced results with an accuracy higher than 88%. Our findings suggest that the development and implementation of a multi-level system improves automation capabilities and increases accuracy in the classification of nerves. Furthermore, our architecture allows for generalisation and flexibility, which can subsequently be extended to other biological control systems.
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      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.010
      Issue No: Vol. 67 (2018)
       
  • Dispersion of relative importance values contributes to the ranking
           uncertainty: Sensitivity analysis of Multiple Criteria Decision-Making
           methods
    • Authors: Vida Maliene; Robert Dixon-Gough; Naglis Malys
      Pages: 286 - 298
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Vida Maliene, Robert Dixon-Gough, Naglis Malys
      Multiple Criteria Decision-Making (MCDM) methods are widely used in research and industrial applications. These methods rely heavily on expert perceptions and are often sensitive to the assumptions made. The reliability and robustness of MCDM analysis can be further tested and verified by a computer simulation and sensitivity analysis. In order to address this, five different MCDM approaches, including Weighted Sum Model (WSM), Weighted Product Model (WPM), revised Analytic Hierarchy Process (rAHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and COmplex PRoportional ASsessment (COPRAS) are explored in the paper. Real data of the case study for assessing housing affordability are used for testing the robustness of alternative ranking and finding the most sensitive criteria to the change of criterion weight. We identify the most critical criteria for any and best ranking alternatives. The paper highlights the significance of sensitivity analysis in assessing the robustness and reliability of MCDM outcomes. Furthermore, randomly generated and model-based data sets are used to establish relationship between the dispersion of relative importance values of alternatives and ranking uncertainty. Our findings demonstrate that the dispersion of relative importance values of alternatives correlate with the Euclidian distances of aggregated values. We conclude that the dispersion of relative importance values contributes directly to the ranking uncertainty and can be used as a measure for identifying critical criteria.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.003
      Issue No: Vol. 67 (2018)
       
  • A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm
           for cluster analysis
    • Authors: R.J. Kuo; T.C. Lin; F.E. Zulvia; C.Y. Tsai
      Pages: 299 - 308
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): R.J. Kuo, T.C. Lin, F.E. Zulvia, C.Y. Tsai
      Cluster analysis is a very useful data mining approach. Although many clustering algorithms have been proposed, it is very difficult to find a clustering method which is suitable for all types of datasets. This study proposes an evolutionary-based clustering algorithm which combines a metaheuristic with a kernel intuitionistic fuzzy c-means (KIFCM) algorithm. The KIFCM algorithm improves the fuzzy c-means (FCM) algorithm by employing an intuitionistic fuzzy set and a kernel function. According to previous studies, the KIFCM algorithm is a promising algorithm. However, it still has a weakness due to its high sensitivity to initial centroids. Thus, this study overcomes this problem by using a metaheuristic algorithm to improve the KIFCM result. The metaheuristic can provide better initial centroids for the KIFCM algorithm. This study applies three metaheuristics, particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC) algorithms. Though the hybrid method is not new, this is the first paper to combine metaheuristics and KIFCM. The proposed algorithms, PSO-KIFCM, GA-KIFCM and ABC-KIFCM algorithms are evaluated using six benchmark datasets. The results are compared with some other clustering algorithms, namely K-means, FCM, Kernel fuzzy c-means (KFCM) and KIFCM algorithms. The results prove that the proposed algorithms achieve better accuracy. Furthermore, the proposed algorithms are applied to solve a case study on customer segmentation. This case study is taken from franchise stores selling women's clothing in Taiwan. For this case study, the proposed algorithms also exhibit better cluster construction than other tested algorithms.
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      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.039
      Issue No: Vol. 67 (2018)
       
  • Residue properties for the arithmetical estimation of the image
           quantization table
    • Authors: Fernando López Hernández; Elena Giménez de Ory; Sergio Ríos Aguilar; Rubén González Crespo
      Pages: 309 - 321
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Fernando López Hernández, Elena Giménez de Ory, Sergio Ríos Aguilar, Rubén González Crespo
      Traditionally, a statistical approach has been used to detect the JPEG quantization table used to compress a bitmap. This approach has the disadvantage that at times false solutions are found. These false solutions may have important implications if, for example, a court expert issues an incorrect assessment on whether an image is forged. This paper develops the concept of residue properties, which enables us to determine the quantization table following an arithmetic approach. This study shows that these properties allow us to ensure that no false solutions are produced, but at the cost of being able to obtain more than one compatible solution. Sometimes we prefer to find this set of possible Q values (quantization values) used, without risking obtaining a false solution. If we choose to obtain a unique answer for Q, then we can perform a statistical analysis on this pruned space of compatible solutions to decide the most probable Q value. In this way, a higher success rate is obtained than if we perform only a statistical soft computing analysis on the total space of solutions.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.017
      Issue No: Vol. 67 (2018)
       
  • 3D fast convex-hull-based evolutionary multiobjective optimization
           algorithm
    • Authors: Jiaqi Zhao; Licheng Jiao; Fang Liu; Vitor Basto Fernandes; Iryna Yevseyeva; Shixiong Xia; Michael T.M. Emmerich
      Pages: 322 - 336
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Jiaqi Zhao, Licheng Jiao, Fang Liu, Vitor Basto Fernandes, Iryna Yevseyeva, Shixiong Xia, Michael T.M. Emmerich
      The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves have been widely used in the machine learning community to analyze the performance of classifiers. The area (or volume) under the convex hull has been used as a scalar indicator for the performance of a set of classifiers in ROC and DET space. Recently, 3D convex-hull-based evolutionary multiobjective optimization algorithm (3DCH-EMOA) has been proposed to maximize the volume of convex hull for binary classification combined with parsimony and three-way classification problems. However, 3DCH-EMOA revealed high consumption of computational resources due to redundant convex hull calculations and a frequent execution of nondominated sorting. In this paper, we introduce incremental convex hull calculation and a fast replacement for non-dominated sorting. While achieving the same high quality results, the computational effort of 3DCH-EMOA can be reduced by orders of magnitude. The average time complexity of 3DCH-EMOA in each generation is reduced from O ( n 2 log n ) to O ( n log n ) per iteration, where n is the population size. Six test function problems are used to test the performance of the newly proposed method, and the algorithms are compared to several state-of-the-art algorithms, including NSGA-III, RVEA, etc., which were not compared to 3DCH-EMOA before. Experimental results show that the new version of the algorithm (3DFCH-EMOA) can speed up 3DCH-EMOA for about 30 times for a typical population size of 300 without reducing the performance of the method. Besides, the proposed algorithm is applied for neural networks pruning, and several UCI datasets are used to test the performance.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.005
      Issue No: Vol. 67 (2018)
       
  • A hybrid financial trading support system using multi-category classifiers
           and random forest
    • Authors: Manoj Thakur; Deepak Kumar
      Pages: 337 - 349
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Manoj Thakur, Deepak Kumar
      This study presents a decision support system for algorithmic trading in the financial market that uses a new hybrid approach for making automatic trading decision. The hybrid approach integrates weighted multicategory generalized eigenvalue support vector machine (WMGEPSVM) and random forest (RF) algorithms (named RF-WMGEPSVM) to generate “Buy/Hold/Sell” signals. The WMGEPSVM technique has an advantage of handling the unbalanced data set effectively. The input variables are generated from a number of technical indicators and oscillators that are widely used in industry by professional financial experts. Selection of relevant input variables can enhance the predictive capability of the prediction algorithms. RF technique is employed to discover the optimal feature subset from a large set of technical indicators. The proposed hybrid system is tested using “walk forward” approach for its capability of taking an automatic trading decision on daily data of five index futures, viz., NASDAQ, DOW JONES, S&P 500, NIFTY 50 and NIFTY BANK. RF-WMGEPSVM achieves the notable improvement over the buy/hold strategy and other predictive models contemplated in this study. It is also observed that combining WMGEPSVM with RF further improves the results. Empirical results confirm the effectiveness of RF-WMGEPSVM in the real market scenarios having bullish, bearish or flat trend.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.006
      Issue No: Vol. 67 (2018)
       
  • A threat assessment method of group targets based on interval-valued
           intuitionistic fuzzy multi-attribute group decision-making
    • Authors: Depeng Kong; Tianqing Chang; Quandong Wang; Haoze Sun; Wenjun Dai
      Pages: 350 - 369
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Depeng Kong, Tianqing Chang, Quandong Wang, Haoze Sun, Wenjun Dai
      Group target (GT) is consisted of multi-class weapons that can collaboratively work and it is a basic application unit in the information warfare. Assessing the threat of GT is required for the optimal decision of troop deployment. However, it is difficult to obtain a reasonable and effective threat assessment result of GT due to the uncertain battlefield information and different judgments from various decision makers (DMs). The study aims to investigate the multi-attribute group decision-making (MAGDM) method for solving the interval-valued intuitionistic fuzzy threat assessment problem of GTs without known attribute weights and DM’s preference weights. Based on the assessment information of DMs, attribute weights are determined with the interval-valued intuitionistic fuzzy entropy. To derive the DM’s preference weights objectively, we construct a nonlinear optimization model to minimize decision makers’ overall decision-making conflict. Moreover, the artificial bee colony algorithm is introduced to solve the nonlinear constrained optimization problem in the optimization model. The decision information of multi-DM is aggregated by the interval-valued intuitionistic fuzzy weighted averaging operator (IVIFWA) with the DMs’ preference weights. In order to describe the attribute closeness degree to the ideal solution, the decision-making judgment matrix is constructed according to the ideal solution closeness degree of each GT’s attribute calculated with the cross-entropy distance. Subsequently, based on the decision-making judgment matrix, the threat degree is calculated according to the weighted average method with the attribute weights. Finally, a case of the threat assessment of group targets is provided to illustrate the implementation process and applicability of the method proposed in this paper.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.015
      Issue No: Vol. 67 (2018)
       
  • Statistical analysis for vortex particle swarm optimization
    • Authors: Helbert Eduardo Espitia; Jorge Iván Sofrony
      Pages: 370 - 386
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Helbert Eduardo Espitia, Jorge Iván Sofrony
      This paper presents the statistical analysis of vortex particle swarm optimization (VPSO) which is a boost algorithm based on self-propelled particle swarms. In order to avoid local minima, the optimization algorithm uses two separated behaviors: translational and dispersion. This idea mimics living organism strategies such as foraging and predator avoidance. The dispersion is given by vortex behavior (circular movements) to scape from local minima. Via suitable parameter configuration is possible to switch between translational (convergence) and circular movements (dispersion). Performance of the algorithm is studied via statistical analysis results using well-known test functions.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.002
      Issue No: Vol. 67 (2018)
       
  • Optimal planning of active distribution networks with hybrid distributed
           energy resources using grid-based multi-objective harmony search algorithm
           
    • Authors: Kayalvizhi S.; Vinod Kumar D.M.
      Pages: 387 - 398
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Kayalvizhi S., Vinod Kumar D.M.
      This paper presents a new Grid-based Multi-objective Harmony Search Algorithm (GrMHS) for optimal planning and operation of Distributed Generation (DG) in an active distribution network. Both dispatchable and non-dispatchable (renewable) distributed generations are considered in this paper. The DGs location, size and power factor of diesel generator are optimized using the proposed algorithm in three objective case to minimize three conflicting objectives such as: energy loss, voltage deviation and cost of DG integration in the distribution network. The locations of the renewable sources are also optimized. As the objectives are conflicting with each other, pareto based GrMHS algorithm is proposed where it employs non dominated sorting for ranking of solutions and environmental selection for the secondary selection of population to the next iteration. The environmental selection establishes grid setting in the objective plane and three selection criterions are proposed. All uncertainties associated with load, wind speed and solar irradiance are included in the planning model. The choice of optimisation algorithm (Harmony Search) is ascertained with comparison of results for loss reduction and the effectiveness of the proposed algorithm is demonstrated with IEEE 33-bus, IEEE 69-bus and Indian 85-bus distribution networks with two objectives. The three objective case is presented with relevant analysis for IEEE 33-bus and Indian 85-bus distribution systems.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.009
      Issue No: Vol. 67 (2018)
       
  • Energy-efficient application assignment in profile-based data center
           management through a Repairing Genetic Algorithm
    • Authors: Meera Vasudevan; Yu-Chu Tian; Maolin Tang; Erhan Kozan; Xueying Zhang
      Pages: 399 - 408
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Meera Vasudevan, Yu-Chu Tian, Maolin Tang, Erhan Kozan, Xueying Zhang
      The massive deployment of data center services and cloud computing comes with exorbitant energy costs and excessive carbon footprint. This demands green initiatives and energy-efficient strategies for greener data centers. Assignment of an application to different virtual machines has a significant impact on both energy consumption and resource utilization in virtual resource management of a data centre. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop a scalable application assignment strategy that maintains a trade-off between energy efficiency and resource utilization. To address this problem, this paper formulates application assignment to virtual machines as a profile-driven optimization problem under constraints. Then, a Repairing Genetic Algorithm (RGA) is presented to solve the large-scale optimization problem. It enhances penalty-based genetic algorithm by incorporating the Longest Cloudlet Fastest Processor (LCFP), from which an initial population is generated, and an infeasible-solution repairing procedure (ISRP). The application assignment with RGA is integrated into a three-layer energy management framework for data centres. Experiments are conducted to demonstrate the effectiveness of the presented approach, e.g., 23% less energy consumption and 43% more resource utilization in comparison with the steady-state Genetic Algorithm (GA) under investigated scenarios.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.016
      Issue No: Vol. 67 (2018)
       
  • The continuous-discrete PSO algorithm for shape formation problem of
           multiple agents in two and three dimensional space
    • Authors: Jun Liu; Hongbin Ma; Xuemei Ren; Tianyun Shi; Ping Li; Xiaoning Ma
      Pages: 409 - 433
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Jun Liu, Hongbin Ma, Xuemei Ren, Tianyun Shi, Ping Li, Xiaoning Ma
      Shape formation problem of agents in the two or three dimensional space is one of the most important and challenging topics in the fields of evolutionary computation and multi-agents system, etc. Firstly, the basic concepts and objective functions of shape formation problem are introduced to deeply understand the considered shape formation problem. Three theorems of shape formation problem with three agents are addressed by the Lagrangian multiplier method, however, the Lagrangian multiplier method difficultly solves optimal shape formation problem where the number of agents is strictly larger than 3 and the number of constraints is larger than 2. In order to tackle the continuous and discrete optimization problem, the continuous-discrete particle swarm optimization (CDPSO) algorithm is developed to search for the rotated angle of the desired shape and the matching pair between points in the initial shape and points in the desired shape. Additionally, the parameters in CDPSO algorithm are set by three theorems on convergence analysis of the random PSO algorithm. To demonstrate the effectiveness and the feasibility of the CDPSO algorithm on the shape formation problem, numerical results not only discuss the optimal virtual helicopters formation between two typical shapes in the three dimensional space, but also provide one searching and rescuing strategy of MH370 plane to minimize the whole moving distance of all virtual rescuing ships. Moreover, the shape conversion problem including multiple agents is also solved by the CDPSO algorithm when the number of agents is equal to 100, 200, 500 and 1000. Additionally, the optimization results and the computational time are compared among the Lagrange multiplier method, CDPSO, CDDE, CDGA, CDPSOI and CDPSOE algorithms.
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      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.02.015
      Issue No: Vol. 67 (2018)
       
  • Multi-objective community detection algorithm with node importance
           analysis in attributed networks
    • Authors: Alireza Moayedikia
      Pages: 434 - 451
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Alireza Moayedikia
      Community detection is the act of grouping similar nodes while separating dissimilar ones. The utility of conventional algorithms are limited as they consider a structure based, single objective formulation in which, nodes are treated with the same importance. However, in real networks such as LinkedIn, nodes are not only connected through their structural properties, but also using their associated attributes. In addition, in real networks nodes interact, and this interaction causes some nodes be more important than others. However, conventional algorithms for community detection, do not consider the interactions exists amongst nodes and therefore their utility is limited. To overcome such limitations, this paper introduces a novel Multi-objective Attributed community detection algorithm with Node Importance Analysis (MANIA). The proposed algorithm considers, (i) two objective functions to evaluate the suitability of communities from structure and attribute perspectives, (ii) incorporates nodes’ attribute information to benefit from their stronger discrimination power and (iii) estimates nodes’ importance using, convergence degree and topology potential field. To prove the efficiency of MANIA, its performance is experimentally tested and compared against other novel community detection algorithms using five real-world datasets in terms of homogeneity and modularity objective functions. The comparisons indicate that MANIA detects more meaningful and interpretable communities and significantly outperforms the rivals.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.014
      Issue No: Vol. 67 (2018)
       
  • Differential evolution mutation operators for constrained multi-objective
           optimization
    • Authors: Xiaobing Yu; Xianrui Yu; Yiqun Lu; Gary G. Yen; Mei Cai
      Pages: 452 - 466
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Xiaobing Yu, Xianrui Yu, Yiqun Lu, Gary G. Yen, Mei Cai
      Many real-world optimization problems belong to constrained multi-objective optimization problems (CMOPs). Handling constraints and optimizing objectives are two equally important goals. With effective and efficient population-based meta-heuristics in mind, how to generate the offspring with good convergence and diversity properties is a problem to be solved. Competitive algorithms based on different evolution (DE) metaphors have been proposed to solve CMOPs over years as the performance of the DE is attractive. The creative idea of the proposed algorithm is to design a novel mutation mechanism for handling infeasible solutions and feasible solutions respectively. The mechanism can produce well distributed Pareto optimal front while satisfying all concerning constraints. The performance of the algorithm is evaluated on nineteen benchmark functions. Compared with three representative constraint handling techniques and latest optimization algorithms, experimental results have indicated that the proposed algorithm is an effective candidate for real-world problems. At last, the proposed method is used to solve combined economic emission dispatch (CEED) problem. The experiment results have further validated the efficiency of the method.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.028
      Issue No: Vol. 67 (2018)
       
  • Multiobjective design of fuzzy neural network controller for wastewater
           treatment process
    • Authors: Hong-Gui Han; Lu Zhang; Hong-Xu Liu; Jun-Fei Qiao
      Pages: 467 - 478
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Hong-Gui Han, Lu Zhang, Hong-Xu Liu, Jun-Fei Qiao
      In this paper, an improved multiobjective optimal control (MOOC) strategy is developed to improve the operational efficiency, satisfy the effluent quality (EQ) and reduce the energy consumption (EC) in wastewater treatment process (WWTP). First, the adaptive kernel function models of the process, which can describe the complex dynamics of EQ and EC, are developed for the proposed MOOC strategy. Meanwhile, a multiobjective optimization problem is constituted to account for WWTP. Second, an improved multiobjective particle swarm optimization (MOPSO) algorithm, using a self-adaptive flight parameters mechanism and a multiobjective gradient (MOG) method, is designed to minimize the established objectives. And then the optimal set-points of dissolved oxygen (SO ) and nitrate (SNO ) are obtained in the treatment process. Third, an adaptive fuzzy neural network controller (FNNC) is applied for realizing the tracking control of the obtained set-points in the proposed MOOC strategy. Finally, Benchmark Simulation Model No.1 (BSM1) is introduced to evaluate the effectiveness of the proposed MOOC strategy. Experimental results show the efficacy of the proposed method.
      Graphical abstract image

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.020
      Issue No: Vol. 67 (2018)
       
  • Methods to improve the ordinal and multiplicative consistency for
           reciprocal preference relations
    • Authors: Yejun Xu; Qianqian Wang; Francisco Javier Cabrerizo; Enrique Herrera-Viedma
      Pages: 479 - 493
      Abstract: Publication date: June 2018
      Source:Applied Soft Computing, Volume 67
      Author(s): Yejun Xu, Qianqian Wang, Francisco Javier Cabrerizo, Enrique Herrera-Viedma
      This paper investigates both the ordinal and multiplicative consistencies for reciprocal preference relations simultaneously. First, the relationship among ordinal consistency, acceptable cardinal consistency and perfect cardinal consistency of reciprocal preference relations are analyzed. Then, based on the basic forms of all ordinal inconsistencies, i.e. 3-cycle cases, a new ordinal consistency index, called OCI(R),is introduced to measure the degree of ordinal consistency for reciprocal preference relations. An algorithm to identify and modify ordinal inconsistent elements in reciprocal preference relations is further developed. Afterwards, a multiplicative inconsistency identification and modification method is developed, in which an induced matrix is constructed on the basis of multiplicative consistency property, to improve the consistency of reciprocal preference relations. Meanwhile, some numerical examples are illustrated to show the effectiveness and efficiency of the developed methods Compared with the existing methods, our proposed methods not only significantly improve both the ordinal and multiplicative consistency but also are appropriate for both strict and non-strict reciprocal preference relations, meanwhile retaining the original preference information as much as possible.

      PubDate: 2018-04-15T04:05:50Z
      DOI: 10.1016/j.asoc.2018.03.034
      Issue No: Vol. 67 (2018)
       
 
 
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