for Journals by Title or ISSN
for Articles by Keywords
help

Publisher: Hindawi   (Total: 335 journals)

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 | Last   [Sort by number of followers]   [Restore default list]

Showing 1 - 200 of 335 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.512, h-index: 32)
Active and Passive Electronic Components     Open Access   (Followers: 7, SJR: 0.157, h-index: 15)
Advances in Acoustics and Vibration     Open Access   (Followers: 25, SJR: 0.259, h-index: 6)
Advances in Agriculture     Open Access   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Astronomy     Open Access   (Followers: 37, SJR: 0.351, h-index: 17)
Advances in Bioinformatics     Open Access   (Followers: 18, SJR: 0.421, h-index: 8)
Advances in Biology     Open Access   (Followers: 8)
Advances in Chemistry     Open Access   (Followers: 14)
Advances in Civil Engineering     Open Access   (Followers: 33, SJR: 0.338, h-index: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 8, SJR: 0.248, h-index: 10)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.231, h-index: 6)
Advances in Ecology     Open Access   (Followers: 16)
Advances in Electrical Engineering     Open Access   (Followers: 20)
Advances in Endocrinology     Open Access   (Followers: 4)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.258, h-index: 7)
Advances in Hematology     Open Access   (Followers: 9, SJR: 0.892, h-index: 18)
Advances in High Energy Physics     Open Access   (Followers: 19, SJR: 0.892, h-index: 19)
Advances in Human-Computer Interaction     Open Access   (Followers: 19, SJR: 0.439, h-index: 9)
Advances in Materials Science and Engineering     Open Access   (Followers: 32, SJR: 0.263, h-index: 11)
Advances in Mathematical Physics     Open Access   (Followers: 5, SJR: 0.332, h-index: 10)
Advances in Medicine     Open Access   (Followers: 2)
Advances in Meteorology     Open Access   (Followers: 18, SJR: 0.498, h-index: 10)
Advances in Multimedia     Open Access   (Followers: 2, SJR: 0.191, h-index: 10)
Advances in Nonlinear Optics     Open Access   (Followers: 5)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Nursing     Open Access   (Followers: 22)
Advances in Operations Research     Open Access   (Followers: 11, SJR: 0.343, h-index: 7)
Advances in Optical Technologies     Open Access   (Followers: 3, SJR: 0.283, h-index: 16)
Advances in OptoElectronics     Open Access   (Followers: 5, SJR: 0.973, h-index: 16)
Advances in Orthopedic Surgery     Open Access   (Followers: 9)
Advances in Orthopedics     Open Access   (Followers: 9)
Advances in Pharmacological Sciences     Open Access   (Followers: 6, SJR: 0.695, h-index: 13)
Advances in Physical Chemistry     Open Access   (Followers: 11, SJR: 0.297, h-index: 7)
Advances in Power Electronics     Open Access   (Followers: 25, SJR: 0.26, h-index: 6)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 20)
Advances in Software Engineering     Open Access   (Followers: 10)
Advances in Tribology     Open Access   (Followers: 10, SJR: 0.267, h-index: 6)
Advances in Urology     Open Access   (Followers: 10, SJR: 0.629, h-index: 16)
Advances in Virology     Open Access   (Followers: 7, SJR: 1.04, h-index: 12)
AIDS Research and Treatment     Open Access   (Followers: 3, SJR: 1.125, h-index: 14)
Analytical Cellular Pathology     Open Access   (Followers: 2, SJR: 0.334, h-index: 12)
Anatomy Research Intl.     Open Access   (Followers: 2)
Anemia     Open Access   (Followers: 4, SJR: 0.991, h-index: 11)
Anesthesiology Research and Practice     Open Access   (Followers: 12, SJR: 0.513, h-index: 12)
Applied and Environmental Soil Science     Open Access   (Followers: 15, SJR: 0.53, h-index: 9)
Applied Bionics and Biomechanics     Open Access   (Followers: 8, SJR: 0.23, h-index: 13)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Archaea     Open Access   (Followers: 3, SJR: 1.248, h-index: 27)
Arthritis     Open Access   (Followers: 4)
Autism Research and Treatment     Open Access   (Followers: 29)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.909, h-index: 17)
Behavioural Neurology     Open Access   (Followers: 7, SJR: 0.696, h-index: 34)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 1.085, h-index: 17)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 9, SJR: 0.286, h-index: 19)
BioMed Research Intl.     Open Access   (Followers: 6, SJR: 0.725, h-index: 59)
Biotechnology Research Intl.     Open Access   (Followers: 2)
Bone Marrow Research     Open Access   (Followers: 2)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 3, SJR: 0.856, h-index: 53)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 4, SJR: 0.409, h-index: 25)
Canadian Respiratory J.     Open Access   (Followers: 1, SJR: 0.503, h-index: 42)
Cardiology Research and Practice     Open Access   (Followers: 8, SJR: 0.941, h-index: 17)
Cardiovascular Psychiatry and Neurology     Open Access   (Followers: 4, SJR: 1.091, h-index: 14)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 2)
Case Reports in Critical Care     Open Access   (Followers: 9)
Case Reports in Dentistry     Open Access   (Followers: 3)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 13)
Case Reports in Endocrinology     Open Access   (SJR: 0.326, h-index: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 2)
Case Reports in Hepatology     Open Access   (Followers: 1)
Case Reports in Immunology     Open Access   (Followers: 4)
Case Reports in Infectious Diseases     Open Access   (Followers: 5)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 4)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 10)
Case Reports in Oncological Medicine     Open Access   (Followers: 2)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 2)
Case Reports in Orthopedics     Open Access   (Followers: 7)
Case Reports in Otolaryngology     Open Access   (Followers: 4)
Case Reports in Pathology     Open Access   (Followers: 4)
Case Reports in Pediatrics     Open Access   (Followers: 5)
Case Reports in Psychiatry     Open Access   (Followers: 10)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 9)
Case Reports in Rheumatology     Open Access   (Followers: 4)
Case Reports in Surgery     Open Access   (Followers: 8)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 8)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Chemotherapy Research and Practice     Open Access   (Followers: 1)
Child Development Research     Open Access   (Followers: 14)
Chinese J. of Engineering     Open Access   (Followers: 2)
Chinese J. of Mathematics     Open Access  
Cholesterol     Open Access   (Followers: 1, SJR: 0.906, h-index: 12)
Chromatography Research Intl.     Open Access   (Followers: 7)
Complexity     Hybrid Journal   (Followers: 6, SJR: 0.526, h-index: 27)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.415, h-index: 22)
Computational Intelligence and Neuroscience     Open Access   (Followers: 9, SJR: 0.232, h-index: 30)
Contrast Media & Molecular Imaging     Open Access   (Followers: 3, SJR: 0.932, h-index: 34)
Critical Care Research and Practice     Open Access   (Followers: 10, SJR: 0.916, h-index: 14)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.8, h-index: 12)
Dataset Papers in Science     Open Access  
Depression Research and Treatment     Open Access   (Followers: 13, SJR: 0.77, h-index: 11)
Dermatology Research and Practice     Open Access   (Followers: 3, SJR: 0.576, h-index: 15)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.651, h-index: 18)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 5, SJR: 0.323, h-index: 24)
Disease Markers     Open Access   (Followers: 1, SJR: 0.774, h-index: 49)
Economics Research Intl.     Open Access   (Followers: 2)
Education Research Intl.     Open Access   (Followers: 18)
Emergency Medicine Intl.     Open Access   (Followers: 7)
Enzyme Research     Open Access   (Followers: 4, SJR: 0.457, h-index: 18)
Epilepsy Research and Treatment     Open Access   (Followers: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 18, SJR: 0.615, h-index: 50)
Experimental Diabetes Research     Open Access   (Followers: 11, SJR: 1.591, h-index: 30)
Gastroenterology Research and Practice     Open Access   (Followers: 3, SJR: 0.664, h-index: 21)
Genetics Research Intl.     Open Access   (Followers: 1)
Geofluids     Open Access   (Followers: 4, SJR: 0.693, h-index: 38)
Hepatitis Research and Treatment     Open Access   (Followers: 6)
HPB Surgery     Open Access   (Followers: 5, SJR: 0.798, h-index: 22)
Indian J. of Materials Science     Open Access  
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 7, SJR: 0.976, h-index: 34)
Influenza Research and Treatment     Open Access   (Followers: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 2, SJR: 0.763, h-index: 15)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 66, SJR: 0.241, h-index: 6)
Intl. J. of Agronomy     Open Access   (Followers: 8, SJR: 0.223, h-index: 2)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 11, SJR: 1.193, h-index: 25)
Intl. J. of Analysis     Open Access  
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.157, h-index: 2)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.385, h-index: 15)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 23)
Intl. J. of Bacteriology     Open Access  
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.485, h-index: 10)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 5, SJR: 0.581, h-index: 23)
Intl. J. of Breast Cancer     Open Access   (Followers: 12)
Intl. J. of Carbohydrate Chemistry     Open Access   (Followers: 7)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 2.658, h-index: 25)
Intl. J. of Chemical Engineering     Open Access   (Followers: 7, SJR: 0.361, h-index: 10)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 11, SJR: 0.213, h-index: 12)
Intl. J. of Corrosion     Open Access   (Followers: 11, SJR: 0.19, h-index: 7)
Intl. J. of Dentistry     Open Access   (Followers: 6, SJR: 0.558, h-index: 11)
Intl. J. of Differential Equations     Open Access   (Followers: 6, SJR: 0.363, h-index: 11)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.144, h-index: 10)
Intl. J. of Ecology     Open Access   (Followers: 6, SJR: 0.8, h-index: 11)
Intl. J. of Electrochemistry     Open Access   (Followers: 7)
Intl. J. of Endocrinology     Open Access   (Followers: 3, SJR: 0.961, h-index: 24)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 3)
Intl. J. of Evolutionary Biology     Open Access   (Followers: 9)
Intl. J. of Family Medicine     Open Access   (Followers: 2)
Intl. J. of Food Science     Open Access   (Followers: 3)
Intl. J. of Forestry Research     Open Access   (Followers: 4)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.721, h-index: 7)
Intl. J. of Geophysics     Open Access   (Followers: 5, SJR: 0.416, h-index: 8)
Intl. J. of Hepatology     Open Access   (Followers: 3)
Intl. J. of Hypertension     Open Access   (Followers: 6, SJR: 0.823, h-index: 20)
Intl. J. of Inflammation     Open Access   (SJR: 0.876, h-index: 14)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 2)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.346, h-index: 27)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6)
Intl. J. of Metals     Open Access   (Followers: 4)
Intl. J. of Microbiology     Open Access   (Followers: 5, SJR: 1.006, h-index: 18)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 3, SJR: 0.167, h-index: 5)
Intl. J. of Molecular Imaging     Open Access  
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.411, h-index: 7)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.926, h-index: 14)
Intl. J. of Oceanography     Open Access   (Followers: 8)
Intl. J. of Optics     Open Access   (Followers: 7, SJR: 0.262, h-index: 7)
Intl. J. of Otolaryngology     Open Access   (Followers: 1)
Intl. J. of Pediatrics     Open Access   (Followers: 5)
Intl. J. of Peptides     Open Access   (Followers: 4, SJR: 0.73, h-index: 16)
Intl. J. of Photoenergy     Open Access   (Followers: 2, SJR: 0.348, h-index: 28)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 1.578, h-index: 20)
Intl. J. of Polymer Science     Open Access   (Followers: 23, SJR: 0.265, h-index: 11)
Intl. J. of Population Research     Open Access   (Followers: 2)
Intl. J. of Proteomics     Open Access   (Followers: 1)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 14, SJR: 0.345, h-index: 4)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.182, h-index: 8)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 5)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 1.015, h-index: 18)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.402, h-index: 19)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 4, SJR: 0.234, h-index: 19)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.753, h-index: 11)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 3, SJR: 0.757, h-index: 14)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.865, h-index: 16)
Intl. J. of Vehicular Technology     Open Access   (Followers: 4, SJR: 0.169, h-index: 6)
Intl. J. of Zoology     Open Access   (Followers: 1, SJR: 0.389, h-index: 8)
Intl. Scholarly Research Notices     Open Access   (Followers: 200)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 7)

        1 2 | Last   [Sort by number of followers]   [Restore default list]

Journal Cover Computational Intelligence and Neuroscience
  [SJR: 0.232]   [H-I: 30]   [9 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1687-5265 - ISSN (Online) 1687-5273
   Published by Hindawi Homepage  [335 journals]
  • Firefly Mating Algorithm for Continuous Optimization Problems

    • Abstract: This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.
      PubDate: Thu, 20 Jul 2017 00:00:00 +000
       
  • Shape Completion Using Deep Boltzmann Machine

    • Abstract: Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model. In this paper, we make use of the hidden activation of the DBM and incorporate it with the convolutional shape features to fit a regression model. We compare the output of the regression model with the incomplete shape feature in order to set a proper and compact mask for sampling from the DBM. The experiment shows that our method can obtain realistic results without any prior information about the incomplete object shape.
      PubDate: Wed, 19 Jul 2017 06:47:41 +000
       
  • Patch-Based Principal Component Analysis for Face Recognition

    • Abstract: We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.
      PubDate: Tue, 11 Jul 2017 09:10:35 +000
       
  • Feature Selection and Parameters Optimization of SVM Using Particle Swarm
           Optimization for Fault Classification in Power Distribution Systems

    • Abstract: Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
      PubDate: Tue, 11 Jul 2017 06:53:26 +000
       
  • Differential Cloud Particles Evolution Algorithm Based on Data-Driven
           Mechanism for Applications of ANN

    • Abstract: Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms.
      PubDate: Thu, 06 Jul 2017 09:50:26 +000
       
  • Characterization of 2-Path Product Signed Graphs with Its Properties

    • Abstract: A signed graph is a simple graph where each edge receives a sign positive or negative. Such graphs are mainly used in social sciences where individuals represent vertices friendly relation between them as a positive edge and enmity as a negative edge. In signed graphs, we define these relationships (edges) as of friendship (“” edge) or hostility (“” edge). A 2-path product signed graph of a signed graph is defined as follows: the vertex set is the same as and two vertices are adjacent if and only if there exists a path of length two between them in . The sign of an edge is the product of marks of vertices in where the mark of vertex in is the product of signs of all edges incident to the vertex. In this paper, we give a characterization of 2-path product signed graphs. Also, some other properties such as sign-compatibility and canonically-sign-compatibility of 2-path product signed graphs are discussed along with isomorphism and switching equivalence of this signed graph with 2-path signed graph.
      PubDate: Thu, 06 Jul 2017 08:41:50 +000
       
  • Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual
           Attention Features

    • Abstract: In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.
      PubDate: Thu, 06 Jul 2017 08:16:14 +000
       
  • Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm

    • Abstract: A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model. These parameters are learned simultaneously. The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained results with other existing results of other methods. The proposed CS method gives an optimal T–S fuzzy model with fewer numbers of rules.
      PubDate: Thu, 06 Jul 2017 00:00:00 +000
       
  • Automatic Image-Based Plant Disease Severity Estimation Using Deep
           Learning

    • Abstract: Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.
      PubDate: Wed, 05 Jul 2017 06:46:21 +000
       
  • MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations
           of Apoptosis Proteins

    • Abstract: Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.
      PubDate: Tue, 04 Jul 2017 10:15:31 +000
       
  • Dorsoventral and Proximodistal Hippocampal Processing Account for the
           Influences of Sleep and Context on Memory (Re)consolidation: A
           Connectionist Model

    • Abstract: The context in which learning occurs is sufficient to reconsolidate stored memories and neuronal reactivation may be crucial to memory consolidation during sleep. The mechanisms of context-dependent and sleep-dependent memory (re)consolidation are unknown but involve the hippocampus. We simulated memory (re)consolidation using a connectionist model of the hippocampus that explicitly accounted for its dorsoventral organization and for CA1 proximodistal processing. Replicating human and rodent (re)consolidation studies yielded the following results. (1) Semantic overlap between memory items and extraneous learning was necessary to explain experimental data and depended crucially on the recurrent networks of dorsal but not ventral CA3. (2) Stimulus-free, sleep-induced internal reactivations of memory patterns produced heterogeneous recruitment of memory items and protected memories from subsequent interference. These simulations further suggested that the decrease in memory resilience when subjects were not allowed to sleep following learning was primarily due to extraneous learning. (3) Partial exposure to the learning context during simulated sleep (i.e., targeted memory reactivation) uniformly increased memory item reactivation and enhanced subsequent recall. Altogether, these results show that the dorsoventral and proximodistal organization of the hippocampus may be important components of the neural mechanisms for context-based and sleep-based memory (re)consolidations.
      PubDate: Mon, 03 Jul 2017 09:10:37 +000
       
  • Object Extraction in Cluttered Environments via a P300-Based IFCE

    • Abstract: One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.
      PubDate: Tue, 27 Jun 2017 09:29:33 +000
       
  • Research of Hubs Location Method for Weighted Brain Network Based on
           NoS-FA

    • Abstract: As a complex network of many interlinked brain regions, there are some central hub regions which play key roles in the structural human brain network based on T1 and diffusion tensor imaging (DTI) technology. Since most studies about hubs location method in the whole human brain network are mainly concerned with the local properties of each single node but not the global properties of all the directly connected nodes, a novel hubs location method based on global importance contribution evaluation index is proposed in this study. The number of streamlines (NoS) is fused with normalized fractional anisotropy (FA) for more comprehensive brain bioinformation. The brain region importance contribution matrix and information transfer efficiency value are constructed, respectively, and then by combining these two factors together we can calculate the importance value of each node and locate the hubs. Profiting from both local and global features of the nodes and the multi-information fusion of human brain biosignals, the experiment results show that this method can detect the brain hubs more accurately and reasonably compared with other methods. Furthermore, the proposed location method is used in impaired brain hubs connectivity analysis of schizophrenia patients and the results are in agreement with previous studies.
      PubDate: Wed, 21 Jun 2017 00:00:00 +000
       
  • Bag of Visual Words Model with Deep Spatial Features for Geographical
           Scene Classification

    • Abstract: With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories.
      PubDate: Mon, 19 Jun 2017 00:00:00 +000
       
  • Random Forest-Based Approach for Maximum Power Point Tracking of
           Photovoltaic Systems Operating under Actual Environmental Conditions

    • Abstract: Many maximum power point tracking (MPPT) algorithms have been developed in recent years to maximize the produced PV energy. These algorithms are not sufficiently robust because of fast-changing environmental conditions, efficiency, accuracy at steady-state value, and dynamics of the tracking algorithm. Thus, this paper proposes a new random forest (RF) model to improve MPPT performance. The RF model has the ability to capture the nonlinear association of patterns between predictors, such as irradiance and temperature, to determine accurate maximum power point. A RF-based tracker is designed for 25 SolarTIFSTF-120P6 PV modules, with the capacity of 3 kW peak using two high-speed sensors. For this purpose, a complete PV system is modeled using 300,000 data samples and simulated using the MATLAB/SIMULINK package. The proposed RF-based MPPT is then tested under actual environmental conditions for 24 days to validate the accuracy and dynamic response. The response of the RF-based MPPT model is also compared with that of the artificial neural network and adaptive neurofuzzy inference system algorithms for further validation. The results show that the proposed MPPT technique gives significant improvement compared with that of other techniques. In addition, the RF model passes the Bland–Altman test, with more than 95 percent acceptability.
      PubDate: Thu, 15 Jun 2017 09:28:03 +000
       
  • Forest Pruning Based on Branch Importance

    • Abstract: A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.
      PubDate: Thu, 01 Jun 2017 00:00:00 +000
       
  • A Combined One-Class SVM and Template-Matching Approach for User-Aided
           Human Fall Detection by Means of Floor Acoustic Features

    • Abstract: The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds. Compared to the OCSVM only approach, the proposed algorithm improves the performance by 10.14% in clean conditions and 4.84% in noisy conditions. Compared to Popescu and Mahnot (2009) the performance improvement is 19.96% in clean conditions and 8.08% in noisy conditions.
      PubDate: Tue, 30 May 2017 00:00:00 +000
       
  • Dynamic Inertia Weight Binary Bat Algorithm with Neighborhood Search

    • Abstract: Binary bat algorithm (BBA) is a binary version of the bat algorithm (BA). It has been proven that BBA is competitive compared to other binary heuristic algorithms. Since the update processes of velocity in the algorithm are consistent with BA, in some cases, this algorithm also faces the premature convergence problem. This paper proposes an improved binary bat algorithm (IBBA) to solve this problem. To evaluate the performance of IBBA, standard benchmark functions and zero-one knapsack problems have been employed. The numeric results obtained by benchmark functions experiment prove that the proposed approach greatly outperforms the original BBA and binary particle swarm optimization (BPSO). Compared with several other heuristic algorithms on zero-one knapsack problems, it also verifies that the proposed algorithm is more able to avoid local minima.
      PubDate: Sun, 28 May 2017 00:00:00 +000
       
  • Antiherding in Financial Decision Increases Valuation of Return on
           Investment: An Event-Related Potential Study

    • Abstract: Using event-related potentials, this study investigated how financial herding or antiherding affected the valuation of subsequent outcomes. For each trial, subjects decided whether to buy the stock according to its net money flow information which could be used to reflect the strength of buying power or selling power of the stock. The return on investment (ROI) as feedback included the increase or decrease percentage after subjects’ responses. Results showed that, compared with herding, antiherding induced larger discrepancies of FRN and P300 amplitude between positive ROI and negative ROI, indicating that individuals under antiherding condition had stronger motivation and paid more attention in the evaluation process of ROI. Moreover, only for positive ROI, the amplitudes of FRN and P300 were modulated by two kinds of behaviors. We suggested that individuals making antiherd decisions were more confident with their own ability and choices, which reduced the positive outcome prediction error and gave more mental resources to evaluate positive outcome. However, negative outcomes evoked no different motivational meaning and negative emotion for individuals between herding and antiherding. The study may provide new insights into neurocognitive processes of herding and antiherding in financial market.
      PubDate: Sun, 28 May 2017 00:00:00 +000
       
  • A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle
           Swarm Optimization

    • Abstract: Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
      PubDate: Thu, 25 May 2017 00:00:00 +000
       
  • Cloud Model-Based Artificial Immune Network for Complex Optimization
           Problem

    • Abstract: This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators—cloning, mutation, and suppression—are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications—finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning—are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.
      PubDate: Wed, 24 May 2017 10:05:58 +000
       
  • Deep Learning for Plant Identification in Natural Environment

    • Abstract: Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.
      PubDate: Mon, 22 May 2017 00:00:00 +000
       
  • Applications of Computational Intelligence in Time Series

    • PubDate: Mon, 22 May 2017 00:00:00 +000
       
  • Robust Grape Detector Based on SVMs and HOG Features

    • Abstract: Detection of grapes in real-life images is a serious task solved by researchers dealing with precision viticulture. In the case of white wine varieties, grape detectors based on SVMs classifiers, in combination with a HOG descriptor, have proven to be very efficient. Simplified versions of the detectors seem to be the best solution for practical applications. They offer the best known performance versus time-complexity ratio. As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image preprocessing level, is ideal means for further improvement of performance of the detectors. In order to enhance the ratio, we explored relevance of the conversion in a context of a detector potential sensitivity to a rotation of berries. For this purpose, we proposed a modification of the conversion, and we designed an appropriate method for a tuning of such modified detectors. To evaluate the effect of the new parameter space on their performance, we developed a specialized visualization method. In order to provide accurate results, we formed new datasets for both tuning and evaluation of the detectors. Our effort resulted in a robust grape detector which is less sensitive to image distortion.
      PubDate: Thu, 18 May 2017 00:00:00 +000
       
  • Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

    • Abstract: Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.
      PubDate: Sun, 14 May 2017 07:07:37 +000
       
  • Development of a Novel Motor Imagery Control Technique and Application in
           a Gaming Environment

    • Abstract: We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player’s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player’s event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players’ scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.
      PubDate: Tue, 09 May 2017 09:41:44 +000
       
  • Improving Classification Performance through an Advanced Ensemble Based
           Heterogeneous Extreme Learning Machines

    • Abstract: Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.
      PubDate: Thu, 04 May 2017 00:00:00 +000
       
  • Comparison of Brain Activation during Motor Imagery and Motor Movement
           Using fNIRS

    • Abstract: Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them.
      PubDate: Thu, 04 May 2017 00:00:00 +000
       
  • Fast Recall for Complex-Valued Hopfield Neural Networks with Projection
           Rules

    • Abstract: Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima. In the present work, we propose a new recall algorithm that eliminates the local minima. We show that our proposed recall algorithm not only accelerated the recall but also improved the noise tolerance through computer simulations.
      PubDate: Wed, 03 May 2017 00:00:00 +000
       
  • A Decision-Based Modified Total Variation Diffusion Method for Impulse
           Noise Removal

    • Abstract: Impulsive noise removal usually employs median filtering, switching median filtering, the total variation method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.
      PubDate: Thu, 27 Apr 2017 00:00:00 +000
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.198.147.221
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016