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Publisher: Hindawi   (Total: 339 journals)

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

Showing 1 - 200 of 339 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 7, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 36, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 53)
Advances in Agriculture     Open Access   (Followers: 9)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Astronomy     Open Access   (Followers: 39, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 17, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 9)
Advances in Chemistry     Open Access   (Followers: 23)
Advances in Civil Engineering     Open Access   (Followers: 43, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Condensed Matter Physics     Open Access   (Followers: 10, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 3, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 31)
Advances in Electronics     Open Access   (Followers: 73)
Advances in Emergency Medicine     Open Access   (Followers: 12)
Advances in Endocrinology     Open Access   (Followers: 5)
Advances in Environmental Chemistry     Open Access   (Followers: 7)
Advances in Epidemiology     Open Access   (Followers: 8)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.161, CiteScore: 1)
Advances in Geology     Open Access   (Followers: 19)
Advances in Geriatrics     Open Access   (Followers: 5)
Advances in Hematology     Open Access   (Followers: 11, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 2)
Advances in High Energy Physics     Open Access   (Followers: 19, SJR: 0.866, CiteScore: 2)
Advances in Human-Computer Interaction     Open Access   (Followers: 20, SJR: 0.186, CiteScore: 1)
Advances in Materials Science and Engineering     Open Access   (Followers: 30, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 4, SJR: 0.218, CiteScore: 1)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 21, SJR: 0.48, CiteScore: 1)
Advances in Multimedia     Open Access   (Followers: 2, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 6)
Advances in Numerical Analysis     Open Access   (Followers: 5)
Advances in Nursing     Open Access   (Followers: 30)
Advances in Operations Research     Open Access   (Followers: 12, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 5)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 8, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological Sciences     Open Access   (Followers: 8, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 10, SJR: 0.179, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 32, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 24)
Advances in Regenerative Medicine     Open Access   (Followers: 3)
Advances in Software Engineering     Open Access   (Followers: 10)
Advances in Statistics     Open Access   (Followers: 4)
Advances in Toxicology     Open Access   (Followers: 2)
Advances in Tribology     Open Access   (Followers: 13, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 9, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 7, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 3, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 2, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 2)
Anemia     Open Access   (Followers: 5, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 14, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 17, SJR: 0.451, CiteScore: 1)
Applied Bionics and Biomechanics     Open Access   (Followers: 8, SJR: 0.288, CiteScore: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 13)
Archaea     Open Access   (Followers: 3, SJR: 0.852, CiteScore: 2)
Arthritis     Open Access   (Followers: 5, SJR: 0.454, CiteScore: 1)
Autism Research and Treatment     Open Access   (Followers: 26)
Autoimmune Diseases     Open Access   (Followers: 4, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 9, SJR: 0.786, CiteScore: 2)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 0.437, CiteScore: 2)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 11, SJR: 0.419, CiteScore: 2)
BioMed Research Intl.     Open Access   (Followers: 4, SJR: 0.935, CiteScore: 3)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2, SJR: 0.531, CiteScore: 1)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 4, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 5, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 1, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 8, SJR: 1.237, CiteScore: 4)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 4, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 9)
Case Reports in Dentistry     Open Access   (Followers: 5, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 14)
Case Reports in Endocrinology     Open Access   (Followers: 1, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 2)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 4)
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: 2)
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, SJR: 0.204, CiteScore: 1)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 5)
Case Reports in Otolaryngology     Open Access   (Followers: 6)
Case Reports in Pathology     Open Access   (Followers: 5)
Case Reports in Pediatrics     Open Access   (Followers: 7)
Case Reports in Psychiatry     Open Access   (Followers: 13)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 9)
Case Reports in Rheumatology     Open Access   (Followers: 6)
Case Reports in Surgery     Open Access   (Followers: 11)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 9)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 6)
Child Development Research     Open Access   (Followers: 17, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Cholesterol     Open Access   (Followers: 1, SJR: 0.424, CiteScore: 1)
Chromatography Research Intl.     Open Access   (Followers: 6)
Complexity     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Intelligence and Neuroscience     Open Access   (Followers: 12, SJR: 0.326, CiteScore: 1)
Contrast Media & Molecular Imaging     Open Access   (Followers: 3, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 11, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 14, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 3, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 5, SJR: 0.279, CiteScore: 1)
Disease Markers     Open Access   (Followers: 1, SJR: 0.9, CiteScore: 2)
Economics Research Intl.     Open Access   (Followers: 1)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 9, SJR: 0.298, CiteScore: 1)
Enzyme Research     Open Access   (Followers: 4, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 20, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 2, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 4, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
HPB Surgery     Open Access   (Followers: 6, SJR: 0.824, CiteScore: 2)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 1.27, CiteScore: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.627, CiteScore: 2)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 74, SJR: 0.232, CiteScore: 1)
Intl. J. of Agronomy     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 11, SJR: 0.787, CiteScore: 3)
Intl. J. of Analysis     Open Access  
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 4, SJR: 0.511, CiteScore: 2)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.501, CiteScore: 2)
Intl. J. of Breast Cancer     Open Access   (Followers: 13, SJR: 1.025, CiteScore: 2)
Intl. J. of Cell Biology     Open Access   (Followers: 3, SJR: 1.887, CiteScore: 4)
Intl. J. of Chemical Engineering     Open Access   (Followers: 8, SJR: 0.327, CiteScore: 1)
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: 10, SJR: 0.287, CiteScore: 2)
Intl. J. of Corrosion     Open Access   (Followers: 10, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 6, SJR: 0.649, CiteScore: 2)
Intl. J. of Differential Equations     Open Access   (Followers: 7, SJR: 0.191, CiteScore: 0)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.296, CiteScore: 2)
Intl. J. of Electrochemistry     Open Access   (Followers: 8)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 5)
Intl. J. of Food Science     Open Access   (Followers: 4, SJR: 0.44, CiteScore: 2)
Intl. J. of Forestry Research     Open Access   (Followers: 3, SJR: 0.373, CiteScore: 1)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.868, CiteScore: 3)
Intl. J. of Geophysics     Open Access   (Followers: 4, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 4, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 6, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 3)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.177, CiteScore: 0)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6, SJR: 0.31, CiteScore: 1)
Intl. J. of Metals     Open Access   (Followers: 4)
Intl. J. of Microbiology     Open Access   (Followers: 4, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 3, SJR: 0.136, CiteScore: 1)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.267, CiteScore: 2)
Intl. J. of Nephrology     Open Access   (Followers: 1, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 7)
Intl. J. of Optics     Open Access   (Followers: 7, SJR: 0.231, CiteScore: 1)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
Intl. J. of Peptides     Open Access   (Followers: 4, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 2, SJR: 0.341, CiteScore: 1)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 0.583, CiteScore: 1)
Intl. J. of Polymer Science     Open Access   (Followers: 24, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 3)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 15)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 4)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 0.645, CiteScore: 2)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.193, CiteScore: 1)
Intl. J. of Spectroscopy     Open Access   (Followers: 7)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, SJR: 0.279, CiteScore: 1)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.573, CiteScore: 2)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 5, SJR: 0.403, CiteScore: 2)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.782, CiteScore: 2)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Intl. Scholarly Research Notices     Open Access   (Followers: 192)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 7)
J. of Addiction     Open Access   (Followers: 14)
J. of Advanced Transportation     Hybrid Journal   (Followers: 13, SJR: 0.581, CiteScore: 1)
J. of Aerodynamics     Open Access   (Followers: 12)

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Journal Cover
Applied Computational Intelligence and Soft Computing
Number of Followers: 13  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-9724 - ISSN (Online) 1687-9732
Published by Hindawi Homepage  [339 journals]
  • Enhancement of Performance for Steam Turbine in Thermal Power Plants Using
           Artificial Neural Network and Electric Circuit Design

    • Abstract: Design and implantation of electric circuit for enhanced performance of steam power plant and artificial neural networks technique are used to control turbine. Artificial neural networks technique is used to control a lot of industrial models practically. Artificial neural network has been applied to control the important variables of turbine in AL–Dura power plant in Baghdad such as pressure, temperature, speed, and humidity. In this study Simulink model was applied in MATLAB program (v 2014 a) by using artificial neural network (ANN). The method of controlling model is by using NARMA to generate data and train network. ANN is offline. ANN requires data to obtain results and for comparison with actual power plant. The values of the input variables have a large effect on the number of nodes and epochs and in hidden layer of the artificial neural network they also affect performance of ANN. The electric circuit of sensors consists of transformer, DC bridge, and voltage regulator. Comparing the results from modeling by ANN and electric circuit with experimental data reveals a good agreement and the maximum deviation between the experimental data and predicted results from ANN and circuit design is less than 1%. The novelty in this paper is applying NARMA controller for the purpose of enhancement of turbine performance.
      PubDate: Sun, 02 Dec 2018 00:00:00 +000
       
  • Simulink-Based Analysis for Coupled Metabolic Systems

    • Abstract: Stability analysis and dynamic simulation are important for researchers to capture the performance and the properties of underling systems. S-systems have good potential for characterizing dynamic interactive behaviour of large scale metabolic and genetic systems. It is important to develop a platform to achieve timely dynamic behaviour of S-systems to various situations. In this study, we first set up the respective block diagrams of S-systems for module-based simulation. We then derive reasonable theorems to examine the stability of S-systems and find out what kinds of environmental situations will make systems stable. Three canonical systems are used to examine the results which are carried out in the Matlab/Simulink environments.
      PubDate: Sun, 02 Dec 2018 00:00:00 +000
       
  • Power Supply Management for an Electric Vehicle Using Fuzzy Logic

    • Abstract: The technology of power electronic systems has diversified into industrial, commercial, and residential areas. Developing a strategy to improve the performance of the electrical energy of an electric vehicle (EV) requires an analysis of the model that describes it. EVs are complex mechatronic systems described by nonlinear models and, therefore, its study is not an easy task. It can improve the performance of a battery bank by creating new batteries that allow for greater storage or by developing a management energy system. This article shows the development of a power supply management system based on fuzzy logic for an electric vehicle, in order to minimize the total energy consumption and optimize the battery bank. The experimental result is shown using the fuzzy controller under standard operating conditions. An increase in battery performance and overall performance of energy consumption is shown. Speed signals acquired show improvements in some dynamic, such as overshoot, settling time, and steady-state error parameters. It is shown that this fuzzy controller increases the overall energy efficiency of the vehicle.
      PubDate: Mon, 05 Nov 2018 09:57:42 +000
       
  • Adjusting Neural Network to a Particular Problem: Neural Network-Based
           Empirical Biological Model for Chlorophyll Concentration in the Upper
           Ocean

    • Abstract: The versatility of the neural network (NN) technique allows it to be successfully applied in many fields of science and to a great variety of problems. For each problem or class of problems, a generic NN technique (e.g., multilayer perceptron (MLP)) usually requires some adjustments, which often are crucial for the development of a successful application. In this paper, we introduce a NN application that demonstrates the importance of such adjustments; moreover, in this case, the adjustments applied to a generic NN technique may be successfully used in many other NN applications. We introduce a NN technique, linking chlorophyll “a” (chl-a) variability—primarily driven by biological processes—with the physical processes of the upper ocean using a NN-based empirical biological model for chl-a. In this study, satellite-derived surface parameter fields, sea-surface temperature (SST) and sea-surface height (SSH), as well as gridded salinity and temperature profiles from 0 to 75m depth are employed as signatures of upper-ocean dynamics. Chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a concentrations. Different methods of optimizing the NN technique are investigated. Results are assessed using the root-mean-square error (RMSE) metric and cross-correlations between observed ocean color (OC) fields and NN output. To reduce the impact of noise in the data and to obtain a stable computation of the NN Jacobian, an ensemble of NN with different weights is constructed. This study demonstrates that the NN technique provides an accurate, computationally cheap method to generate long (up to 10 years) time series of consistent chl-a concentration that are in good agreement with chl-a data observed by different satellite sensors during the relevant period. The presented NN demonstrates a very good ability to generalize in terms of both space and time. Consequently, the NN-based empirical biological model for chl-a can be used in oceanic models, coupled climate prediction systems, and data assimilation systems to dynamically consider biological processes in the upper ocean.
      PubDate: Thu, 01 Nov 2018 00:00:00 +000
       
  • Development of Decision Support Model for Selecting a Maintenance Plan
           Using a Fuzzy MCDM Approach: A Theoretical Framework

    • Abstract: In complex decision making, using multicriteria decision-making (MCDM) methodologies is the most scientific way to ensure an informed and justified decision between several alternatives. MCDMs have been used in different ways and with several applications that proved their efficiency in achieving this goal. In this research, the advantages and disadvantages of the different MCDM methodologies are studied, along with the different techniques implemented to increase their accuracy and precision. The main aim of the study is to develop a hybrid MCDM process that combines the strengths of several MCDM methods and apply it to choose the best fit maintenance policy/strategy for industrial application. Moreover, fuzzy linguistic terms are utilized in all of the used MCDM techniques in order to eliminate the uncertainty and ambiguity of the results. Through an extensive literature review performed on studies that have used MCDM methods in a hybrid context and using fuzzy linguistic terms, a model is developed to use fuzzy DEMATEL-AHP-TOPSIS hybrid technique. The model with its application is the first of its kind, which combines the strengths of fuzzy DEMATEL in establishing interrelationships between several criteria, as well as performing a pairwise comparison between the criteria for prioritization using the fuzzy AHP method. Thereafter, the alternatives are compared using fuzzy TOPSIS method by establishing negative and positive solutions and calculating the relative closeness for each of the alternatives. Furthermore, six main criteria, twenty criteria, and five alternatives are selected from the literature for the model application.
      PubDate: Thu, 01 Nov 2018 00:00:00 +000
       
  • Two Iterative Methods for Solving Linear Interval Systems

    • Abstract: Conjugate gradient is an iterative method that solves a linear system , where is a positive definite matrix. We present this new iterative method for solving linear interval systems , where is a diagonally dominant interval matrix, as defined in this paper. Our method is based on conjugate gradient algorithm in the context view of interval numbers. Numerical experiments show that the new interval modified conjugate gradient method minimizes the norm of the difference of and at every step while the norm is sufficiently small. In addition, we present another iterative method that solves , where is a diagonally dominant interval matrix. This method, using the idea of steepest descent, finds exact solution for linear interval systems, where ; we present a proof that indicates that this iterative method is convergent. Also, our numerical experiments illustrate the efficiency of the proposed methods.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Performance Assessment of Multiple Classifiers Based on Ensemble Feature
           Selection Scheme for Sentiment Analysis

    • Abstract: Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying various numbers of methodologies. Feature generation and selection are consequent for text mining as the high-dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability or incompetency of the widely used feature selection methods (IG, Chi-square, and Gini Index) with unigram and bigram feature set on four machine learning classification algorithms (MNB, SVM, KNN, and ME). The proposed methods are evaluated on the basis of three standard datasets, namely, IMDb movie review and electronics and kitchen product review dataset. Initially, unigram and bigram features are extracted by applying n-gram method. In addition, we generate a composite features vector CompUniBi (unigram + bigram), which is sent to the feature selection methods Information Gain (IG), Gini Index (GI), and Chi-square (CHI) to get an optimal feature subset by assigning a score to each of the features. These methods offer a ranking to the features depending on their score; thus a prominent feature vector (CompIG, CompGI, and CompCHI) can be generated easily for classification. Finally, the machine learning classifiers SVM, MNB, KNN, and ME used prominent feature vector for classifying the review document into either positive or negative. The performance of the algorithm is measured by evaluation methods such as precision, recall, and F-measure. Experimental results show that the composite feature vector achieved a better performance than unigram feature, which is encouraging as well as comparable to the related research. The best results were obtained from the combination of Information Gain with SVM in terms of highest accuracy.
      PubDate: Mon, 01 Oct 2018 00:00:00 +000
       
  • Concepts, Methods, and Performances of Particle Swarm Optimization,
           Backpropagation, and Neural Networks

    • Abstract: With the advancement of Machine Learning, since its beginning and over the last years, a special attention has been given to the Artificial Neural Network. As an inspiration from natural selection of animal groups and human’s neural system, the Artificial Neural Network also known as Neural Networks has become the new computational power which is used for solving real world problems. Neural Networks alone as a concept involve various methods for achieving their success; thus, this review paper describes an overview of such methods called Particle Swarm Optimization, Backpropagation, and Neural Network itself, respectively. A brief explanation of the concepts, history, performances, advantages, and disadvantages is given, followed by the latest researches done on these methods. A description of solutions and applications on various industrial sectors such as Medicine or Information Technology has been provided. The last part briefly discusses the directions, current, and future challenges of Neural Networks towards achieving the highest success rate in solving real world problems.
      PubDate: Mon, 03 Sep 2018 06:46:51 +000
       
  • A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation
           for Uncorrelated Multivariate Time Series

    • Abstract: The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windows and . We then find the most similar subsequence () to the subsequence before this gap and the most similar one () to the subsequence after the gap . To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window following and the one preceding . The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.
      PubDate: Thu, 09 Aug 2018 06:52:33 +000
       
  • Explicit Content Detection System: An Approach towards a Safe and Ethical
           Environment

    • Abstract: An explicit content detection (ECD) system to detect Not Suitable For Work (NSFW) media (i.e., image/ video) content is proposed. The proposed ECD system is based on residual network (i.e., deep learning model) which returns a probability to indicate the explicitness in media content. The value is further compared with a defined threshold to decide whether the content is explicit or nonexplicit. The proposed system not only differentiates between explicit/nonexplicit contents but also indicates the degree of explicitness in any media content, i.e., high, medium, or low. In addition, the system also identifies the media files with tampered extension and label them as suspicious. The experimental result shows that the proposed model provides an accuracy of ~ 95% when tested on our image and video datasets.
      PubDate: Wed, 04 Jul 2018 06:30:59 +000
       
  • Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect
           Sensor

    • Abstract: This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond.
      PubDate: Sun, 03 Jun 2018 00:00:00 +000
       
  • Imaging, Vision, and Pattern Recognition

    • PubDate: Thu, 03 May 2018 08:11:42 +000
       
  • Real Time Eye Detector with Cascaded Convolutional Neural Networks

    • Abstract: An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.
      PubDate: Sun, 22 Apr 2018 00:00:00 +000
       
  • On the Feature Selection and Classification Based on Information Gain for
           Document Sentiment Analysis

    • Abstract: Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.
      PubDate: Mon, 19 Feb 2018 00:00:00 +000
       
  • A Comparison Study on Rule Extraction from Neural Network Ensembles,
           Boosted Shallow Trees, and SVMs

    • Abstract: One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experiments were performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP architecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs), and Support Vector Machines (SVM). The complexity of rulesets was measured with the average number of generated rules and average number of antecedents per rule. From the 25 used classification problems, the most complex rulesets were generated from BSTs trained by “gentle boosting” and “real boosting.” Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules generated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest fidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to those reported in the literature proved to be competitive.
      PubDate: Tue, 09 Jan 2018 07:22:24 +000
       
  • Saliency Aggregation: Multifeature and Neighbor Based Salient Region
           Detection for Social Images

    • Abstract: The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detection of social images using both image appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance features and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency model to enhance salient regions. The aggregation method is dependent on individual images and considers the performance gaps appropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations to promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method performs well on not only the new dataset but also several state-of-the-art saliency datasets.
      PubDate: Mon, 01 Jan 2018 09:50:58 +000
       
 
 
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