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  Subjects -> MATHEMATICS (Total: 1040 journals)
    - APPLIED MATHEMATICS (83 journals)
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    - MATHEMATICS (770 journals)
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    - PROBABILITIES AND MATH STATISTICS (98 journals)

MATHEMATICS (770 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 5)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 7)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 38)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 12)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 9)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 21)
Advances in Decision Sciences     Open Access   (Followers: 3)
Advances in Difference Equations     Open Access   (Followers: 3)
Advances in Fixed Point Theory     Open Access   (Followers: 8)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 17)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 10)
Advances in Materials Science     Open Access   (Followers: 17)
Advances in Mathematical Physics     Open Access   (Followers: 7)
Advances in Mathematics     Full-text available via subscription   (Followers: 15)
Advances in Nonlinear Analysis     Open Access  
Advances in Numerical Analysis     Open Access   (Followers: 7)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 8)
Advances in Pure Mathematics     Open Access   (Followers: 9)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 7)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 5)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 13)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 2)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 7)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Open Access   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 8)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 6)
American Journal of Operations Research     Open Access   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 11)
Anadol University Journal of Science and Technology B : Theoritical Sciences     Open Access  
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access  
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 6)
Analysis Mathematica     Full-text available via subscription  
Analysis. International mathematical journal of analysis and its applications     Hybrid Journal   (Followers: 3)
Annales Mathematicae Silesianae     Open Access   (Followers: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 13)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 7)
Annals of Mathematics     Full-text available via subscription   (Followers: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of PDE     Hybrid Journal  
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 1)
Annuaire du Collège de France     Open Access   (Followers: 6)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 3)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics     Open Access   (Followers: 8)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 10)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal   (Followers: 1)
Applied Mathematics and Nonlinear Sciences     Open Access  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 4)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 4)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Armenian Journal of Mathematics     Open Access   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 25)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 3)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 5)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 2)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access   (Followers: 1)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 1)
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access   (Followers: 2)
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal   (Followers: 1)
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 2)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 20)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 13)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 3)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 2)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the Iranian Mathematical Society     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Mathematics / Journal canadien de mathématiques     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 20)
Canadian Mathematical Bulletin     Hybrid Journal  
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access  
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Clean Air Journal     Full-text available via subscription   (Followers: 1)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Collectanea Mathematica     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 15)
Commentarii Mathematici Helvetici     Hybrid Journal  
Communications in Advanced Mathematical Sciences     Open Access  
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 4)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 4)
Complex Analysis and its Synergies     Open Access   (Followers: 3)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Compositio Mathematica     Full-text available via subscription  
Comptes Rendus Mathematique     Full-text available via subscription  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 10)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 11)
Concrete Operators     Open Access   (Followers: 5)
Confluentes Mathematici     Hybrid Journal  
Contributions to Game Theory and Management     Open Access  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 13)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 8)
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 15)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 31)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 4)
Differentsial'nye Uravneniya     Open Access  
Digital Experiences in Mathematics Education     Hybrid Journal  
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 2)
Diskretnaya Matematika     Full-text available via subscription  
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Akademii Nauk     Open Access  
Doklady Mathematics     Hybrid Journal  

        1 2 3 4 | Last

Similar Journals
Journal Cover
Cognitive Computation
Journal Prestige (SJR): 0.908
Citation Impact (citeScore): 4
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1866-9964 - ISSN (Online) 1866-9956
Published by Springer-Verlag Homepage  [2574 journals]
  • Ensemble p -Laplacian Regularization for Scene Image Recognition
    • Abstract: Recently, manifold regularized semi-supervised learning (MRSSL) received considerable attention, because it successfully exploits the geometry of the intrinsic data probability distribution to leverage the performance of a learning model. As a natural nonlinear generalization of graph Laplacian, p-Laplacian has been proved having the rich theoretical foundations to better preserve the local structure. However, it is difficult to determine the fitting graph p-Lapalcian, i.e., the parameter p, which is a critical factor for the performance of graph p-Laplacian. Therefore, we develop an ensemble p-Laplacian regularization (EpLapR) to fully approximate the intrinsic manifold of the data distribution. EpLapR incorporates multiple graphs into a regularization term in order to sufficiently explore the complementation of graph p-Laplacian. Specifically, we construct a fused graph by introducing an optimization approach to assign suitable weights on different p value graphs. And then, we conduct semi-supervised learning framework on the fused graph. Extensive experiments on UC-Merced dataset and Scene 15 dataset demonstrate the effectiveness and efficiency of the proposed method.
      PubDate: 2019-03-22
       
  • A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic
           Aperture Radar Image Recognition
    • Abstract: Synthetic aperture radar (SAR) automatic target recognition (ATR) technology is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural network (CNN)-based SAR ATR methods. However, the performance of CNN significantly deteriorates when the labeled samples are insufficient. To effectively utilize the unlabeled samples, we present a novel semi-supervised CNN method. In the training process of our method, the information contained in the unlabeled samples is integrated into the loss function of CNN. Specifically, we first utilize CNN to obtain the class probabilities of the unlabeled samples. Thresholding processing is performed to optimize the class probabilities so that the reliability of the unlabeled samples is improved. Afterward, the optimized class probabilities are used to calculate the scatter matrices of the linear discriminant analysis (LDA) method. Finally, the loss function of CNN is modified by the scatter matrices. We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of our method is significantly higher than other semi-supervised methods. It has been proved that our method can effectively improve the SAR ATR accuracy when labeled samples are insufficient.
      PubDate: 2019-03-19
       
  • Evolutionary Design of Linguistic Fuzzy Regression Systems with Adaptive
           Defuzzification in Big Data Environments
    • Abstract: This paper is positioned in the area of the use of cognitive computation techniques to design intelligent systems for big data scenarios, specifically the use of evolutionary algorithms to design data-driven linguistic fuzzy rule-based systems for regression and control. On the one hand, data-driven approaches have been extensively employed to create rule bases for fuzzy regression and control from examples. On the other, adaptive defuzzification is a well-known mechanism used to significantly improve the accuracy of fuzzy systems. When dealing with large-scale scenarios, the aforementioned methods must be redesigned to allow scalability. Our proposal is based on a distributed MapReduce schema, relying on two ideas: first, a simple adaptation of a classic data-driven method to quickly obtain a set of rules, and, second, a novel scalable strategy that uses evolutionary adaptive defuzzification to achieve better behavior through cooperation among rules. Some different regression problems were used to validate our methodology through an experimental study developed and included at the end of our paper. Therefore, the proposed approach allows scalability while tackling applications of linguistic fuzzy rule-based systems for regression with adaptive defuzzification in large-scale data scenarios. This paper thus examines the use of some relevant techniques for cognitive computing when working with a vast volume of examples, a common occurrence when dealing with the design of artificial intelligent systems that perform reasoning in a similar way as humans.
      PubDate: 2019-03-19
       
  • Improvements on Correlation Coefficients of Hesitant Fuzzy Sets and Their
           Applications
    • Abstract: Hesitant fuzzy set (HFS) can express the hesitancy and uncertainty according to human’s cognitions and knowledge. The decision making with HFSs can be regarded as a cognitive computation process. Decision making based on information measures is a hot topic, among which correlation coefficient is an important direction. Although many correlation coefficients of HFSs have been proposed in the previous papers, they suffer from different counter-intuitions to a certain extent. Therefore, we mainly focus on improving these counter-intuitions of the existing correlation coefficients of HFSs in this paper. We point out the counter-intuitions of the existing correlation coefficients of HFSs and analyze the reasons of them in the view of the rigorous mathematics and stochastic process rules. We improve these counter-intuitions and develop the correct versions. Moreover, we use two examples about medical diagnosis and cluster analysis to compare the improved correlation coefficients with the existing ones. The improved correlation coefficients can handle the examples well. Further, combining with the comparison analysis, the accuracy and discrimination property of the improved correlation coefficients are demonstrated in detail, which shows the advantages of them. The notion of the improved correlation coefficients can benefit other types of fuzzy sets too.
      PubDate: 2019-03-19
       
  • Multi-Granulation-Based Graphical Analytics of Three-Way Bipolar
           Neutrosophic Contexts
    • Abstract: Recently, a three-way fuzzy concept lattice and its graphical structure analytics has given a mathematical way to deal with cognitive concept learning based on its truth, false, and uncertain regions, independently. In this process, a major problem was addressed while existence of bipolar information in a three-way decision space. To address this problem, the current paper aimed at introducing bipolar neutrosophic graph representation of concept lattice and its granular-based processing for cognitive concept learning. In addition, the proposed method is illustrated with an example for better understanding. Cognitive computing provides a way to mimic with human brain and its uncertainty beyond the binary values. To characterize these types of bipolar attributes based on its acceptation, rejection, and uncertain part, the three-way bipolar neutrosophic context and its concept lattice is introduced in this paper. In addition, another method is proposed to extract some of the bipolar cognitive concepts based on user required bipolar truth, bipolar indeterminacy, and falsity membership values, independently. This paper provides a graphical structure visualization of the three-way bipolar information at user defined granules. It is also shown that the extracted information from both of the proposed methods are concordant with each other. It is also shown that, the proposed method provides an adequate way to model the three-way bipolar cognitive concepts when compared to other available approaches. This paper introduces a method to model the three-way bipolar cognitive context using the properties of bipolar neutrosophic graph and its lattice structure. The line diagram is drawn based on their lower neighbors within O( C n2 m3) time complexity. In addition, another method is proposed to refine the three-way bipolar neutrosophic cognitive concepts at user defined granulation within O(n6) or O(m6) time complexity with an illustrative example. However, the proposed method is unable to measure the changes in the three-way bipolar neutrosophic cognitive concepts at the given phase of time. Due to that, the author will focus on resolving this issue of bipolar neutrosophic context in near future.
      PubDate: 2019-03-18
       
  • Feature Selection and Evolutionary Rule Learning for Big Data in Smart
           Building Energy Management
    • Abstract: Since buildings are one of the largest sources of energy consumption in most cities of the world, energy management is one of the major concerns in their design. To ameliorate this problem, buildings are becoming smarter by the incorporation of intelligent supervision and control systems. Data captured by the sensors can be interpreted and processed by rule-based computation methods of biological inspiration (such as genetic fuzzy systems, GFS) for predicting the future behavior of the building in a knowledge-based interpretable human-like manner. GFS are computational models inspired in human cognition which use evolutionary computation (inspired in the natural evolution) to automatically learn fuzzy rules which contain explicit imprecise knowledge about a system or process. This knowledge, represented using fuzzy rules that involve fuzzy linguistic variables and values, is used to perform approximate reasoning on the input values for obtaining inferred values for the output variables. In energy management of buildings, these rules allow a smart control of the system actuators to reduce the building average energy consumption. However, the large amount of data produced on a per second basis complicates the generation of accurate and interpretable models by means of traditional methods. In this paper, we present an evolutionary computation-based approach, namely a genetic fuzzy system, to build scalable and interpretable knowledge bases for predicting energy consumption in smart buildings. For accomplishing this task, we propose a cognitive computation system for multi-step prediction based on S-FRULER, a state-of-the-art scalable distributed GFS, coupled with a feature subset selection method to automatically select the most relevant features for different time steps. S-FRULER is able to learn a fuzzy rule-based system made up of Takagi-Sugeno-Kang (TSK) rules that are able to predict the output values using both linguistic imprecise knowledge (represented by fuzzy sets) and fuzzy inference. Experiments with real data on two different problems related with the energy management revealed an average improvement of 6% on accuracy with respect to S-FRULER without feature selection, and with knowledge bases with a lower number of variables.
      PubDate: 2019-03-09
       
  • Determination of Temporal Stock Investment Styles via Biclustering Trading
           Patterns
    • Abstract: Due to the effects of many deterministic and stochastic factors, it has always been a challenging goal to gain good profits from the stock market. Many methods based on different theories have been proposed in the past decades. However, there has been little research about determining the temporal investment style (i.e., short term, middle term, or long term) for the stock. In this paper, we propose a method to find suitable stock investment styles in terms of investment time. Firstly, biclustering is applied to a matrix that is composed of technical indicators of each trading day to discover trading patterns (regarded as trading rules). Subsequently a k-nearest neighbor (KNN) algorithm is employed to transform the trading rules to the trading actions (i.e., the buy, sell, or no-action signals). Finally, a min-max and quantization strategy is designed for determination of the temporal investment style of the stock. The proposed method was tested on 30 stocks from US bear, bull, and flat markets. The experimental results validate its usefulness.
      PubDate: 2019-03-08
       
  • Discriminant Zero-Shot Learning with Center Loss
    • Abstract: Current work on zero-shot learning (ZSL) generally does not focus on the discriminative ability of the models, which is important for differentiating between classes since our brain focuses on the discriminating part of the object to classify it. For generalized ZSL (GZSL), the fact that the outputs of the model are not comparable leads to a degraded performance. We propose a new ZSL method with a center loss to make the instances from the same class more compact by extracting their discriminative parts. Further, we introduce a varying learning rate to accelerate the model selection process. We also demonstrate how to boost the performance of GZSL by rectifying the outputs of the model to make the outputs be comparable. Experimental results on four benchmarks, including SUN, CUB, AWA2, and aPY, demonstrate the superiority of the proposed method, therein achieving state-of-the-art performance.
      PubDate: 2019-03-07
       
  • Multi-Scale Mahalanobis Kernel-Based Support Vector Machine for
           Classification of High-Resolution Remote Sensing Images
    • Abstract: Support vector machine (SVM) is a powerful cognitive and learning algorithm in the domain of pattern recognition and image classification. However, the generalization ability of SVM is limited when processing classification of high-resolution remote sensing images. One chief reason for this is that the Euclidean distance-based distance matrix in traditional SVM treats different samples equally and overlooks the global distribution of samples. To construct a more effective SVM-based classification method, this paper proposes a multi-scale Mahalanobis kernel-based SVM classifier. In this new method, we first introduce a Mahalanobis distance kernel to improve the global cognitive learning ability of SVM. Then, the Mahalanobis distance kernel is embedded to the multi-scale kernel learning (MSKL) to construct a novel multi-scale Mahalanobis kernel, in which the parameters are optimized by a bio-inspired algorithm, named differential evolution. Finally, the new method is extended to the classification of high-resolution remote sensing images based on the spatial-spectral features. The comparison experiments of five public UCI datasets and two high-resolution remote sensing images verify that the Mahalanobis distance-based method can obtain more accurate classification results than that of the Euclidean distance-based method. In addition, the proposed method produced the best classification results in all the experiments. The global cognitive learning ability of Mahalanobis distance-based method is stronger than that of the Euclidean distance-based method. In addition, this study indicates that the optimized MSKL are potential for the interpretation and understanding of complicated high-resolution remote sensing scene.
      PubDate: 2019-03-02
       
  • Cognitively Inspired Feature Extraction and Speech Recognition for
           Automated Hearing Loss Testing
    • Abstract: Hearing loss, a partial or total inability to hear, is one of the most commonly reported disabilities. A hearing test can be carried out by an audiologist to assess a patient’s auditory system. However, the procedure requires an appointment, which can result in delays and practitioner fees. In addition, there are often challenges associated with the unavailability of equipment and qualified practitioners, particularly in remote areas. This paper presents a novel idea that automatically identifies any hearing impairment based on a cognitively inspired feature extraction and speech recognition approach. The proposed system uses an adaptive filter bank with weighted Mel-frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated hearing test method to achieve comparable results to the clinical ground truth, established by the expert audiologist’s tests. The overall absolute error of the proposed model when compared with the expert audiologist test is less than 4.9 dB and 4.4 dB for the pure tone and speech audiometry tests, respectively. The overall accuracy achieved is 96.67% with a hidden Markov model (HMM). The proposed method potentially offers a second opinion to audiologists, and serves as a cost-effective pre-screening test to predict hearing loss at an early stage. In future work, authors intend to explore the application of advanced deep learning and optimization approaches to further enhance the performance of the automated testing prototype considering imperfect datasets with real-world background noise.
      PubDate: 2019-02-13
       
  • ReUS : a Real-time Unsupervised System For Monitoring Opinion Streams
    • Abstract: An actual challenge within the sentiment analysis research area is the extraction of polarity values associated with specific aspects (or opinion targets) contained in user-generated content. This task, called aspect-based sentiment analysis, brings new challenges like the disambiguation of words’ role within a text and the inference of correct polarity values based on the domain in which a text occurs. The former requires strategies able to understand how each word is used in a specific context in order to annotate it as aspect or not. The latter need to be addressed with unsupervised solutions in order to make a system efficient for real-time tasks and at the same time flexible in order to adopt it in any domain without requiring the training of sentiment models. Finally, the deployment of such a system into real-world scenarios needs the development of usable solutions for accessing and analyzing data. This paper presents the ReUS platform: a system integrating an unsupervised approach, based on open information extraction strategies, for performing real-time aspect-based sentiment analysis together with facilities supporting decision-makers in the analysis and visualization of collected data. The ReUS platform has been validated from a quantitative and qualitative perspectives. First, the aspect extraction and polarity inference capabilities have been evaluated on three datasets used in likewise editions of SemEval. Second, a user group has been invited to judge the usability of the platform. The developed platform demonstrated to be suitable for being used into real-world scenarios requiring (i) the capability of processing real-time opinion-based documents streams and (ii) the availability of usable facilities for analyzing and visualizing collected data. Examples of possible analysis and visualizations include the presentation of lists ranking aspects by the importance of their polarity values computed within the whole data repository. This kind of analysis enables, for instance, the discovery of product issues.
      PubDate: 2019-02-07
       
  • A Line Feature Extraction Method for Finger-Knuckle-Print Verification
    • Abstract: Due to its mobility and reliability, the outer finger-knuckle-print (FKP) possesses several advantages over other biometric traits of the hand. However, most existing state-of-the-art methods utilize either local features alone or together with global features for FKP verification. These methods often demand high computational cost despite their high verification accuracy. In this paper, we propose a novel and fast matrix projection method for extracting line features from the finger-knuckle-print for person verification. Essentially, both the horizontal and the vertical knuckle lines are extracted by projecting the knuckle print image onto a shift-and-difference matrix. Such a matrix enables directional image shifting and subtraction within a single matrix multiplication. The resultant difference image then goes through a sigmoidal activation for contrast enhancement. Subsequently, the Fourier spectrum of the contrast enhanced image is adopted as the holistic features of the given finger-knuckle-print image. The entire process of extracting the proposed features is expressed in an analytic form to facilitate a fast vectorized implementation. For cognition performance enhancement, the two directional line features are subsequently fused at the score level by minimizing the error counts of the extreme learning machine kernel. Extensive experiments are performed to compare the proposed method with competing methods using three public finger-knuckle-print databases. Our experimental results show encouraging performance in terms of verification accuracy and computational efficiency.
      PubDate: 2019-02-01
       
  • Back to the Roots: Multi- X Evolutionary Computation
    • Abstract: Over the years, evolutionary computation has come to be recognized as one of the leading algorithmic paradigms in the arena of global black box optimization. The distinguishing facets of evolutionary methods, inspired by Darwin’s foundational principles of natural selection, stem mainly from their population-based search strategy—which gives rise to the phenomenon of implicit parallelism. Precisely, even as an evolutionary algorithm manipulates a population of a few candidate solutions (or: individuals), it is able to simultaneously sample, evaluate, and process a vast number of regions of the search space. This behavior is in effect analogous to our inherent cognitive ability of processing diverse information streams (such as sight and sound) with apparent simultaneity in different regions of our brain. For this reason, evolutionary algorithms have emerged as the method of choice for those search and optimization problems where a collection of multiple target solutions (that may be scattered throughout the search space) are to be found in a single run. With the above in mind, in this paper we return to the roots of evolutionary computation, with the aim of shedding light on a variety of problem settings that are uniquely suited for exploiting the implicit parallelism of evolutionary algorithms. Our discussions cover established concepts of multi-objective and multi-modal optimization, as well as new (schema) theories pertaining to emerging problem formulations that entail multiple searches to be carried out at once. We capture associated research activities under the umbrella term of multi-X evolutionary computation, where X, as of now, represents the following list: {“objective,” “modal,” “task,” “level,” “hard,” “disciplinary,” “form”}. With this, we hope that the present position paper will serve as a catalyst for effecting further research efforts into such areas of optimization problem-solving that are well-aligned with the fundamentals of evolutionary computation; in turn prompting the steady update of the list X with new applications in the future.
      PubDate: 2019-02-01
       
  • Region-Enhanced Multi-layer Extreme Learning Machine
    • Abstract: Deep neural networks have made significant achievements in representation learning of traditionally man-made features, especially in terms of complex objects. Over the decades, this learning process has attracted thousands of researchers and has been widely used in the speech, visual, and text recognition fields. One deep network multi-layer extreme learning machine (ML-ELM) achieves a good performance in representation learning while inheriting the advantages of faster learning and the approximating capability of the extreme learning machine (ELM). However, as with most deep networks, the ML-ELM’s algorithmic performance largely depends on the probability distribution of the training data. In this paper, we propose an improved ML-ELM made via using the local significant regions at the input end to enhance the contributions of these regions according to the idea of the selective attention mechanism. To avoid involving and exploring the complex principle of the attention system and to focus on the clarification of our local regional enhancement idea, the paper only selects two typical attention regions. One is the geometric central region, which is normally the important region to attract human attention due to the focal attention mechanism. The other is the task-driven interest region, with facial recognition as an example. The comprehensive experiments are done on the three public datasets of MNIST, NORB, and ORL. The comparison experiment results demonstrate that our proposed region-enhanced ML-ELM (RE-ML-ELM) achieves performance increases in important feature learning by utilizing the apriori knowledge of attention and has a higher recognition rate than that of the normal ML-ELM and the basic ELM. Moreover, it benefits from the non-iterative parameter training method of other ELMs, and our proposed algorithm outperforms most state-of-the-art deep networks such as deep belief network(DBN), in the aspects of training efficiency. Furthermore, because of the deep structure with fewer hidden nodes at each layer, our proposed RE-ML-ELM achieves a comparable training efficiency to that of the ML-ELM but has a higher training speed with the basic ELM, which is normally the width single network that has more hidden nodes to obtain the similar recognition accuracy with the deep networks. Based on our idea of combining the apriori knowledge of the human selective attention system with the data learning, our proposed region-enhanced ML-ELM increases the image classification performance. We believe that the idea of intentionally combining psychological knowledge with the most algorithms based on data-driven learning has the potential to improve their cognitive computing ability.
      PubDate: 2019-02-01
       
  • 3D Local Spatio-temporal Ternary Patterns for Moving Object Detection in
           Complex Scenes
    • Abstract: Humans possess natural cognitive vision to perceive objects in a 3D space and are able to differentiate foreground and background moving objects using their shape, colour and texture. Moving object detection is a leading and challenging task in complex scenes which involve illumination variation, blurriness, camouflage, moving background objects, etc. Inspired by human cognitive vision, a novel descriptor named 3D local spatio-temporal ternary patterns (3D-LStTP) is proposed for moving object detection. The 3D-LStTP collects multidirectional spatio-temporal information from three consecutive frames in a video by forming a 3D grid structure. The background models are constructed by using texture and colour features. The results obtained after modelling are integrated for foreground moving object detection in complex scenes. The performance of proposed algorithm is validated by conducting five experiments on Fish4Knowledge dataset, four experiments on I2R dataset and four experiments on Change Detection dataset. Qualitative and quantitative analyses are carried out on benchmark datasets. The results after investigation prove that the proposed method outperforms the state-of-the-art techniques for moving object detection in terms of ROC, TPR, FPR, Precision and F- measure.
      PubDate: 2019-02-01
       
  • A New Decision-Making Method Based on Interval-Valued Linguistic
           Intuitionistic Fuzzy Information
    • Abstract: In real decision-making, because of the particularity of human cognition activity, it is difficult to depict the decision information by exact numbers, especially, for complex decision information, how to express and aggregate them is an important work for solving these decision-making problems. In order to express the complex fuzzy information accurately, we proposed the concept of interval-valued linguistic intuitionistic fuzzy numbers (IVLIFNs), where their membership function and non-membership function are represented by interval-valued linguistic terms, and then we developed the operational rules, score function, accuracy function, and comparison method of them. Considering that the Maclaurin symmetric mean (MSM) operator has a good characteristic in dealing with the interrelationships among multi-parameters, and it also is a generalization of arithmetic aggregation operator, Bonferroni mean (BM) operator, and geometric aggregation operator, we further proposed the interval-valued linguistic intuitionistic fuzzy MSM (IVLIFMSM) operator, the weighted interval-valued linguistic intuitionistic fuzzy MSM (WIVLIFMSM) operator, and proved some related properties of them. We gave an illustrative example to demonstrate the steps and the effectiveness of the proposed method by the comparison with existing methods. IVLIFNs can more conveniently express the complex fuzzy information in qualitative environment by considering the cognition of decision-makers, and the proposed method can consider the interrelationship among multiple input arguments, so it can make the decision-making results more reasonable. In a word, the proposed method is more scientific and flexible in solving multiple attribute decision-making (MADM) problems than some existing methods.
      PubDate: 2019-02-01
       
  • Stochastic Multiple-Attribute Decision Making Method Based on Stochastic
           Dominance and Almost Stochastic Dominance Rules with an Application to
           Online Purchase Decisions
    • Abstract: Although some stochastic multiple-attribute decision making (SMADM) methods based on the stochastic dominance (SD) rules have been proposed, there is still the limitation that the dominance relations between some pairs of alternatives cannot be identified. In this paper, almost stochastic dominance (ASD) rules are used as supplements of the SD rules to identify dominance relations between the pairs of alternatives, and a new method for SMADM based on the SD and ASD rules is proposed. In the method, a procedure for identifying the dominance relation between each pair of alternatives based on the SD and ASD rules is given. Then, according to the identified dominance relation, the priority degree that one alternative is superior to another alternative concerning each attribute is calculated. Further, according to the obtained priority degrees, an approach for ranking alternatives is proposed using the simple weighted method. Finally, the proposed method is applied to the selection of passenger car(s) based on online ratings, and a comparison between the proposed method and the existing methods based on a numerical example is given. The proposed method can obtain more precise ranking results of alternatives. ASD rules are important supplements of the SD rules for identifying dominance relations. Based on the SD and ASD rules, the proposed SMADM method is important for developing theories and methods for SMADM.
      PubDate: 2019-02-01
       
  • A Study of Arabic Social Media Users—Posting Behavior and
           Author’s Gender Prediction
    • Abstract: Social media opens up numerous possibilities to study human interaction and collective behavior in an unprecedented scale. It opened a whole new venue for research under the name “social computing”. Researchers are interested in profiling individuals (e.g., gender, age group), groups, community, and networking. We are interested in studying the collective behavior of Arabic social media users. Most studies covering Arabic social media has focused on the sentiment analysis of, say tweets. This study, however, looks into who and when users interact with the Arabic social media. Specifically, there are two objectives of this work. First, studying the demographic posting behavior of social media users from two different perspectives: gender and educational level. Second, author profiling. Identifying author’s gender of a social media post. We use Saudi Arabia, a very prolific country when it comes to social media in general, as a backdrop for this study. The results in this study are based on mining huge amount of metadata of a popular local social media forum covering the period 2011–14 inclusive. The extracted features (normalized list of k highest scoring words, and likewise for stems) from the posts were used to train classifiers to identify the author’s gender. We used two different classifiers, Support Vector Machine (SVM) with linear kernel and 1-NN (1-nearest neighbor), and experimented with different sizes for the list of features. When the number of features (size of the features vector) is small (≤ 50) both classifiers perform equally well in identifying the author’s gender, but we risk overfitting the data. The classifiers achieved their best result when using 100 features. The 1-NN classifier delivered a better performance, achieving a balanced accuracy of 93.16% vs 87.33% for the SVM in predicting the author’s gender. And for a larger set of features, SVM delivered a better performance and more stable behavior than 1-NN, but still nowhere close to its best performance. We used t test to confirm our assessment that the difference between the performance of both classifiers is statistically significant. Based on that, we recommend using 100 features, and we get our best result using 1-NN with a balanced accuracy of 93.16%.
      PubDate: 2019-02-01
       
  • Travel Time Functions Prediction for Time-Dependent Networks
    • Abstract: The studies on the TDN (time-dependent network), in which the travel time of the same road segment varies depending on the time of the day, have attracted much attention of researchers, but there is little work focusing on the travel time functions prediction problem. Though traditional methods for travel time or travel speed prediction problem can be used to generate the travel time functions, they have some limitations due to the need of less breakpoints, fine granularity, and long-term prediction. In this paper, we study the travel time functions prediction problem for TDN based on taxi trajectory data. In order to maintain a high degree of accuracy in fine-grained and long-predicted situations, we take into account not only the traffic incidents but also the data sparsity. Specifically, a traffic incident detection method is proposed based on k-means algorithm and a downstream-based strategy is proposed to estimate the speeds of segments considering the data sparsity. To make the breakpoints of function not so much, a prediction algorithm based on classification using ELM (extreme learning machine) is proposed, which predicts the speed classes taking both the weather and the adjacent segment conditions into account. In addition, a transformation method is presented to convert the discrete travel speeds into piecewise linear functions satisfying FIFO (First-In-First-Out) property. The experimental results show that ELM outperforms SVM (support vector machine) with regard to both the training time and prediction accuracy. Moreover, it also can be seen that both the weather conditions and the adjacent segment conditions have impact on the prediction accuracy.
      PubDate: 2019-02-01
       
  • Learning with Similarity Functions: a Tensor-Based Framework
    • Abstract: Machine learning algorithms are typically designed to deal with data represented as vectors. Several major applications, however, involve multi-way data, such as video sequences and multi-sensory arrays. In those cases, tensors endow a more consistent way to capture multi-modal relations, which may be lost by a conventional remapping of original data into a vector representation. This paper presents a tensor-oriented machine learning framework, and shows that the theory of learning with similarity functions provides an effective paradigm to support this framework. The proposed approach adopts a specific similarity function, which defines a measure of similarity between a pair of tensors. The performance of the tensor-based framework is evaluated on a set of complex, real-world, pattern-recognition problems. Experimental results confirm the effectiveness of the framework, which compares favorably with state-of-the-art machine learning methodologies that can accept tensors as inputs. Indeed, a formal analysis proves that the framework is more efficient than state-of-the-art methodologies also in terms of computational cost. The paper thus provides two main outcomes: (1) a theoretical framework that enables the use of tensor-oriented similarity notions and (2) a cognitively inspired notion of similarity that leads to computationally efficient predictors.
      PubDate: 2019-02-01
       
 
 
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