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  Subjects -> MATHEMATICS (Total: 885 journals)
    - APPLIED MATHEMATICS (71 journals)
    - GEOMETRY AND TOPOLOGY (20 journals)
    - MATHEMATICS (656 journals)
    - MATHEMATICS (GENERAL) (42 journals)
    - NUMERICAL ANALYSIS (19 journals)
    - PROBABILITIES AND MATH STATISTICS (77 journals)

MATHEMATICS (656 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 3)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 3)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 8)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 11)
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: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Decision Sciences     Open Access   (Followers: 5)
Advances in Difference Equations     Open Access   (Followers: 1)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 2)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Mathematical Physics     Open Access   (Followers: 5)
Advances in Mathematics     Full-text available via subscription   (Followers: 10)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 6)
Advances in Pure Mathematics     Open Access   (Followers: 4)
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: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 7)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access  
Algebra and Logic     Hybrid Journal   (Followers: 4)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Full-text available via subscription   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Biostatistics     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 7)
American Journal of Operations Research     Open Access   (Followers: 5)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 7)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access   (Followers: 1)
Analysis     Hybrid Journal   (Followers: 2)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 3)
Analysis Mathematica     Full-text available via subscription  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales UMCS, 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: 3)
Annals of Data Science     Hybrid Journal   (Followers: 9)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription  
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
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  
Annuaire du Collège de France     Open Access   (Followers: 5)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 1)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access  
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 19)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Journal of Current Engineering & Maths     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 2)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access  
Baltic International Yearbook of Cognition, Logic and Communication     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
BoEM - Boletim online de Educação Matemática     Open Access  
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: 1)
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: 7)
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: 1)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of the Brazilian Mathematical Society, New Series     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 Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 18)
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
ChemSusChem     Hybrid Journal   (Followers: 7)
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: 2)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Full-text available via subscription   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 1)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Concrete Operators     Open Access   (Followers: 4)
Confluentes Mathematici     Hybrid Journal  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 14)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 11)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 28)
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: 2)
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 3)
Energy for Sustainable Development     Hybrid Journal   (Followers: 9)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Ensino da Matemática em Debate     Open Access  
Entropy     Open Access   (Followers: 5)
ESAIM: Control Optimisation and Calculus of Variations     Full-text available via subscription   (Followers: 1)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 5)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 2)
Experimental Mathematics     Hybrid Journal   (Followers: 4)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Fasciculi Mathematici     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 4)
Fixed Point Theory and Applications     Open Access   (Followers: 1)

        1 2 3 4 | Last

Journal Cover Cognitive Computation
  [SJR: 0.692]   [H-I: 19]   [4 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1866-9964 - ISSN (Online) 1866-9956
   Published by Springer-Verlag Homepage  [2353 journals]
  • Effective Use of Evaluation Measures for the Validation of Best Classifier
           in Urdu Sentiment Analysis
    • Authors: Neelam Mukhtar; Mohammad Abid Khan; Nadia Chiragh
      Pages: 446 - 456
      Abstract: Abstract Sentiment analysis (SA) can help in decision making, drawing conclusion, or recommending appropriate solution for different business, political, or other problems. At the same time reliable ways are also required to verify the results that are achieved after SA. In the frame of biologically inspired approaches for machine learning, getting reliable result is challenging but important. Properly verified and validated results are always appreciated and preferred by the research community. The strategy of achieving reliable result is adopted in this research by using three standard evaluation measures. First, SA of Urdu is performed. After collection and annotation of data, five classifiers, i.e., PART, Naives Bayes mutinomial Text, Lib SVM (support vector machine), decision tree (J48), and k nearest neighbor (KNN, IBK) are employed using Weka. After using 10-fold cross-validation, three top most classifiers, i.e., Lib SVM, J48, and IBK are selected on the basis of high accuracy, precision, recall, and F-measure. Further, IBK resulted as the best classifier among the three. For verification of this result, labels of the sentences (positive, negative, or neutral) are predicted by using training and test data, followed by the application of the three standard evaluation measures, i.e., McNemar’s test, kappa statistic, and root mean squared error. IBK performs much better than the other two classifiers. To make this result more reliable, a number of steps are taken including the use of three evaluation measures for getting a confirmed and validated result which is the main contribution of this research. It is concluded with confidence that IBK is the best classifier in this case.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9481-5
      Issue No: Vol. 9, No. 4 (2017)
       
  • Cognitive Modeling of the Natural Behavior of the Varroa destructor Mite
           on Video
    • Authors: Melvin Ramírez-Bogantes; Juan P. Prendas-Rojas; Geovanni Figueroa-Mata; Rafael A. Calderon; Oscar Salas-Huertas; Carlos M. Travieso
      Pages: 482 - 493
      Abstract: Abstract The present work offers an innovative and automatic approach for detecting, tracking, analyzing, and reporting the natural behavior of the Varroa destructor mite and its activity from videos provided by the Tropical Apicultural Research Center (CINAT) in Costa Rica. These videos correspond to the presence of V. destructor in capped Africanized worker honeybee cells in a controlled environment. The main objective of this paper is to present an automatic report of the identification of the mite behavior based on mite information (bioinspired information). First, a calibration system was implemented to enhance the frame. This calibration was achieved by searching the movement-active area (MAA) and the geometrical definition of the V. destructor mite. Then, an automatic detection and tracking was applied. Finally, an automatic classification was used to establish the mite activity. This approach reached up to 92.83% for all processes: detection, tracking, behavior analysis, and activity reporting, in real time and showing a cognitive model of the mite. The proposed approach provides an automatic tool and objective measurement against manual and qualitative methods traditionally applied in this kind of analysis, with a significant potential to be used as a reference in the modeling of the behavior of the V. destructor mite.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9471-7
      Issue No: Vol. 9, No. 4 (2017)
       
  • A Novel Clustering Algorithm in a Neutrosophic Recommender System for
           Medical Diagnosis
    • Authors: Nguyen Dang Thanh; Mumtaz Ali; Le Hoang Son
      Pages: 526 - 544
      Abstract: Abstract Decision-making processes have been extensively used in artificial intelligence and cognitive sciences to explain and improve individual and social perception. As one of the most typical decision-making problems, medical diagnosis is used to analyze the relationship between symptoms and diseases according to uncertain and inconsistent information. It is essential to investigate the structure of a set of records on different levels such that similar patients can be treated concurrently within a group. In this paper, we propose a novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. First, we define new algebraic structures for the system such as lattices, De Morgan algebra, Kleen algebra, MV algebra, BCK algebra, Stone algebra, and Brouwerian algebra. Based on these algebraic structures, we construct a neutrosophic recommender similarity matrix and a neutrosophic recommender equivalence matrix. A consecutive series of compositions between the neutrosophic recommender similarity matrices is performed to obtain the neutrosophic recommender equivalence matrix. From this matrix, a λ-cutting matrix is defined to conduct clustering among the neutrosophic recommender systems. Regarding the values of clustering validity indices, the Davies-Bouldin (DB) of the proposed method is approximately 20% better than those of the methods of Sahin (Neutrosophic Sets and Systems. 2014;2:18–24), Ye (J Intell Syst. 2014;23(4):379–89), and Ye (Soft Computing 2016;1–7). Analogously, the IFV and simplified silhouette with criterion (SSWC) of the proposal are better than those of the relevant methods with the improvement percentages being 30 and 70%, respectively. The results show that the proposed method is better than the related algorithms in terms of clustering quality whilst its computational time is slightly slower. The contributions of this research is significant in both algorithmic aspects of computational intelligence and and practical applications.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9462-8
      Issue No: Vol. 9, No. 4 (2017)
       
  • Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction
           of Adverse Cardiac Events
    • Authors: Nan Liu; Jeffrey Tadashi Sakamoto; Jiuwen Cao; Zhi Xiong Koh; Andrew Fu Wah Ho; Zhiping Lin; Marcus Eng Hock Ong
      Pages: 545 - 554
      Abstract: Abstract Accurate prediction of adverse cardiac events for the emergency department (ED) chest pain patients is essential in risk stratification due to the current ambiguity in diagnosing acute coronary syndrome. While most current practices rely on human decision by measuring clinical vital signs, computerized solutions are gaining popularity. We have previously proposed an ensemble-based scoring system (ESS). In this paper, we aim to extend the ESS system using extreme learning machine (ELM), a fast learning algorithm for neural networks. We recruited patients from the ED of Singapore General Hospital, and extracted features such as heart rate variability, 12-lead ECG parameters, and vital signs. We also proposed a novel algorithm called ESS-ELM to predict adverse cardiac events. Different from the original ESS algorithm, ESS-ELM uses the under-sampling technique only in model training. Our proposed method was compared to the original ESS algorithm and several clinical scores in predicting patient outcome. With a cohort of 797 recruited patients, we demonstrated that ESS-ELM outperformed the original ESS algorithm and three established clinical scores, namely HEART, TIMI, and GRACE, in terms of receiver operating characteristic analysis. Furthermore, we have investigated the impact of hidden node number and ensemble size on the predictive performance. ELM has demonstrated the flexibility in its integration with the ESS algorithm. Experiments showed the value of ESS-ELM in prediction of adverse cardiac events. Future works may include the use of new ELM-based learning methods and further validation with a new cohort of patients.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9455-7
      Issue No: Vol. 9, No. 4 (2017)
       
  • Common Subspace Learning via Cross-Domain Extreme Learning Machine
    • Authors: Yan Liu; Lei Zhang; Pingling Deng; Zheng He
      Pages: 555 - 563
      Abstract: Abstract Extreme learning machine (ELM) is proposed for solving a single-layer feed-forward network (SLFN) with fast learning speed and has been confirmed to be effective and efficient for pattern classification and regression in different fields. ELM originally focuses on the supervised, semi-supervised, and unsupervised learning problems, but just in the single domain. To our best knowledge, ELM with cross-domain learning capability in subspace learning has not been exploited very well. Inspired by a cognitive-based extreme learning machine technique (Cognit Comput. 6:376–390, 1; Cognit Comput. 7:263–278, 2.), this paper proposes a unified subspace transfer framework called cross-domain extreme learning machine (CdELM), which aims at learning a common (shared) subspace across domains. Three merits of the proposed CdELM are included: (1) A cross-domain subspace shared by source and target domains is achieved based on domain adaptation; (2) ELM is well exploited in the cross-domain shared subspace learning framework, and a new perspective is brought for ELM theory in heterogeneous data analysis; (3) the proposed method is a subspace learning framework and can be combined with different classifiers in recognition phase, such as ELM, SVM, nearest neighbor, etc. Experiments on our electronic nose olfaction datasets demonstrate that the proposed CdELM method significantly outperforms other compared methods.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9473-5
      Issue No: Vol. 9, No. 4 (2017)
       
  • Neuron Pruning-Based Discriminative Extreme Learning Machine for Pattern
           Classification
    • Authors: Tan Guo; Lei Zhang; Xiaoheng Tan
      Pages: 581 - 595
      Abstract: Abstract Extreme learning machine (ELM), as a newly developed learning paradigm for the generalized single hidden layer feedforward neural networks, has been widely studied due to its unique characteristics, i.e., fast training, good generalization, and universal approximation/classification ability. A novel framework of discriminative extreme learning machine (DELM) is developed for pattern classification. In DELM, the margins between different classes are enlarged as much as possible through a technique called ε-dragging. DELM is further extended to pruning DELM (P-DELM) using L2,1-norm regularization. The performance of DELM is compared with several state-of-the-art methods on public face databases. The simulation results show the effectiveness of DELM for face recognition when there are posture, facial expression, and illumination variations. P-DELM can distinguish the importance of different hidden neurons and remove the worthless ones. The model can achieve promising performance with fewer hidden neurons and less prediction time on several benchmark datasets. In DELM model, the margins between different classes are enlarged by learning a nonnegative label relaxation matrix. The experiments validate the effectiveness of DELM. Furthermore, DELM is extended to P-DELM based on L2,1-norm regularization. The developed P-DELM can naturally distinguish the importance of different hidden neurons, which will lead to a more compact network by neuron pruning. Experimental validations on some benchmark datasets show the advantages of the proposed P-DELM method.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9474-4
      Issue No: Vol. 9, No. 4 (2017)
       
  • Sentence-Level Emotion Detection Framework Using Rule-Based Classification
    • Authors: Muhammad Zubair Asghar; Aurangzeb Khan; Afsana Bibi; Fazal Masud Kundi; Hussain Ahmad
      Abstract: Abstract Emotion detection and analysis aims at developing applications that can detect and analyse emotions expressed by the users in a given text. Such applications have received considerable attention from experts in computer science, psychology, communications and health care. Emotion-based sentiment analysis can be performed using supervised and unsupervised techniques. The existing studies using supervised and unsupervised emotion-based sentiment analysis are based on Ekman’s basic emotion model; have limited coverage of emotion-words, polarity shifters and negations; and lack emoticons and slang. The problems associated with existing approaches can be overcome by the development of an effective, sentence-level emotion-detection sentiment analysis system under a rule-based classification scheme with extended lexicon support and an enhanced model of emotion signals: emotion words, polarity shifters, negations, emoticons and slang. In this work, we propose a rule-based framework for emotion-based sentiment classification at the sentence level obtained from user reviews. The main contribution of this work is to integrate cognitive-based emotion theory (e.g. Ekman’s model) with sentiment analysis-based computational techniques (e.g. detection of emotion words, emoticons and slang) to detect and classify emotions from natural language text. The main focus is to improve the performance of state-of-the-art methods by including additional emotion-related signals, such as emotion words, emoticons, slang, polarity shifters and negations, to efficiently detect and classify emotions in user reviews. The improved results in terms of accuracy, precision, recall and F-measure demonstrate the superiority of the proposed method’s classification results compared with baseline methods. The framework is generalized and capable of classifying emotions in any domain.
      PubDate: 2017-09-05
      DOI: 10.1007/s12559-017-9503-3
       
  • Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self
           Model for Self-Recognition
    • Authors: Yi Zeng; Yuxuan Zhao; Jun Bai; Bo Xu
      Abstract: Abstract The neural correlates and nature of self-consciousness is an advanced topic in Cognitive Neuroscience. Only a few animal species have been testified to be with this cognitive ability. From artificial intelligence and robotics point of view, few efforts are deeply rooted in the neural correlates and brain mechanisms of biological self-consciousness. Despite the fact that the scientific understanding of biological self-consciousness is still in preliminary stage, we make our efforts to integrate and adopt known biological findings of self-consciousness to build a brain-inspired model for robot self-consciousness. In this paper, we propose a brain-inspired robot bodily self model based on extensions to primate mirror neuron system and apply it to humanoid robot for self recognition. In this model, the robot firstly learns the correlations between self-generated actions and visual feedbacks in motion by learning with spike timing dependent plasticity (STDP), and then learns the appearance of body part with the expectation that the visual feedback is consistent with its motion. Based on this model, the robot uses multisensory integration to learn its own body in real world and in mirror. Then it can distinguish itself from others. In a mirror test setting with three robots with the same appearance, with the proposed brain-inspired robot bodily self model, each of them can recognize itself in the mirror after these robots make random movements at the same time. The theoretic modeling and experimental validations indicate that the brain-inspired robot bodily self model is biologically inspired, and computationally feasible as a foundation for robot self recognition.
      PubDate: 2017-08-31
      DOI: 10.1007/s12559-017-9505-1
       
  • A Novel Technique for Detecting Plagiarism in Documents Exploiting
           Information Sources
    • Authors: Mansi Sahi; Vishal Gupta
      Abstract: Abstract Plagiarism takes place when we use any person’s work without giving due acknowledgment. There are several fields where the text similarity is involved like web document retrieval, information mining, and searching related articles. Several approaches have been introduced for detecting plagiarism in the text documents based on the syntactic structure of the text, string similarity, fingerprinting, semantic meaning underlying the text, etc. The basic limitation of plagiarism detection systems these days is that they fail to detect tough cases of plagiarism. The proposed plagiarism detection approach is the hybrid of semantic and syntactic similarity between the text documents. This novel approach exploits linguistic information sources non-linearly using the lexical database for finding the relatedness between text documents. The proposed approach uses semantic knowledge to perform cognitive-inspired computing. The framework is capable of detecting intelligent plagiarism cases like a verbatim copy, paraphrasing, rewording in a sentence, and sentence transformation. The approach has been evaluated on the standard PAN-PC-11 dataset. The experiments show that our technique has outperformed other strong baseline techniques in terms of precision, recall, F-measure, and plagiarism detection (PlagDet) score.
      PubDate: 2017-08-22
      DOI: 10.1007/s12559-017-9502-4
       
  • Online Extreme Learning Machine with Hybrid Sampling Strategy for
           Sequential Imbalanced Data
    • Authors: Wentao Mao; Mengxue Jiang; Jinwan Wang; Yuan Li
      Abstract: Abstract In real applications of cognitive computation, data with imbalanced classes are used to be collected sequentially. In this situation, some of current machine learning algorithms, e.g., support vector machine, will obtain weak classification performance, especially on minority class. To solve this problem, a new hybrid sampling online extreme learning machine (ELM) on sequential imbalanced data is proposed in this paper. The key idea is keeping the majority and minority classes balanced with similar sequential distribution characteristic of the original data. This method includes two stages. At the offline stage, we introduce the principal curve to build confidence regions of minority and majority classes respectively. Based on these two confidence zones, over-sampling of minority class and under-sampling of majority class are both conducted to generate new synthetic samples, and then, the initial ELM model is established. At the online stage, we first choose the most valuable ones from the synthetic samples of majority class in terms of sample importance. Afterwards, a new online fast leave-one-out cross validation (LOO CV) algorithm utilizing Cholesky decomposition is proposed to determine whether to update the ELM network weight at online stage or not. We also prove theoretically that the proposed method has upper bound of information loss. Experimental results on seven UCI datasets and one real-world air pollutant forecasting dataset show that, compared with ELM, OS-ELM, meta-cognitive OS-ELM, and OSELM with SMOTE strategy, the proposed method can simultaneously improve the classification performance of minority and majority classes in terms of accuracy, G-mean value, and ROC curve. As a conclusion, the proposed hybrid sampling online extreme learning machine can be effectively applied to the sequential data imbalance problem with better generalization performance and numerical stability.
      PubDate: 2017-08-17
      DOI: 10.1007/s12559-017-9504-2
       
  • Learning Word Representations for Sentiment Analysis
    • Authors: Yang Li; Quan Pan; Tao Yang; Suhang Wang; Jiliang Tang; Erik Cambria
      Abstract: Abstract Word embedding has been proven to be a useful model for various natural language processing tasks. Traditional word embedding methods merely take into account word distributions independently from any specific tasks. Hence, the resulting representations could be sub-optimal for a given task. In the context of sentiment analysis, there are various types of prior knowledge available, e.g., sentiment labels of documents from available datasets or polarity values of words from sentiment lexicons. We incorporate such prior sentiment information at both word level and document level in order to investigate the influence each word has on the sentiment label of both target word and context words. By evaluating the performance of sentiment analysis in each category, we find the best way of incorporating prior sentiment information. Experimental results on real-world datasets demonstrate that the word representations learnt by DLJT2 can significantly improve the sentiment analysis performance. We prove that incorporating prior sentiment knowledge into the embedding process has the potential to learn better representations for sentiment analysis.
      PubDate: 2017-08-17
      DOI: 10.1007/s12559-017-9492-2
       
  • A Bayesian Assessment of Real-World Behavior During Multitasking
    • Authors: Jeroen H.M. Bergmann; Joan Fei; David A Green; Amir Hussain; Newton Howard
      Abstract: Abstract Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monitored in healthy individuals with a wearable sensor network during single and multitask conditions. Motor performance was quantified by the median frequencies (f m) of hand trajectories and wrist accelerations. The probability that multitasking occurred based on the obtained motor information was estimated using a Naïve Bayes Model, with a specific focus on the single and triple loading conditions. The Bayesian probability estimator showed task distinction for the wrist accelerometer data at the high and low value ranges. The likelihood of encountering a certain motor performance during well-established everyday activities, such as preparing a simple meal, changed when additional (cognitive) tasks were performed. Within a healthy population, the probability of lower acceleration frequency patterns increases when people are asked to multitask. Cognitive decline due to aging or disease might yield even greater differences.
      PubDate: 2017-08-12
      DOI: 10.1007/s12559-017-9500-6
       
  • Multi-Criteria Decision-Making Method Based on Distance Measure and
           Choquet Integral for Linguistic Z-Numbers
    • Authors: Jian-qiang Wang; Yong-xi Cao; Hong-yu Zhang
      Abstract: Abstract Z-numbers are a new concept considering both the description of cognitive information and the reliability of information. Linguistic terms are useful tools to adequately and effectively model real-life cognitive information, as well as to characterize the randomness of events. However, a form of Z-numbers, in which their two components are in the form of linguistic terms, is rarely studied, although it is common in decision-making problems. In terms of Z-numbers and linguistic term sets, we provided the definition of linguistic Z-numbers as a form of Z-numbers or a subclass of Z-numbers. Then, we defined some operations of linguistic Z-numbers and proposed a comparison method based on the score and accuracy functions of linguistic Z-numbers. We also presented the distance measure of linguistic Z-numbers. Next, we developed an extended TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method based on the Choquet integral for multi-criteria decision-making (MCDM) problems with linguistic Z-numbers. Finally, we provided an example concerning the selection of medical inquiry applications to demonstrate the feasibility of our proposed approach. We then verified the applicability and superiority of our approach through comparative analyses with other existing methods. Illustrative and comparative analyses indicated that the proposed approach was valid and feasible for different decision-makers and cognitive environments. Furthermore, the final ranking results of the proposed approach were closer to real decision-making processes. Linguistic Z-numbers can flexibly characterize real cognitive information as well as describe the reliability of information. This method not only is a more comprehensive reflection of the decision-makers’ cognition but also is more in line with expression habits. The proposed method inherited the merits of the classical TODIM method and considers the interactivity of criteria; therefore, the proposed method was effective for dealing with real-life MCDM problems. Consideration about bounded rational and the interactivity of criteria made final outcomes convincing and consistent with real decision-making.
      PubDate: 2017-08-07
      DOI: 10.1007/s12559-017-9493-1
       
  • Incremental Adaptive Learning Vector Quantization for Character
           Recognition with Continuous Style Adaptation
    • Authors: Yuan-Yuan Shen; Cheng-Lin Liu
      Abstract: Abstract Incremental learning enables continuous model adaptation based on a constantly arriving data stream. It is a way relevant to human cognitive system, which learns to predict objects in a changing world. Incremental learning for character recognition is a typical scenario that characters appear sequentially and the font/writing style changes irregularly. In the paper, we investigate how to classify characters incrementally (i.e., input patterns appear once at a time). A reasonable assumption is that adjacent characters from the same font or the same writer share the same style in a short period while style variation occurs in characters printed by different fonts or written by different persons during a long period. The challenging issue here is how to take advantage of the local style consistency and adapt to the continuous style variation as well incrementally. For this purpose, we propose a continuous incremental adaptive learning vector quantization (CIALVQ) method, which incrementally learns a self-adaptive style transfer matrix for mapping input patterns from style-conscious space onto style-free space. After style transformation, this problem is casted into a common character recognition task and an incremental learning vector quantization (ILVQ) classifier is used. In this framework, we consider two learning modes: supervised incremental learning and active incremental learning. In the latter mode, samples receiving low confidence from the classifier are requested class labels. We evaluated the classification performance of CIALVQ in two scenarios, interleaved test-then-train and style-specific classification on NIST hand-printed data sets. The results show that local style consistency improves the accuracies of both two test scenarios, and for both supervised and active incremental learning modes.
      PubDate: 2017-08-04
      DOI: 10.1007/s12559-017-9491-3
       
  • Parkinson’s Disease and Aging: Analysis of Their Effect in Phonation and
           Articulation of Speech
    • Authors: T. Arias-Vergara; J. C. Vásquez-Correa; J. R. Orozco-Arroyave
      Abstract: Abstract Parkinson’s disease (PD) is a neurological disorder that affects the communication ability of patients. There is interest in the research community to study acoustic measures that provide objective information to model PD speech. Although there are several studies in the literature that consider different characteristics of Parkinson’s speech like phonation and articulation, there are no studies including the aging process as another possible source of impairments in speech. The aim of this work is to analyze the vowel articulation and phonation of Parkinson’s patients compared with respect to two groups of healthy people: (1) young speakers with ages ranging from 22 to 50 years and (2) people with ages matched with respect to the Parkinson’s patients. Each participant repeated the sustained phonation of the five Spanish vowels three times and those utterances per speaker are modeled by using phonation and articulation features. Feature selection is applied to eliminate redundant information in the features space, and the automatic discrimination of the three groups of speakers is performed using a multi-class Support Vector Machine (SVM) following a one vs. all strategy, speaker independent. The results are compared to those obtained using a cognitive-inspired classifier which is based on neural networks (NN). The results indicate that the phonation and articulation capabilities of young speakers clearly differ from those exhibited by the elderly speakers (with and without PD). To the best of our knowledge, this is the first paper introducing experimental evidence to support the fact that age matching is necessary to perform more accurate and robust evaluations of pathological speech signals, especially considering diseases suffered by elderly people, like Parkinson’s. Additionally, the comparison among groups of speakers at different ages is necessary in order to understand the natural change in speech due to the aging process.
      PubDate: 2017-08-04
      DOI: 10.1007/s12559-017-9497-x
       
  • Human Reading Knowledge Inspired Text Line Extraction
    • Authors: Liuan Wang; Seiichi Uchida; Anna Zhu; Jun Sun
      Abstract: Abstract Text in images contains exact semantic information and the text knowledge can be utilized in many image cognition and understanding applications. The human reading habits can provide the clues of text line structure for text line extraction. In this paper, we propose a novel human reading knowledge inspired text line extraction method based on k-shortest paths global optimization. Firstly, the candidate character extraction is reformulated as Maximal Stable Extremal Region (MSER) algorithm on gray, red, blue, and green channels of the target images, and the extracted MSERs are fed into Convolutional Neural Network (CNN) to remove the noise components. Then, the directed graph is built upon the character component nodes with edges inspired by human reading sense. The directed graph can automatically construct the relationship to eliminate the disorder of candidate text components. The text line paths optimization is inspired by the human reading ability in planning of a text line path sequentially. Therefore, the text line extraction problem can be solved using the k-shortest paths optimization algorithm by taking advantage of the human reading sense structure of the directed graph. It can extract the text lines iteratively to avoid the exhaustive searching and obtain global optimized text line number. The proposed method achieves the f-measure of 0.820 and 0.812 on public ICDAR2011 and ICDAR2013 dataset, respectively. The experimental results demonstrate the effectiveness of the proposed human reading knowledge inspired text line extraction method in comparison with state-of-the-art methods This paper presents one human reading knowledge inspired text line extraction method, which approves that the human reading knowledge can benefit the text line extraction and image text discovery.
      PubDate: 2017-08-02
      DOI: 10.1007/s12559-017-9490-4
       
  • An Interval Neutrosophic Projection-Based VIKOR Method for Selecting
           Doctors
    • Authors: Junhua Hu; Li Pan; Xiaohong Chen
      Abstract: Abstract Mobile healthcare applications are emerging as an innovative and practical technology resource to provide automated and efficient medical services online. However, vagueness and uncertainty commonly exist during the process of selecting doctors online and few studies have addressed these problems. In this paper, we employed interval neutrosophic sets (INSs) to process evaluation information. We proposed and normalized an improved projection measurement for INSs to overcome the shortcomings in extant projection measurements. Additionally, we presented a projection-based difference measure combined with the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to establish a projection-based VIKOR method. Moreover, we introduced transition functions to transform online evaluation information into INSs and employed the maximizing deviation method to obtain weights for criteria. It is a practical problem for patients to choose a suitable doctor on a mobile healthcare application. This problem can be solved by applying the proposed method. Finally, we verified the validity of the method by comparison with several existing methods, and then we conducted sensitivity analysis to demonstrate the method’s reliability. The doctor selection problem can be effectively solved by the projection-based VIKOR method for INSs. Our comparison indicates that the proposed method is more appropriate than other methods, and the final ranking results are more precise than the actual ranking list found online. INSs are effective in describing vagueness and uncertainty in multi-criteria decision-making problems. The method proposed in this paper was viable and valid for use in doctor selection processes with information expressed by INSs.
      PubDate: 2017-08-02
      DOI: 10.1007/s12559-017-9499-8
       
  • A Novel Manifold Regularized Online Semi-supervised Learning Model
    • Authors: Shuguang Ding; Xuanyang Xi; Zhiyong Liu; Hong Qiao; Bo Zhang
      Abstract: Abstract In the process of human learning, training samples are often obtained successively. Therefore, many human learning tasks exhibit online and semi-supervision characteristics, that is, the observations arrive in sequence and the corresponding labels are presented very sporadically. In this paper, we propose a novel manifold regularized model in a reproducing kernel Hilbert space (RKHS) to solve the online semi-supervised learning (OS2L) problems. The proposed algorithm, named Model-Based Online Manifold Regularization (MOMR), is derived by solving a constrained optimization problem. Different from the stochastic gradient algorithm used for solving the online version of the primal problem of Laplacian support vector machine (LapSVM), the proposed algorithm can obtain an exact solution iteratively by solving its Lagrange dual problem. Meanwhile, to improve the computational efficiency, a fast algorithm is presented by introducing an approximate technique to compute the derivative of the manifold term in the proposed model. Furthermore, several buffering strategies are introduced to improve the scalability of the proposed algorithms and theoretical results show the reliability of the proposed algorithms. Finally, the proposed algorithms are experimentally shown to have a comparable performance to the standard batch manifold regularization algorithm.
      PubDate: 2017-08-02
      DOI: 10.1007/s12559-017-9489-x
       
  • Leveraging Spatial Context Disparity for Power Line Detection
    • Authors: Chaofeng Pan; Haotian Shan; Xianbin Cao; Xuelong Li; Dapeng Wu
      Abstract: Abstract For the safety of low flying aircraft, it will become increasingly important that an aircraft should have the ability to detect and avoid small obstacles in the low flying environment. In recent years, using context information to assist in detecting power lines has shown great potential to better detect power lines at a remote distance. Therefore, how to adequately use the context information for a better detection is a hot issue of concern. This paper proposes a novel auxiliary assisted power line detection method, in which the spatial context disparity of auxiliaries is quantitatively and uniformly evaluated for the first time. As a cognitive strategy, the spatial context disparity depends on two factors, the spatial context peakedness and the spatial context difference. With this cognitive method, objects that achieve high spatial context disparity scores are more suitable for being the auxiliaries of the power lines. Experimental results show that, owing to the spatial context disparity, the proposed method can acquire proper auxiliaries with abundant context information to support the detection, so that better power line detections are achieved comparing to traditional power line detection methods. The proposed power line detection method, which can automatically choose the optimal auxiliaries, is effective and has the potential for practical use in ensuring the flight safety of unmanned air vehicles (UAVs) in the low flying environment.
      PubDate: 2017-08-02
      DOI: 10.1007/s12559-017-9488-y
       
  • Motor Imagery EEG Classification Based on Kernel Hierarchical Extreme
           Learning Machine
    • Authors: Lijuan Duan; Menghu Bao; Song Cui; Yuanhua Qiao; Jun Miao
      Abstract: Abstract As connections from the brain to an external device, Brain-Computer Interface (BCI) systems are a crucial aspect of assisted communication and control. When equipped with well-designed feature extraction and classification approaches, information can be accurately acquired from the brain using such systems. The Hierarchical Extreme Learning Machine (HELM) has been developed as an effective and accurate classification approach due to its deep structure and extreme learning mechanism. A classification system for motor imagery EEG signals is proposed based on the HELM combined with a kernel, herein called the Kernel Hierarchical Extreme Learning Machine (KHELM). Principle Component Analysis (PCA) is used to reduce the dimensionality of the data, and Linear Discriminant Analysis (LDA) is introduced to push the features away from different classes. To demonstrate the performance, the proposed system is applied to the BCI competition 2003 Dataset Ia, and the results are compared with those from state-of-the-art methods; we find that the accuracy is up to 94.54%.
      PubDate: 2017-08-01
      DOI: 10.1007/s12559-017-9494-0
       
 
 
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