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  Subjects -> MATHEMATICS (Total: 955 journals)
    - APPLIED MATHEMATICS (82 journals)
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    - MATHEMATICS (706 journals)
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MATHEMATICS (706 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 4)
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: 7)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 27)
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: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
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: 6)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 18)
Advances in Decision Sciences     Open Access   (Followers: 3)
Advances in Difference Equations     Open Access   (Followers: 3)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 2)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Mathematical Physics     Open Access   (Followers: 3)
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: 12)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 6)
Advances in Pure Mathematics     Open Access   (Followers: 5)
Advances in Science and Research (ASR)     Open Access   (Followers: 4)
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: 11)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access  
Algebra and Logic     Hybrid Journal   (Followers: 5)
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 Computational and Applied Mathematics     Open Access   (Followers: 5)
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: 5)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 8)
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: 4)
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: 11)
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: 12)
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)
ANZIAM Journal     Open Access   (Followers: 1)
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: 11)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 6)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 6)
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   (Followers: 3)
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: 2)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Armenian Journal of Mathematics     Open Access  
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 20)
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: 6)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 4)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
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)
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: 21)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 11)
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 Symbolic Logic     Full-text available via subscription   (Followers: 2)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 1)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4)
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   (Followers: 2)
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: 1)
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: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 2)
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: 8)
Compositio Mathematica     Full-text available via subscription   (Followers: 1)
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: 5)
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: 8)
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: 12)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 9)
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 10)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 29)
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: 3)
Differentsial'nye Uravneniya     Open Access  
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: 1)
Diskretnaya Matematika     Full-text available via subscription  
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Akademii Nauk     Open Access  
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Eco Matemático     Open Access  
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Differential Equations     Open Access  
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: 4)

        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  [2351 journals]
  • Learning from Few Samples with Memory Network
    • Authors: Shufei Zhang; Kaizhu Huang; Rui Zhang; Amir Hussain
      Pages: 15 - 22
      Abstract: Neural networks (NN) have achieved great successes in pattern recognition and machine learning. However, the success of a NN usually relies on the provision of a sufficiently large number of data samples as training data. When fed with a limited data set, a NN’s performance may be degraded significantly. In this paper, a novel NN structure is proposed called a memory network. It is inspired by the cognitive mechanism of human beings, which can learn effectively, even from limited data. Taking advantage of the memory from previous samples, the new model achieves a remarkable improvement in performance when trained using limited data. The memory network is demonstrated here using the multi-layer perceptron (MLP) as a base model. However, it would be straightforward to extend the idea to other neural networks, e.g., convolutional neural networks (CNN). In this paper, the memory network structure is detailed, the training algorithm is presented, and a series of experiments are conducted to validate the proposed framework. Experimental results show that the proposed model outperforms traditional MLP-based models as well as other competitive algorithms in response to two real benchmark data sets.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9507-z
      Issue No: Vol. 10, No. 1 (2018)
       
  • Lagrange Programming Neural Network Approaches for Robust Time-of-Arrival
           Localization
    • Authors: Hao Wang; Ruibin Feng; Andrew Chi Sing Leung; K. F. Tsang
      Pages: 23 - 34
      Abstract: There are two interesting properties in human brain. One is its massively interconnected structure. Another one is that human can handle outlier data effectively. For instance, human is able to recognize an object from an image with non-Gaussian noise. Artificial neural network is one of biologically inspired techniques. From the structural point of view, many neural network models have massively interconnected structures. Since the traditional analog neural network approach cannot handle an l 1-norm-like objective function, it cannot be used to handle outlier data. This paper proposes two neural network models for the robust source localization problem in the time-of-arrival (TOA) model. Our development is based on the Lagrange programming neural network (LPNN) approach. To alleviate the influence of outliers, this paper introduces an l 1-norm objective function. However, in the traditional LPNN approach, the constraints and the objective function must be differentiable. We devise two methods to handle the non-differentiable l 1-norm term. The first method introduces an approximation to replace the l 1-norm term. The second one uses the concept of hidden state from the locally competitive algorithm (LCA) to avoid the computation of the gradient vector at non-differentiable points. We also present the local stability of the two proposed models. From the simulations, our proposed methods are capable to handle the outliers and their error performances are better than many existing TOA algorithms.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9495-z
      Issue No: Vol. 10, No. 1 (2018)
       
  • Attentional Bias Pattern Recognition in Spiking Neural Networks from
           Spatio-Temporal EEG Data
    • Authors: Zohreh Gholami Doborjeh; Maryam G. Doborjeh; Nikola Kasabov
      Pages: 35 - 48
      Abstract: When facing with different marketing product features, consumers are unaware of the important role of external stimuli on their decision-making behaviour. Neuromarketing background suggested that consumers might be seduced by the attentional bias which can direct their decision. This study aims at modelling and visualisation of the brain activity patterns generated by marketing product features with respect to the spatio-temporal relationships between the continuous EEG data streams. This research utilises brain-like Spiking Neural Network (SNN) models for analysing spatio-temporal brain patterns generated by attentional bias. The model was applied to Electroencephalogram (EEG) data for investigating the effectiveness of attentional bias on consumer preference towards marketing stimuli. Our experimental results have shown that consumers were more likely to get distracted by product features that are related to their subconscious preferences. This paper proofs that consumers pay the highest attention to non-target stimuli when they were presented with attractive features. This study provided a proof of principle for the role of attentional bias on concern-related human preferences. It represents knowledge discovery in the prediction of consumer preferences in the field of neuromarketing. The SNN-based models performed superior not only in achieving a higher classification of EEG data related to different stimuli in comparison with traditional methods, but it most importantly enables a better interpretation and understanding of underpinning brain functions against marketing stimuli.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9517-x
      Issue No: Vol. 10, No. 1 (2018)
       
  • A Novel Manifold Regularized Online Semi-supervised Learning Model
    • Authors: Shuguang Ding; Xuanyang Xi; Zhiyong Liu; Hong Qiao; Bo Zhang
      Pages: 49 - 61
      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: 2018-02-01
      DOI: 10.1007/s12559-017-9489-x
      Issue No: Vol. 10, No. 1 (2018)
       
  • SSDM 2 : a Two-Stage Semantic Sequential Dependence Model Framework for
           Biomedical Question Answering
    • Authors: Bo-Wen Zhang; Xu-Cheng Yin
      Pages: 73 - 83
      Abstract: Biomedical question answering is a hot and challenging topic in artificial intelligence and natural language processing as it helps to analyze multiple, large, and fast-growing biomedical knowledge sources. Most researchers manage to address the problem through constructing a knowledge base but these approaches require much expertise as well as workload. In this paper, we propose a two-stage semantic sequential dependence model (SSDM2 ) framework based on a cognitive-inspired model and sequential dependence model (SDM) to answer biomedical questions with relevant snippets in academic papers. Concretely, we firstly search relevant articles and generate candidate snippets with a SSDM, which is proposed to integrate the semantic and sequential information within questions together. Afterwards, another SSDM is utilized to measure the relevances between the questions and corresponding candidate snippets and rank these snippets. A biomedical question answering system is constructed based on the proposed framework and evaluated on 3-year BioASQ 2013-15 benchmarks. Statistics indicate the proposed framework SSDM2 outperforms several state-of-the-art baselines and BioASQ participants. The proposed SSDM2 is an effective and robust framework for biomedical question answering.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9525-x
      Issue No: Vol. 10, No. 1 (2018)
       
  • Human Reading Knowledge Inspired Text Line Extraction
    • Authors: Liuan Wang; Seiichi Uchida; Anna Zhu; Jun Sun
      Pages: 84 - 93
      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: 2018-02-01
      DOI: 10.1007/s12559-017-9490-4
      Issue No: Vol. 10, No. 1 (2018)
       
  • A Semi-blind Model with Parameter Identification for Building Temperature
           Estimation
    • Authors: Xing Luo; Xu Zhu; Eng Gee Lim; Yi Huang
      Pages: 105 - 116
      Abstract: An accurate thermal model for building enables the heating system (HS) to work efficiently as well as save energy. Thermal modelling often requires physical parameters of the building, which are difficult to be accurately determined. The aim of this work is to develop an optimal thermal model for better understanding of thermal dynamics with the goal of using this to estimate temperature variation in a few hours ahead within building. Based on the characteristics of thermal motion, a conventional physics-based (PB) model for building temperature estimation is introduced first. Afterwards, in order to refine the model and improve the actual performance, we propose an innovative semi-blind (SB) model based on data-driven approaches. Additionally, the methodologies including self-adaptive algorithms (SAAs) and grey prediction technique (GPT) have been applied in dealing with the integrated parameters estimation (IPE) process to ensure the practicability of the implemented model. The proposed model schema is validated by testing in a laboratory. The results indicate that the proposed approach achieves much higher accuracy in estimating temperature variation than the conventional PB model, with only limited knowledge of the building characteristics. The root mean square deviation (RMSD) of SB model and PB model are 0.18 and 0.43, respectively. According to the results, it can be concluded that the proposed SB model is able to appropriately estimate the internal temperature values and great improvement has been achieved comparing with the original thermal model.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9486-0
      Issue No: Vol. 10, No. 1 (2018)
       
  • The Impact of Sentiment Features on the Sentiment Polarity Classification
           in Persian Reviews
    • Authors: Ehsan Asgarian; Mohsen Kahani; Shahla Sharifi
      Pages: 117 - 135
      Abstract: Natural language processing (NLP) techniques can prove relevant to a variety of specialties in the field of cognitive science, including sentiment analysis. This paper investigates the impact of NLP tools, various sentiment features, and sentiment lexicon generation approaches to sentiment polarity classification of internet reviews written in Persian language. For this purpose, a comprehensive Persian WordNet (FerdowsNet), with high recall and proper precision (based on Princeton WordNet), was developed. Using FerdowsNet and a generated corpus of reviews, a Persian sentiment lexicon was developed using (i) mapping to the SentiWordNet and (ii) a semi-supervised learning method, after which the results of both methods were compared. In addition to sentiment words, a set of various features were extracted and applied to the sentiment classification. Then, by employing various well-known feature selection approaches and state-of-the art machine learning methods, a sentiment classification for Persian text reviews was carried out. The obtained results demonstrate the critical role of sentiment lexicon quality in improving the quality of sentiment classification in Persian language.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9513-1
      Issue No: Vol. 10, No. 1 (2018)
       
  • Detecting Multiple Coexisting Emotions in Microblogs with Convolutional
           Neural Networks
    • Authors: Shi Feng; Yaqi Wang; Kaisong Song; Daling Wang; Ge Yu
      Pages: 136 - 155
      Abstract: Analyzing human sentiments and emotions is a critical problem in cognitive computing. One fundamental task of sentiment analysis is to infer the sentiment polarity or emotion category of subjective text, such as microblogs. Most existing methods treat sentiment classification as a type of single-label supervised learning problem that classifies a microblog according to sentiment polarity or a single-labeled emotion. However, multiple fine-grained emotions may coexist in a single tweet or sentence of a microblog. We regard emotion detection in microblogs as a multi-label classification problem. First, we develop a graph-based algorithm to automatically build emotion lexicons, which are further utilized to construct distant-supervised corpora from massive microblog datasets. Then, a ranking-based multi-label convolutional neural network model (RM-CNN) that considers the order and relevance of labels is proposed to address emotion detection in microblogs. The RM-CNN model is pre-trained using the distant-supervised corpus and then fine-tuned using specific training data without the need for any manually designed features. Extensive experiments on two real-world datasets demonstrate substantial improvements of our proposed RM-CNN model over the state-of-the-art baseline methods in terms of multi-label classification metrics. We propose an effective RM-CNN model with a distant-supervised learning framework for detecting multiple coexisting emotions in the short text of microblogs.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9521-1
      Issue No: Vol. 10, No. 1 (2018)
       
  • A Framework for Building an Arabic Multi-disciplinary Ontology from
           Multiple Resources
    • Authors: Ahmad Hawalah
      Pages: 156 - 164
      Abstract: Over recent years, the Internet has become people’s main source of information, with many databases and web pages being added and accessed every day. This continued growth in the amount of information available has led to frustration and difficulty for those attempting to find a specific piece of information. As such, many techniques are widely used to retrieve useful information and to mine valuable data; indeed, these techniques make it possible to discover hidden relations and patterns. Most of the above-mentioned techniques have been used primarily to process and analyse English text, but not Arabic text. Limited Arabic resources (e.g. datasets, databases, and ontologies), also make analysing and processing Arabic text a difficult task. As such, in this paper, we propose a framework for building an Arabic ontology from multiple resources. Thus, we will first extract and build an Arabic ontology from a publicly available directory, following which, we will enhance this ontology with rich data from the Internet. We will then use an Arabic online directory to construct a multi-disciplinary ontology that provides a hierarchical representation of topics in a conceptual way. Following this, we introduce an enhanced technique to enrich these ontologies with sufficient information and proper annotation for each concept. Finally, by using common information retrieval evaluation techniques, we confirm the viability of the proposed approach.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9460-x
      Issue No: Vol. 10, No. 1 (2018)
       
  • Clustering-Oriented Multiple Convolutional Neural Networks for Single
           Image Super-Resolution
    • Authors: Peng Ren; Wenjian Sun; Chunbo Luo; Amir Hussain
      Pages: 165 - 178
      Abstract: In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural categories, most existing deep learning models for image super-resolution tend to exploit an indiscriminate scheme for processing one whole image. Inspired by the human cognitive mechanism, we propose a multiple convolutional neural network framework trained based on different textural clusters of image local patches. To this end, we commence by grouping patches into K clusters via K-means, which enables each cluster center to encode image priors of a certain texture category. We then train K convolutional neural networks for super-resolution based on the K clusters of patches separately, such that the multiple convolutional neural networks comprehensively capture the patch textural variability. Furthermore, each convolutional neural network characterizes one specific texture category and is used for restoring patches belonging to the cluster. In this way, the texture variation within a whole image is characterized by assigning local patches to their closest cluster centers, and the super-resolution of each local patch is conducted via the convolutional neural network trained by its cluster. Our proposed framework not only exploits the deep learning capability of convolutional neural networks but also adapts them to depict texture diversities for super-resolution. Experimental super-resolution evaluations on benchmark image datasets validate that our framework achieves state-of-the-art performance in terms of peak signal-to-noise ratio and structural similarity. Our multiple convolutional neural network framework provides an enhanced image super-resolution strategy over existing single-mode deep learning models.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9512-2
      Issue No: Vol. 10, No. 1 (2018)
       
  • Reducing and Stretching Deep Convolutional Activation Features for
           Accurate Image Classification
    • Authors: Guoqiang Zhong; Shoujun Yan; Kaizhu Huang; Yajuan Cai; Junyu Dong
      Pages: 179 - 186
      Abstract: In order to extract effective representations of data using deep learning models, deep convolutional activation feature (DeCAF) is usually considered. However, since the deep models for learning DeCAF are generally pre-trained, the dimensionality of DeCAF is simply fixed to a constant number (e.g., 4096D). In this case, one may ask whether DeCAF is good enough for image classification and whether we can further improve its performance' In this paper, to answer these two challenging questions, we propose a new model called RS-DeCAF based on “reducing” and “stretching” the dimensionality of DeCAF. In the implementation of RS-DeCAF, we reduce the dimensionality of DeCAF using dimensionality reduction methods and increase its dimensionality by stretching the weight matrix between successive layers. To improve the performance of RS-DeCAF, we also present a modified version of RS-DeCAF by applying the fine-tuning operation. Extensive experiments on several image classification tasks show that RS-DeCAF not only improves DeCAF but also outperforms previous “stretching” approaches. More importantly, from the results, we find that RS-DeCAF can generally achieve the highest classification accuracy when its dimensionality is two to four times of that of DeCAF.
      PubDate: 2018-02-01
      DOI: 10.1007/s12559-017-9515-z
      Issue No: Vol. 10, No. 1 (2018)
       
  • On Intuitionistic Fuzzy Copula Aggregation Operators in Multiple-
           Attribute Decision Making
    • Authors: Zhifu Tao; Bing Han; Huayou Chen
      Abstract: Operations of intuitionistic fuzzy values have been widely studied and have attracted significant interest. In this paper, some other operations on intuitionistic fuzzy values on the basis of Archimedean copulas and corresponding co-copulas are introduced. Such novel operations can show the relevance between intuitionistic fuzzy values. A family of weighted aggregation operators are developed according to the proposed operations, i.e., the intuitionistic fuzzy copula aggregation operator. The properties of the novel operations and the weighted aggregation operators are also considered. In the end, we provide a modified maximizing deviation decision procedure for multiple attributes decision making under intuitionistic fuzzy environment, and show a case study to illustrate the application of the proposed approach.
      PubDate: 2018-02-12
      DOI: 10.1007/s12559-018-9545-1
       
  • Rank-Adaptive Non-Negative Matrix Factorization
    • Authors: Dong Shan; Xinzheng Xu; Tianming Liang; Shifei Ding
      Abstract: Dimension reduction is a challenge task in data processing, especially in high-dimensional data processing area. Non-negative matrix factorization (NMF), as a classical dimension reduction method, has a contribution to the parts-based representation for the characteristics of non-negative constraints in the NMF algorithm. In this paper, the NMF algorithm is introduced to extract local features for dimension reduction. Considering the problem of which NMF is required to define the number of the decomposition rank manually, we proposed a rank-adaptive NMF algorithm, in which the affinity propagation (AP) clustering algorithm is introduced to determine adaptively the number of the decomposition rank of NMF. Then, the rank-adaptive NMF algorithm is used to extract features for the original image. After that, a low-dimensional representation of the original image is obtained through the projection from the original images to the feature space. Finally, we used extreme learning machine (ELM) and k-nearest neighbor (KNN) as the classifier to classify those low-dimensional feature representations. The experimental results demonstrate that the decomposition rank determined by the AP clustering algorithm can reflect the characteristics of the original data. When it is combined with the classification algorithm ELM or KNN and applied to handwritten character recognition, the proposed method not only reduces the dimension of original images but also performs well in terms of classification accuracy and time consumption. A new rank-adaptive NMF algorithm is proposed based on the AP clustering algorithm and the original NMF algorithm. According to this algorithm, the low-dimensional representation of the original data can be obtained without any prior knowledge. In addition, the proposed rank-adaptive NMF algorithm combined with the ELM and KNN classification algorithms performs well.
      PubDate: 2018-02-07
      DOI: 10.1007/s12559-018-9546-0
       
  • D-Intuitionistic Hesitant Fuzzy Sets and their Application in Multiple
           Attribute Decision Making
    • Authors: Xihua Li; Xiaohong Chen
      Abstract: Hesitant fuzzy sets (HFSs) and generalized hesitant fuzzy sets (GHFSs) provide useful tools for uncertain information processing in situations in which decision makers have doubts among several possible membership degrees. In practice, however, decision makers may have a degree of belief for hesitant memberships based on their knowledge and experience. The aim of our study is to propose a new manifestation of uncertain information, called D-intuitionistic hesitant fuzzy sets (D-IHFSs), by combining D numbers and GHFSs. First, arithmetic operations, score functions, and comparison laws related to D-IHFSs are introduced. Next, an extension principle is proposed for the application of aggregation operators of GHFSs to the D-intuitionistic hesitant fuzzy environment. Finally, a decision-making approach based on D-IHFSs is developed. An illustrative example shows the effectiveness and flexibility of D-IHFSs to handle uncertainties, such as fuzziness, hesitation, and incompleteness. D-IHFSs, combining D numbers and GHFSs, improve decision makers’ ability to handle uncertain information.
      PubDate: 2018-02-04
      DOI: 10.1007/s12559-018-9544-2
       
  • A Novel Spatiotemporal Longitudinal Methodology for Predicting Obesity
           Using Near Infra r ed Spectroscopy (NIRS) Cerebral Functional Activity
           Data
    • Authors: Ahsan Abdullah; Amir Hussain; Imtiaz Hussain Khan
      Abstract: Globally, there has been a dramatic increase in obesity, with prevalence in males and females expected to increase to 18 and 21%, respectively (NCD Risk Factor Collaboration, Lancet 387(10026):1377–96, 2016). However, there are hardly any data-analytic calorie-based cognitive studies, especially using non-invasive near infrared spectroscopy (NIRS) data that predict obesity using predictive data mining. Obesity is linked with neurodegenerative diseases, diabetes, and cardiovascular diseases. Thus, understanding, predicting, preventing, and managing obesity have the potential to save the lives of millions. Behavioral studies suggest that overeating in obese individuals is triggered by exaggerated brain reward center (BRC) activity to high-calorie food stimuli (Shefer et al., Neurosci Biobehav Rev 37(10):2489–503, 2013). In this paper, details of a novel research methodology are presented for a 24-month longitudinal study using a 44-channel NIRS device with the subjects in a natural environment. The proposed methodology consists of using visual stimuli of low/high calorie food items under fasting and satiated conditions for three types of subjects. The experiments consist of block design, longitudinal plan, data smoothing, BRC activation mapping, stereotactic normalization, generating paired t-test maps under fasting and non-fasting conditions and subsequently using Naïve Bayes modeling to generate obesity prediction maps for the control subjects. The simulated results consist of generation of Bayesian prediction maps using layers of paired t-test cerebral activity maps for the four BRC functional regions considered for three types of subjects, i.e., obese, control, and control subjects fed high calorie diet. We have demonstrated how cerebral functional activity data in response to visual food stimuli can be used to predict obesity in the non-obese, thus offering a non-invasive preventive measure.
      PubDate: 2018-01-30
      DOI: 10.1007/s12559-017-9541-x
       
  • Special Issue Editorial: Cognitively-Inspired Computing for Knowledge
           Discovery
    • Authors: Kaizhu Huang; Rui Zhang; Xiaobo Jin; Amir Hussain
      PubDate: 2018-01-23
      DOI: 10.1007/s12559-017-9532-y
       
  • Implicit Heterogeneous Features Embedding in Deep Knowledge Tracing
    • Authors: Haiqin Yang; Lap Pong Cheung
      Abstract: Deep recurrent neural networks have been successfully applied to knowledge tracing, namely, deep knowledge tracing (DKT), which aims to automatically trace students’ knowledge states by mining their exercise performance data. Two main issues exist in the current DKT models: First, the complexity of the DKT models increases the tension of psychological interpretation. Second, the input of existing DKT models is only the exercise tags representing via one-hot encoding. The correlation between the hidden knowledge components and students’ responses to the exercises heavily relies on training the DKT models. The existing rich and informative features are excluded in the training, which may yield sub-optimal performance. To utilize the information embedded in these features, researchers have proposed a manual method to pre-process the features, i.e., discretizing them based on the inner characteristics of individual features. However, the proposed method requires many feature engineering efforts and is infeasible when the selected features are huge. To tackle the above issues, we design an automatic system to embed the heterogeneous features implicitly and effectively into the original DKT model. More specifically, we apply tree-based classifiers to predict whether the student can correctly answer the exercise given the heterogeneous features, an effective way to capture how the student deviates from others in the exercise. The predicted response and the true response are then encoded into a 4-bit one-hot encoding and concatenated with the original one-hot encoding features on the exercise tags to train a long short-term memory (LSTM) model, which can output the probability that a student will answer the exercise correctly on the corresponding exercise. We conduct a thorough evaluation on two educational datasets and demonstrate the merits and observations of our proposal.
      PubDate: 2017-12-15
      DOI: 10.1007/s12559-017-9522-0
       
  • Very Fast Semantic Image Segmentation Using Hierarchical Dilation and
           Feature Refining
    • Authors: Qingqun Ning; Jianke Zhu; Chun Chen
      Abstract: With the rapid development of deep learning techniques, semantic image segmentation has been considerably improved recently, which is viewed as the key problem of scene understanding in computer vision. These advances are built upon the capability of complex architectures for deep neural network. In this paper, we present a novel deep neural network architecture designed for semantic image segmentation. In order to improve the segmentation accuracy, we introduce a novel hierarchical dilation block to effectively enlarge the size of receptive field and enable multi-scale processing in fully convolutional neural network. Moreover, we exploit the technique of bypass and intermediate supervision to capture the context information during upsampling and refining coarse features. We have conducted extensive experiments on several popular semantic segmentation testbeds, including Cityscapes, CamVid, Kitti, and Helen facial datasets. The experimental results demonstrate that our proposed approach runs two times faster than the state-of-the-art method. Our full system is able to obtain realtime inference performance on 1080P images using a PC with single GPU. It executes a network forwarding at 200fps in our experiment while retaining high accuracy. Our proposed approach not only runs faster than the existing realtime methods but also performs on par with them.
      PubDate: 2017-12-05
      DOI: 10.1007/s12559-017-9530-0
       
  • Cognitive Fusion of Thermal and Visible Imagery for Effective Detection
           and Tracking of Pedestrians in Videos
    • Authors: Yijun Yan; Jinchang Ren; Huimin Zhao; Genyun Sun; Zheng Wang; Jiangbin Zheng; Stephen Marshall; John Soraghan
      Abstract: In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by a two-stage background subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge-based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. To evaluate the proposed method, a publicly available color-thermal benchmark dataset Object Tracking and Classification in and Beyond the Visible Spectrum is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. Promising results have shown that the proposed fusion-based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem.
      PubDate: 2017-12-04
      DOI: 10.1007/s12559-017-9529-6
       
 
 
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