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  Subjects -> MATHEMATICS (Total: 889 journals)
    - APPLIED MATHEMATICS (73 journals)
    - GEOMETRY AND TOPOLOGY (20 journals)
    - MATHEMATICS (659 journals)
    - MATHEMATICS (GENERAL) (42 journals)
    - NUMERICAL ANALYSIS (19 journals)
    - PROBABILITIES AND MATH STATISTICS (76 journals)

MATHEMATICS (659 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: 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: 25)
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: 8)
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: 2)
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: 9)
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: 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: 7)
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   (Followers: 1)
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: 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: 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   (Followers: 1)
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: 9)
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: 20)
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)
Eco Matemático     Open Access  
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)

        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  [2352 journals]
  • Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial
           Expression Recognition
    • Authors: Guihua Wen; Zhi Hou; Huihui Li; Danyang Li; Lijun Jiang; Eryang Xun
      Pages: 597 - 610
      Abstract: Convolutional neural network (CNN) is a very effective method to recognize facial emotions. However, the preprocessing and selection of parameters of these methods heavily depend on the human experience and require a large amount of trial-and-errors. This paper presents an ensemble of convolutional neural networks method with probability-based fusion for facial expression recognition, where the architecture of CNN was adapted by using the convolutional rectified linear layer as the first layer and multiple hidden maxout layers. It was constructed by randomly varying parameters and architecture around the optimal values for CNN, where each CNN as the base classifier was trained to output a probability for each class. These probabilities were then fused through the probability-based fusion method. The conducted experiments on benchmark data sets validated our method, which had better accuracy than the compared methods. The proposed method was novel and efficient for facial expression recognition.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9472-6
      Issue No: Vol. 9, No. 5 (2017)
       
  • Removal of Electrooculogram Artifacts from Electroencephalogram Using
           
    • Authors: Banghua Yang; Tao Zhang; Yunyuan Zhang; Wanquan Liu; Jianguo Wang; Kaiwen Duan
      Pages: 626 - 633
      Abstract: Electrooculogram (EOG) is one of the major artifacts in the design of electroencephalogram (EEG)-based brain computer interfaces (BCIs). That removing EOG artifacts automatically while retaining more neural data will benefit for further feature extraction and classification. In order to remove EOG artifacts automatically as well as reserve more useful information from raw EEG, this paper proposes a novel blind source separation method called CCA-EEMD (canonical correlation analysis, ensemble empirical mode decomposition). Technically, the major steps of CCA-EEMD are as follows: Firstly, the multiple-channel original EEG signals are separated into several uncorrelated components using CCA. Then, the EOG component can be identified automatically by its kurtosis value. Next, the identified EOG component is decomposed into several intrinsic mode functions (IMFs) by EEMD. The IMFs uncorrelated to the EOG component are recognized and retained, and a new component will be constructed by the retained IMFs. Finally, the clean EEG signals are reconstructed. Keep in mind that the novelty of this paper is that the identified EOG component is not removed directly but used to extract neural EEG data, which would keep more effective information. Our tests with the data of seven subjects demonstrate that the proposed method has distinct advantages over other two commonly used methods in terms of average root mean square error [37.71 ± 0.14 (CCA-EEMD), 44.72 ± 0.13 (CCA), 49.59 ± 0.16 (ICA)], signal-to-noise ratio [3.59 ± 0.24 (CCA-EEMD), −6.53 ± 0.18(CCA), −8.43 ± 0.26 (ICA)], and classification accuracy [0.88 ± 0.002 (CCA-EEMD), 0.79 ± 0.001 (CCA), 0.73 ± 0.002 (ICA)]. The proposed method can not only remove EOG artifacts automatically but also keep the integrity of EEG data to the maximum extent.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9478-0
      Issue No: Vol. 9, No. 5 (2017)
       
  • Lane Boundary Detection Algorithm Based on Vector Fuzzy Connectedness
    • Authors: Lingling Fang; Xianghai Wang
      Pages: 634 - 645
      Abstract: In most actual autonomous guided vehicles (AGV), path finding and navigational control systems are usually implemented using images captured by cameras mounted on the vehicles. This paper presents and discusses a lane boundary detection technique that is necessary for the task of autonomous driving. In this paper, a new method called vector fuzzy connectedness (VFC) is presented to detect and estimate road lane boundaries. First, a preprocessed technique is used to obtain a skeleton image. Based on the result, the curvatures of the left and right lane boundaries are estimated, and the control points are found by the VFC method. Finally, the non-uniform b-spline (NUBS) interpolation method is introduced to construct the road lane boundaries. The proposed VFC method integrates the vector concept and fuzzy connectedness into the lane boundary detection algorithm. As shown in the example results, the proposed method can extract various road lane shapes and types from real road frames even under complex road environments. For navigation tasks, it is necessary to determine the position of the vehicle relative to the road. These results prove that the proposed detection method can assist in a number of actual AGV assistant applications. In the future, some intelligent techniques will be applied to test the AGV system with obstacle avoidance conditions on real world roads.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9483-3
      Issue No: Vol. 9, No. 5 (2017)
       
  • Storages Are Not Forever
    • Authors: Erik Cambria; Anupam Chattopadhyay; Eike Linn; Bappaditya Mandal; Bebo White
      Pages: 646 - 658
      Abstract: Not unlike the concern over diminishing fossil fuel, information technology is bringing its own share of future worries. We chose to look closely into one concern in this paper, namely the limited amount of data storage. By a simple extrapolatory analysis, it is shown that we are on the way to exhaust our storage capacity in less than two centuries with current technology and no recycling. This can be taken as a note of caution to expand research initiative in several directions: firstly, bringing forth innovative data analysis techniques to represent, learn, and aggregate useful knowledge while filtering out noise from data; secondly, tap onto the interplay between storage and computing to minimize storage allocation; thirdly, explore ingenious solutions to expand storage capacity. Throughout this paper, we delve deeper into the state-of-the-art research and also put forth novel propositions in all of the abovementioned directions, including space- and time-efficient data representation, intelligent data aggregation, in-memory computing, extra-terrestrial storage, and data curation. The main aim of this paper is to raise awareness on the storage limitation we are about to face if current technology is adopted and the storage utilization growth rate persists. In the manuscript, we propose some storage solutions and a better utilization of storage capacity through a global DIKW hierarchy.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9482-4
      Issue No: Vol. 9, No. 5 (2017)
       
  • FE-ELM: A New Friend Recommendation Model with Extreme Learning Machine
    • Authors: Zhen Zhang; Xiangguo Zhao; Guoren Wang
      Pages: 659 - 670
      Abstract: Friend recommendation is one of the most popular services in location-based social network (LBSN) platforms, which recommends interested or familiar people to users. Except for the original social property and textual property in social networks, LBSN specially owns the spatial-temporal property. However, none of the existing methods fully utilized all the three properties (i.e., just one or two), which may lead to the low recommendation accuracy. Moreover, these existing methods are usually inefficient. In this paper, we propose a new friend recommendation model to solve the above shortcomings of the existing methods, called feature extraction-extreme learning machine (FE-ELM), where friend recommendation is regarded as a binary classification problem. Classification is an important task in cognitive computation community. First, we use new strategies in our FE-ELM model to extract the spatial-temporal feature, social feature, and textual feature. These features make full use of all above properties of LBSN and ensure the recommendation accuracy. Second, our FE-ELM model also takes advantage of the extreme learning machine (ELM) classifier. ELM has fast learning speed and ensures the recommendation efficiency. Extensive experiments verify the accuracy and efficiency of FE-ELM model.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9484-2
      Issue No: Vol. 9, No. 5 (2017)
       
  • An Efficient Corpus-Based Stemmer
    • Authors: Jasmeet Singh; Vishal Gupta
      Pages: 671 - 688
      Abstract: Word stemming is a linguistic process in which the various inflected word forms are matched to their base form. It is among the basic text pre-processing approaches used in Natural Language Processing and Information Retrieval. Stemming is employed at the text pre-processing stage to solve the issue of vocabulary mismatch or to reduce the size of the word vocabulary, and consequently also the dimensionality of training data for statistical models. In this article, we present a fully unsupervised corpus-based text stemming method which clusters morphologically related words based on lexical knowledge. The proposed method performs cognitive-inspired computing to discover morphologically related words from the corpus without any human intervention or language-specific knowledge. The performance of the proposed method is evaluated in inflection removal (approximating lemmas) and Information Retrieval tasks. The retrieval experiments in four different languages using standard Text Retrieval Conference, Cross-Language Evaluation Forum, and Forum for Information Retrieval Evaluation collections show that the proposed stemming method performs significantly better than no stemming. In the case of highly inflectional languages, Marathi and Hungarian, the improvement in Mean Average Precision is nearly 50% as compared to unstemmed words. Moreover, the proposed unsupervised stemming method outperforms state-of-the-art strong language-independent and rule-based stemming methods in all the languages. Besides Information Retrieval, the proposed stemming method also performs significantly better in inflection removal experiments. The proposed unsupervised language-independent stemming method can be used as a multipurpose tool for various tasks such as the approximation of lemmas, improving retrieval performance or other Natural Language Processing applications.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9479-z
      Issue No: Vol. 9, No. 5 (2017)
       
  • A Study on Text-Score Disagreement in Online Reviews
    • Authors: Michela Fazzolari; Vittoria Cozza; Marinella Petrocchi; Angelo Spognardi
      Pages: 689 - 701
      Abstract: In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understand the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that (1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa), and (2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from TripAdvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well-established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that—on a scale of five stars—those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews—on an initial very large dataset—that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement,which conveniently convey to the user a summary of positive and negative features of the review target.
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9496-y
      Issue No: Vol. 9, No. 5 (2017)
       
  • A Comparative Study of In-Air Trajectories at Short and Long Distances in
           Online Handwriting
    • Authors: Carlos Alonso-Martinez; Marcos Faundez-Zanuy; Jiri Mekyska
      Pages: 712 - 720
      Abstract: Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current acquisition devices do not provide height formation. However, it is quite straightforward to differentiate movements at two different heights (a) short distance: height lower or equal to 1 cm above a surface of digitizer, the digitizer provides x and y coordinates; (b) long distance: height exceeding 1 cm, the only information available is a time stamp that indicates the time that a specific stroke has spent at long distance. Although short distance has been used in several papers, long distances have been ignored and will be investigated in this paper. In this paper, we will analyze a large set of databases (BIOSECUR-ID, EMOTHAW, PaHaW, OXYGEN-THERAPY, and SALT), which contain a total amount of 663 users and 17,951 files. We have specifically studied (a) the percentage of time spent on-surface, in-air at short distance, and in-air at long distance for different user profiles (pathological and healthy users) and different tasks; (b) the potential use of these signals to improve classification rates. Our experimental results reveal that long distance movements represent a very small portion of the total execution time (0.5% in the case of signatures and 10.4% for uppercase words of BIOSECUR-ID, which is the largest database). In addition, significant differences have been found in the comparison of pathological versus control group for letter “l” in PaHaW database (p = 0.0157) and crossed pentagons in SALT database (p = 0.0122).
      PubDate: 2017-10-01
      DOI: 10.1007/s12559-017-9501-5
      Issue No: Vol. 9, No. 5 (2017)
       
  • The Impact of Sentiment Features on the Sentiment Polarity Classification
           in Persian Reviews
    • Authors: Ehsan Asgarian; Mohsen Kahani; Shahla Sharifi
      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: 2017-11-07
      DOI: 10.1007/s12559-017-9513-1
       
  • The Importance of Pen Motion Pattern Groups for Semi-Automatic
           Classification of Handwriting into Mental Workload Classes
    • Authors: Murad Badarna; Ilan Shimshoni; Gil Luria; Sara Rosenblum
      Abstract: In this paper, we introduce the pen motion pattern groups (PMPGs) and their contribution to the classification of handwriting into cognitive mental workload classes. We demonstrate the importance of PMPGs by providing an efficient semi-automatic machine learning-based classification framework that distinguishes between handwritten texts written by the same person under different mental workloads. Our evaluation framework is non-language-dependent since we used stroke features, which are not language-specific, and it takes into account the variability in behavioral biometrics between different writers. The handwritten text data was collected using the Computerized Penmanship Evaluation Tool. This digitizer provided accurate temporal measures throughout the writing. As a first stage, the participants were asked to write a given text in the Hebrew language. Then, as a second stage, the participants’ cognitive workload was manipulated by asking them to hold a number in their memory during the entire writing task. In our experiments, we show that incorporating the PMPGs into the classification process yielded an average cognitive load discrimination accuracy of 92.16%, which decreased to 72.90% when the PMPGs were not considered. The separation of handwritten strokes into PMPGs allows us to account for the fact that the strokes are affected differently under different cognitive mental workloads. This novel distinction between PMPGs is important since the handwriting process in each PMPG is different from a perceptual motor and brain-hand control point of view. Moreover, most of the features that are influenced by cognitive workload are those that cannot be discerned by an expert when looking at a handwritten text on paper, such as azimuth, tilt, velocity, acceleration, and pressure.
      PubDate: 2017-11-07
      DOI: 10.1007/s12559-017-9520-2
       
  • Detecting Multiple Coexisting Emotions in Microblogs with Convolutional
           Neural Networks
    • Authors: Shi Feng; Yaqi Wang; Kaisong Song; Daling Wang; Ge Yu
      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: 2017-11-07
      DOI: 10.1007/s12559-017-9521-1
       
  • Learning from Few Samples with Memory Network
    • Authors: Shufei Zhang; Kaizhu Huang; Rui Zhang; Amir Hussain
      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: 2017-10-25
      DOI: 10.1007/s12559-017-9507-z
       
  • A Machine Learning Approach to Detect Router Advertisement Flooding
           Attacks in Next-Generation IPv6 Networks
    • Authors: Mohammed Anbar; Rosni Abdullah; Bassam Naji Al-Tamimi; Amir Hussain
      Abstract: Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attacks. The proposed technique exploits information gain ratio (IGR) and principal component analysis (PCA) for feature selection and a support vector machine (SVM)-based predictor model, which can also detect input traffic anomaly. A real benchmark dataset obtained from National Advanced IPv6 Center of Excellence laboratory is used to evaluate the proposed technique. The evaluation process is conducted with two experiments. The first experiment investigates the effect of IGR and PCA feature selection methods to identify the most contributed features for the SVM training model. The second experiment evaluates the capability of SVM to detect RA flooding attacks. The results show that the proposed technique demonstrates excellent detection accuracy and is thus an effective choice for detecting RA flooding attacks. The main contribution of this study is identification of a set of new features that are related to RA flooding attack by utilizing IGR and PCA algorithms. The proposed technique in this paper can effectively detect the presence of RA flooding attack in IPv6 network.
      PubDate: 2017-10-23
      DOI: 10.1007/s12559-017-9519-8
       
  • An Online Sequential Learning Non-parametric Value-at-Risk Model for
           High-Dimensional Time Series
    • Authors: Heng-Guo Zhang; Libo Wu; Yan Song; Chi-Wei Su; Qingping Wang; Fei Su
      Abstract: Online Value-at-Risk (VaR) analysis in high-dimensional space remains a challenge in the era of big data. In this paper, we propose an online sequential learning non-parametric VaR model called OS-GELM which is an autonomous cognitive system. This model uses a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process and an online sequential extreme learning machine (OS-ELM) to cognitively calculate VaR, which can be used for online risk analysis. The proposed model not only learns the data one-by-one or chunk-by-chunk but also calculates VaR in real time by extending OS-ELM from machine learning to the non-parametric GARCH process. The GARCH process is also extended to one-by-one and chunk-by-chunk mode. In OS-GELM, the parameters of hidden nodes are randomly selected. The output weights are analytically determined based on the sequentially arriving data. In addition, the generalization performance of the OS-GELM model attains a small training error and generates the smallest norm of weights. Experimentally obtained VaRs are compared with those given by GARCH-type models and conventional OS-ELM. The computational results demonstrate that the OS-GELM model obtains more accurate results and is better at forecasting the online VaR. OS-GELM model is an autonomous cognitive system to dynamically calculate Value-at-Risk, which can be used for online financial risk assessment about human being’s behavior. The OS-GELM model can calculate VaR in real time, which can be used as a tool for online risk management. OS-GELM can handle any bounded, non-constant, piecewise-continuous membership function to realize real-time VaR monitoring.
      PubDate: 2017-10-14
      DOI: 10.1007/s12559-017-9516-y
       
  • Anatomical Pattern Analysis for Decoding Visual Stimuli in Human Brains
    • Authors: Muhammad Yousefnezhad; Daoqiang Zhang
      Abstract: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxel Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noise in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on four visual categories (words, consonants, objects, and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.
      PubDate: 2017-10-13
      DOI: 10.1007/s12559-017-9518-9
       
  • End-to-End Lifelong Learning: a Framework to Achieve Plasticities of both
           the Feature and Classifier Constructions
    • Authors: Wangli Hao; Junsong Fan; Zhaoxiang Zhang; Guibo Zhu
      Abstract: Plasticity in our brain offers us promising ability to learn and know the world. Although great successes have been achieved in many fields, few bio-inspired machine learning methods have mimicked this ability. Consequently, when meeting large-scale or time-varying data, these bio-inspired methods are infeasible, due to the reasons that they lack plasticity and need all training data loaded into memory. Furthermore, even the popular deep convolutional neural network (CNN) models have relatively fixed structures and cannot process time varying data well. Through incremental methodologies, this paper aims at exploring an end-to-end lifelong learning framework to achieve plasticities of both the feature and classifier constructions. The proposed model mainly comprises of three parts: Gabor filters followed by max pooling layer offering shift and scale tolerance to input samples, incremental unsupervised feature extraction, and incremental SVM trying to achieve plasticities of both the feature learning and classifier construction. Different from CNN, plasticity in our model has no back propogation (BP) process and does not need huge parameters. Our incremental models, including IncPCANet and IncKmeansNet, have achieved better results than PCANet and KmeansNet on minist and Caltech101 datasets respectively. Meanwhile, IncPCANet and IncKmeansNet show promising plasticity of feature extraction and classifier construction when the distribution of data changes. Lots of experiments have validated the performance of our model and verified a physiological hypothesis that plasticity exists in high level layer better than that in low level layer.
      PubDate: 2017-10-07
      DOI: 10.1007/s12559-017-9514-0
       
  • Attentional Bias Pattern Recognition in Spiking Neural Networks from
           Spatio-Temporal EEG Data
    • Authors: Zohreh Gholami Doborjeh; Maryam G. Doborjeh; Nikola Kasabov
      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: 2017-10-06
      DOI: 10.1007/s12559-017-9517-x
       
  • Reducing and Stretching Deep Convolutional Activation Features for
           Accurate Image Classification
    • Authors: Guoqiang Zhong; Shoujun Yan; Kaizhu Huang; Yajuan Cai; Junyu Dong
      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: 2017-10-04
      DOI: 10.1007/s12559-017-9515-z
       
  • A Primal Neural Network for Online Equality-Constrained Quadratic
           Programming
    • Authors: Ke Chen; Zhaoxiang Zhang
      Abstract: This paper aims at solving online equality-constrained quadratic programming problem, which is widely encountered in science and engineering, e.g., computer vision and pattern recognition, digital signal processing, and robotics. Recurrent neural networks such as conventional GradientNet and ZhangNet are considered as powerful solvers for such a problem in light of its high computational efficiency and capability of circuit realisation. In this paper, an improved primal recurrent neural network and its electronic implementation are proposed and analysed. Compared to the existing recurrent networks, i.e. GradientNet and ZhangNet, our network can theoretically guarantee superior global exponential convergence. Robustness performance of our such neural model is also analysed under a large model implementation error, with the upper bound of stead-state solution error estimated. Simulation results demonstrate theoretical analysis on the proposed model, which also verify the effectiveness of the proposed model for online equality-constrained quadratic programming.
      PubDate: 2017-10-04
      DOI: 10.1007/s12559-017-9510-4
       
  • Clustering-Oriented Multiple Convolutional Neural Networks for Single
           Image Super-Resolution
    • Authors: Peng Ren; Wenjian Sun; Chunbo Luo; Amir Hussain
      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: 2017-10-04
      DOI: 10.1007/s12559-017-9512-2
       
 
 
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