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  Subjects -> COMPUTER SCIENCE (Total: 1969 journals)
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COMPUTER SCIENCE (1147 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 11)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 67)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 24)
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 35)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 31)
Applied Medical Informatics     Open Access   (Followers: 9)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 8)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 233)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 119)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 12)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 13)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 18)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 47)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
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 and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 12)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 27)
Computer     Full-text available via subscription   (Followers: 78)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 8)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 13)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 10)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 9)
Computer Science Review     Hybrid Journal   (Followers: 10)

        1 2 3 4 5 6 | 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  [2329 journals]
  • Cognitively Inspired Artificial Bee Colony Clustering for Cognitive
           Wireless Sensor Networks
    • Authors: Sung-Soo Kim; Sean McLoone; Ji-Hwan Byeon; Seokcheon Lee; Hongbo Liu
      Pages: 207 - 224
      Abstract: Abstract The swarm cognitive behavior of bees readily translates to swarm intelligence with “social cognition,” thus giving rise to the rapid promotion of survival skills and resource allocation. This paper presents a novel cognitively inspired artificial bee colony clustering (ABCC) algorithm with a clustering evaluation model to manage the energy consumption in cognitive wireless sensor networks (CWSNs). The ABCC algorithm can optimally align with the dynamics of the sensor nodes and cluster heads in CWSNs. These sensor nodes and cluster heads adapt to topological changes in the network graph over time. One of the major challenges with employing CWSNs is to maximize the lifetime of the networks. The ABCC algorithm is able to reduce and balance the energy consumption of nodes across the networks. Artificial bee colony (ABC) optimization is attractive for this application as the cognitive behaviors of artificial bees match perfectly with the intrinsic dynamics in cognitive wireless sensor networks. Additionally, it employs fewer control parameters compared to other heuristic algorithms, making identification of optimal parameter settings easier. Simulation results illustrate that the ABCC algorithm outperforms particle swarm optimisation (PSO), group search optimization (GSO), low-energy adaptive clustering hierarchy (LEACH), LEACH-centralized (LEACH-C), and hybrid energy-efficient distributed clustering (HEED) for energy management in CWSNs. Our proposed algorithm is increasingly superior to these other approaches as the number of nodes in the network grows.
      PubDate: 2017-04-01
      DOI: 10.1007/s12559-016-9447-z
      Issue No: Vol. 9, No. 2 (2017)
       
  • Observer-Based Stabilization Control of Time-Delay T-S Fuzzy Systems via
           the Non-Uniform Delay Partitioning Approach
    • Authors: Xiaohong Nian; Miaoping Sun; Hua Guo; Haibo Wang; Liqiong Dai
      Pages: 225 - 236
      Abstract: Abstract It is well known that intelligent control technology plays an important role in the design of many control systems, and intelligent control has aroused wide attention from scholars. Fuzzy control is also the case because fuzzy control is one branch of intelligent control. The Takagi-Sugeno (T-S) fuzzy model is an effective approach when dealing with complex nonlinear systems, and the advantages of fuzzy controller design is that the linear control methods can be used. In addition, nonlinearity and time delay are inherent and not all states are available in many practical system. Therefore, the observer-based stabilization control for time-delay T-S fuzzy systems is of great significance. With the help of the non-uniform delay partitioning approach, a novel method is put forward to analyze the stability of the time-delay T-S fuzzy system and design the observer-based feedback controller via the parallel distributed compensation (PDC) scheme. The sufficient conditions of asymptotic stability for both nominal and uncertain time-delay T-S fuzzy system are derived based on the Lyapunov stability theory and linear matrix inequality (LMI) techniques. What is more, the solving methods to obtain the controller gain matrices, observer gain matrices, and upper bound of time delay are presented. Two illustrative examples are given to demonstrate the effectiveness and verify the superiority of our developed methods. From the simulation results, it can be found that the most prominent advantages of our proposed methods lie on larger delay bound and less decision variables compared with other related methods. The problem of observer-based stabilization control for continuous nonlinear time-delay systems is investigated in this paper, and the delay-dependent stability criteria are derived to achieve greater delay bound by virtual of the non-uniform delay partitioning approach. Numerical examples further confirmed the effectiveness and advantages of our developed methods.
      PubDate: 2017-04-01
      DOI: 10.1007/s12559-017-9448-6
      Issue No: Vol. 9, No. 2 (2017)
       
  • A Multiple Criteria Decision Making Model with Entropy Weight in an
           Interval-Transformed Hesitant Fuzzy Environment
    • Authors: B. Farhadinia
      Abstract: Abstract This article first aims to critically review the existing literature on entropy measures for hesitant fuzzy elements (HFEs), and then introduces the concept of interval-transformed HFE (ITHFE) which bridges HFEs and interval-valued fuzzy sets (IVFSs). As discussed later, this bridge will also benefit researchers in terms of opening up more directions for future work, concentrating on HFE entropy measures. By taking the concept of ITHFE into account, we here exploit three features of an interval value including its lower and upper bounds, and the range of possible values to define a new class of entropy measures for HFEs. Then, we introduce the axiomatic framework of the new measures of entropy for HFEs, and two families of HFE entropy measures are also constructed. A comparison results shows that the proposed entropy measures for HFEs are more confident in distinguishing different HFEs rather than the most existing entropy measures. Finally, a multiple attribute decision making problem based on TOPSIS is applied to a case study of the health-care waste management.
      PubDate: 2017-05-25
      DOI: 10.1007/s12559-017-9480-6
       
  • Cognitive Modeling of the Natural Behavior of the Varroa destructor Mite
           on Video
    • Authors: Melvin Ramírez-Bogantes; Juan P. Prendas-Rojas; Geovanni Figueroa-Mata; Rafael A. Calderon; Oscar Salas-Huertas; Carlos M. Travieso
      Abstract: Abstract The present work offers an innovative and automatic approach for detecting, tracking, analyzing, and reporting the natural behavior of the Varroa destructor mite and its activity from videos provided by the Tropical Apicultural Research Center (CINAT) in Costa Rica. These videos correspond to the presence of V. destructor in capped Africanized worker honeybee cells in a controlled environment. The main objective of this paper is to present an automatic report of the identification of the mite behavior based on mite information (bioinspired information). First, a calibration system was implemented to enhance the frame. This calibration was achieved by searching the movement-active area (MAA) and the geometrical definition of the V. destructor mite. Then, an automatic detection and tracking was applied. Finally, an automatic classification was used to establish the mite activity. This approach reached up to 92.83% for all processes: detection, tracking, behavior analysis, and activity reporting, in real time and showing a cognitive model of the mite. The proposed approach provides an automatic tool and objective measurement against manual and qualitative methods traditionally applied in this kind of analysis, with a significant potential to be used as a reference in the modeling of the behavior of the V. destructor mite.
      PubDate: 2017-05-20
      DOI: 10.1007/s12559-017-9471-7
       
  • Orthogonal Echo State Networks and Stochastic Evaluations of Likelihoods
    • Authors: N. Michael Mayer; Ying-Hao Yu
      Abstract: Abstract We report about probabilistic likelihood estimates that are performed on time series using an echo state network with orthogonal recurrent connectivity. The results from tests using synthetic stochastic input time series with temporal inference indicate that the capability of the network to infer depends on the balance between input strength and recurrent activity. This balance has an influence on the network with regard to the quality of inference from the short-term input history versus inference that accounts for influences that date back a long time. Sensitivity of such networks against noise and the finite accuracy of network states in the recurrent layer are investigated. In addition, a measure based on mutual information between the output time series and the reservoir is introduced. Finally, different types of recurrent connectivity are evaluated. Orthogonal matrices not only show the best results of all investigated connectivity types overall but also in the way how the network performance scales with the size of the recurrent layer.
      PubDate: 2017-05-16
      DOI: 10.1007/s12559-017-9466-4
       
  • Neuron Pruning-Based Discriminative Extreme Learning Machine for Pattern
           Classification
    • Authors: Tan Guo; Lei Zhang; Xiaoheng Tan
      Abstract: Abstract Extreme learning machine (ELM), as a newly developed learning paradigm for the generalized single hidden layer feedforward neural networks, has been widely studied due to its unique characteristics, i.e., fast training, good generalization, and universal approximation/classification ability. A novel framework of discriminative extreme learning machine (DELM) is developed for pattern classification. In DELM, the margins between different classes are enlarged as much as possible through a technique called ε-dragging. DELM is further extended to pruning DELM (P-DELM) using L2,1-norm regularization. The performance of DELM is compared with several state-of-the-art methods on public face databases. The simulation results show the effectiveness of DELM for face recognition when there are posture, facial expression, and illumination variations. P-DELM can distinguish the importance of different hidden neurons and remove the worthless ones. The model can achieve promising performance with fewer hidden neurons and less prediction time on several benchmark datasets. In DELM model, the margins between different classes are enlarged by learning a nonnegative label relaxation matrix. The experiments validate the effectiveness of DELM. Furthermore, DELM is extended to P-DELM based on L2,1-norm regularization. The developed P-DELM can naturally distinguish the importance of different hidden neurons, which will lead to a more compact network by neuron pruning. Experimental validations on some benchmark datasets show the advantages of the proposed P-DELM method.
      PubDate: 2017-05-11
      DOI: 10.1007/s12559-017-9474-4
       
  • 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
      Abstract: 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: 2017-05-10
      DOI: 10.1007/s12559-017-9472-6
       
  • DOA Estimation of Excavation Devices with ELM and MUSIC-Based Hybrid
           Algorithm
    • Authors: Jianzhong Wang; Kai Ye; Jiuwen Cao; Tianlei Wang; Anke Xue; Yuhua Cheng; Chun Yin
      Abstract: Abstract Underground pipelines suffered severe external breakage caused by excavation devices due to arbitral road excavation. Acoustic signal-based recognition has recently shown effectiveness in underground pipeline network surveillance. However, merely relying on recognition may lead to a high false alarm rate. The reason is that underground pipelines are generally paved along a fixed direction and excavations out of the region also trigger the surveillance system. To enhance the reliability of the surveillance system, the direction-of-arrival (DOA) estimation of target sources is combined into the recognition algorithm to reduce false detections in this paper. Two hybrid recognition algorithms are developed. The first one employs extreme learning machine (ELM) for acoustic recognition followed by a focusing matrix-based multiple signal classification algorithm (ELM-MUSIC) for DOA estimation. The second introduces a decision matrix (DM) to characterize the statistic distribution of results obtained by ELM-MUSIC. Real acoustic signals collected by a cross-layer sensor array are conducted for performance comparison. Four representative excavation devices working in a metro construction site are used to generate the signal. Multiple scenarios of the experiments are designed. Comparisons show that the proposed ELM-MUSIC and DM algorithms outperform the conventional focusing matrix based MUSIC (F-MUSIC). In addition, the improved DM method is capable of localizing multiple devices working in order. Two hybrid acoustic signal recognition and source direction estimation algorithms are developed for excavation device classification in this paper. The novel recognition combining DOA estimation scheme can work efficiently for underground pipeline network protection in the real-world complex environment.
      PubDate: 2017-05-08
      DOI: 10.1007/s12559-017-9475-3
       
  • A Review of Sentiment Analysis Research in Chinese Language
    • Authors: Haiyun Peng; Erik Cambria; Amir Hussain
      Abstract: Abstract Research on sentiment analysis in English language has undergone major developments in recent years. Chinese sentiment analysis research, however, has not evolved significantly despite the exponential growth of Chinese e-business and e-markets. This review paper aims to study past, present, and future of Chinese sentiment analysis from both monolingual and multilingual perspectives. The constructions of sentiment corpora and lexica are first introduced and summarized. Following, a survey of monolingual sentiment classification in Chinese via three different classification frameworks is conducted. Finally, sentiment classification based on the multilingual approach is introduced. After an overview of the literature, we propose that a more human-like (cognitive) representation of Chinese concepts and their inter-connections could overcome the scarceness of available resources and, hence, improve the state of the art. With the increasing expansion of Chinese language on the Web, sentiment analysis in Chinese is becoming an increasingly important research field. Concept-level sentiment analysis, in particular, is an exciting yet challenging direction for such research field which holds great promise for the future.
      PubDate: 2017-05-08
      DOI: 10.1007/s12559-017-9470-8
       
  • Common Subspace Learning via Cross-Domain Extreme Learning Machine
    • Authors: Yan Liu; Lei Zhang; Pingling Deng; Zheng He
      Abstract: Abstract Extreme learning machine (ELM) is proposed for solving a single-layer feed-forward network (SLFN) with fast learning speed and has been confirmed to be effective and efficient for pattern classification and regression in different fields. ELM originally focuses on the supervised, semi-supervised, and unsupervised learning problems, but just in the single domain. To our best knowledge, ELM with cross-domain learning capability in subspace learning has not been exploited very well. Inspired by a cognitive-based extreme learning machine technique (Cognit Comput. 6:376–390, 1; Cognit Comput. 7:263–278, 2.), this paper proposes a unified subspace transfer framework called cross-domain extreme learning machine (CdELM), which aims at learning a common (shared) subspace across domains. Three merits of the proposed CdELM are included: (1) A cross-domain subspace shared by source and target domains is achieved based on domain adaptation; (2) ELM is well exploited in the cross-domain shared subspace learning framework, and a new perspective is brought for ELM theory in heterogeneous data analysis; (3) the proposed method is a subspace learning framework and can be combined with different classifiers in recognition phase, such as ELM, SVM, nearest neighbor, etc. Experiments on our electronic nose olfaction datasets demonstrate that the proposed CdELM method significantly outperforms other compared methods.
      PubDate: 2017-05-05
      DOI: 10.1007/s12559-017-9473-5
       
  • Echo State Property of Deep Reservoir Computing Networks
    • Authors: Claudio Gallicchio; Alessio Micheli
      Abstract: In the last years, the Reservoir Computing (RC) framework has emerged as a state of-the-art approach for efficient learning in temporal domains. Recently, within the RC context, deep Echo State Network (ESN) models have been proposed. Being composed of a stack of multiple non-linear reservoir layers, deep ESNs potentially allow to exploit the advantages of a hierarchical temporal feature representation at different levels of abstraction, at the same time preserving the training efficiency typical of the RC methodology. In this paper, we generalize to the case of deep architectures the fundamental RC conditions related to the Echo State Property (ESP), based on the study of stability and contractivity of the resulting dynamical system. Besides providing a necessary condition and a sufficient condition for the ESP of layered RC networks, the results of our analysis provide also insights on the nature of the state dynamics in hierarchically organized recurrent models. In particular, we find out that by adding layers to a deep reservoir architecture, the regime of network’s dynamics can only be driven towards (equally or) less stable behaviors. Moreover, our investigation shows the intrinsic ability of temporal dynamics differentiation at the different levels in a deep recurrent architecture, with higher layers in the stack characterized by less contractive dynamics. Such theoretical insights are further supported by experimental results that show the effect of layering in terms of a progressively increased short-term memory capacity of the recurrent models.
      PubDate: 2017-05-05
      DOI: 10.1007/s12559-017-9461-9
       
  • Reservoir Computing with Both Neuronal Intrinsic Plasticity and
           Multi-Clustered Structure
    • Authors: Fangzheng Xue; Qian Li; Hongjun Zhou; Xiumin Li
      Abstract: Abstract In the echo state networks, both reservoir states and network structure are essential for the performance of reservoir computing. In neuroscience, it has been confirmed that a single neuron can adaptively change its intrinsic excitability to fit various synaptic inputs. This mechanism is called intrinsic plasticity (IP) mechanism in the literature. This adaptive adjustment of neuronal response to external inputs is believed to maximize input-output mutual information. Meanwhile, the existence of multi-clustered structure with small-world-like property in the brain has been strongly supported by many neurophysiological experiments. Thus, it is advisable to consider both the intrinsic plasticity and multi-clustered structure of a reservoir network, rather than a random network with a non-adaptive reservoir response. In this paper, reservoir models with neuronal intrinsic plasticity and multi-clustered structure are investigated. The effects of two types of IP rules on the performance of several computational tasks have been investigated in detail by combining neuronal IP with multi-clustered reservoir structures. The first type is the Triesch’s IP rule, which drives the output activities of neurons to approximate exponential distributions; another is the Li’s IP rule, which generates a Gaussian distribution of neuronal firing. Results show that both the multi-clustered structures and IP rules can improve the computational accuracy of reservoir computing. However, before the application of the IP rules, the enhancement of computational performance for multi-clustered reservoirs is minor. Both IP rules contribute to improvement of the computational performance, where the Li’s IP rule is more advantageous than the Triesch’s IP. The results indicate that the combination of multi-clustered reservoir structures and IP learning can increase the dynamic diversity of reservoir states, especially for the IP’s learning. The adaptive tuning of reservoir states based on IP improves the dynamic complexity of neuronal activity, which helps train output weights. This biologically inspired reservoir model may give insights for the optimization of reservoir computing.
      PubDate: 2017-05-04
      DOI: 10.1007/s12559-017-9467-3
       
  • A Multiple-Input Strategy to Efficient Integrated Photonic Reservoir
           Computing
    • Authors: Andrew Katumba; Matthias Freiberger; Peter Bienstman; Joni Dambre
      Abstract: Abstract Photonic reservoir computing has evolved into a viable contender for the next generation of analog computing platforms as industry looks beyond standard transistor-based computing architectures. Integrated photonic reservoir computing, particularly on the silicon-on-insulator platform, presents a CMOS-compatible, wide bandwidth, parallel platform for implementation of optical reservoirs. A number of demonstrations of the applicability of this platform for processing optical telecommunication signals have been made in the recent past. In this work, we take it a stage further by performing an architectural search for designs that yield the best performance while maintaining power efficiency. We present numerical simulations for an optical circuit model of a 16-node integrated photonic reservoir with the input signal injected in combinations of 2, 4, and 8 nodes, or into all 16 nodes. The reservoir is composed of a network of passive photonic integrated circuit components with the required nonlinearity introduced at the readout point with a photodetector. The resulting error performance on the temporal XOR task for these multiple input cases is compared with that of the typical case of input to a single node. We additionally introduce for the first time in our simulations a realistic model of a photodetector. Based on this, we carry out a full power-level exploration for each of the above input strategies. Multiple-input reservoirs achieve better performance and power efficiency than single-input reservoirs. For the same input power level, multiple-input reservoirs yield lower error rates. The best multiple-input reservoir designs can achieve the error rates of single-input ones with at least two orders of magnitude less total input power. These results can be generally attributed to the increase in richness of the reservoir dynamics and the fact that signals stay longer within the reservoir. If we account for all loss and noise contributions, the minimum input power for error-free performance for the optimal design is found to be in the ≈1 mW range.
      PubDate: 2017-04-28
      DOI: 10.1007/s12559-017-9465-5
       
  • Advances in Biologically Inspired Reservoir Computing
    • Authors: Simone Scardapane; John B. Butcher; Filippo M. Bianchi; Zeeshan K. Malik
      PubDate: 2017-04-28
      DOI: 10.1007/s12559-017-9469-1
       
  • Optimizing Echo State Networks for Static Pattern Recognition
    • Authors: Adam J. Wootton; Sarah L. Taylor; Charles R. Day; Peter W. Haycock
      Abstract: Abstract Static pattern recognition requires a machine to classify an object on the basis of a combination of attributes and is typically performed using machine learning techniques such as support vector machines and multilayer perceptrons. Unusually, in this study, we applied a successful time-series processing neural network architecture, the echo state network (ESN), to a static pattern recognition task. The networks were presented with clamped input data patterns, but in this work, they were allowed to run until their output units delivered a stable set of output activations, in a similar fashion to previous work that focused on the behaviour of ESN reservoir units. Our aim was to see if the short-term memory developed by the reservoir and the clamped inputs could deliver improved overall classification accuracy. The study utilized a challenging, high dimensional, real-world plant species spectroradiometry classification dataset with the objective of accurately detecting one of the world’s top 100 invasive plant species. Surprisingly, the ESNs performed equally well with both unsettled and settled reservoirs. Delivering a classification accuracy of 96.60%, the clamped ESNs outperformed three widely used machine learning techniques, namely support vector machines, extreme learning machines and multilayer perceptrons. Contrary to past work, where inputs were clamped until reservoir stabilization, it was found that it was possible to obtain similar classification accuracy (96.49%) by clamping the input patterns for just two repeats. The chief contribution of this work is that a recurrent architecture can get good classification accuracy, even while the reservoir is still in an unstable state.
      PubDate: 2017-04-28
      DOI: 10.1007/s12559-017-9468-2
       
  • A Novel Clustering Algorithm in a Neutrosophic Recommender System for
           Medical Diagnosis
    • Authors: Nguyen Dang Thanh; Mumtaz Ali; Le Hoang Son
      Abstract: Abstract Decision-making processes have been extensively used in artificial intelligence and cognitive sciences to explain and improve individual and social perception. As one of the most typical decision-making problems, medical diagnosis is used to analyze the relationship between symptoms and diseases according to uncertain and inconsistent information. It is essential to investigate the structure of a set of records on different levels such that similar patients can be treated concurrently within a group. In this paper, we propose a novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. First, we define new algebraic structures for the system such as lattices, De Morgan algebra, Kleen algebra, MV algebra, BCK algebra, Stone algebra, and Brouwerian algebra. Based on these algebraic structures, we construct a neutrosophic recommender similarity matrix and a neutrosophic recommender equivalence matrix. A consecutive series of compositions between the neutrosophic recommender similarity matrices is performed to obtain the neutrosophic recommender equivalence matrix. From this matrix, a λ-cutting matrix is defined to conduct clustering among the neutrosophic recommender systems. Regarding the values of clustering validity indices, the Davies-Bouldin (DB) of the proposed method is approximately 20% better than those of the methods of Sahin (Neutrosophic Sets and Systems. 2014;2:18–24), Ye (J Intell Syst. 2014;23(4):379–89), and Ye (Soft Computing 2016;1–7). Analogously, the IFV and simplified silhouette with criterion (SSWC) of the proposal are better than those of the relevant methods with the improvement percentages being 30 and 70%, respectively. The results show that the proposed method is better than the related algorithms in terms of clustering quality whilst its computational time is slightly slower. The contributions of this research is significant in both algorithmic aspects of computational intelligence and and practical applications.
      PubDate: 2017-04-10
      DOI: 10.1007/s12559-017-9462-8
       
  • An Investigation of the Dynamical Transitions in Harmonically Driven
           Random Networks of Firing-Rate Neurons
    • Authors: Kyriacos Nikiforou; Pedro A. M. Mediano; Murray Shanahan
      Abstract: Abstract Continuous-time recurrent neural networks are widely used as models of neural dynamics and also have applications in machine learning. But their dynamics are not yet well understood, especially when they are driven by external stimuli. In this article, we study the response of stable and unstable networks to different harmonically oscillating stimuli by varying a parameter ρ, the ratio between the timescale of the network and the stimulus, and use the dimensionality of the network’s attractor as an estimate of the complexity of this response. Additionally, we propose a novel technique for exploring the stationary points and locally linear dynamics of these networks in order to understand the origin of input-dependent dynamical transitions. Attractors in both stable and unstable networks show a peak in dimensionality for intermediate values of ρ, with the latter consistently showing a higher dimensionality than the former, which exhibit a resonance-like phenomenon. We explain changes in the dimensionality of a network’s dynamics in terms of changes in the underlying structure of its vector field by analysing stationary points. Furthermore, we uncover the coexistence of underlying attractors with various geometric forms in unstable networks. As ρ is increased, our visualisation technique shows the network passing through a series of phase transitions with its trajectory taking on a sequence of qualitatively distinct figure-of-eight, cylinder, and spiral shapes. These findings bring us one step closer to a comprehensive theory of this important class of neural networks by revealing the subtle structure of their dynamics under different conditions.
      PubDate: 2017-04-07
      DOI: 10.1007/s12559-017-9464-6
       
  • Reservoir Computing with an Ensemble of Time-Delay Reservoirs
    • Authors: Silvia Ortín; Luis Pesquera
      Abstract: Abstract Reservoir computing (RC) has attracted a lot of attention in the field of machine learning because of its promising performance in a broad range of applications. However, it is difficult to implement standard RC in hardware. Reservoir computers with a single nonlinear neuron subject to delayed feedback (delay-based RC) allow efficient hardware implementation with similar performance to standard RC. We propose and study two different ways to build ensembles of delay-based RC with several delayed neurons (time-delay reservoirs): one using decoupled neurons and the other using coupled neurons through the feedback lines. In both cases, the outputs of the different neurons are linearly combined to solve some benchmark tasks. Simulation results show that these schemes achieve better performance than the single-neuron case. Moreover, the proposed architectures boost the RC processing speed with respect to the single-neuron case. Both schemes are found to be robust against small mismatches between delayed neuron parameters.
      PubDate: 2017-04-05
      DOI: 10.1007/s12559-017-9463-7
       
  • A Framework for Building an Arabic Multi-disciplinary Ontology from
           Multiple Resources
    • Authors: Ahmad Hawalah
      Abstract: 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: 2017-04-03
      DOI: 10.1007/s12559-017-9460-x
       
  • Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical
           Machine Translation
    • Authors: Yan Liu; Chi Man Vong; Pak Kin Wong
      Abstract: Abstract In statistical machine translation (SMT), a possibly infinite number of translation hypotheses can be decoded from a source sentence, among which re-ranking is applied to sort out the best translation result. Undoubtedly, re-ranking is an essential component of SMT for effective and efficient translation. A novel re-ranking method called Scaled Sorted Classification Re-ranking (SSCR) based on extreme learning machine (ELM) classification and minimum error rate training (MERT) is proposed. SSCR contains four steps: (1) the input features are normalized to the range of 0 to 1; (2) an ELM classification model is constructed for hypothesis ranking; (3) each translation hypothesis is ranked using the ELM classification model; and (4) the highest ranked subset of hypotheses are selected, in which the hypothesis with best predicted score based on MERT (system score) is returned as the final translation result. Compared with the baseline score (lower bound), SSCR with ELM classification can raise the translation quality up to 6.7% in IWSLT 2014 Chinese to English corpus. Compared with the state-of-the-art rank boosting, SSCR has a relatively 7.8% of improvement on BLEU in a larger WMT 2015 English-to-French corpus. Moreover, the training time of the proposed method is about 160 times faster than traditional regression-based re-ranking.
      PubDate: 2017-02-17
      DOI: 10.1007/s12559-017-9452-x
       
 
 
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