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  • A modified deep neural network enables identification of foliage under complex background
    Connect. Sci. (IF 0.673) Pub Date : 2019-04-26
    Xiaolong Zhu; Junhao Zuo; Honge Ren

    For the sake of enhancing the identification ability of current network and meeting the needs of the high accuracy of distinguishing similar small objects (foliage) in the complex scenes, this paper proposes a modified region-based fully convolutional network which adopts Inception V3 accompanying with residual connection as the main framework. Incorporating deep residual learning module into Inception V3 can not only save the computational cost by factorising convolutions, but also mitigate the vanishing gradients causing the increasing depth of the network. Additionally, this combination can alleviate the degradation problem in the process of extracting features and providing proposals. Experimental results show that the modified approach can identify out different leaves with similar characteristics in one scene, and demonstrate the superiority of our proposed approach over some state-of-the-art deep neural networks, when it comes to recognise foliage in complicated environments.

  • Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems
    Connect. Sci. (IF 0.673) Pub Date : 2019-04-25
    Arfan Ali Nagra; Fei Han; Qing Hua Ling; Muhammad Abubaker; Farooq Ahmad; Sumet Mehta; Abeo Timothy Apasiba

    Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.

  • A fuzzy irregular cellular automata-based method for the vertex colouring problem
    Connect. Sci. (IF 0.673) Pub Date : 2019-08-19
    Mostafa Kashani; Saeid Gorgin; Seyed Vahab Shojaedini

    Vertex colouring is among the most important problems in graph theory which has been widely applied across different real-world problems. In vertex colouring problem (VCP), the goal is to assign a distinct colour to each vertex of the graph in such a way that no two adjacent vertices have the same colour. This paper presents a fuzzy irregular cellular automaton (FICA) for finding a near-optimal solution for the VCP. FICA is an extension fuzzy cellular automaton (FCA) in which the cells of the automaton can be arranged in an irregular structure. The aim of the proposed method is to reap the benefits of both FCA and irregular cellular automata while minimising their drawbacks. To evaluate the proposed method, various computer simulations have been conducted on a variety of graphs. The results suggest that the proposed method is able to achieve better results in terms of the minimum number of required colours and the execution time of the algorithm, compared to other peer algorithms.

  • Fractional power series neural network for solving delay fractional optimal control problems
    Connect. Sci. (IF 0.673) Pub Date : 2019-05-08
    Farzaneh Kheyrinataj; Alireza Nazemi

    In this paper, we develop a numerical method for solving the delay optimal control problems of fractional-order. The fractional derivatives are considered in the Caputo sense. The process begins with the assumption that the problem is first transformed into an equivalent problem with a fractional dynamical system without delay, using a Padé approximation. We then try to approximate the solution of the Hamiltonian conditions based on the Pontryagin minimum principle. The main feature is to implement nonlinear polynomial expansions in a neural network adaptive structure. The transfer functions of the employed neural network follow a fractional power series. The proposed technique does not use sigmoid or hyperbolic tangent nonlinear transfer functions commonly adopted in conventional neural networks at the output. Instead, linear transfer functions are employed which lead to explicit fractional power series formulae for the fractional optimal control problem. To do this, we use trial solutions for the states, Lagrange multipliers and control functions where these trial solutions are constructed by fractional power series neural network model. We then minimise the error function using an unconstrained optimisation scheme where weight parameters (or coefficients of the series) and biases associated with all neurons are unknown. Some numerical examples are given to illustrate the effectiveness of the proposed scheme.

  • A methodology to forecast some attributes of an automatic storyteller’s outputs
    Connect. Sci. (IF 0.673) Pub Date : 2019-04-25
    Iván Guerrero Román; Rafael Pérez y Pérez

    This paper reports an extension of the program MEXICA, an automatic plot generator. The purpose of this project is to build a framework that permits studying the relations between MEXICA’s processes, its knowledge structures and the features of the produced narratives. We describe a methodology to analyse the features of the agent’s knowledge-base, to further establish correlations between such features and a set of general characteristics of the tales that they produce. Next, we make use of those correlations to forecast some properties of the future tales to be developed by different MEXICAs agents with different knowledge-bases. For this task, we introduce the S-graphs, representations of the similarity and organisation of the knowledge-structures. The results we obtained indicate that we are able to correctly forecast some of the features of tales to be produced; however, much more work is required.

  • Fuzzy kernel feature selection with multi-objective differential evolution algorithm
    Connect. Sci. (IF 0.673) Pub Date : 2019-07-09
    Emrah Hancer

    In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response.

  • Solving optimal control problems of the time-delayed systems by a neural network framework
    Connect. Sci. (IF 0.673) Pub Date : 2019-04-17
    Alireza Nazemi; Ensieh Fayyazi; Marzieh Mortezaee

    A numerical method using neural networks for solving time-delayed optimal control problems is studied. The problem is first transformed into one without a time-delayed argument, using a Páde approximation. We try to approximate the solution of the Hamiltonian conditions based on the Pontryagin minimum principle (PMP). For this purpose, we introduce an error function that contains all PMP conditions. We then minimise the error function where weights and biases associated with all neurons are unknown. Substituting the optimal values of the weights and biases in the trial solutions, we obtain the optimal solution of the original problem. Several examples are given to show the efficiency of the method.

  • Event-triggered H∞ anti-synchronisation for delayed neural networks with discontinuous neuron activations via non-fragile control strategy
    Connect. Sci. (IF 0.673) Pub Date : 2019-04-15
    Min Liu; Huaiqin Wu; Jinde Cao; Yanning Wang

    This paper treats of the global event-triggered anti-synchronisation issue for discontinuous neural networks with the mixed time-varying delays and random feedback gain fluctuation via non-fragile control strategy. The random gain uncertainties are described by stochastic variables satisfying the Bernoulli distribution. Firstly, the novel hybrid controllers, which are composed of the non-fragile controller and the event-triggered controller, are designed. Then, based on Clarke's non-smooth analysis theory, general free-weighting matrix method, the Lyapunov-Krasovskii functional approach and Wirtinger-based multiple integral inequality analysis technology, the global event-triggered non-fragile anti-synchronisation conditions are established in terms of linear matrix inequalities (LMIs). In addition, under the considered external disturbance, the conditions with respect to the global event-triggered non-fragile H∞ anti-synchronisation are also addressed in forms of LMIs. Finally, two illustrative examples are provided to verify the effectiveness of the designed event-triggered non-fragile control scheme and the validity of theoretical results.

  • Exploring the usability of the text-based CAPTCHA on tablet computers
    Connect. Sci. (IF 0.673) Pub Date : 2019-05-07
    Darko Brodić; Alessia Amelio

    This paper analyses and discusses the usability aspect of the text-based CAPTCHA in terms of response time and success in solving the CAPTCHA on tablet computers. The response time is the time spent by the user to find a solution to the CAPTCHA. The analysis is separately conducted on text-based CAPTCHA with only text and numbers. Then, the results are compared and the differences in response time and success in solving the two types of CAPTCHA are underlined. This is accomplished by asking 125 Internet users to solve the text-based CAPTCHA on the tablet computer. Their gender, age, education level, Internet experience, response time and success in solving two types of text-based CAPTCHA are collected in a dataset. Then, advanced statistical analysis by association rule mining is performed. It shows the dependence of the response time and success in solving the CAPTCHA on co-occurrence of gender, age, education level and Internet experience and the strength of this dependence by support, confidence and lift measures. This study provides relevant information for designing new CAPTCHAs which may be more accustomed to specific types of Internet users.

  • Social coordination in toddler's word learning: interacting systems of perception and action.
    Connect. Sci. (IF 0.673) Pub Date : 2008-06-01
    Alfredo F Pereira,Linda B Smith,Chen Yu

    We measured turn-taking in terms of hand and head movements and asked if the global rhythm of the participants' body activity relates to word learning. Six dyads composed of parents and toddlers (M = 18 months) interacted in a tabletop task wearing motion-tracking sensors on their hands and head. Parents were instructed to teach the labels of 10 novel objects and the child was later tested on a name-comprehension task. Using dynamic time warping, we compared the motion data of all body-part pairs, within and between partners. For every dyad, we also computed an overall measure of the quality of the interaction, that takes into consideration the state of interaction when the parent uttered an object label and the overall smoothness of the turn-taking. The overall interaction quality measure was correlated with the total number of words learned.In particular, head movements were inversely related to other partner's hand movements, and the degree of bodily coupling of parent and toddler predicted the words that children learned during the interaction. The implications of joint body dynamics to understanding joint coordination of activity in a social interaction, its scaffolding effect on the child's learning and its use in the development of artificial systems are discussed.

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Springer Nature 2019高下载量文章和章节