311 research outputs found

    A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

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    The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

    A Study on Intrusion Detection System in Wireless Sensor Networks

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    The technology of Wireless Sensor Networks (WSNs) has become most significant in present day. WSNs are extensively used in applications like military, industry, health, smart homes and smart cities. All the applications of WSN require secure communication between the sensor nodes and the base station. Adversary compromises at the sensor nodes to introduce different attacks into WSN. Hence, suitable Intrusion Detection System (IDS) is essential in WSN to defend against the security attack. IDS approaches for WSN are classified based on the mechanism used to detect the attacks. In this paper, we present the taxonomy of security attacks, different IDS mechanisms for detecting attacks and performance metrics used to assess the IDS algorithm for WSNs. Future research directions on IDS in WSN are also discussed

    Adaptive input selection and evolving neural fuzzy networks modeling

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    This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. Candidate models with larger and smaller number of input variables than the current model are constructed and tested concurrently. The procedure employs a statistical test in each learning step to choose the best model amongst the current and candidate models. Membership functions can be added or deleted to adjust input space granulation and the neural network structure. Granulation and structure adaptation depend of the modeling error. The weights of the neural networks are updated using a gradient-descent algorithm with optimal learning rate. Prediction and nonlinear system identification examples illustrate the usefulness of the approach. Comparisons with state of the art evolving fuzzy modeling alternatives are performed to evaluate performance from the point of view of modeling error. Simulation results show that the evolving adaptive input selection modeling neural network approach achieves as high as, or higher performance than the remaining evolving modeling methods81314CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG305906/2014-3não temnão te

    A Markov-Based Intrusion Tolerance Finite Automaton

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    It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton

    Tweet categorization by combining content and structural knowledge

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    Twitter is a worldwide social media platform where millions of people frequently express ideas and opinions about any topic. This widespread success makes the analysis of tweets an interesting and possibly lucrative task, being those tweets rarely objective and becoming the targeting for large-scale analysis. In this paper, we explore the idea of integrating two fundamental aspects of a tweet, the proper textual content and its underlying structural information, when addressing the tweet categorization task. Thus, not only we analyze textual content of tweets but also analyze the structural information provided by the relationship between tweets and users, and we propose different methods for effectively combining both kinds of feature models extracted from the different knowledge sources. In order to test our approach, we address the specific task of determining the political opinion of Twitter users within their political context, observing that our most refined knowledge integration approach performs remarkably better (about 5 points above) than the textual-based classic modelMinisterio de Economía y Competitividad TIN2012-38536-C03-02Junta de Andalucía P11-TIC-7684 M

    Application of RBFNNs Incorporating MIMO Processes for Simultaneous River Flow Forecasting

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    Simultaneous flow forecasting using multi-input multi-output (MIMO) processes is an efficient technique for accurate flow forecasting on river systems. The present study demonstrates the capability of radial basis function neural networks (RBFNN) incorporating MIMO processes in simultaneous river flow forecasting. The river system considered in the present study was the Barak river system, Assam, India. Hourly concurrent discharge data were collected from the Central Water Commission, Shillong, India from multiple sections of the Barak river system. The forecasts were tested for short-range time horizons, i.e. 1, 3, 6 and 12 hours in advance, and a comparative analysis was done using the popular Nonlinear Autoregressive with Exogenous Inputs (NARX) time series model. The result shows that MIMO-NARX provided higher prediction accuracy than MIMO-RBFNN, even at longer lead times when compared to following various statistical criterions

    Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

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    https://ieeexplore.ieee.org/document/8632897Breast cancer prognostic modeling is difficult since it is governed by many diverse factors. Given the low median survival and large scale breast cancer data, which comes from high throughput technology, the accurate and reliable prognosis of breast cancer is becoming increasingly difficult. While accurate and timely prognosis may save many patients from going through painful and expensive treatments, it may also help oncologists in managing the disease more efficiently and effectively. Data analytics augmented by machine-learning algorithms have been proposed in past for breast cancer prognosis; and however, most of these could not perform well owing to the heterogeneous nature of available data and model interpretability related issues. A robust prognostic modeling approach is proposed here whereby a Pareto optimal set of deep neural networks (DNNs) exhibiting equally good performance metrics is obtained. The set of DNNs is initialized and their hyperparameters are optimized using the evolutionary algorithm, NSGAIII. The final DNN model is selected from the Pareto optimal set of many DNNs using a fuzzy inferencing approach. Contrary to using DNNs as the black box, the proposed scheme allows understanding how various performance metrics (such as accuracy, sensitivity, F1, and so on) change with changes in hyperparameters. This enhanced interpretability can be further used to improve or modify the behavior of DNNs. The heterogeneous breast cancer database requires preprocessing for better interpretation of categorical variables in order to improve prognosis from classifiers. Furthermore, we propose to use a neural network-based entity-embedding method for categorical features with high cardinality. This approach can provide a vector representation of categorical features in multidimensional space with enhanced interpretability. It is shown with evidence that DNNs optimized using evolutionary algorithms exhibit improved performance over other classifiers mentioned in this paper

    Predication of Drug Target Interaction using Reliable Multicast Routing in Wireless Ad hoc Molecular Network

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    Wireless Ad hoc Molecular network consists of atoms in terms of nodes in the absence of administrative point. In this connection, there is a need to adopt molecular analogy to define the architecture. Prediction of Drug Target Interaction (DTI) is a major impact in molecular ad hoc network. Efforts were made to combine such information with data to define DTI and to construct biological space. The concept of Conditional Random Field (CRF) is used in our proposed reliable multicast routing to integrate genomic, chemical and functional data to predict DTI. Reliability of links was also maintained to increase the network performance. Based on the extensive simulation results, the proposed reliable routing protocol achieves better performance than existing schemes
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