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Open access

M. Javaid, M. Abbas, Jia-Bao Liu, W. C. Teh and Jinde Cao

Abstract

A topological property or index of a network is a numeric number which characterises the whole structure of the underlying network. It is used to predict the certain changes in the bio, chemical and physical activities of the networks. The 4-layered probabilistic neural networks are more general than the 3-layered probabilistic neural networks. Javaid and Cao [Neural Comput. and Applic., DOI 10.1007/s00521-017-2972-1] and Liu et al. [Journal of Artificial Intelligence and Soft Computing Research, 8(2018), 225-266] studied the certain degree and distance based topological indices (TI’s) of the 3-layered probabilistic neural networks. In this paper, we extend this study to the 4-layered probabilistic neural networks and compute the certain degree-based TI’s. In the end, a comparison between all the computed indices is included and it is also proved that the TI’s of the 4-layered probabilistic neural networks are better being strictly greater than the 3-layered probabilistic neural networks.

Open access

Ryotaro Kamimura

Abstract

The present paper1 aims to propose a new type of information-theoretic method to maximize mutual information between inputs and outputs. The importance of mutual information in neural networks is well known, but the actual implementation of mutual information maximization has been quite difficult to undertake. In addition, mutual information has not extensively been used in neural networks, meaning that its applicability is very limited. To overcome the shortcoming of mutual information maximization, we present it here in a very simplified manner by supposing that mutual information is already maximized before learning, or at least at the beginning of learning. The method was applied to three data sets (crab data set, wholesale data set, and human resources data set) and examined in terms of generalization performance and connection weights. The results showed that by disentangling connection weights, maximizing mutual information made it possible to explicitly interpret the relations between inputs and outputs.

Open access

Parisa Rastin, Basarab Matei, Guénaël Cabanes, Nistor Grozavu and Younès Bennani

Abstract

Collaborative Clustering is a data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The idea of collaborative clustering is that each collaborator shares some information about the segmentation (structure) of its local data and improve its own clustering with the information provided by the other learners. This paper analyses the impact of the quality and the diversity of the potential learners to the quality of the collaboration for topological collaborative clustering algorithms based on the learning of a Self-Organizing Map (SOM). Experimental analysis on real data-sets showed that the diversity between learners impact the quality of the collaboration. We also showed that some internal indexes of quality are a good estimator of the increase of quality due to the collaboration.

Open access

Michal Mizera, Pawel Nowotarski, Aleksander Byrski and Marek Kisiel-Dorohinicki

Abstract

Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

Open access

Md Meftahul Ferdaus, Sreenatha G. Anavatti, Matthew A. Garratt and Mahardhika Pratama

Abstract

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller.

Open access

Yun Wei, Ying Yu, Lifeng Xu, Wei Huang, Jianhua Guo, Ying Wan and Jinde Cao

Abstract

Vehicle emission calculation is critical for evaluating motor vehicle related environmental protection policies. Currently, many studies calculate vehicle emissions from integrating the microscopic traffic simulation model and the vehicle emission model. However, conventionally vehicle emission models are presented as a stand-alone software, requiring a laborious processing of the simulated second-by-second vehicle activity data. This is inefficient, in particular, when multiple runs of vehicle emission calculations are needed. Therefore, an integrated vehicle emission computation system is proposed around a microscopic traffic simulation model. In doing so, the relational database technique is used to store the simulated traffic activity data, and these data are used in emission computation through a built-in emission computation module developed based on the IVE model. In order to ensure the validity of the simulated vehicle activity data, the simulation model is calibrated using the genetic algorithm. The proposed system was implemented for a central urban region of Nanjing city. Hourly vehicle emissions of three types of vehicles were computed using the proposed system for the afternoon peak period, and the results were compared with those computed directly from the IVE software with a trivial difference in the results from the proposed system and the IVE software, indicating the validity of the proposed system. In addition, it was found for the study region that passenger cars are critical for controlling CO, buses are critical for controlling CO and VOC, and trucks are critical for controlling NOx and CO2. Future work is to test the proposed system in more traffic management and control strategies, and more vehicle emission models are to be incorporated in the system.

Open access

Yuchen Hou and Lawrence B. Holder

Abstract

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with the stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We analyze the node embeddings to confirm that closeness in embedding space correlates with stronger relationships as measured by the link weight. We anticipate this new approach will provide effective solutions to more graph mining tasks.

Open access

Igor Aizenberg, Antonio Luchetta, Stefano Manetti and Maria Cristina Piccirilli

Abstract

A procedure for the identification of lumped models of distributed parameter electromagnetic systems is presented in this paper. A Frequency Response Analysis (FRA) of the device to be modeled is performed, executing repeated measurements or intensive simulations. The method can be used to extract the values of the components. The fundamental brick of this architecture is a multi-valued neuron (MVN), used in a multilayer neural network (MLMVN); the neuron is modified in order to use arbitrary complex-valued inputs, which represent the frequency response of the device. It is shown that this modification requires just a slight change in the MLMVN learning algorithm. The method is tested over three completely different examples to clearly explain its generality.

Open access

Keiko Ono, Yoshiko Hanada, Masahito Kumano and Masahiro Kimura

Abstract

In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit f requent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its activation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.

Open access

Gustavo Botelho de Souza, Daniel Felipe da Silva Santos, Rafael Gonçalves Pires, Aparecido Nilceu Marana and João Paulo Papa

Abstract

Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.