Showing papers in "Engineering Applications of Artificial Intelligence in 2016"

TL;DR: A discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems and an improvement in the basic structure of the classic bat algorithm are proposed.

TL;DR: This paper proposes a simple, efficient, and effective feature weighting approach, called deep feature weighted (DFW), which estimates the conditional probabilities of naive Bayes by deeply computing feature weighted frequencies from training data.

TL;DR: Empirical results demonstrate that the proposed novel ensemble learning paradigm statistically outperforms all considered benchmark models in both prediction accuracy and effectiveness and is a promising tool to predict complicated time series with high volatility and irregularity.

TL;DR: A sentiment analysis system for automatic recognition of emotions in text, using an ensemble of classifiers based on the notion of combining knowledge-based and statistical machine learning classification methods aiming to benefit from their merits and minimize their drawbacks.

TL;DR: Numerical and experimental results show accurate identification of the natural frequencies and damping ratios even when the signal is embedded in high-level noise demonstrating that the proposed methodology provides a powerful approach to estimate the modal parameters of a civil structure using ambient vibration excitations.

TL;DR: A new method to optimize traffic flow, based on reinforcement learning is proposed, which uses Q-learning to learn policies dictating the maximum driving speed that is allowed on a highway, such that traffic congestion is reduced.

TL;DR: A framework is illustrated to select pairs of opinionated representative yet comparative sentences with specific product features from reviews of competitive products, and three greedy algorithms are proposed to analyze this problem for suboptimal solutions.

TL;DR: An automated diagnosis network of VOBE for high-speed train via a deep learning approach, which improves the accuracy of fault diagnosis for VOBEs to 9095% in HSRs and outperforms both KNN and ANN-BP.

TL;DR: The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of the algorithm is comparable with other state-of-the-art algorithms.

TL;DR: The proposed deep neural network (DNN) is created from an improved denoising auto-encoder reformed by a wavelet transform (WT) method, which showed significant improvement in SNR and RMSE compared with the individual processing with either a WT or DAE, thus providing promising approaches for ECG signal enhancement.

TL;DR: In this article, the robustness and security issue of IWT (integer wavelet transform) and SVD (singular value decomposition) based watermarking is explored, where Singular values are used for the watermark embedding.

TL;DR: The developed diagnostic system for detecting the onset of degradation, isolating the degrading bearing, classifying the type of defect is based on an hierarchical structure of K-Nearest Neighbours classifiers.

TL;DR: Compared with state-of-the-art LMS-Bayes and M 10 - ML methods, the proposed SAE-MV method can distinguish the most categories of halftone images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance.

TL;DR: This work proposes a new metaheuristic, Yin-Yang-Pair Optimization (YYPO), which is a low complexity stochastic algorithm which works with two points and generates additional points depending on the number of decision variables in the optimization problem.

TL;DR: A novel algorithm combining the capabilities of chaotic maps and the golden section search method in order to solve nonlinear optimization problems and performs effectively for the engineering applications such as the gear train deign problem.

TL;DR: An efficient PSO-based algorithm namely HUIM-BPSOsig is proposed to efficiently find HUIs and it first sets the number of discovered high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of a particle based on transaction- Weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process.

TL;DR: The findings show that the new model has the best performance, which on one hand testifies the correctness of the defect analysis, and on the other hand validates the effectiveness of the structure reform of the traditional GM(1,N) model.

TL;DR: A particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process, which performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden, not to behidden, and database similarity.

TL;DR: A novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability is proposed.

TL;DR: A novel technical approach based on domain mapping in Axiomatic Design and the quality and reliability data from product lifecycle and the integrated application of artificial intelligence techniques of Rough Set and fuzzy TOPSIS to compute the weight of root causes for complex product infant failure is put forward.

TL;DR: Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02% and 96.03% on the test data.

TL;DR: A self-adaptive intelligence grey predictive model with an alterable structure that has the advantages of adjustable parameters and is characterised by its variable structure as a homogenous/non-homogenous exponent model or as a single-variable linear-auto-regression model.

TL;DR: An artificial intelligence (AI)-based methodology for integrating affective design, engineering, and marketing for defining design specifications of new products is proposed by which the concerns of the three processes can be considered simultaneously in the early design stage.

TL;DR: A new hybrid heuristic approach is proposed that combines the Quantum Particle Swarm Optimization technique with a local search method to solve the Multidimensional Knapsack Problem and incorporates a heuristic repair operator that uses problem-specific knowledge instead of the penalty function technique commonly used for constrained problems.

TL;DR: A novel picture fuzzy clustering algorithm for complex data called PFCA-CD that deals with both mix data type and distinct data structures that results in better clustering quality than others through clustering validity indices.

TL;DR: Study results show that STDNN outperforms the Naive, ARIMA, and STARIMA models in prediction accuracy and has considerable advantages in travel time prediction.

TL;DR: An unsupervised steganalysis method that combines artificial training sets and supervised classification is proposed that bypasses the problem of Cover Source Mismatch since it removes the need of a training database when the authors have a large enough testing set.

TL;DR: An integrated framework that aims to introduce a semantic mapping method and to use this semantic map, as a means to provide a hierarchical navigation solution, for mobile robots.

TL;DR: An improved grey model based on particle swarm optimization algorithm named PGM(1,1) is proposed for time series prediction, targeting at minimizing the average relative errors between the restored value and real value of the model to avoid the problem caused by background value optimization.

TL;DR: The novelty of this research is in its combining of the three above mentioned approaches to develop a new classifier which can be applied to detect network intrusion, with incremental learning capability, by adapting the weight of key features.