International Journal of Computational Intelligence and Applications

Piecewise Aggregate Approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and well-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies viz. fault detection on industrial gas turbines and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and APCA alternatives. The method is therefore demonstrated to provide a robust and readily implemented algorithm for rapid FE and identification for datasets.

Cuckoo Search (CS) is a recent addition to the field of swarm-based metaheuristics. It has been shown to be an efficient approach for global optimization. Moreover, its application for solving Multi-objective Optimization (MOO) shows very promising results as well. In multi-objective context, a bounded archive is required to store the set of nondominated solutions. But, what is the best archiving strategy to use in order to maintain a bounded set with good characteristics is a critical issue that may lead to a questionable choice. In this work, the behavior of the developed multi-objective CS is studied under several archiving strategies. An extensive experimental study has been conducted using several test problems and two performance metrics related to convergence and diversity. A nonparametric test for statistical analysis is performed. In addition, we used a Multi-Objective Particle Swarm Optimization (MOPSO) for further analysis and comparison. The results revealed that archiving strategies play an important role as they can impact differently on the quality of obtained fronts depending on the problem’s characteristics. Also, this study confirms that the proposed MOCS algorithm is a very promising approach for MOPs compared to the widely used MOPSO.

In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian–Bernoulli and Bernoulli–Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence. A suitable feature representation from the raw ECG data can therefore be extracted in an unsupervised way. In order to enhance the performance of DBN, a fine-tuning process is carried out, which uses backpropagation by adding a softmax regression layer on the top of the resulting hidden representation layer to perform multiclass classification. The method is then validated by experiments on the well-known MIT-BIH arrhythmia database. Considering the real clinical application, the inter-patient heartbeat dataset is divided into two sets and grouped into four classes (N, S, V, F) following the recommendations of AAMI. The experiment results show our approach achieves better performance with less feature learning time than traditional hand-designed methods on the classification of ECG arrhythmias.

This paper proposes a novel versatile genetic algorithm (GA) for solving the graph distribution problem. The new GA is based mainly on an inspirational idea that exploits the Newton’s universal gravitation law to introduce a novel GA fitness function. By this,the new GA preserves the workload balancing property between the different sites of the graph network and reduces the inter-processors communications overhead. Moreover, three main variants of the novel GA are developed. The two first; centralized and distributed variants, are developed to conduct a graph distribution over homogeneous architectures. The third variant is a distributed one devoted for heterogeneous architectures where the impact of the auto-adaptation features of the GA emerges. The results obtained and the comparative studies show the effectiveness of the proposed methods.

We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem (ETP). This approach is defined with three phases: pre-processing phase, construction phase and enhancement phase. The pre-processing phase relies on our variable ordering heuristic as well as a form of transitive closure for discovering implicit constraints. The construction phase uses a variant of the Tabu Search with conflicts dictionary. The enhancement phase includes Hill Climbing (HC), Simulated Annealing (SA) and our updated version of the extended “Great Deluge” algorithm. In order to evaluate the performance of the different phases of our proposed approach, we conducted several experiments on instances taken from ITC 2007 benchmarking datasets. The results are very promising and competitive with the well known ETP solvers.

With lower operational costs, many small and medium-sized enterprises (SMEs) trade via e-commerce, but without the abilities to develop the expensive payment system. Therefore, a cash flow service provider plays critical roles to complete the online transactions. A cash flow service provider must precisely predict those outsourcing customers to serve the customers well under the correctly prepared facilities. Since the adaptive neuro-fuzzy inference systems (ANFIS) have demonstrated prediction efficiency for fuzzy circumstances in many fields, this study attempts to innovate deploying the ANFIS model on the time series predictions for e-commerce cash flow service customers. Moreover, this study takes an e-commerce cash flow service provider in Taiwan for numerical analysis. For the ANFIS predictions, an acceptable prediction error rate of 5.6% is achieved. The results show that fashion industry tops the highest customers share for outsourcing the cash flow services; and the credit cards top the highest share in the payment media choices.