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In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person's signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.

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Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.

Telecommunication carrier is by definition companies that are authorized by a regulatory agency to operate a telecommunications system. In the work of Ching-Ter et al. (2011), the problem of routing management still stands to confuse many telecommunication-related companies among which are: equipment manufacturers, platform vendors, service operators, and billing system. Through their study, they observed that routing control is dependent not only on equipment but also on the operational flow of the company. Unlike Internet Service Provider (ISP) where its providers often control the entire network topology; the same is not of telecommunication operators. A full range of service is only possible to the reach of its customers by contracting partners such as service carriers. In the case of customer calls or user calls, these operators provide service according to an internal and pre-deployed routing logic to its service providers or carriers depending upon the destination or the service traffic to available routes. That is, a clear guide is instituted for the engineering staff on path deploying for the next period (or billing cycle) to come while achieving multiple pre-defined goals automatically. In this instance, decision-making which is the science of recognizing and determining choices based on the values and partialities of the decision maker becomes necessary (Diaz et al. 2015; Caron and Daniels 2013; Anderson and Mansingh 2014). Making a choice implies that there are alternatives to be considered, and in such a case we do not only distinguish as many of these choices but also take the one that best fits