Ant Colony Optimization Based Subset Feature Selection in Speech Processing: Constructing Graphs with Degree Sequences

Feature selection or the process of selecting the most discriminating feature subset is an essential practice in speech processing that significantly affects the performance of classification. However, the volume of features that presents in speech processing makes the feature selection perplexing. Moreover, finding the optimal feature subset is a NP-hard problem (2n). Thus, a good searching strategy is required to avoid evaluating large number of combinations in the whole feature subsets. As a result, in recent years, many heuristic based search algorithms are developed to address this NP-hard problem. One of the several meta heuristic algorithms that is applied in many application domains to solve feature selection problem is Ant Colony Optimization (ACO) based algorithms.  ACO based algorithms are nature-inspired from the foraging behavior of actual ants. The success of an ACO based feature selection algorithm depends on the choice of the construction graph with respect to runtime behavior. While most ACO based feature selection algorithms use fully connected graphs, this paper proposes ACO based algorithm that uses graphs with prescribed degree sequences. In this method, the degree of the graph representing the search space will be predicted and the construction graph that satisfies the predicted degree will be generated. This research direction on graph representation for ACO algorithms may offer possibilities to reduce computation complexity from O(n2) to O(nm) in which m is the number of edges. This paper outlines some popular optimization based feature selection algorithms in the field of speech processing applications and overviewed ACO algorithm and its main variants. In addition to that, ACO based feature selection is explained and its application in various speech processing tasks is reviewed. Finally, a degree based graph construction for ACO algorithms is proposed

COMPUTER BASED ENGLISH SPEAKING TEST BASED ON ARTIFICAL NEURAL NETWORK

English testing is a most common test conducted around the world for evaluating an individual’s English capabilities in mostly reading, writing, speaking, and listening domain. With increased cost and higher subjective assessment attached in some tests, there is required to change the test from traditional method to computer based. In this study, a proposed method for conducting speaking test for English based on objective assessment method. The proposed system is able to identify different dialects based on unit analysis of syllable along with phonetic errors. The proposed system is based on pronunciation parameters and neural network for evaluation purpose. The PSO algorithm is used for training the artificial neural network. The experiment result conducted for validating the proposed system shows promising performanc

Water filtration by using apple and banana peels as activated carbon

Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent

Two-Dimensional Gel Electrophoresis Image Registration Using Block-Matching Techniques and Deformation Models

[Abstract] Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique—based on successive deformation, search, fitting, filtering, and interpolation stages—is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D–PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.Galicia. Consellería de Economía e Industria; 10MDS014CTGalicia. Consellería de Economía e Industria; 10SIN105004PRInstituto de Salud Carlos III; PI13/0028

Intelligent Decision Making Using Evolutionary System for Optimizing Product-Mix Model

The development and deployment of managerial decision support system represents an emerging trend in the business and organizational field in which the increased application of Decision Support Systems (DSS) can be compiling by Intelligent Systems (IS). Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions. Competitive business pressures and a desire to leverage existing information technology investments have led many firms to explore the benefits of evolutionary system data management solutions such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). This study proposes a new model for product mix problem based on evolutionary system for optimizing constraint values as well as objective function. The formulations of the objective function for the minimization problem. This technology is designed to help businesses to finding multi objective functions

Differential Evolution to Optimize Hidden Markov Models Training: Application to Facial Expression Recognition

The base system in this paper uses Hidden Markov Models (HMMs) to model dynamic relationships among facial features in facial behavior interpretation and understanding field. The input of HMMs is a new set of derived features from geometrical distances obtained from detected and automatically tracked facial points. Numerical data representation which is in the form of multi-time series is transformed to a symbolic representation in order to reduce dimensionality, extract the most pertinent information and give a meaningful representation to humans. The main problem of the use of HMMs is that the training is generally trapped in local minima, so we used the Differential Evolution (DE) algorithm to offer more diversity and so limit as much as possible the occurrence of stagnation. For this reason, this paper proposes to enhance HMM learning abilities by the use of DE as an optimization tool, instead of the classical Baum and Welch algorithm. Obtained results are compared against the traditional learning approach and significant improvements have been obtained.</p

What makes trading strategies based on chart pattern recognition profitable?

[EN] Automating chart pattern recognition is a relevant issue addressed by researchers and practitioners when designing a system that considers technical analysis for trading purposes. This article proposes the design of a trading system that takes into account any generic pattern that has been proven to be profitable in the past, without restricting the search to the specific technical patterns reported in the literature, hence the term generic pattern recognition. A fast version of dynamic time warping, the University College Riverside subsequence search suite (called the UCR suite), is employed for the pattern recognition task in an effort to produce trading signals in realistic timescales. This article evaluates the significance of the relation between the system's profitability and (a) the pattern length, (b) the take-profit and stop-loss levels and (c) the performance consensus of past patterns. The trading system is assessed under the mean¿variance perspective by using 560 NYSE stocks. The results obtained by the different parameter configurations are reported, controlling for both data-snooping and transaction costs. On average, the proposed system dominates the market index in the mean¿variance sense. Although transaction costs reduce the profitability of the proposed trading system, 92.5% of the experiments are profitable if the analysis is reduced to the parameter values aligned with the technical analysisTsinaslanidis, P.; Guijarro, F. (2021). What makes trading strategies based on chart pattern recognition profitable?. Expert Systems. 38(5):1-17. https://doi.org/10.1111/exsy.12596S11738

A Review on Speech Recognition Methods

Voice recognition is the identification of a speaker on the basis of the characteristics of voices. For this, features of speech patterns that differ between individuals are used to achieve the objective. In this paper speaker recognition system are discussed. Implementation of speaker's voice recognition system with MATLAB makes possible use of voice for real life applications. This paper provides a brief review of different DSP based techniques applied for speech recognition