Volume 25, Issue 3

This paper applies a genetic algorithm (GA) to the autonomous evolution of digital art, eliminating the need for a human in the loop. Creative applications of GAs face the challenge of producing art or music to fit a wide range of human tastes. One approach is to use a human in the loop to determine the fitness function in order to direct the selection and evolution. Another approach, which this paper explores, is to define an objective fitness function to automate evolution without the need for human input. In this paper, several features of digital art are identified and used as the basis for fitness functions. The resulting images are recognizable for the intended evolution of the fitness function used. This indicates the potential of an approach to create more robust algorithmic fitness functions capable of evolving creative applications autonomously.

The problem of coverage optimization is the challengingly important and key part in the research and application of ecology sensor network related with the ecological monitoring of Poyang Lake. A modified differential evolution algorithm (PSI-DE) combined with particle swarm intelligence is proposed to solve the coverage optimization problem. First, an improved version of the mutation rule combined with self-cognitive and social-cognitive items is introduced. Then, the influence on the coverage optimization performance of the PSI-DE algorithm brought by the five factors – namely, population size, number of iterations, sensing radius size, raster size, and number of nodes – is discussed and analyzed. The statistical results about the best coverage rate, average coverage rate, worst coverage rate, and variance are respectively obtained through a lot of simulation experiments. A series of the coverage rate curves, the line chart, and the node layout are drawn in this paper, and finally, the figures and the statistical results are proven to confirm each other.

Plagiarism in free text has become a common occurrence due to the wide availability of voluminous information resources. Automatic plagiarism detection systems aim to identify plagiarized content present in large repositories. This task is rendered difficult by the use of sophisticated plagiarism techniques such as paraphrasing and summarization, which mask the occurrence of plagiarism. In this work, a monolingual plagiarism detection technique has been developed to tackle cases of paraphrased plagiarism. A support vector machine based paraphrase recognition system, which works by extracting lexical, syntactic, and semantic features from input text has been used. Both sentence-level and passage-level approaches have been investigated. The performance of the system has been evaluated on various corpora, and the passage level approach has registered promising results.

Decomposing product development project into various tasks and grouping them are important activities in product development. Many scholars devoted their efforts to solving this problem and proposed some useful methods. However, the research work of task decomposition and grouping for customer collaboration in product development is still lacking. Therefore, this study first decomposes product development tasks and analyzes its executability. Then, by using an integrated numerical design structure matrix and adaptive genetic algorithm (AGA) approach, tasks are divided into different groups, tasks in the same group have high correlation degree, and tasks in the different groups have low correlation degree. To illustrate the process of task decomposition and grouping methods proposed in this paper, a mobile phone structural development case is applied. Moreover, standard generic algorithm (SGA) and particle swarm optimization (PSO) are used to compare with AGA to verify the effectiveness of AGA.

As a neutrosophic number, which consists of a determinate part and an indeterminate part, can more easily and better express incomplete and indeterminate information that exists commonly in real situations, the main purposes of this paper are to provide a neutrosophic number tool for group decision-making problems with indeterminate information under a neutrosophic number environment and to develop a de-neutrosophication method and a possibility degree ranking method for neutrosophic numbers from the probability viewpoint as a methodological support for group decision-making problems. In group decision-making problems with neutrosophic numbers, through the de-neutrosophication and possibility degree ranking order of neutrosophic numbers, the ranking order of alternatives is performed well as the possibility degree ranking method has the intuitive meaning from the probability viewpoint, and then the best one(s) can be determined as well. Finally, two illustrative examples show the applications and effectiveness of the proposed method.

The classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency recognition. The system designed for this particular task relies on the Gaussian mixture model (GMM), which is the most widely used probabilistic modeling technique in ASR. The GMM parameters are estimated from Mel frequency cepstral coefficients (MFCCs). This statistical speaker-modeling technique represents the fundamental characteristic sounds of speech signal. Using this model, we build a dysfluency recognizer that is capable of recognizing dysfluencies irrespective of a person as well as what is being said. The performance of the system is evaluated for different types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection using speech samples from the University College London Archive of Stuttered Speech (UCLASS).

The human ear has been deemed to be a source of data for person identification in recent years. Ear biometrics has distinct advantages, such as visibility from a distance and ease with which images could be captured. This paper elaborates on a novel approach to ear biometrics. We propose moment of inertia-based biometric for the ears in any random orientation. The features concerned are the moment of inertia about the major and minor axes, corresponding radii of gyration, and the planar surface area of the ear. The databases of the said features were collected through ear images of 600 subjects. Principal component analysis of the features demonstrated that the radius of gyration with respect to the major axis, moment of inertia about the minor axis, and radius of gyration about the minor axis are significant attributes contributing to major variability. The person identification system developed showed recognition rates of 99% with just three attributes, when compared with the 96% recognition rate when all five attributes were considered. The evaluation of the system was done on several metrics. All metrics were found to be insignificant in their magnitude, which is suggestive of robustness and excellent authentication performance.

For the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

The quality of data is an important task in the data mining. The validity of mining algorithms is reduced if data is not of good quality. The quality of data can be assessed in terms of missing values (MV) as well as noise present in the data set. Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain’s (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study.

In this work, we present a combination of edge feature and distribution of the gradient orientation of an object technique for content-based image retrieval (CBIR). First, the bidimensional empirical mode decomposition (BEMD) technique is employed to get the edge features of an image. Later, the information about the gradient orientation is obtained by the histogram of oriented gradient (HOG) descriptor. These two features are extracted from the images and stored in the database for further usage. When the user submits the query image, the features are extracted in same way and compared with the features of the data set images. Based on the similarity, the relevant images have been selected as a resultant set. These images are ranked from higher similarity to lower similarity and displayed on the user interface. The experiments are carried out using the Columbia Object Image Library (COIL-100) dataset. The COIL-100 database is a collection of 7200 color images belonging to 100 various objects, each with 72 different orientations. Our proposed method results are high with precision and recall values of 93.00 and 77.70, respectively. Taken individually, the precision and recall values for BEMD are 82.25 and 68.54 and for HOG are 85.00, 71.10, respectively. The observation from the experimental result is that the combined method performs better than the individual methods. Experiments are conducted in the presence of noise, and the robustness of the method is verified.