International Journal on Artificial Intelligence Tools

Due to the increasing growth of data, many methods are proposed to extract useful data and remove noisy data. Instance selection is one of these methods which selects some instances of a data set and removes others. This paper proposes a new instance selection algorithm based on ReliefF, which is a feature selection algorithm. In the proposed algorithm, based on the Jaccard index, the nearest instances of each class are found for each instance. Then, based on the nearest neighbor’s set, the weight of each instance is calculated. Finally, only instances with more weights are selected. This algorithm can reduce data at a specified rate and have the ability to run parallel on the instances. It can work on a variety of data sets with nominal and numeric data with missing values and is also suitable for working with imbalanced data sets. The proposed algorithm tests on three data sets. Results show that the proposed algorithm can reduce the volume of data, without a significant change in classification accuracy of these datasets.

In this paper, a new two-level recurrent neural network language model (RNNLM) based on the continuous bag-of-words (CBOW) model for application to sentence classification is presented. The vector representations of words learned by a neural network language model have been shown to carry semantic sentiment and are useful in various natural language processing tasks. A disadvantage of CBOW is that it only considers the fixed length of a context because its basic structure is a neural network with a fixed length of input. In contrast, the RNNLM does not have a size limit for a context but only considers the previous context’s words. Therefore, the advantage of RNNLM is complementary to the disadvantage of CBOW. Herein, our proposed model encodes many linguistic patterns and improves upon sentiment analysis and question classification benchmarks compared to previously reported methods.

The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.

Artificial bee colony (ABC) is a quite popular optimization approach that has been used in many fields, with its not only standard form but also improved versions. In this paper, new versions of ABC algorithm to solve TSP are introduced and described in detail. One of these is the combinatorial version of standard ABC, called combinatorial ABC (CABC) algorithm. The other one is an improved version of CABC algorithm, called quick CABC (qCABC) algorithm. In order to see the efficiency of the new versions, 15 different TSP benchmarks are considered and the results generated are compared with different well-known optimization methods. The simulation results show that, both CABC and qCABC algorithms demonstrate good performance for TSP and also the new definition in quick ABC (qABC) improves the convergence performance of CABC on TSP.

This work aims to explore resources and alternatives for 3D facial biometry using Binary Patterns. A 3D facial geometry image is converted into two 2D representations, appointed as descriptors: A Depth Map and an Azimuthal Projection Distance Image. The first is known as traditional facial geometry, and the second is normal facial geometry that is able to capture the information of different geometries. The characteristics of Local Binary Patterns, Local Phase Quantisers and Gabor Binary Patterns were used with the Depth Map and Azimuthal Projection Distance Image to produce six new facial descriptors: 3D Local Binary Patterns, Local Azimuthal Binary Patterns, Local Depth Phase Quantisers, Local Azimuthal Phase Patterns, and Local Depth Gabor Binary Pattern Magnitudes and Phases. Then, this work uses the immune concept to propose a new approach to realize facial biometry, where the eight new facial descriptors were applied to an Artificial Intelligence algorithm named Artificial Immune Systems of Negative Selection. The analysis of the results shows the efficiency, robustness, precision and reliability of this approach, encouraging further research in this area.