Training radial basis function networks using biogeography-based optimizer

Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.

     

Numbering with spiral pattern to prove authenticity and integrity in medical images

With the rapid development of technology in digital multimedia, manipulation and misconduct of digital data are soaring. The watermarking procedure is seen as helpful to protect the security of digital images which are highly confidential such as medical images. In this study, an efficient watermarking method for greyscale images is proposed. The scheme is designed to achieve good numbering pattern, exact detection and image recovery. The proposed scheme uses a unique spiral pattern numbering before implementing the block-based mechanism for embedding. The experiment tests the difference between using normal pattern and unique spiral pattern in numbering, and the results show that the proposed method produces a great quality value of embedded image with great tamper localization and recovery ability. This function can help in proving authenticity and integrity of medical images in the system.     

ANALYSIS AND SYNTHESIS OF LEARNING AGENT'S COMMUNICATIVE BEHAVIOR

This paper is about people. It is about understanding how learning and communication mutually influence one another, allowing people to infer each other's communicative behavior. In order to understand how people learn to communicate, we refer to existing theories. They are the logical theories of learning and communication, situated cognition, and activity theory. Thus, this paper is about applying existing theories of analyzing conversations, human learning, and memory to a range of scenarios of actual human conversations. It also introduces a new way of analyzing conversations. We have recorded and observed actual human communications on the Web. We have applied those theories to analyze these communication scenarios. We describe the preliminary results on the analyses of the communication scenarios. In particular, we show our analysis of the recorded conversational structures. We illustrate how the re-enacting and re-sequencing of conversational structures is adapted to the context (i.e., environment) moment by moment. From our analyses, we found that people have internal rules (e.g., a combinatorial rule system). These internal rules can be related to how a person learns, adapts, and merges protocols situated in their context of communication. Our long term goal is to make use of these analyses to improve human communication on the Grid.     

Study of the TRIM transistor

TRIM Devices can be classified as plasma-operated structures. This paper considers this possibility and forms a theoretical model which takes into account the effects of high level injection and punch through operation. It offers a simple cylindrical model for the purpose of analysis and compares its results with those of a computer simulated one-dimensional model, which can be used with any plasma operated device. The 1-D model uses the general transport equations to form a set of differential equations which can be integrated by an iterative method. The results achieved from these models are then compared with those obtained experimentally.