Valeri Mladenov - Böcker

The European Computing Conference offers a unique forum for establishing new collaborations within present or upcoming research projects, exchanging useful ideas, presenting recent research results, participating in discussions and establishing new academic collaborations, linking university with the industry. Engineers and Scientists working on various areas of Systems Theory, Applied Mathematics, Simulation, Numerical and Computational Methods and Parallel Computing present the latest findings, advances, and current trends on a wide range of topics. This proceedings volume will be of interest to students, researchers, and practicing engineers.

Software testing has been considered so important that organizations can assign teams only for testing activities. Testing is an important activity to ensure software quality. Tests are usually run several times to certify that code ma- tenance did not accidentally insert defects into working parts of the software. In such situations, test teams shall be able to estimate the required effort to execute test cases in its schedules and to request more resources or negotiate deadlines when necessary. When regarding model-based testing approaches that we use in our documented Integrated and Optimized Software Testing methodology (IOSTP) [1–4], a high number of test cases can be automatically generated. As team resources are limited, it may be not practical to execute all generated test cases. Their complexity usually determines the effort required to execute them and it can be used for planning test resources and test suites. Several software development estimation models have been proposed over the years. However, these models do not estimate the effort for executing a given test suite, since their estimations are based on software development complexity instead of its test planning, test case design and test execution complexity. According to our reading of the literature (e. g. [5–14]), ‘‘best practices’’ in model-based effort estimation include: Local calibration (or LC); i. e. using local data to set two special tuning parameters; Stratification; i. e. given a database of past projects, and a current project to be estimated, restrict local calibration to just those records from similar projects.

The European Computing Conference offers a unique forum for establishing new collaborations within present or upcoming research projects, exchanging useful ideas, presenting recent research results, participating in discussions and establishing new academic collaborations, linking university with the industry.

Software testing has been considered so important that organizations can assign teams only for testing activities. However, these models do not estimate the effort for executing a given test suite, since their estimations are based on software development complexity instead of its test planning, test case design and test execution complexity.

This book provides readers with modern computational techniques for solving variety of problems from electrical, mechanical, civil and chemical engineering.

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.

This volume constitutes the refereed proceedings of the 15th International Conference on Engineering Applications of Neural Networks, EANN 2014, held in Sofia, Bulgaria, in September 2014. The 18 revised full papers presented together with 5 short papers were carefully reviewed and selected from 37 submissions. The papers demonstrate a variety of applications of neural networks and other computational intelligence approaches to challenging problems relevant to society and the economy. These include areas such as: environmental engineering, facial expression recognition, classification with parallelization algorithms, control of autonomous unmanned aerial vehicles, intelligent transport, flood forecasting, classification of medical images, renewable energy systems, intrusion detection, fault classification and general engineering.

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.

This book provides readers with modern computational techniques for solving variety of problems from electrical, mechanical, civil and chemical engineering. Mathematical methods are presented in a unified manner, so they can be applied consistently to problems in applied electromagnetics, strength of materials, fluid mechanics, heat and mass transfer, environmental engineering, biomedical engineering, signal processing, automatic control and more.