A multiobjective metaheuristic approach for morphological filters on many-core architectures

Mathematical Morphology (MM) is a set-theoretic technique for the analysis of geometrical structures. It provides a powerful tool for image processing, but is hampered by significant computational requirements. These requirements can be substantially reduced by decomposing complex operators into sequences of simpler operators, at the cost of degradation of the quality of the results. This decomposition also directly translates to streaming task graphs, a programming model that maps well to the kind of systolic architectures typically associated with many-core systems. There is however a trade-off between mappings that implement high-quality filters and mappings that offer high performance in many-core systems. The approach presented in this paper exploits a multi-objective evolutionary algorithm as a design-time tool to investigate trade-offs between the quality of the MM decomposition and computational performance. The evolutionary process performs an analysis of filter quality vs computational performance and generates a set of task graphs and mappings that represent different trade-offs between the two objectives. It then outputs a Pareto front of mapping solutions, allowing the designer to select an implementation that matches application-specific requirements. The performance of the tool is benchmarked on a morphological filter for the detection of features in a high-resolution PCB image

Design methodologies and architectures of hardware-based evolutionary algorithms for aerospace optimisation applications on FPGAS

This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospace platforms and providing effective performance speedups over traditional methods

Optimized deep learning model for early detection and classification of lung cancer on CT images.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Recently, researchers have shown an increased interest in the early diagnosis and detection of lung cancer using the characteristics of computed tomography (CT) images. The accurate classification of lung cancer assists the physician to know the targeted treatment, reducing mortality, and as a result, supporting human survival. Several studies have been carried out on lung cancer detection using a convolutional neural network (CNN) models. However, it still remains a challenge to improve the model’s performance. Moreover, CNN models have some limitations that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best parameter values for weights and bias. To address the problem of selecting the best combination of weights and bias needed for the classification of lung cancer in CT images, this study proposes a hybrid of Ebola optimization search algorithm (EOSA) and the CNN model. We proposed a hybrid deep learning model with preprocessing features for lung cancer classification using publicly accessible Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) dataset. The proposed EOSA-CNN hybrid model was trained using 80% of the cases to obtain the optimal configuration, while the remaining 20% was applied for validation. Also, we compared the proposed model with similar five hybrid algorithms and the traditional CNN. The results indicated that EOSA-CNN scored 0.9321 classification accuracy. Furthermore, the result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, 0.9071 for normal, benign, and malignant cases, respectively. This confirmed that the hybrid algorithm provides a good solution for the classification of lung cancer

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Efficient Learning Machines

Computer scienc

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field