Multi-classifier ensemble based on dynamic weights

In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the real-time importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified

SOM based Face Recognition using Steganography and DWT Compression Techniques

Biometrics is used in day to day life of human beings to access mobile phones, computers, vehicles etc. In this paper, we propose SOM based Face Recognition using Steganography and DWT Compression Techniques. The various available standard face databases such as ORL, JAFFE, NRI, YALE, Indian male and Indian female are used to test the performance of the algorithm. The number of face images per person is reduced using stenography compression technique. The preprocessing techniques such as resize and Gaussian filter are applied on reduced number of face images for uniform size and good quality of images. The initial features are extracted using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The DWT and LBP features are fused using arithmetic additions and connected to input of Self Organizing Map (SOM). The final features are extracted from SOM. The test image features are compared with face database images using Euclidian Distance (ED) to compute performance parameters. It is observed that the performance of the proposed method is better than the existing methods

Gravitational Search and Harmony Search Algorithms for Solving the Chemical Kinetics Optimization Problems

The article is dedicated to the analysis of the global optimization algorithms application to the solution of inverse problems of chemical kinetics. Two heuristic algorithms are considered - the gravitational search algorithm and the harmony algorithm. The article describes the algorithms, as well as the application of these algorithms to the optimization of test functions. After that, these algorithms are used to search for the kinetic parameters of two chemical processes – propane pre-reforming on Ni-catalyst and catalytic isomerization of pentane-hexane fraction. For the first process both algorithms showed approximately the same solution, while for the second problem the gravitational search algorithm showed a smaller value of the minimizing function. Wherefore, it is concluded that on large-scale problems it is better to use the gravitational search algorithm rather than the harmony algorithm, while obtaining a smaller value of the minimizing function in a minimum time. On low-scale problems both algorithms showed approximately the same result, while demonstrating the coincidence of the calculated data with the experimental ones

Colour and texture image analysis in a Local Binary Pattern framework

In this Thesis we use colour and Local Binary Pattern based texture analysis for image classification and reconstruction. In complementary work we offer a new texture description called the Sudoku transform, an extension of the Local Binary Pattern. Our new method when used to classify members of benchmark datasets shows a performance increment over traditional methods including the Local Binary Pattern. Finally we consider the invertibility of texture descriptions and show how with our new method - Quadratic Reconstruction - that a highly accurate image can be recovered purely from its textural information

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

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

Optimal demand-supply energy management in smart grids

Everything goes down if you do not have power: the financial sector, refineries and water. The grid underlies the rest of the country’s critical infrastructure. This thesis focuses on four specific problems to balance demand-supply gap with higher reliability, efficiency and economical operation of the modern power grid. The first part investigates the economic dispatch problem with uncertain power sources. The classic economic dispatch problems seek thermal power generation to meet the demand most efficiently. However, this project exploits two different power sources such as wind and solar power generation into the standard optimal power flow framework. The stochastic nature of renewable energy sources (RES) is modeled using Weibull and Lognormal probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. The calculation of best power dispatch is proposed using a cost function. The second part investigates demand-side management (DSM) strategies to minimize energy wastage by changing the time pattern and magnitude of utility load at the consumer side. The main objective of DSM is to flatten the demand curve by encouraging end-users to shift energy consumption to off-peak hours or to consume less power during peak times. It is more appropriate to follow the generation pattern in many cases instead of flattening the demand curve. It becomes more challenging when the future grid accommodates the penetration of distributed energy resources in a greater manner. In both scenarios, there is an ultimate need to control energy consumption. Effective DSM strategies would help to get an accurate balance between both ends, i.e., the supply-side and demand-side, effectively reducing power demand peaks and more efficient operation of the whole system. The gap between power demand and supply can be balanced if power peak loads are minimized. The third part of the thesis then focuses on modeling the consumption behavior of end-users. For this purpose, a novel artificial intelligence and machine learning-based forecasting model is developed to analyze big data in the smart grid. Three modules namely feature selection, feature extraction and classification are proposed to solve big data problems such as feature redundancy and high dimensionality to generate quality data for classifier training and better prediction results. The last part of this thesis investigates the problem of electricity theft to minimize non technical losses and power disruptions in the power grid. Electricity theft with its many facets usually has an enormous cost to utilities compared to non-payment because of energy wastage and power quality problems. With the recognition of the internet of things (IoT) technologies and data-driven approaches, power utilities have enough tools to combat electricity theft and fraud. An integrated framework is proposed that combines three distinct modules such as data preprocessing, data class balancing and final classification to make accurate electrical consumption theft predictions in smart grids. The result of our solution to balance the electricity demand-supply gap can provide helpful information to grid planners seeking to improve the resilience of the power grid to outages and disturbances. All parts of this thesis include extensive experimental results on case studies, including realistic large-scale instances

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Applied Methuerstic 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