Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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Aisimam - An Artificial immune system based intelligent multiangent model

The goal of this thesis is to develop a biological model for multiagent systems. This thesis explores artificial immune systems, a novel evolutionary paradigm based on the immunological principles. Artificial Immune systems (AIS) are found to be powerful to solve complex computational tasks. The main focus of the thesis is to develop a generic mathematical model that uses the principles of the human immune system in multiagent systems (MAS). The components and properties of the human immune system are studied. On understanding the concepts of A/5, a literature survey of multiagent systems is performed to understand and compare the multiagent concepts and AIS concepts. An analogy between the immune system parameters and the agent theory was derived. Then, an intelligent multiagent model named AISIMAM is derived. It exploits several properties and features of the immune system in multiagent systems. In other words, the intelligence of the immune systems to kill the antigen and the characteristics of the agents are combined in the model. The model is expressed in terms of mathematical expressions. The model is applied to a specific application namely the mine detection and defusion. The simulations are done in MATLAB that runs on a PC. The experimental results of AISIMAM applied to the mine detection problem are discussed. The results are successful and shows that AISIMAM could be an alternative solution to agent based problems. Artificial Immune System is also applied to a pattern recognition problem. The problem experimented is a color image classification problem useful in a real time industrial application. The images are those of wooden components that need to be classified according to the color and type of wood. To solve the classification task, a simple negative selection and genetic algorithm based A/5 algorithm was developed and simulated. The results are compared with the radial basis function approach applied to the same set of input images

eXplainable data processing

Seminario realizado en U & P U Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT), Changa-388421, Gujarat, India 2021[EN]Deep Learning y has created many new opportunities, it has unfortunately also become a means for achieving ill-intentioned goals. Fake news, disinformation campaigns, and manipulated images and videos have plagued the internet which has had serious consequences on our society. The myriad of information available online means that it may be difficult to distinguish between true and fake news, leading many users to unknowingly share fake news, contributing to the spread of misinformation. The use of Deep Learning to create fake images and videos has become known as deepfake. This means that there are ever more effective and realistic forms of deception on the internet, making it more difficult for internet users to distinguish reality from fictio

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions

Simulators: evolutionary multi-agent system for object recognition in satellite image.

Miu, Hoi Shun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 170-182).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Statement --- p.4Chapter 1.2 --- Contributions --- p.5Chapter 1.3 --- Thesis Organization --- p.6Chapter 2 --- Background --- p.8Chapter 2.1 --- Multi-agent Systems --- p.8Chapter 2.1.1 --- Agent Architectures --- p.9Chapter 2.1.2 --- Multi-agent system frameworks --- p.12Chapter 2.1.3 --- The Advantages and Disadvantages of Multi-agent Systems --- p.15Chapter 2.2 --- Evolutionary Computation --- p.16Chapter 2.2.1 --- Genetic Algorithms --- p.17Chapter 2.2.2 --- Genetic Programming --- p.18Chapter 2.2.3 --- Evolutionary Strategies --- p.19Chapter 2.2.4 --- Evolutionary Programming --- p.19Chapter 2.3 --- Object Recognition --- p.19Chapter 2.3.1 --- Knowledge Representation --- p.20Chapter 2.3.2 --- Object Recognition Methods --- p.21Chapter 2.4 --- Evolutionary Multi-agent Systems --- p.25Chapter 2.4.1 --- Competitive Coevolutionary Agents --- p.26Chapter 2.4.2 --- Cooperative Coevolutionary Agents --- p.26Chapter 2.4.3 --- Cellular Automata --- p.27Chapter 2.4.4 --- Emergent Behavior --- p.28Chapter 2.4.5 --- Evolutionary Agents for Image processing and Pattern Recog- nition --- p.29Chapter 3 --- System Architecture and Agent Behaviors in SIMULATORS --- p.33Chapter 3.1 --- Organization of the System --- p.34Chapter 3.1.1 --- General Architecture of Object Recognition System --- p.34Chapter 3.1.2 --- Introduction to SIMULATORS --- p.35Chapter 3.1.3 --- System Flow of SIMULATORS --- p.37Chapter 3.1.4 --- Layered Digital Image Environment --- p.39Chapter 3.2 --- Architecture of Autonomous Agents --- p.41Chapter 3.2.1 --- Internal Object Model in an Agent --- p.41Chapter 3.2.2 --- Current State of an Agent --- p.46Chapter 3.2.3 --- Local Information Sensor --- p.46Chapter 3.2.4 --- Direction Density Vector --- p.47Chapter 3.3 --- Agent Behaviors --- p.48Chapter 3.3.1 --- Feature Target Marking --- p.49Chapter 3.3.2 --- Reproduction --- p.49Chapter 3.3.3 --- Diffusion --- p.52Chapter 3.3.4 --- Vanishing --- p.54Chapter 3.4 --- Clustering for Autonomous Agent Training --- p.56Chapter 3.4.1 --- Introduction --- p.56Chapter 3.4.2 --- Creating the Internal Object Model --- p.58Chapter 3.5 --- Summary --- p.63Chapter 4 --- Evolutionary Algorithms for Multi Agent System --- p.64Chapter 4.1 --- Evolutionary Agent Behaviors in SIMULATORS --- p.65Chapter 4.1.1 --- Overview --- p.65Chapter 4.1.2 --- Evolutionary Autonomous Agents --- p.66Chapter 4.1.3 --- Reproduction --- p.68Chapter 4.1.4 --- Fitness Function --- p.68Chapter 4.1.5 --- Direction Density Vector Propagation --- p.73Chapter 4.1.6 --- Mutation --- p.73Chapter 4.2 --- Agents Voting Mechanism --- p.74Chapter 4.2.1 --- Overview --- p.74Chapter 4.2.2 --- Voting for Cooperative Agents --- p.75Chapter 4.3 --- Evolutionary Multi Agent Object Recognition --- p.79Chapter 4.4 --- Summary --- p.81Chapter 5 --- Experimental Results and Applications --- p.82Chapter 5.1 --- Experiment Methodology --- p.82Chapter 5.1.1 --- Introduction to Fung Shui Woodland --- p.83Chapter 5.1.2 --- Testing Images --- p.83Chapter 5.1.3 --- Creating Internal Object Model --- p.85Chapter 5.1.4 --- Experiment Parameters --- p.86Chapter 5.2 --- Experimental Results of Fung Shui Woodland Recognition --- p.92Chapter 5.2.1 --- Experiment 1: artificial0l --- p.92Chapter 5.2.2 --- Experiment 2: artificial0l´ؤnoise --- p.92Chapter 5.2.3 --- Experiment 3: artificial02 --- p.93Chapter 5.2.4 --- Experiment 4: FungShui0l --- p.93Chapter 5.2.5 --- Experiment 5: FungShui0l´ؤnoise --- p.94Chapter 5.2.6 --- Experiments 6 to 11: FungShui02 to FungShui07 --- p.94Chapter 5.3 --- Discussion --- p.119Chapter 5.4 --- An Example of Eyes Detection --- p.124Chapter 5.4.1 --- Result of the Eyes Detection --- p.128Chapter 5.5 --- Summary --- p.132Chapter 6 --- Conclusion --- p.133Chapter 6.1 --- Summary --- p.133Chapter 6.2 --- Future Work --- p.136Chapter A --- The Figures in the Experiments --- p.13

Managing smart cities with deepint.net

In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities