Automata-based Adaptive Behavior for Economical Modelling Using Game Theory

In this chapter, we deal with some specific domains of applications to game theory. This is one of the major class of models in the new approaches of modelling in the economic domain. For that, we use genetic automata which allow to build adaptive strategies for the players. We explain how the automata-based formalism proposed - matrix representation of automata with multiplicities - allows to define semi-distance between the strategy behaviors. With that tools, we are able to generate an automatic processus to compute emergent systems of entities whose behaviors are represented by these genetic automata

Automata-based adaptive behavior for economic modeling using game theory

In this paper, we deal with some specific domains of applications to game theory. This is one of the major class of models in the new approaches of modelling in the economic domain. For that, we use genetic automata which allow to buid adaptive strategies for the players. We explain how the automata-based formalism proposed - matrix representation of automata with multiplicities - allows to define a semi-distance between the strategy behaviors. With that tools, we are able to generate an automatic processus to compute emergent systems of entities whose behaviors are represented by these genetic automata

Fine-tuning U-net for medical image segmentation based on activation function, optimizer and pooling layer

U-net convolutional neural network (CNN) is a famous architecture developed to deal with medical images. Fine-tuning CNNs is a common technique used to enhance their performance by selecting the building blocks which can provide the ultimate results. This paper introduces a method for tuning U-net architecture to improve its performance in medical image segmentation. The experiment is conducted using an x-ray image segmentation approach. The performance of U-net CNN in lung x-ray image segmentation is studied with different activation functions, optimizers, and pooling-bottleneck-layers. The analysis focuses on creating a method that can be applied for tuning U-net, like CNNs. It also provides the best activation function, optimizer, and pooling layer to enhance U-net CNN’s performance on x-ray image segmentation. The findings of this research showed that a U-net architecture worked supremely when we used the LeakyReLU activation function and average pooling layer as well as RMSProb optimizer. The U-net model accuracy is raised from 89.59 to 93.81% when trained and tested with lung x-ray images and uses the LeakyReLU activation function, average pooling layer, and RMSProb optimizer. The fine-tuned model also enhanced accuracy results with three other datasets

Modeling Spatial Organization with Swarm Intelligence Processes

International audienceUrban Dynamics modeling needs to implement spatial organization emergence in order to describe the development of services evolution and their usage within spatial centers. In this paper, we propose an extension of the nest building algorithm with multi-center, multi-criteria and adaptive processes. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. Typically, this model is suitable to analyse and simulate urban dynamics like the evolution of cultural equipment in urban area

URBAN DYNAMICS MODELLING USING ANT NEST BUILDING

International audienceUrban dynamics deal with spatial organizations where a great complexity of interactions appears. Social and economic aspects interact and environmental objectives are nowadays a major purpose for sustainable urban development. We propose some generic modelling processes able to face with this complexity, in order to simulate the evolution of the city centers. These organizational centers need a multi-criteria description for their evolution, including feed-back phenomena of them over their environment and components. We propose a swarm intelligence algorithm, using social-insect collective behavior. We combine a decentralized approach, based on emergent clustering mixed with spatial constraints or attractions, as an extension of the ant nest building algorithm with multi-center. Typically, this model is currently used by ourself, to model and analyze cultural equipment dynamics in urban area

ON THE USE OF GENERALIZED DERANGEMENTS FOR SCHELLING'S MODEL OF SEGREGATION

International audienceThis paper proposes a definition of Schelling's model of segregation using generalized derangements. Many of urban or territorial modellings are based on decentralized approaches where rule-based systems have to be integrated inside a whole interaction system to describe complex phenomena. The goal of these decentralized modellings is to deal with emergent computing able to detect dynamically emergent organizations in an unsupervized way, thanks to complex systems theory. The convergence of these modern computings is generally hard to study because of the use of asynchronised processes dealing with a number of autonomous entities which are acting and interacting, in non linear way, during the whole simulation. Our approach is to define a non sequential-dependant algorithm, thanks to generalized derangements, and so to use this efficient tool to study some properties on the evolutive process

Modélisation adaptative pour l'émergence spatiale dans les systèmes complexes

The aim of this work concerns the implementation of swarm intelligence models in order to study the spatial emergence of organizations within self-organized systems, under multi-criteria constraints. The scientific context of the modeling formalism is developped in this document. A methodology is presented and leads to the development of a complex heuristic linking bio-inspirated elementary models, based on ant algorithms. An application is developped and concerns the service/user modeling - specifically for cultural services - in urban dynamics. Models of adaptive mechanisms of services during their usage are also proposed.L'objectif de ce travail consiste à mettre en place des modèles d'intelligence en essaim pour l'étude de l'émergence spatiale d'organisations dans des systèmes complexes auto-organisés, sous des contraintes multi-critères. Le contexte scientique de la formalisation dans le cadre de la modélisation des systèmes complexes est développé dans ce document. Une méthodologie est présentée et conduit au développement d'une heuristique complexe tissant des liens entre des modèles élémentaires bio-inspirés des algorithmes de type fourmis. Une application est développée et concerne la modélisation de l'usage de services - notamment des services culturels - en dynamique urbaine, ainsi que la modélisation des mécanismes d'adaptation de ces services en fonction de leurs usages

Hybrid Framework for Diabetic Retinopathy Stage Measurement Using Convolutional Neural Network and a Fuzzy Rules Inference System

Diabetic retinopathy (DR) is an increasingly common eye disorder that gradually damages the retina. Identification at the early stage can significantly reduce the severity of vision loss. Deep learning techniques provide detection for retinal images based on data size and quality, as the error rate increases with low-quality images and unbalanced data classes. This paper proposes a hybrid intelligent framework of a conventional neural network and a fuzzy inference system to measure the stages of DR automatically, Diabetic Retinopathy Stage Measurement using Conventional Neural Network and Fuzzy Inference System (DRSM-CNNFIS). The fuzzy inference used human experts’ rules to overcome data dependency problems. At first, the Conventional Neural Network (CNN) model was used for feature extraction, and then fuzzy rules were used to measure diabetic retinopathy stage percentage. The framework is trained using images from Kaggle datasets (Diabetic Retinopathy Detection, 2022). The efficacy of this framework outperformed the other models with regard to accuracy, macro average precision, macro average recall, and macro average F1 score: 0.9281, 0.7142, 0.7753, and 0.7301, respectively. The evaluation results indicate that the proposed framework, without any segmentation process, has a similar performance for all the classes, while the other classification models (Dense-Net-201, Inception-ResNet ResNet-50, Xception, and Ensemble methods) have different levels of performance for each class classification

Classification of Mobile Customers Behavior and Usage Patterns using Self-Organizing Neural Networks

Mobile usage is witnessing a booming growth attributed to advances in smartphone technologies, the extremely high penetration rate and the availability of popular mobile applications. Telecommunication markets have been injecting huge investments to fulfil the sheer demand on wireless network and mobile services as a result. Such potentials highlights the importance of behavioral segmentation of mobile network users to target different sectors of customers with efficient marketing strategies and ensure customer retention in light of the intense competition. A major hurdle in applying this approach is the number of dimensions underlying customer preferences which makes it hard to visualize similarities among customers and formulate behavioral segments correctly and efficiently. In this paper, we use self-organizing maps, to detect different usage patterns of mobile users. The proposed system is tested using a large sample of customers’ data provided by major mobile operator in Jordan. The study detected different behavioural segments in this market and highlights the role of data users in modern mobile markets. In this context, we give detailed analysis of our results on user behavioral segmentation