A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

Purpose The purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs. Design/methodology/approach The node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA. Findings This study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions. Research limitations/implications For simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered. Originality/value This research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

A Hybrid Intelligent System Framework for Detecting and Predicting Risk of Cardiovascular Disease

Number of deaths is increasing everyday especially due to heart disease in the present-day world. The cardiovascular diseases can appear due to family history, obesity, smoking etc. Prevention of death due to heart failure and proper treatment requires on time and accurate diagnosis of the disease. In this study we predict heart disease using a system that is based on machine learning using heart disease dataset. In this paper we try analysing and predicting heart diseases occurring by applying algorithms such as K-Nearest Neighbour, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, XG Boost. We have taken the dataset from UCI Machine Learning Repository. The algorithms results based on different factors like cholesterol, age, gender etc. The resulting performance provides efficiency in diagnosing the disease.

USE OF FUZZY PETRI NETWORKS FOR INFORMATION DESCRIPTION OF THE HOUSING ENVIRONMENT

The current state of comfort of an apartment building indicates many problems that exist in this area. First, there is a need for clear planning of comfortable housing, which should take into account the current socio – economic needs of the population. Secondly, there is no clear definition of comfort levels of an apartment building. Also, there is a need to identify the main factors that affect the life and comfort of the population. The article is based on two main areas of research. The first is to present a description of the information model of a hybrid intelligent system (GIS) for the comfort of the living environment of an apartment building. This tool allows you to model the effects of interaction, adaptation, which can be observed in the system when making decisions. The study also simulates complex systems based on the use of fuzzy Petri nets (FPs) to describe comfort. MP and its various types are one of the classes of models that provide an opportunity to present the structure of integrated organizational – information systems and complexes, as well as logical – temporal processes of system operation. Fuzzy Petri nets are divided into two parts. The first part describes the structure of the network, it is usually standard, the processes of initial marking and movement of markers on the network are determined by means and methods of fuzzy sets and fuzzy logic. The biggest area of their application is complex objects. Fuzzy logic is used to formalize fuzzy concepts in terms of semantics and provides efficient information processing along with clear data. With the help of this network the endurance of modeling systems and the optimality of its structure are investigated. To date, a large number of different types of Petri nets are known, which provide an opportunity to present the structure of the models in the absence of analytical representation of the influence of certain factors.

An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches

(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.