DYNAMIC THRESHOLDING GA-BASED ECG FEATURE SELECTION IN CARDIOVASCULAR DISEASE DIAGNOSIS

Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified

Modeling ADSL Traffic on an IP Backbone Link

Measurements from an Internet backbone link carrying TCP traffic towards different ADSL areas are analyzed in this paper. For traffic analysis, we adopt a flow based approach and the popular mice/elephants dichotomy. The originality of the experimental data reported in this paper, when compared with previous measurements from very high speed backbone links, is in that commercial traffic comprises a significant part due to peer-to-peer applications. This kind of traffic exhibits some remarkable properties in terms of mice and elephants, which are described in this paper. It turns out that by adopting a suitable level of aggregation, the bit rate of mice can be described by means of a Gaussian process. The bit rate of elephants is smoother than that of mice and can also be well approximated by a Gaussian process

DYNAMIC THRESHOLDING GA-BASED ECG FEATURE SELECTION IN CARDIOVASCULAR DISEASE DIAGNOSIS

Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified

Inverting sampled ADSL traffic

On the basis of a reference model for ADSL traffic on an IP backbone link, established in an earlier study, we show in this paper that it is possible to infer the characteristics of long flows by performing a deterministic $1/N$ packet sampling. By using the fact that the number of active long flows can be represented by means of the number of customers in an $M/G/\infty$ queue with Weibullian service times, we derive some probabilistic properties of sampled data. These properties are then used to infer the characteristics of original flows. The method is illustrated by considering an actual traffic trace captured in the France Telecom IP backbone network. Experimental data show that the method proves quite efficient

Etude des méthodes d'échantillonnage des flux pour la mesure dans les réseaux large bande

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Towards a Self-adaptive Trust Management Model for VANETs

International audienc

Trust management in vehicular ad hoc networks: a survey

International audienceThe vehicular ad hoc networks (VANETs) provide a variety of applications that aim to ensure a safe and comfort driving experience. These applications rely on the communication and the exchange of data between vehicles. These entities are exposed to many security threats that may affect the reliability of the provided applications. Accordingly, there is a need for a trust management scheme that has to cope with the security threats and the high dynamicity of the network topology. In this paper, we survey the recent advances in trust management for VANETs. The aim of this paper is to show the importance of an adaptive trust model that can deal with the requirements of each class of applications. Therefore, we have presented well-defined criteria to point out the key issues of the existing studies and to set up some insights for research within this scope

Trust management in vehicular ad hoc networks: a survey

International audienceThe vehicular ad hoc networks (VANETs) provide a variety of applications that aim to ensure a safe and comfort driving experience. These applications rely on the communication and the exchange of data between vehicles. These entities are exposed to many security threats that may affect the reliability of the provided applications. Accordingly, there is a need for a trust management scheme that has to cope with the security threats and the high dynamicity of the network topology. In this paper, we survey the recent advances in trust management for VANETs. The aim of this paper is to show the importance of an adaptive trust model that can deal with the requirements of each class of applications. Therefore, we have presented well-defined criteria to point out the key issues of the existing studies and to set up some insights for research within this scope

Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

International audienc