LEAESN: Predicting DDoS attack in healthcare systems based on Lyapunov Exponent Analysis and Echo State Neural Networks

2021 ◽  
Author(s):  
Hossein Salemi ◽  
Habib Rostami ◽  
Saeed Talatian-Azad ◽  
Mohammad Reza Khosravi
Keyword(s):  
Neural Networks ◽  
Lyapunov Exponent ◽  
Healthcare Systems ◽  
Ddos Attack

scholarly journals Lyapunov exponent analysis for multilayer neural networks

2021 ◽  
Vol 12 (4) ◽  
pp. 674-684
Author(s):  
Misaki Kondo ◽  
Satoshi Sunada ◽  
Tomoaki Niiyama
Keyword(s):  
Neural Networks ◽  
Lyapunov Exponent ◽  
Multilayer Neural Networks

scholarly journals Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

2020 ◽  
Author(s):  
Junghwan Lee ◽  
Casey Ta ◽  
Jae Hyun Kim ◽  
Cong Liu ◽  
Chunhua Weng
Keyword(s):  
Neural Networks ◽  
Mechanical Ventilation ◽  
Risk Management ◽  
Recurrent Neural Networks ◽  
Healthcare Systems ◽  
The Novel ◽  
Health Records ◽  
Proposed Model ◽  
Severity Prediction ◽  
Novel Coronavirus

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) using recurrent neural networks (RNN). The predicted severity risk score represents the probability for a person to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission

Evaluating Lyapunov exponent spectra with neural networks

2013 ◽  
Vol 51 ◽  
pp. 13-21 ◽  
Author(s):  
A. Maus ◽  
J.C. Sprott
Keyword(s):  
Neural Networks ◽  
Lyapunov Exponent

scholarly journals TOWARDS AUTOMATIC EPILEPTIC SEIZURE DETECTION IN EEGS BASED ON NEURAL NETWORKS AND LARGEST LYAPUNOV EXPONENT

2014 ◽  
pp. 36-47
Author(s):  
Vladimir Golovko ◽  
Svetlana Artsiomenka ◽  
Volha Kisten ◽  
Victor Evstigneev
Keyword(s):  
Neural Networks ◽  
Lyapunov Exponent ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Activity ◽  
Largest Lyapunov Exponent ◽  
Eeg Signals ◽  
Paroxysmal Activity ◽  
Data Set ◽  
Epileptic Seizure Detection

Over the past few decades, application of neural networks and chaos theory to electroencephalogram (EEG) analysis has grown rapidly due to the complex and nonlinear nature of EEG data. We report a novel method for epileptic seizure detection that is depending on the maximal short-term Lyapunov exponent (STLmax). The proposed approach is based on the automatic segmentation of the EEG into time segments that correspond to epileptic and non-epileptic activity. The STL-max is then computed from both categories of EEG signal and used for classification of epileptic and non-epileptic EEG segments throughout the recording. Neural network techniques are proposed both for segmentation of EEG signals and computation of STLmax. The data set from hospital have been used for experiments performing. It consists of 21 records during 8 seconds of eight adult patients. Furthermore the publicly available data were used for experiments. The main advantages of presented neural technique is its ability to detect rapidly the small EEG time segments as the epileptic or non-epileptic activity, training without desired data set about epileptic and non-epileptic activity in EEG signals. The proposed approach permits to detect exactly the epileptic and non-epileptic EEG segments of different duration and shape in order to identify a pathological activity in a remission state as well as detect a paroxysmal activity in a preictal period.

DDoS-attack detection using artificial neural networks in Matlab

2019 ◽  
Author(s):  
Leonid M. Kupershtein ◽  
Tatiana B. Martyniuk ◽  
Olesia P. Voitovych ◽  
Bohdan V. Kulchytskyi ◽  
Andrii V. Kozhemiako ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Attack Detection ◽  
Ddos Attack ◽  
Artificial Neural ◽  
Ddos Attack Detection

scholarly journals Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks

Entropy ◽  
2016 ◽  
Vol 18 (10) ◽  
pp. 350 ◽  
Author(s):  
Khundrakpam Johnson Singh ◽  
Khelchandra Thongam ◽  
Tanmay De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Attack Detection ◽  
Application Layer ◽  
Ddos Attack ◽  
Artificial Neural ◽  
Ddos Attack Detection

Toward Identifying Mild Cognitive Impairment in Hispanic and African American Adults

2017 ◽  
Vol 2 (2) ◽  
pp. 110-116
Author(s):  
Valarie B. Fleming ◽  
Joyce L. Harris
Keyword(s):  
Older Adults ◽  
African American ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Healthcare Access ◽  
Communication Disorders ◽  
Healthcare Systems ◽  
Prevention Education ◽  
Education Management ◽  
African American Adults

Across the breadth of acquired neurogenic communication disorders, mild cognitive impairment (MCI) may go undetected, underreported, and untreated. In addition to stigma and distrust of healthcare systems, other barriers contribute to decreased identification, healthcare access, and service utilization for Hispanic and African American adults with MCI. Speech-language pathologists (SLPs) have significant roles in prevention, education, management, and support of older adults, the population must susceptible to MCI.

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