An atrial fibrillation detection system based on machine learning algorithm with mix-domain features and hardware acceleration *

2021 ◽  
Author(s):  
Chao Chen ◽  
Caiyun Ma ◽  
Yantao Xing ◽  
Zinan Li ◽  
Hongxiang Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Learning Algorithm ◽  
Detection System ◽  
Hardware Acceleration ◽  
Machine Learning Algorithm ◽  
Atrial Fibrillation Detection

scholarly journals Effective and efficient network anomaly detection system using machine learning algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Mukrimah Nawir ◽  
Amiza Amir ◽  
Naimah Yaakob ◽  
Ong Bi Lynn
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Processing Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

A Survey on Intrusion Detection in Wired and Wireless Network for Future IoT Deployment

2020 ◽  
pp. 119-144
Author(s):  
Vasaki Ponnusamy ◽  
Said Bakhshad ◽  
Bobby Sharma ◽  
Robithoh Annur ◽  
Teh Boon Seong
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Computer System ◽  
Wireless Network ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithm ◽  
Problem Statement ◽  
Malicious Activity ◽  
Alarm Message

An intrusion detection system (IDS) works as an alarm mechanism for computer systems. It detects any malicious activity that happened to the computer system and it alerts an alarm message to notify the user there is malicious activity. There are IDS that are able to take action when malicious or anomalous networks are detected, which include suspending the traffic sent from suspicious IP addresses. The problem statement for this project is to find out the most accurate machine learning algorithm and the types of IDS with different placement strategies. When it comes to the deployment of a wireless network, IDS is not as easy a task as deploying a traditional network IDS. There are many unexpected complexities of the problem of reliable intrusion detection in a wireless network. The motivation of this research is to find the most suitable classification techniques that are able to increase the accuracy of an IDS. Machine learning is useful for the upcoming trend; it provides better accuracy in the detection of malicious traffic.

Intelligent fall detection system using traditional and non-traditional machine learning algorithm based on MQTT

2021 ◽  
Author(s):  
C. Y. Cheong ◽  
C. C. Lim ◽  
Y. F. Chong ◽  
V. Vikneswaran ◽  
A. F. Salleh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Fall Detection ◽  
Machine Learning Algorithm

scholarly journals Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

2017 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Carol Gu ◽  
Heidi Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Intensive Care ◽  
Length Of Stay ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithm ◽  
Implementation Phase ◽  
Before And After ◽  
Sepsis Detection

AbstractIntroductionSepsis is a major health crisis in US hospitals, and several clinical identification systems have been designed to help care providers with early diagnosis of sepsis. However, many of these systems demonstrate low specificity or sensitivity, which limits their clinical utility. We evaluate the effects of a machine learning algodiagnostic (MLA) sepsis prediction and detection system using a before-and-after clinical study performed at Cabell Huntington Hospital (CHH) in Huntington, West Virginia. Prior to this study, CHH utilized the St. John’s Sepsis Agent (SJSA) as a rules-based sepsis detection system.MethodsThe Predictive algoRithm for EValuation and Intervention in SEpsis (PREVISE) study was carried out between July 1, 2017 and August 30, 2017. All patients over the age of 18 who were admitted to the emergency department or intensive care units at CHH were monitored during the study. We assessed pre-implementation baseline metrics during the month of July, 2017, when the SJSA was active. During implementation in the month of August, 2017, SJSA and the MLA concurrently monitored patients for sepsis risk. At the conclusion of the study period, the primary outcome of sepsis-related in-hospital mortality and secondary outcome of sepsis-related hospital length of stay were compared between the two groups.ResultsSepsis-related in-hospital mortality decreased from 3.97% to 2.64%, a 33.5% relative decrease (P = 0.038), and sepsis-related length of stay decreased from 2.99 days in the pre-implementation phase to 2.48 days in the post-implementation phase, a 17.1% relative reduction (P < 0.001).ConclusionReductions in patient mortality and length-of-stay were observed with use of a machine learning algorithm for early sepsis detection in the emergency department and intensive care units at Cabell Huntington Hospital, and may present a method for improving patient outcomes.Trial RegistrationClinicalTrials.gov, NCT03235193, retrospectively registered on July 27th 2017.

scholarly journals FPGA-based Hardware Acceleration for SVM Machine Learning Algorithm

2021 ◽  
Vol 229 ◽  
pp. 01024
Author(s):  
Jakjoud Fatimazahra ◽  
Hatim Anas ◽  
Abella Bouaaddi
Keyword(s):  
Machine Learning ◽  
Object Recognition ◽  
Execution Time ◽  
Learning Algorithm ◽  
Hardware Acceleration ◽  
Machine Learning Algorithm ◽  
Computing Power ◽  
Recognition Algorithms ◽  
Plant Recognition ◽  
And Performance

object recognition algorithms are both large consumers of computing power and memory which affects the quality and performance especially when it comes to large image datasets, in this paper we propose an algorithm for fruit/plant recognition that we will accelerate it using the PYNQ Board to evaluate the execution time and the accuracy of the classifier.

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