An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm

2021 ◽  
Author(s):  
Mohamed H. Mahmoud ◽  
Salman Alamery ◽  
H. Fouad ◽  
Amir Altinawi ◽  
Ahmed E. Youssef
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Learning Algorithm ◽  
Detection System ◽  
Automatic Detection ◽  
Machine Learning Algorithm ◽  
Inductive Machine Learning ◽  
Automatic Detection System

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

2019 ◽  
Vol 23 (5-6) ◽  
pp. 801-817 ◽  
Author(s):  
Diana Yacchirema ◽  
Jara Suárez de Puga ◽  
Carlos Palau ◽  
Manuel Esteve
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Learning Algorithm ◽  
Detection System ◽  
Fall Detection ◽  
Machine Learning Algorithm ◽  
Ensemble Machine Learning

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
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