Image-Edge Detection System Based on Supervised Machine Learning Algorithm

2021 ◽  
pp. 257-267
Author(s):  
Kaiyong Li ◽  
Haixia Wang ◽  
Linwei Ouyang ◽  
Bo Zhao
Keyword(s):  
Machine Learning ◽  
Edge Detection ◽  
Learning Algorithm ◽  
Detection System ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Image Edge Detection ◽  
Image Edge

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.

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 AI3 PREDICTION OF BREAST CANCER USING K-NEAREST NEIGHBOUR: A SUPERVISED MACHINE LEARNING ALGORITHM

2020 ◽  
Vol 23 ◽  
pp. S1
Author(s):  
S. Pandey ◽  
A. Sharma ◽  
M.K. Siddiqui ◽  
D. Singla ◽  
J. Vanderpuye-Orgle
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Nearest Neighbour
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