An Analysis of Segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems With Machine Learning Models

2020 ◽  
Vol 20 (6) ◽  
pp. 3303-3313 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Hsiang-Yun Huang ◽  
Steen Jun-Ping Hsu ◽  
Chia-Tai Chan
Keyword(s):  
Machine Learning ◽  
Fall Detection ◽  
Learning Models ◽  
Detection Systems ◽  
Machine Learning Models

Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models

2018 ◽  
Vol 18 (23) ◽  
pp. 9882-9890 ◽  
Author(s):  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Steen Jun-Ping Hsu ◽  
Chia-Tai Chan
Keyword(s):  
Machine Learning ◽  
Sampling Rate ◽  
Fall Detection ◽  
Learning Models ◽  
Detection Systems ◽  
Machine Learning Models

scholarly journals Review Study of Hoax Email Characteristic

2018 ◽  
Vol 7 (3.2) ◽  
pp. 778 ◽  
Author(s):  
SY. Yuliani ◽  
Shahrin Sahib ◽  
Mohd Faizal Abdollah ◽  
Mohammed Nasser Al-Mhiqani ◽  
Aldy Rialdy Atmadja
Keyword(s):  
Machine Learning ◽  
Fake News ◽  
Learning Models ◽  
Detection Systems ◽  
Review Study ◽  
Machine Learning Models

Hoax on email is one form of attack in the cyber world where an email account will be sent with fake news that has many goals to take advantage or raise the rating of sales of a product. A Hoax can affect many people by damaging the credibility of the image of a person or group. The phenomenon of this hoax would cause anxiety in the community and even more bad effects because of the potential for the wrong power of the news or information. In this paper we review the Hoax detection systems, Types of Hoax, and machine learning models that has been used to detect the Hoax. This work serves as a basis for further studies on Hoax detection systems.  

scholarly journals Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 203 ◽  
Author(s):  
Martin Sarnovsky ◽  
Jan Paralic
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Models ◽  
Knowledge Model ◽  
Detection Systems ◽  
Knowledge Based ◽  
Knowledge Models ◽  
Multi Stage ◽  
Machine Learning Approach ◽  
Machine Learning Models

Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.

scholarly journals Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
K Nearest Neighbors ◽  
Detection Systems ◽  
Iot Devices ◽  
Application Developers ◽  
Machine Learning Models

This study aims to analyze the performance of machine learning models for detecting Internet of Things malware utilizing a recent IoT dataset. Experiments on the IoT dataset were conducted with nine well-known machine learning techniques, consisting of Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks (NN), Random Forest (RF), Bagging (BG), and Stacking (ST). The results show that the proposed model attains 100% accuracy in detecting IoT malware for DT, SVM, RF, BG; about 99.9% percent for LR, NB, KNN, NN; and only 28.16% for ST classifier. This study also shows higher performance than other proposed machine learning models evaluated on the same dataset. Therefore, the results of this study can help both the researchers and application developers in designing and building intelligent malware detection systems for IoT devices.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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