scholarly journals Congestion Control in Wireless Communication Network Using Fuzzy Logic and Machine Learning Techniques

2014 ◽  
Vol 03 (11) ◽  
pp. 12825-12832 ◽  
Author(s):  
Abhishak Sawhney ◽  
Ritu Bhatia ◽  
Payal Mahajan
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Fuzzy Logic ◽  
Wireless Communication ◽  
Congestion Control ◽  
Machine Learning Techniques ◽  
Wireless Communication Network ◽  
Learning Techniques

scholarly journals Wireless communication network security intelligent monitoring system based on machine learning

2021 ◽  
Vol 2083 (3) ◽  
pp. 032045
Author(s):  
Hongkun Liu ◽  
Nianci Wang ◽  
Sirong Liang
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Network Security ◽  
Data Collection ◽  
Wireless Communication ◽  
Monitoring System ◽  
Intelligent Monitoring ◽  
Security Vulnerability ◽  
Wireless Communication Network ◽  
Monitoring Accuracy

Abstract Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.

scholarly journals Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 74
Author(s):  
Bhavesh Pandya ◽  
Amir Pourabdollah ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Random Forest ◽  
Web Services ◽  
Real Time ◽  
Fall Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Time Fall

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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