scholarly journals Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

Symmetry ◽  
2017 ◽  
Vol 9 (9) ◽  
pp. 197 ◽  
Author(s):  
Kamran Siddique ◽  
Zahid Akhtar ◽  
Haeng-gon Lee ◽  
Woongsup Kim ◽  
Yangwoo Kim
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Anomaly Detection ◽  
High Speed ◽  
Machine Learning Techniques ◽  
Data Networks ◽  
Bulk Synchronous Parallel ◽  
Learning Techniques

Review on Anomaly Detection in Mobile Networks Using Traditional Learning, Machine Learning and Deep Learning

2020 ◽  
Vol 17 (11) ◽  
pp. 4789-4796
Author(s):  
T. S. Prabhakar ◽  
M. N. Veena
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Mobile Networks ◽  
High Speed ◽  
Network Activity ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Artificial Neural Network Method ◽  
Learning Machine

Increasing usage of smart phones involves in the developing large amount of data and high speed internet is used for transfers this large amount of data. This in-turn gives rise to the development of various attacks to hack the data. Anomaly detection in the network analyzes the pattern in the network activity and found the abnormality in the network. The accurate detection of abnormality in network helps to prevent the attackers to steal the data. Many researches were conducted to improve the performance of anomaly detection in the mobile networks. Traditional methods results for performance of anomaly detection are not much effective. Machine learning techniques are used for the anomaly detection to increase the performance. The deep learning techniques are applied to increase the detection rate and decrease the false positive. Both the techniques machine learning uses k-means and Deep learning uses Artificial Neural Network method provides the considerable performance in anomaly detection.

scholarly journals Estimating the CAP greening effect by machine learning techniques: A big data ex post analysis

2021 ◽  
Vol 119 ◽  
pp. 44-53
Author(s):  
Danilo Bertoni ◽  
Giacomo Aletti ◽  
Daniele Cavicchioli ◽  
Alessandra Micheletti ◽  
Roberto Pretolani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Machine Learning Techniques ◽  
Ex Post ◽  
Learning Techniques ◽  
Ex Post Analysis

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

scholarly journals IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2857
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Francisco Nóvoa
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Detection Rate ◽  
Security Vulnerabilities ◽  
Smart Systems ◽  
Learning Techniques ◽  
Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

Sign in / Sign up

Export Citation Format

Share Document