… The motivation behind Vehicular Ad Hoc Networks (VANETs) is to improve traffic safety and
… In this paper, we present a machine learning approach to classify multiple misbehaviors in …

… The second work towards cooperative traffic solutions using machine learning methods …
In this phase, reinforcement learning techniques and deep learning networks are exploited to …

… Through this broad experimental evaluation, we show that using machine learning in an
online decentralized approach is feasible in stringent constraints of embedded systems, that it …

… Using Machine Learning for LOS Classification This research used three different machine
learning models to categorize LOS. This paper reports that KNN, SVM, and RF achieved the …

… ) to develop a soft mobility predictor using neural learning machines, and 2) to demonstrate
… extreme learning machine (ELM) models where the rationale behind the machine learning …

… Machine learning has become an essential toolkit for complex analytic processing. Data
is typically stored in large data warehouses with multiple dimension hierarchies. Often, data …

… in Multi-hop mobile ad Hoc Networks (MANETs) by utilizing system learning techniques. …
of integrating machine learning models into the network performance of a mobile ad hoc …

… RR is featured by integrating machine learning (ML) in MANET operations. Ad-hoc routing
protocols that … The machine learning for neighbor selection relies on the attributes analyzed. …

… When traffic or packet is coming from the internet, the router will pass the data to the IDS
server, the IDS server will do the traffic collection process and machine learning process. …

… ad-hoc networks such as vehicular ad-hoc networks (VANETs), robot ad-hoc networks (RANETs),
ship ad-hoc networks (SANETs), smartphone ad hoc networks (SPANs), and wireless …