Machine Learning for Congestion Management and Routability Prediction within FPGA Placement

2020 ◽  
Vol 25 (5) ◽  
pp. 1-25
Author(s):  
Hannah Szentimrey ◽  
Abeer Al-Hyari ◽  
Jeremy Foxcroft ◽  
Timothy Martin ◽  
David Noel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Congestion Management ◽  
Fpga Placement

scholarly journals Machine Learning-Based Cooperative Spectrum Sensing in Dynamic Segmentation Enabled Cognitive Radio Vehicular Network

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1169
Author(s):  
Mohammad Asif Hossain ◽  
Rafidah Md Noor ◽  
Kok-Lim Alvin Yau ◽  
Saaidal Razalli Azzuhri ◽  
Muhammad Reza Z’aba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Ad Hoc ◽  
Cooperative Spectrum Sensing ◽  
Learning Algorithm ◽  
Congestion Management ◽  
Secondary Users ◽  
Dynamic Segmentation ◽  
Services Integration

A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naïve Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users’ activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing.

Traffic Congestion Management System : An Approach with Machine Learning

2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Ankush Jain ◽  
Ankur Kumar
Keyword(s):  
Machine Learning ◽  
Traffic Congestion ◽  
Management System ◽  
Congestion Management ◽  
Negative Influence ◽  
Learning Technology ◽  
Rapid Urbanization ◽  
R Programming Language ◽  
Modern Cities ◽  
R Programming

Traffic congestion is one of the major issues that is faced in most of the modern cities. It results from rapid urbanization and has a negative influence upon the society. Hence there is an urgent need for solutions to tackle this issue. This paper presents a traffic congestion management system which will help in predicting and controlling the traffic. The system will make use of Machine Learning technology in R programming language and Big Data analysis.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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