A Novel Approach for Youtube Video Spam Detection using Markov Decision Process

2018 ◽  
Author(s):  
Simran Kanodia ◽  
Rachna Sasheendran ◽  
Vinod Pathari
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Spam Detection ◽  
Novel Approach ◽  
Markov Decision

scholarly journals N-Element Beam Antennas System Design for Wireless Sensor Node Using Semi-Markov Decision Process with Variable Sojourn Time

2020 ◽  
pp. 289-298
Author(s):  
Abayomi Ajofoyinbo ◽  
◽  
David O. Olowokere ◽  
Oye Ibidapo-Obe
Keyword(s):  
System Design ◽  
Markov Decision Process ◽  
Decision Process ◽  
Sojourn Time ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Novel Approach ◽  
Entire Duration ◽  
Markov Decision

The paper presents N-element switchable beam antennas (BAs) system design for Wireless Sensor Node (WSN), in which the operation of the BAs was characterized by semi-Markov Decision Process (SMDP) with variable sojourn time. A matrix-based switching methodology was introduced for selecting an operational BA, based on the received signal power by each BA. Optimal analysis was carried-out to obtain optimal results in terms of the maximum total of sum of discounted reward in current states. Also developed in the study is the methodology for switching a BA from non-operational to operational state and vice-versa. The effectiveness of this switchable BAs system design was tested via numerical analysis implemented in MATLAB software. Numerical results show that this novel approach enables the WSN equipped with BAs to select and maintain an operational BA in receive (or transmit) mode for the entire duration of packets reception (or transmission). The authors found no paper in the existing literature that provides this capability.

Exploitation-Oriented Learning PS-r#

2009 ◽  
Vol 13 (6) ◽  
pp. 624-630 ◽  
Author(s):  
Kazuteru Miyazaki ◽  
◽  
Shigenobu Kobayashi ◽  
Keyword(s):  
Random Walk ◽  
Reinforcement Learning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Sensory Input ◽  
Numerical Examples ◽  
Novel Approach ◽  
Partially Observed ◽  
Markov Decision ◽  
Directed Learning

Exploitation-oriented learning (XoL) is a novel approach to goal-directed learning from interaction. Reinforcement learning is much more focused on learning and ensures optimality in Markov decision process (MDP) environments, XoL involves learning a rational policy that obtains rewards continuously and very quickly. PS-r*, a form of XoL, involves learning a useful rational policy not inferior to the random walk in the partially observed Markov decision process (POMDP) where reward types number one. PS-r*, however, requires O(MN2) memory where N is the number of sensory input types and M is an action. We propose PS-r#for learning a useful rational policy in the POMDP using O(MN) memory. PS-r#effectiveness is confirmed in numerical examples.

scholarly journals Development of a Shipment Policy for Collection Centers

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1385
Author(s):  
Irais Mora-Ochomogo ◽  
Marco Serrato ◽  
Jaime Mora-Vargas ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Climate Change ◽  
Natural Disasters ◽  
Markov Decision Process ◽  
Decision Process ◽  
Necessary Conditions ◽  
Decision Makers ◽  
Humanitarian Organizations ◽  
The World ◽  
Markov Decision ◽  
Unsatisfied Demand

Natural disasters represent a latent threat for every country in the world. Due to climate change and other factors, statistics show that they continue to be on the rise. This situation presents a challenge for the communities and the humanitarian organizations to be better prepared and react faster to natural disasters. In some countries, in-kind donations represent a high percentage of the supply for the operations, which presents additional challenges. This research proposes a Markov Decision Process (MDP) model to resemble operations in collection centers, where in-kind donations are received, sorted, packed, and sent to the affected areas. The decision addressed is when to send a shipment considering the uncertainty of the donations’ supply and the demand, as well as the logistics costs and the penalty of unsatisfied demand. As a result of the MDP a Monotone Optimal Non-Decreasing Policy (MONDP) is proposed, which provides valuable insights for decision-makers within this field. Moreover, the necessary conditions to prove the existence of such MONDP are presented.

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