scholarly journals Reinforcement Learning-Based Television White Space Database

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0947
Author(s):  
Armie E. Pakzad ◽  
Raine Mattheus Manuel ◽  
Jerrick Spencer Uy ◽  
Xavier Francis Asuncion ◽  
Joshua Vincent Ligayo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
High Frequency ◽  
Learning Approach ◽  
Very High Frequency ◽  
Machine Learning Technique ◽  
Secondary Users ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Very High

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.

scholarly journals A Survey on Machine learning and IoT

2019 ◽  
Vol 4 (2) ◽  
pp. 99-113
Author(s):  
Inkreswari Retno Hardini
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Human Brain ◽  
Human Life ◽  
Learning Approach ◽  
Machine Learning Technique ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Application Industry

Internet of Things is an intelligence devices that can connect to any other devices. Based on that ability to connect and its intelligence, it makes people to create an intelligence device and capable to learn like human brain. To accomplish that goal, machine learning is the solution. Combination between machine learning and IoT generate a revolution in human life and application industry. The combination also generate new trend in market. Therefore, in this paper will be discuss about machine learning including machine learning technique and algorithm and also discuss about IoT including architecture and elements. Then in this paper will discuss about some researches that have already done that combine IoT with machine learning approach, including issue and challenge in those researches.

scholarly journals Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review

2021 ◽  
Vol 10 (16) ◽  
pp. e170101623665
Author(s):  
Clayton Gerber Mangini ◽  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Pharmaceutical Products ◽  
Cold Chain ◽  
Machine Learning Technique ◽  
Thermal Mapping ◽  
Extensive Field ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Main Factors

The cold chain is crucial to ensure the quality and effectiveness of transported and stored medicines. For this, it is necessary to carry out the thermal mapping of routes for drugs transported between 15°C and 30°C, so that the most assertive decision can be taken without raising costs. This study aims to identify the main factors influencing the thermal mapping of pharmaceutical products in the cold chain and applying the machine learning technique. The method used for this systematic review is the Prisma, where the identification, screening, eligibility, and inclusion stages were analyzed. After analyzing 75 articles, the result shows that only eight papers were consistent with the use of modeling in the medicine cold chain distribution. Thus, it can be concluded that there is an extensive field to be researched regarding the use of prediction algorithms in the cold chain of drugs and vaccines.

scholarly journals Obfuscated computer virus detection using machine learning algorithm

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Tan Hui Xin ◽  
Ismahani Ismail ◽  
Ban Mohammed Khammas
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Virus Detection ◽  
Computer Virus ◽  
Machine Learning Technique ◽  
Memory Space ◽  
Alternative Approach ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
String Feature

Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the traditional signature based detection method and investigated the use of machine learning technique for obfuscated computer virus detection. In this work, text strings are used and have been extracted from virus program codes as the features to generate a suitable classifier model that can correctly classify obfuscated virus files. Text string feature is used as it is informative and potentially only use small amount of memory space. Results show that unknown files can be correctly classified with 99.5% accuracy using SMO classifier model. Thus, it is believed that current computer virus defense can be strengthening through machine learning approach.

scholarly journals Individual Yarn Fibre Extraction from Micro CT: Multilevel Machine Learning Approach

2020 ◽  
Author(s):  
Petr Henys ◽  
Lukáš Čapek
Keyword(s):  
Machine Learning ◽  
Point Of View ◽  
Machine Learning Technique ◽  
Computational Point ◽  
Textile Materials ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
The Individual ◽  
Woven Textile ◽  
Tomography Data

The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73-100% with processing time 2-5s on the tested samples.

scholarly journals Deep Reinforcement Learning of Transition States

2021 ◽  
Author(s):  
Jun Zhang ◽  
Yao-Kun Lei ◽  
Zhen Zhang ◽  
Xu Han ◽  
Maodong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Reinforcement Learning ◽  
Transition State ◽  
Chemical Reaction ◽  
Reaction Mechanisms ◽  
Md Simulations ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Chemical Reaction Mechanisms

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach, called RL‡, to automatically unravel chemical reaction mechanisms. In RL‡, locating the transition state of a...

scholarly journals The general design of the automation for multiple fields using reinforcement learning algorithm

2022 ◽  
Vol 25 (1) ◽  
pp. 481
Author(s):  
Vijaya Kumar Reddy Radha ◽  
Anantha N. Lakshmipathi ◽  
Ravi Kumar Tirandasu ◽  
Paruchuri Ravi Prakash
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Optimization Methods ◽  
Graph Representation ◽  
Machine Learning Technique ◽  
Learning Society ◽  
Learning Technique ◽  
Learning Concept

<p>Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.</p>

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