-
Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach
Imeh Umoren,
Esther Polycarp,
Godwin Ansa
Issue:
Volume 5, Issue 2, December 2021
Pages:
46-55
Received:
8 June 2021
Accepted:
21 July 2021
Published:
27 August 2021
Abstract: Spectrum Scheduling is an efficient scheme of improving spectrum utilization for faster communications, higher definition media (HDM) and data transmission. Radio spectrum is very limited in supply resulting in enormous problems related to scarcity. It owes the physical support for wireless communication, both fixed applications and mobile broadband. Basically, effective use of the spectrum depends on the channel settings, sensing performance, detection of spectrum prospect as well as effective transmission of both Primary Users (PUs) and Secondary Users (SUs) packets at a specific time slot. In order to improve spectrum utilization this paper adopted quantitative method which employs Probability Theorem to identify the probabilities of both primary Users (PUs) and secondary users (SUs) in the spectrum datasets allocation and further used conditional probability to compare two Frequency Bands i.e., High Frequency (HF) and Very High Frequency (VHF). The result indicates available spectrum holes (SH) left unutilized in the Secondary User (SU) resulting in the need for spectrum scheduling for the SU. The procedure makes the secondary users occupy a probability of 0.002mhz compared to the primary users on 0.00004mhz utilization. This further indicates that some spectrum holes were left unutilized by the license users (Primary Users). However, spectrum allocation is one of the major issues of improving spectrum efficiency and has become a considerable tool in cognitive wireless networks (CWN). Consequently, the goal of spectrum allocation is to assign leisure spectrum resources efficiently to achieve the optimal Quality of Service (QOS and cognitive user requirements of wireless network. Again, classification of spectrum allocation was carried out through difference methods. Firstly, we employ a probability theorem to identify the probability of both Primary Users (PUs) and Secondary Users (SUs) in the allocated spectrum data sets. Secondly, conditional probability was used to compare two frequency band based on primary and secondary allocation policies designed to identify the specific allocation of each band. Thirdly, Machine Learning (ML) Algorithm based on Decision Tree - Supervised Learning (DTSL) approach was adopted to classified our data sets. The result yielded 68% which correctly classified instances based on the total records of sixty-nine (69) data sets. Research findings demonstrate a highly optimized spectrum scheduling for efficient networks service provisions.
Abstract: Spectrum Scheduling is an efficient scheme of improving spectrum utilization for faster communications, higher definition media (HDM) and data transmission. Radio spectrum is very limited in supply resulting in enormous problems related to scarcity. It owes the physical support for wireless communication, both fixed applications and mobile broadban...
Show More
-
Artificial Corona-Inspired Optimization Algorithm: Theoretical Foundations, Analysis, and Applications
Issue:
Volume 5, Issue 2, December 2021
Pages:
56-65
Received:
28 July 2021
Accepted:
18 August 2021
Published:
27 August 2021
Abstract: One of the important parts of computer science is Artificial Intelligence (AI). It deals with the development of machines that can take decisions like humans on their own. Currently, AI can solve many difficult real-world problems because it works much better and faster than humans. Researchers of operations research also are turning their heads towards AI instead of traditional systems. Meanwhile, there are several AI models to solve mathematical optimization problems. They depend heavily on a random search, but many of their solutions have been efficient at finding absolute optimum. This means that it is necessary to choose another optimization model to get quite the optimum value. This paper introduces an artificially intelligent algorithm in order to find the optimal solution for a given computational problem that minimizes or maximizes a particular function. It is inspired by the corona virus that spreads throughout the world and infects healthy people. Its structure simulates the stages of virus transmission and treatment. Because the starting point is so important for converging to the global optimum, corona virus approach has guided researchers to select the starting point and parameters. Actually, this point depends on three real numbers as the corona virus affects three main parts of the human body (nose, throat, respiratory). The proposed algorithm has been found to be an optimal key to different applications. It doesn't require any derivative information and it is simple in implementation with few parameters setting. Finally, some numerical examples are presented to illustrate the algorithm studied here. The computational results show that it has high performance in finding an optimal solution within reasonable time.
Abstract: One of the important parts of computer science is Artificial Intelligence (AI). It deals with the development of machines that can take decisions like humans on their own. Currently, AI can solve many difficult real-world problems because it works much better and faster than humans. Researchers of operations research also are turning their heads to...
Show More
-
An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier
Adetokunbo MacGregor John-Otumu,
Md Mahmudur Rahman,
Christiana Ugochinyere Oko
Issue:
Volume 5, Issue 2, December 2021
Pages:
66-75
Received:
12 October 2021
Accepted:
1 November 2021
Published:
5 November 2021
Abstract: An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.
Abstract: An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from...
Show More
-
Modern Invisible Hazard of Urban Air Environment Pollution When Operating Vehicles That Causes Large Economic Damage
Vadim Azarov,
Vadim Kutenev
Issue:
Volume 5, Issue 2, December 2021
Pages:
76-81
Received:
17 August 2021
Accepted:
29 September 2021
Published:
25 November 2021
Abstract: Currently, the planet population is terrified of the deaths of more than 4 million people from the coronavirus as they do not know that, according to the WHO, about 8 million of the population die annually in silence from urban atmosphere pollution by and with hazardous substances and particulate matters from the industry and automobile transport operation. These materials show the results of Russian studies proving that current urban pollution shall be defined not only by hazardous substances and particulate matters emitted with vehicle exhaust gases, but also by particulate matters from vehicle operation, first of all, from asphalt roadway wear, from tyre wear and from brake systems wear, which are not legally regulated either by nations or at the international level (UN Regulations) yet. The Russian studies (2015-2017) are presented regarding the comparative analysis of average emissions of particulate matters less than 2.5 microns (µm) from different sources: with exhaust gases (EG) (25%); from wear of brake systems (5%); from wear of tyres (8%) and from wear of roadways (65%), which were substantially confirmed by the studies conducted in Great Britain: from EG – 32%; from tyres – 18%; from brakes – 18% and from wear of roadways – 40%. Based on these results of the comprehensive studies, calculations of economic damage caused by the ecological situations and technogenic disasters of the current and future periods analyzed above, which amount to 65 quadrillion (65•1015) US dollars for the today's world and ca. 100 million dollars for the Russian Federation. According to the data of the World Health Organization (WHO), as of 2018, 9 out of 10 people around the world breathe air with high concentrations of pollutants. For that very reason, from 7 to 8 million people die annually because of the consequences of breathing the air containing particulate matters less than 2.5-10 µm in size which are able to penetrate deep inside the lungs and cardiovascular system, causing such diseases as stroke, cardiac diseases, lung cancer, chronic obstructive pulmonary disease and respiratory infections, including pneumonia.
Abstract: Currently, the planet population is terrified of the deaths of more than 4 million people from the coronavirus as they do not know that, according to the WHO, about 8 million of the population die annually in silence from urban atmosphere pollution by and with hazardous substances and particulate matters from the industry and automobile transport o...
Show More
