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Bulletin of Electrical Engineering and Informatics

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Published by Institute of Advanced Engineering and Science

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Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering Engineering

Bulletin of Electrical Engineering and Informatics ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication, computer engineering, computer science, information technology and informatics from the global world. The journal publishes original papers in the field of electrical (power), electronics, instrumentation & control, telecommunication and computer engineering; computer science; information technology and informatics. Authors must strictly follow the guide for authors. Please read these instructions carefully and follow them strictly. In this way you will help ensure that the review and publication of your paper is as efficient and quick as possible. The editors reserve the right to reject manuscripts that are not in accordance with these instructions.

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Articles 45 Documents

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Search results for , issue "Vol 8, No 2: June 2019" : 45 Documents clear

High availability of data using Automatic Selection Algorithm (ASA) in distributed stream processing systems Sultan Alshamrani; Hesham Alhumyani; Quadri Waseem; Isbudeen Noor Mohamed
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

High Availability of data is one of the most critical requirements of a distributed stream processing systems (DSPS). We can achieve high availability using available recovering techniques, which include (active backup, passive backup and upstream backup). Each recovery technique has its own advantages and disadvantages. They are used for different type of failures based on the type and the nature of the failures. This paper presents an Automatic Selection Algorithm (ASA) which will help in selecting the best recovery techniques based on the type of failures. We intend to use together all different recovery approaches available (i.e., active standby, passive standby, and upstream standby) at nodes in a distributed stream-processing system (DSPS) based upon the system requirements and a failure type). By doing this, we will achieve all benefits of fastest recovery, precise recovery and a lower runtime overhead in a single solution. We evaluate our automatic selection algorithm (ASA) approach as an algorithm selector during the runtime of stream processing. Moreover, we also evaluated its efficiency in comparison with the time factor. The experimental results show that our approach is 95% efficient and fast than other conventional manual failure recovery approaches and is hence totally automatic in nature.

An efficient algorithm for monitoring virtual machines in clouds Sultan Alshamrani
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Cloud computing systems consist of a pool of Virtual Machines (VMs), which are installed physically on the provider's set up. The main aim of the VMs is to offer the service to the end users. With the current increasing demand for the cloud VMs, there is always a huge requirement to secure the cloud systems. To keep these cloud systems secured, they need a continuous and a proper monitoring. For the purpose of monitoring, several algorithms are available with FVMs. FVM is a forensic virtual machine which monitors the threats among the VMs. Our formulated algorithm runs on FVM. In this paper, we formulate the Random-Start-Round-Robin algorithm for monitoring inside FVM.

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status Salami Ifedapo Abdullahi; Mohamed Hadi Habaebi; Noreha Abd Malik
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network is used.

A novel bio-inspired routing algorithm based on ACO for WSNs Afsah Sharmin; F. Anwar; S. M. A. Motakabber
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

The methods to achieve efficient routing in energy constrained wireless sensor networks (WSNs) is a fundamental issue in networking research. A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. The issue of path selection to reach the nodes and vital correspondence parameters, for example, the versatility of nodes, their constrained vitality, the node residual energy and route length are considered since the communications parameters and imperatives must be taken into account by the imperative systems that mediate in the correspondence procedure, and the focal points of the subterranean insect framework have been utilized furthermore. Utilizing the novel technique and considering both the node mobility and the existing energy of the nodes, an optimal route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified using MATLAB and the simulation results demonstrate that new ACO based algorithm achieved improved performance, about 30% improvement compared with the traditional ACO algorithm, and faster convergence to determine the best cost route, and recorded an improvement in the energy consumption of the nodes per transmission.

The disruptometer: an artificial intelligence algorithm for market insights Mimi Aminah binti Wan Nordin; Dmitry Vedenyapin; Muhammad Fahreza Alghifari; Teddy Surya Gunawan
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the analysts ‘studies.

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