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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Mining knowledge graphs to map heterogeneous relations between the internet of things patterns Vusi Sithole; Linda Marshall
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5066-5080

Abstract

Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.
Analyzing the instructions vulnerability of dense convolutional network on GPUS Khalid Adam; Izzeldin I. Mohd; Younis Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4481-4488

Abstract

Recently, deep neural networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the graphics processing units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy.
Substrate treatment for the increment of electric power potential from plants microbial fuel cells Melisa Acosta-Coll; Adalberto Ospino-Castro; Stalin Carbonell-Navarro; Jaider Escobar-Duque; Rafael Peña-Gallardo; Ronald Zamora-Musa
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp1933-1941

Abstract

Plants microbial fuel cells (PMFC) is novel sytem that generates renewable, clean, and sustainable electricity with minimal environmental impact. However, PMFC has limitations in power generation and current density, since its production values is lower than other renewable technologies. Different studies show that the highest limitation for energy generation through MFC is the high resistivity of the cathode, and the solution is to replace the metallic electrodes with non-metallic materials to obtain a better performance, however, the application of these materials requires complex interdisciplinary work. This study conducted three experimental tests using metallic electrodes for the extraction of electrons and combined a black earth substrate with different natural materials, types of plants, and water to determine their influence in the increment of the electric power output.
A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context Amina Ouatiq; Kamal ElGuemmat; Khalifa Mansouri; Mohammed Qbadou
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp515-523

Abstract

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.
Particle swarm optimization-based stator resistance observer for speed sensorless induction motor drive Ho, Sang Dang; Palacky, Petr; Kuchar, Martin; Brandstetter, Pavel; Tran, Cuong Dinh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp815-826

Abstract

This paper presents a different technique for the online stator resistance estimation using a particle swarm optimization (PSO) based algorithm for rotor flux oriented control schemes of induction motor drives without a rotor speed sensor. First, a conventional proportional-integral controller-based stator resistance estimation technique is used for a speed sensorless control scheme with two different model reference adaptive system (MRAS) concepts. Finally, a novel method for the stator resistance estimation based on the PSO algorithm is presented for the two MRAS-type observers. Simulation results in the Matlab/Simulink environment show good adaptability of the proposed estimation model while the stator resistance is varied to 200% of the nominal value. The results also confirm more accurate stator resistance and rotor speed estimation in comparison with the conventional technique.
Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training Morteza Jouyban; Mahdieh Khorashadizade
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5277-5285

Abstract

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.
Design and development of handover simulator model in 5G cellular network Abdulkarem Basil Abdulkarem; Lukman Audah
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3310-3318

Abstract

In the modern era of technology, the high speed internet is the most important part of human life. The current available network is reckoned to be slow in speed and not be up to snuff for data transmission regarding business applications. The objective of handover mechanism is to reassign the current session handle by internet gadget. The globe needs the next generation high mobility and throughput performance based internet model. This research paper explains the proposed method of design and development for handover based 5G cellular network. In comparison to the traditional method, we propose to control the handovers between base-stations using a concentric method. The channel simulator is applied over the range of the frequencies from 500 MHz to 150 GHz and radio frequency for the 700 MHz bandwidth. The performance of the simulation system is calculated on the basis of handover preparation and completion time regarding base station as well as number of users. From this experiment we achieve the 7.08 ms handover preparation time and 9.98 ms handover completion time. The author recommended the minimum handover completion time, perform the high speed for 5G cellular networks.
Smart cities: Understanding policies, standards, applications and case studies Surender Reddy Salkuti
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3137-3144

Abstract

This paper presents the integration of required basic facilities of living such as healthcare, education, and infrastructure for building the smart cities. The administrations of smart cities should have the smart governance, safety measures with cultural and social stimulus. Four building blocks of smart cities, i.e., people and environment, smart utilities, smart technology and smart administration are described in the present paper. The aim of this paper is to give a clearer perspective of the key decisions with spatial reference that may assume a key part in the plan of a smart city technique. Application of various technologies, for examples big data, artificial intelligence, machine learning, internet of things (IoT), cloud computing, block chain technology to the smart cities are discussed in this paper. Various challenges of smart cities such as information technology (IT) infrastructure, cost, privacy, security, efficiency, fossil fuel dependency and congested commutes with proposed solutions are also presented in this paper.
Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder Yashaswini Kunjali Ajeeth Kumar; Adithya Kishore Saxena
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp620-629

Abstract

In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely
Best strategy to control data on internet-of-robotic-things in heterogeneous networks Wisam Mahmood Lafta; Ahmed A. Alkadhmawee; Mohammed A. Altaha
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1830-1838

Abstract

The control and transmission of huge data constitute an immense challenge in various types of networks (wired and wireless). Congestion caused by the high traffic and low throughput of huge data continues to be major problems in a heterogeneous platforms such as internet of things (IoT) technology and internet-of-robotic-things (IoRT). The heterogeneous network requires new models and mechanisms to deal with the increased challenges posed by IoT and IoRT. Accordingly, eliminating the issues that emerge has compelled finding improved solutions as a new strategy. This study proposed a new strategy called routing information and distance vector (RIDV) to create the best improvement of a heterogeneous network. The RIDV strategy activates the routing information protocol (RIPv2) on a router in wire network parallel with the ad-hoc on-demand distance vector (AODV) protocol on the wireless network. The RIDV strategy is used to solve the problems of the diversity of heterogeneous networks as the basis of the infrastructure IoRT technology. Hence, this strategy can reduce or avoid congestion through the use of enhanced and effective best routing protocols. Simulation results using OPNET show that the proposed method improved the quality of service (QoS) compared with other related strategies and AODV and RIPv1 protocols in terms of data drop, traffic drop, queue delay, and throughput.

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