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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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Towards smart modeling of mechanical properties of a bio composite based on a machine learning Aziz Moumen; Abdelghani Lakhdar; Zineb Laabid; Khalifa Mansouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3138-3145

Abstract

The main interest in many research problems in polymer bio composites and machine learning (ML) is the development of predictive models to one or several variables of interest by the use of suitable independent inputs or variables. Nevertheless, these fields have generally adopted several approaches, while bio composite behavior modeling is usually based on phenomenological theories and physical models. These latter are more robust and precise, but they are generally under the restricted predictive ability due to the particular set of conditions. On the other hand, Machine learning models can be highly efficient in the modeling phase by allowing the management of high and massive dimensional sets of data to predict the best behavior of bio composites. In this situation, biomaterial scientists would like to benefit from the comprehension and implementation of the powerful ML models to characterize or predict the bio composites. In this study, we implement a smart methodology employing supervised neural network models to predict the bio composites properties presenting more significant environmental and economic advantages than composites reinforced by synthetic fibers.
Protein secondary structure prediction by a neural network architecture with simple positioning algorithm techniques Romana Rahman Ema; Sharmin Sultana; Shakil Ahmed Shaj; Syed Md. Galib
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4380-4390

Abstract

Protein secondary structure is an immense achievement of bioinformatics. It's an amino acid residue in a polypeptide backbone. In this paper, an innovative method has been proposed for predicting protein secondary structures based on 3-state protein secondary structure by neural network architecture with simple positioning algorithm (SIMPA) technique. Q3 (3-state prediction of protein secondary structure) is a fundamental methodology for our approach. Initially, the prediction of the secondary structure of the protein using the Q3 prediction method has been done. For this, a model has been built from its primary structure. Then it will retrieve the percentage of amino acid sequences from the original sequence to the accuracy of the predicted sequence. Utilizing the SIMPA technique from the 3-state secondary structure predicted sequence, the percentage of dissimilar residues of the three types (α-helix, β-sheet and coil) of Q3 has been extracted. Then the verification of the Q3 predicted accuracy through the SIMPA technique was done. Finally using a new method of neural network, it is verified that the Q3 prediction method gives good results from the neural network approach.
Formation control of non-identical multi-agent systems Djati Wibowo Djamari; Muhamad Rausyan Fikri; Bentang Arief Budiman; Farid Triawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2721-2732

Abstract

The problem considered in this work is formation control for non-identical linear multi-agent systems (MASs) under a time-varying communication network. The size of the formation is scalable via a scaling factor determined by a leader agent. Past works on scalable formation are limited to identical agents under a fixed communication network. In addition, the formation scaling variable is updated under a leader-follower network. Differently, this work considers a leaderless undirected network in addition to a leader-follower network to update the formation scaling variable. The control law to achieve scalable formation is based on the internal model principle and consensus algorithm. A biased reference output, updated in a distributed manner, is introduced such that each agent tracks a different reference output. Numerical examples show the effectiveness of the proposed method.
Enhancing highly-collaborative access control system using a new role-mapping algorithm Doaa Abdelfattah; Hesham A. Hassan; Fatma A. Omara
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2765-2782

Abstract

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, role based access-control (RBAC) Model is deployed in large organizations. The work in this paper is mainly considering the authorization scalability problem, which comes out due to the increase of shared resources and/or the number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with role-to-role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed role-mapping algorithm. A comparative study has been performed to evaluate the performance of the proposed algorithm with concerning the rule-store size and the authorization response time. According to the results, it is found that the proposed algorithm achieves more saving in the number of stored role-mapping rules which minimizes the rule-store size and reduces the authorization response time. Additionally, the RTR model using the proposed algorithm has been implemented by applying a concurrent approach to achieve more saving in the authorization response time. Therefore, it would be suitable in highly-collaborative cloud environments
A secure trust-based protocol for hierarchical routing in wireless sensor network Maha Al-Sadoon; Ahmed Jedidi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3838-3849

Abstract

Wireless sensor networks (WSNs) became the backbone of the internet of things (IoT). IoT applications are vital and demand specific quality of service (QoS) requirements. In addition, security has become a primary concern to provide secure communication between wireless nodes, with additional challenges related to the node’s computational resources. Particular, the design of secure and resource efficient routing protocol is a critical issue in the current deployment of WSNs. Therefore, this paper proposes a novel secure-trust aware routing protocol (ST2A) that provides secure and reliable routing. The proposed protocol establishes communication routes based on calculated trust value in joint with a novel cluster head selection algorithm in the hierarchical routing process. The proposed trust-aware routing algorithm improves the routing security in WSN and optimizes many performance metrics related to WSNs unique characteristics. The results of simulation validate the feasibility of the proposed algorithm for enhancing the network lifetime up to 18% and data delivery by 17% as compared with some state-of-the-art routing algorithms.
Analyzing sentiment dynamics from sparse text coronavirus disease-19 vaccination using natural language processing model Jalaja Govindappa; Kavitha Channegowda
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4054-4066

Abstract

Social media platforms enable people exchange their thoughts, reactions, emotions regarding all aspects of their lives. Therefore, sentiment analysis using textual data is widely practiced field. Due to large textual content available on social media, sentiment analysis is usually considered a text classification task. The high feature dimension is an important issue that needs to be resolved by examining text meaningfully. The proposed study considers a case study of coronavirus (COVID) vaccination to conclude public opinions about prospects for vaccination. Text corpus of tweets is collected, published between December 12, 2020, and July 13, 2021 is considered. The proposed model is developed considering phase-by-phase data analysis process, followed by an assessment of important information about the collected tweets on coronavirus disease (COVID-19) vaccine using two sentiment analyzer methods and probabilistic models for validation and knowledge analysis. The result indicated that public sentiment is more positive than negative. The study also presented statistics of trends in vaccination progress in the top countries from early 2021 to July 2021. The scope of study is enormous regarding sentiment analysis based on keyword and document modeling. The proposed work offers an effective mechanism for a decision-making system to understand public opinion and accordingly assists policymakers in health measures and vaccination campaigns.
Integrated tripartite modules for intelligent traffic light system Emad I. Abdul Kareem; Haider K. Hoomod
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2971-2985

Abstract

The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
Electrical impedance’s effects of flying insects on selectivity of electrical insecticides Lahouaria Neddar; Samir Flazi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2214-2219

Abstract

The history of controlling destructive insects in the twentieth century makes it clear that the difficulties we encounter today in dealing with these types of insects come from our almost total reliance on one single controlling method, namely the use of chemical insecticides. These toxic and suspected carcinogenic products pose a serious threat to agriculture and the environment. However, the possibility of directing researchers in developing a new way considered as more efficient, more selective and less toxic has proved to be possible. The principle of this approach is based on an attractive effect and an electric effect. Nevertheless, the development of a bio and selective electrical system requires taking into account certain parameters involved in the attraction of insects and electrical discharge such as the electrical impedance. The results showed that the threshold at which the insect is disturbed depends on its conductivity.
Priority based flow control protocol for internet of things built on light fidelity Vatsala, Belathuru Ramanna; Chitradurga, Vidyaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4449-4456

Abstract

Excessive usage of Internet by most of the applications that use internet of things (IoT) has resulted in need for high bandwidth network. Light fidelity (LiFi) is one such network having bandwidth in terms of GigaHertz, but LiFi has a limited propagation range hence it can be deployed only in the local area. When IoT nodes are connected using LiFi network in the local area they start pushing large data to the cloud there by arising need for flow control. Some of the IoT applications such as patient monitoring systems and nuclear systems, generate critical data. The protocol for flow control in this case should be based on priority of data since critical data with high priority have to be transmitted first. We develop a flow control protocol named priority based flow control protocol (PFCP) by providing priority to flows that carry critical data especially in IoT system that use LiFi network. We evaluate performance of different transmission control protocol (TCP) variants and modify TCP variant that yields maximum goodput according to the priority based protocol developed and demonstrate that flows that carry critical data are given priority compared to non-prioritized flows
Combined Chebyshev and logistic maps to generate pseudorandom number generator for internet of things Abdulghafour Jassim, Sameeh; Farhan, Alaa Kadhim
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3287-3297

Abstract

Sensitive data exchanging among things over the Internet must be protected by a powerful cryptographic system. Conventional cryptographic such as advanced encryption standard (AES), and respiratory sinus arrhythmia (RSA) are not effective enough to protect internet of things (IoT) because of certain inveterate IoT properties like limited memory, computation, and bandwidth. Nowadays, chaotic maps with high sensitivity to initial conditions, strong ergodicity, and non-periodicity have been widely used in IoT security applications. So, it is suitable for IoT. Also, in a stream cipher method, the user needs to deliver the keystream to all clients in advance. Consequently, this paper proposed a method to solve the keys distribution problem based on combine both Chebyshev and logistic maps techniques as well as a master key to generate a random key. The suggested method was compared with the other stream cipher algorithms (Chacha20, RC4, Salsa20) by utilizing the same plaintext and master key as input parameters and the results were successful in the statistical national institute of standards and technology (NIST) test. Simultaneously, the suggestion was evaluated through different evaluation methods like statistical NIST test, histogram, Shannon entropy, correlation coefficient analysis, keyspace and key sensitivity, and others. All mentioned tests are passed successfully. Therefore, the suggested approach was proved it is effective in security issues.

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