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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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Audio compression using transforms and high order entropy encoding Zainab J. Ahmed; Loay E. George; Raad Ahmed Hadi
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.pp3459-3469

Abstract

Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low & multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity.
Files cryptography based on one-time pad algorithm Ahmad Mohamad Al-Smadi; Ahmad Al-Smadi; Roba Mahmoud Ali Aloglah; Nisrein Abu-darwish; Ahed Abugabah
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.pp2335-2342

Abstract

The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it’s successful without losing any information.
Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy Prakruthi Mandya Krishnegowda; Komarasamy Ganesan
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.pp585-595

Abstract

Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.
High frequency of low noise amplifier architecture for WiMAX application: A review Abu Bakar Ibrahim; Che Zalina Zulkifli; Shamsul Arrieya Ariffin; Nurul Husna Kahar
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.pp2153-2164

Abstract

The low noise amplifier (LNA) circuit is exceptionally imperative as it promotes and initializes general execution performance and quality of the mobile communication system. LNA's design in radio frequency (R.F.) circuit requires the trade-off numerous imperative features' including gain, noise figure (N.F.), bandwidth, stability, sensitivity, power consumption, and complexity. Improvements to the LNA's overall performance should be made to fulfil the worldwide interoperability for microwave access (WiMAX) specifications' prerequisites. The development of front-end receiver, particularly the LNA, is genuinely pivotal for long-distance communications up to 50 km for a particular system with particular requirements. The LNA architecture has recently been designed to concentrate on a single transistor, cascode, or cascade constrained in gain, bandwidth, and noise figure.
The adoption of bitcoins technology: The difference between perceived future expectation and intention to use bitcoins: Does social influence matter? Ibrahim Almarashdeh; Kamal Eldin Eldaw; Mutasem Alsmadi; Fahad Alghamdi; Ghaith Jaradat; Ahmad Althunibat; Malek Alzaqebah; Rami Mustafa A. Mohammad
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.pp5351-5366

Abstract

Bitcoin is a decentralized system that tries to become a solution to the shortcomings of fiat and gold-based currencies. Considering its newness, the adoption level of bitcoin is yet understood. Hence, several variables are proposed in this work in examining user perceptions regarding performance expectancy, effort expectancy, trust, adoption risk, decentralization and social influence interplay, with the context of user’s future expectation and behavioral intentions to use bitcoins. Data were gathered from 293 completed questionnaire and analised using AMOS 18. The outcomes prove the sound predictability of the proposed model regarding user’s future expectations and intentions toward bitcoins. All hypotheses were supported, they were significantly affecting the dependent variables. Social influence was found as the highest predictor of behavioral intention to negatively utilize bitcoins. The significant impact of social influence, adoption risk and effort expectancy which affect behavioral intention to use bitcoins the most, are demonstrated in this study. Bitcoins should thus, present an effective, feasible and personalized program which will assist efficient usage among users. Additionally, the impacts of social influence, adoption risk and perceived trust on behavioral intention to utilize new technology were compared, and their direct path was tested together, for the first time in this context.
An energy aware scheme for layered chain in underwater wireless sensor networks using genetic algorithm Sihem Souiki; Sidi Mohamed Hadj Irid; Mourad Hadjila
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.pp4272-4280

Abstract

Extending the network lifetime is a very challenging problem that needs to be taken into account during routing data in wireless sensor networks in general and particularly in underwater wireless sensor networks (UWSN). For this purpose, the present paper proposes a multilayer chain based on genetic algorithm routing (MCGA) for routing data from nodes to the sink. This algorithm consists to create a limited number of local chains constructed by using genetic algorithm in order to obtain the shortest path between nodes; furthermore, a leader node (LN) is elected in each chain followed by constructing a global chain containing LNs. The selection of the LN in the closest chain to the sink is as follows: Initially, the closest node to sink is elected LN in this latter because all nodes have initially the same energy value; then the future selection of the LN is based on the residual energy of the nodes. LNs in the other chains are selected based on the proximity to the previous LNs. Data transmission is performed in two steps: intra-chain transmission and inter-chain transmission. Furthermore, MCGA is simulated for different scenarios of mobility and density of nodes in the networks. The performance evaluation of the proposed technique shows a considerable reduction in terms of energy consumption and network lifespan.
Using deep learning models for learning semantic text similarity of Arabic questions Mahmoud Hammad; Mohammed Al-Smadi; Qanita Bani Baker; Sa’ad A. Al-Zboon
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.pp3519-3528

Abstract

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%.
Solving multiple linear regression problem using artificial neural network Mohammad S. Khrisat; Ziad A. Alqadi
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.pp770-775

Abstract

Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.
The design of an efficient class E-LCCL capacitive power transfer system through frequency tuning method Khairul Kamarudin Hasan; Shakir Saat; Yusmarnita Yusop; Huzaimah Husin; Nor Diyana Md Sin
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.pp1095-1104

Abstract

In this work, the optimum zero voltage switching (ZVS) of Class E-LCCL capacitive power transfer (CPT) was determined via frequency tuning method. Through this an efficient system can be guanranteed although there is a change in the capacitive plates distance. This study used a Class-E LCCL inverter, as it can operate at a high alternate current frequency, besides producing low switching losses and minimal power losses. Specifically, this study conducted simulations and experiments to analyse the performance of an LCCL CPT System at 1 MHz operating frequency and 24 V DC supply voltage. Using an air gap distance of 0.1 cm, the designed CPT system prototype successfully achieved an output power of 10W and an efficiency of 95.45%. This study also found that by tuning the resonant frequency of the Class E-LCCL system, the optimum ZVS can be obtained although capacitive plate distance was varied from 1-3 cm via experimental. The results of this study could benefit medical implant and portable device development, consumer electronics, and environments that involve electrical hazards.
Development of durian leaf disease detection on Android device A. L. Sabarre; A. S. Navidad; D. S. Torbela; J. J. Adtoon
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.pp4962-4971

Abstract

Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.

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