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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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Monte Carlo simulation convergences’ percentage and position in future reliability evaluation Nur Nabihah Rusyda Roslan; NoorFatin Farhanie Mohd Fauzi; Mohd Ikhwan Muhammad Ridzuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6218-6227

Abstract

Reliability assessment is a needed assessment in today's world. It is required not only for system design but also to ensure the power delivered reaches the consumer. It is usual for fault to occur, but it is best if the fault can be predicted and the way to overcome it can be prepared in advance. Monte Carlo simulation is a standard method of assessing reliability since it is a time-based evaluation that nearly represents the actual situation. However, sequential Monte Carlo (SMC) typically took long-time simulation. A convergence element can be implemented into the simulation to ensure that the time taken to compute the simulation can be reduced. The SMC can be done with and without convergence. SMC with convergence has high accuracy compared to the SMC without convergence, as it takes a long time and has a high possibility of not getting accurate output. In this research, the SMC is subjected to five different convergence items to determine which converge simulation is the fastest while providing better performance for reliability evaluation. There are two types of convergence positions, namely input convergence and output convergence. Overall, output convergence shows the best result compared to input convergence.
Person identification based on facial biometrics in different lighting conditions Marem H. Abdulabas; Noor D. Al-Shakarchy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2086-2092

Abstract

Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
Internet of things based automated monitoring for indoor aeroponic system Teuku Muhammad Roffi; Charisma Aulia Jamhari
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp270-277

Abstract

On the ever-rising urgency of global food security, efforts are required to develop a robust farming technique. This includes the capability of farming in non-agricultural land or indoor spaces. Farming in the air medium, i.e., aeroponic, has persistently stepped in as a viable solution. Aeroponic farming allows efficient water usage while preventing soil related diseases and pests. With the assistance of light emitting diodes (LEDs) and precise electronic monitoring and control, aeroponic may become the suitable farming technique of the future. This work presents an aeroponic system capable of automated monitoring and control of farming parameters. The system achieved both robustness in indoor farming and remote access by employing LED as an artificial lighting system and the internet-of-things (IoT) connectivity, respectively. The test result demonstrated that the system successfully maintained the root chamber temperature below 30 °C with a typical average temperature of 28.8 °C. The system managed a humidity level which prevented plants from drying out. It was also evident that the LED assistance significantly improved the growth quality of Ipomea reptans. The system, data, and analysis presented in this work is expected to facilitate further development of a robust food production system in overcoming the global food crisis.
Classification of COVID-19 from CT chest images using Convolutional Wavelet Neural Network Dina A. AbdulQader; Asma T. Saadoon; Marwa T. Naser; Ali Hassan Jabbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1078-1085

Abstract

Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convolutional neural network that uses other activation functions (exponential linear unit (ELU), rectified linear unit (ReLU), Swish, Leaky ReLU, Sigmoid), and the result is that utilizing CWNN gave better results for all performance metrics (accuracy, sensitivity, specificity, precision, and F1-score). The results obtained show that the prediction accuracies of CWNN were 99.97%, 99.9%, 99.97%, and 99.04% when using wavelet filters (rational function with quadratic poles (RASP1), (RASP2), and polynomials windowed (POLYWOG1), superposed logistic function (SLOG1)) as activation function, respectively. Using this algorithm can reduce the time required for the radiologist to detect whether a patient has COVID or not with very high accuracy.
Ship routing optimization using bacterial foraging optimization algorithm for safety and efficient navigation Phan Van Hung; Dang Quang Viet; Nguyen Minh Duc; Thanh Dat Le
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2309-2315

Abstract

Efficient operation plays a vital role to develop a sustainable shipping fleet with cost competitive. The requirements for economic efficiency, energy efficiency, reducing emissions, and increasing safety and security lead to an innovative model in the optimal weather routing system. The vessel routing is influenced by the quality of meteorological and oceanographic data such as wind, waves, and currents. In this study, the model optimization of weather routing considers the meteorological and oceanographic information, ship's characteristics combined with an adaptive bacterial foraging optimization algorithm (BFOA) will be introduced and applied to the ship’ navigation at sea. The simulation results will be evaluated the effectiveness and reliability of the model. This model will support ships’ navigation to be safer and more comfortable, operate more efficiently and reduce emissions.
Machine learning for Arabic phonemes recognition using electrolarynx speech Zinah Jaffar Mohammed Ameen; Abdulkareem Abdulrahman Kadhim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp400-412

Abstract

Automatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85%.
Best S-box amongst differently sized S-boxes based on the avalanche effect in ‎the advance encryption standard algorithm Hadeel Mohammed Taher; Seddiq Qais Abd Al-Rahman; Shihab A. Shawkat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6535-6544

Abstract

Substitution boxes are essential nonlinear modules that are popular in block ‎cipher algorithms. They ‎also play a significant role in the security area because of ‎their robustness to different linear ‎cryptanalysis. Each element of the state in a S-‎box is nonlinearly replaced using a lookup table. This ‎research presents the S-‎box, one of the fundamental parts of the advanced encryption standard ‎‎(AES) ‎algorithm. The S-box represents the confusion part in the AES. However, when ‎information ‎is shared between different devices in an authorized manner, the ‎algorithm should be able to ‎combine a sufficient number of confusion layers to ‎guarantee the avalanche effect (AE). ‎Subsequently, this research selects the best ‎S-box by comparing different sizes (4×4, 8×8, and ‎‎16×16) and measuring them ‎on the basis of the million-bit encryption. The AE is the main criterion ‎used in ‎choosing the best S-box. A robust and strong cryptography algorithm should be ‎able to ‎confirm the AEs. Results indicate that the 16×16 S-box with a 52% AE ‎ratio is the superior S-box
Energy-efficient non-orthogonal multiple access for wireless communication system Muhamad Firdaus Darus; Fakrulradzi Idris; Norlezah Hashim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1654-1668

Abstract

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.
Hybrid information security system via combination of compression, cryptography, and image steganography Wid Akeel Awadh; Ali Salah Alasady; Alaa Khalaf Hamoud
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6574-6584

Abstract

Today, the world is experiencing a new paradigm characterized by dynamism and rapid change due to revolutions that have gone through information and digital communication technologies, this raised many security and capacity concerns about information security transmitted via the Internet network. Cryptography and steganography are two of the most extensively that are used to ensure information security. Those techniques alone are not suitable for high security of information, so in this paper, we proposed a new system was proposed of hiding information within the image to optimize security and capacity. This system provides a sequence of steps by compressing the secret image using discrete wavelet transform (DWT) algorithm, then using the advanced encryption standard (AES) algorithm for encryption compressed data. The least significant bit (LSB) technique has been applied to hide the encrypted data. The results show that the proposed system is able to optimize the stego-image quality (PSNR value of 47.8 dB) and structural similarity index (SSIM value of 0.92). In addition, the results of the experiment proved that the combination of techniques maintains stego-image quality by 68%, improves system performance by 44%, and increases the size of secret data compared to using each technique alone. This study may contribute to solving the problem of the security and capacity of information when sent over the internet.
Efficiency of two decoders based on hash techniques and syndrome calculation over a Rayleigh channel Seddiq El Kasmi Alaoui; Zouhair Chiba; Hamza Faham; Mohammed El Assad; Said Nouh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1880-1890

Abstract

The explosive growth of connected devices demands high quality and reliability in data transmission and storage. Error correction codes (ECCs) contribute to this in ways that are not very apparent to the end user, yet indispensable and effective at the most basic level of transmission. This paper presents an investigation of the performance and analysis of two decoders that are based on hash techniques and syndrome calculation over a Rayleigh channel. These decoders under study consist of two main features: a reduced complexity compared to other competitors and good error correction performance over an additive white gaussian noise (AWGN) channel. When applied to decode some linear block codes such as Bose, Ray-Chaudhuri, and Hocquenghem (BCH) and quadratic residue (QR) codes over a Rayleigh channel, the experiment and comparison results of these decoders have shown their efficiency in terms of guaranteed performance measured in bit error rate (BER). For example, the coding gain obtained by syndrome decoding and hash techniques (SDHT) when it is applied to decode BCH (31, 11, 11) equals 34.5 dB, i.e., a reduction rate of 75% compared to the case where the exchange is carried out without coding and decoding process.

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