International Journal of Electrical and Computer Engineering (IJECE)

Journal Information
ISSN / EISSN : 2088-8708 / 2088-8708
Total articles ≅ 3,862
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Praveen Naik, Vandana Reddy, Ramesha Shettigar
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2128-2133; doi:10.11591/ijece.v11i3.pp2128-2133

During the gestation period women experience Braxton Hicks which is called the false labor, contractions during the second trimester. These contractions are not in regular intervals and also they are often unnoticed. The real labour or the true labour contractions develop late in the third trimester of the gestation usually beyond 36th week (excluding pre-term birth). Some women often fail to identify these pains in the third trimester of the gestation where an efficient facial recognition algorithm along with the support vector machine (SVM) helps them to identify these pains and take optimum care of themselves. The authors in this paper convey a mechanism to identify the pains effectively by creating a database of images pertaining to the pregnant women, her emotional states through out the pregnancy. Using MATLAB the algorithm of decision tree is implemented and the values obtained from them help us analyze the pain type efficiently.
Fouad Farah, Mustapha El Alaoui, Abdellali Elboutahiri, Mounir Ouremchi, Karim El Khadiri, Ahmed Tahiri, Hassan Qjidaa
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1994-2002; doi:10.11591/ijece.v11i3.pp1994-2002

A new architecture of Li-Ion battery charger with charge mode selection is presented in this work. To ensure high efficiency, good accuracy and complete protection mode, we propose an architecture based on variable current source, temperature detector and power control. To avoid the risk of damage, the Li- Ion batteries charging process must change between three modes of current (trickle current (TC), constant current (CC), and constant voltage (CV)) in order to charge the battery with degrading current. However, the interest of this study is to develop a fast battery charger with high accuracy that is able to switch between charging modes without reducing its power efficiency, and to guarantee a complete protection mode. The proposed charger circuit is designed to control the charging process in three modes using the charging mode selection. The obtained results show that the Li-ion batteries can be successfully charged in a short time without reducing their efficiency. The proposed charger is implemented in 180 nm CMOS technology with a maximum charging current equal to 1 A and a maximum battery voltage equal to 4.22 V, (with input range 2.7-4.5 V). The chip area is 1.5 mm2 and the power efficiency is 90.09 %.
Hana Yousuf, Michael Lahzi, , Khaled Shaalan
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2315-2326; doi:10.11591/ijece.v11i3.pp2315-2326

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Joonsung Park, Doo Heon Song, Kwang Baek Kim
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2653-2659; doi:10.11591/ijece.v11i3.pp2653-2659

Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification.
Muhammad Khaerul Naim Mursalim, Ade Kurniawan
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2467-2476; doi:10.11591/ijece.v11i3.pp2467-2476

COVID-19, which originated from Wuhan, rapidly spread throughout the world and became a public health crisis. Recognizing the positive cases at the earliest stage was crucial in order to restrain the spread of this virus and to perform medical treatment quickly for patients affected. However, the limited supply of RT-PCR as a diagnosis tool caused greatly delay in obtaining examination results of the suspected patients. Previous research stated that using radiologic images could be utilized to detect COVID-19 before the symptoms appeared. With the rapid development of Artificial intelligence in medical imaging in recent years, deep learning as the core of this technology could achieve human-level-performance in diagnostic accuracy. In this paper, deep learning was implemented to detect COVID-19 using a chest X-ray dataset. The proposed model employed a multi-kernel convolution neural network (CNN) block combined with pre-trained ResNet-34 to overcome an imbalanced dataset. The model block adopted different kernel sizes as follows 1x1, 3x3, 5x5, and 7x7. The findings show that the proposed model is capable of performing binary and three class classification tasks with an accuracy of 100% and 93.51% in the validation phase and 95% and 83% in the test phase, respectively.
Ali H. Jabbar, Imad S. Alshawi
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2432-2442; doi:10.11591/ijece.v11i3.pp2432-2442

Uneven energy consumption (UEC) is latent trouble in wireless sensor networks (WSNs) that feature a multiple motion pattern and a multi-hop routing. UEC often splits the network, reduces network life, and leads to performance degradation. Sometimes, improving energy consumption is more complicated because it does not reduce energy consumption only, but it also extends network life. This makes energy consumption balancing critical to WSN design calling for energy-efficient routing protocols that increase network life. Some energy-saving protocols have been applied to make the energy consumption among all nodes inside the network equilibrate in the expectancy and end power in almost all nodes simultaneously. This work has suggested a protocol of energy-saving routing named spider monkey optimization routing protocol (SMORP), which aims to probe the issue of network life in WSNs. The proposed protocol reduces excessive routing messages that may lead to the wastage of significant energy by recycling frequent information from the source node into the sink. This routing protocol can choose the optimal routing path. That is the preferable node can be chosen from nodes of the candidate in the sending ways by preferring the energy of maximum residual, the minimum traffic load, and the least distance to the sink. Simulation results have proved the effectiveness of the proposed protocol in terms of decreasing end-to-end delay, reducing energy consumption compared to well-known routing protocols.
Nishara Nizamuddin, Ahed Abugabah
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2443-2456; doi:10.11591/ijece.v11i3.pp2443-2456

The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a risk of error. The insurance sector faces a threat of failure due to losing and misconception of policies and information. We present a decentralized IPFS and blockchain-based framework for the auto insurance sector that regulates the activities in terms of insurance claims for automobiles and automates payments. This article also discusses how blockchain technology’s features can be useful for the decentralized autonomous vehicle’s ecosystem.
Moon Junghoon, Yoo Sunggoo, Lee Jongtae
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2378-2385; doi:10.11591/ijece.v11i3.pp2378-2385

This study investigates the effect of IT investment portfolios on the performance of mobile business services, as well as the moderating role of IT savvy. This study pulls the concept of IT investment mandates into the conceptual research framework of mobile investment. A survey for the IT specialists working at 123 enterprise-level companies was conducted and hierarchical regression analysis was adopted. Our results show that IT investment and organizational IT capabilities influence the performance of the mobile office and that IT savvy plays as a moderator in the relationship between investment mandates and mobile office performance. This research also may indicate that transactional assets are most helpful factors for a change by the adoption of mobile technology. This study is a rare research paper to explain the impact of IT investment portfolios on the mobile office performance in an academic methodology.
Obaida M. Al-Hazaimeh
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2203-2210; doi:10.11591/ijece.v11i3.pp2203-2210

Over the past few decades, many algorithms have been proposed to improve the performance of speech encryption over un-secure channel (i.e., Internet). In this paper, the security level was enhanced using a dynamic dual chaotic based on Hénon chaotic map. In the proposed algorithm, the speech elements are shuffled in a random fashion. Moreover, when both Hénon state variables are free to be used for shuffling the index is toggled randomly between them according to toggle bit. After index shuffling each speech element is modified with XOR operation between the original speech element value and the key that is selected randomly from the updated key table. The same chaotic map is used to initiate the empty or full table and provide new table entries from the values that are already shuffled. The experimental results show that the proposed crypto-system is simple, fast with extra random toggling behavior. The high order of substitution make it sensitive to initial condition, common cryptanalysis attacks such as linear and differential attacks are infeasible.
J. S. Norbakyah, M. I. Nordiyana, I. N. Anida, A. F. Ayob, A. R. Salisa
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 2054-2061; doi:10.11591/ijece.v11i3.pp2054-2061

Driving cycles are series of data points that represent vehicle speed versus time sequenced profile developed for specific road, route, city or certain location. It is widely utilized in the application of vehicle manufacturers, environmentalists and traffic engineers. Since the vehicles are one of the higher air pollution sources, driving cycle is needed to evaluate the fuel consumption and exhaust emissions. The main objectives in this study are to develop and characterize the driving cycle for myBAS in Kuala Terengganu city using established k-means clustering method and to analyse the fuel consumption and emissions using advanced vehicle simulator (ADVISOR). Operation of myBAS offers 7 trunk routes and one feeder route. The research covered on two operation routes of myBAS which is Kuala Terengganu city-feeder and from Kuala Terengganu to Jeti Merang where the speed-time data is collected using on-board measurement method. In general, driving cycle is made up of a few micro-trips, defined as the trip made between two idling periods. These micro-trips cluster by using the k-means clustering method and matrix laboratory software (MATLAB) is used in developing myBAS driving cycle. Typically, developing the driving cycle based on the real-world in resulting improved the fuel economy and emissions of myBAS.
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