cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 454   455   456   457   458 
Landslide early warning systems: a perspective from the internet of things Vladimir Henao-Céspedes; Yeison Alberto Garcés-Gómez; María Nancy Marín Olaya
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.pp2214-2222

Abstract

Populations located in the vicinity of slopes and soils derived from volcanic ash are constantly at risk due to the possibility of landslides. Such is the case of the city of Manizales, Colombia, which, due to its geomorphological characteristics, has experienced a significant number of landslides that have caused human and economic losses. The Internet of things (IoT) has allowed important technological advances for monitoring, thanks to the low cost and wide coverage of IoT-based systems. Slope monitoring and the development of landslide early warning systems (EWS) have been positively impacted by IoT developments, which shows a relationship. The objective of this article is to review, from the scientific production, the relationship between IoT and EWS. For this purpose, a fragmenting-deriving-combining methodology is applied to focus on a research trends analysis of the subject, from macro-areas such as IoT and EWS to micro areas such as EWS by IoT-based landslides. Finally, the analysis concluded that the conceptual models of IoT and EWS for landslides have some correspondence in some of their layers.
A reconfigurable dual port antenna system for underlay/interweave cognitive radio Laith Wajeeh Abdullah; Adheed H. Sallomi; Ali Khalid Jassim
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.pp443-453

Abstract

An antenna system that is reconfigurable in frequency is presented in this paper as a novel dual port design that serves both undelay and interweave cognitive radio. This 25×40×0.8 mm3 system is composed of two wide slot antennas: the first is designed as an ultra-wideband (UWB) antenna with controllable band rejection capabilities, while the second antenna is reconfigurable for communication purposes. Three slots are etched into the patch of the UWB antenna to obtain band notching in wireless local area network/Xband/International Telecommunication Union bands (WLAN/Xband/ITU) bands which can be controlled by a positive-intrinsic-negative (PIN) diode across each slot. The configuration states of these three diodes are all useable that produces seven band rejection modes plus the UWB operation mode. The second antenna is configured by five PIN diodes to operate either in Cband, WLAN or Xband regions which results in three interweave modes when setting the first antenna for UWB sensing. The design is simulated by computer simulation technology (CST) v.10. S21 results shows good isolation while input reflection coefficient and realized gain results prove system’s scanning, filtering and communication capabilities. This system is new that it gathers the undelay/interweave operation in a single design and when considering its large number of operation modes it looks adequate for many cognitive radio applications.
Natural language processing for Albanian: a state-of-the-art survey Muhamet Kastrati; Marenglen Biba
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.pp6432-6439

Abstract

Due to its wide applicability, natural language processing (NLP) has attracted significant research efforts to the machine learning and deep learning research community. Despite this, research works investigating NLP for the Albanian language are still limited. However, to the best of our knowledge, there is no literature review available, which presents a clear picture of what has been studied, argued, and established in the area. The main objective of this survey is to comprehensively review, analyze and discuss the state-of-the-art in NLP for the Albanian language. Here, we present an extensive study concerning the contribution of several authors that have contributed to the application of NLP to the Albanian language. Also, we present an overview of research carried out in the typical applications of NLP for the Albanian language. Finally, some future challenges and limitations of the area are discussed.
A novel sketch based face recognition in unconstrained video for criminal investigation Napa Lakshmi; Megha P. Arakeri
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.pp1499-1509

Abstract

Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.
Machine learning for prediction models to mitigate the voltage deviation in photovoltaic-rich distributed network Mohammed Baniyounis; Samer Z. Salah; Jasim A. Ghaeb
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.pp55-68

Abstract

The voltage deviation is one of the most crucial power quality issues that occur in electrical power systems. Renewable energy plays a vital role in electrical distribution networks due to the high economic returns. However, the presence of photovoltaic systems changes the nature of the energy flow in the grid and causes many problems such as voltage deviation. In this work, several predictive models are examined for voltage regulation in the Jordanian Sabha distribution network equipped with photovoltaic farms. The augmented grey wolf optimizer is used to train the different predictive models. To evaluate the performance of models, a value of one for regression factor and a low value for root mean square error, mean square error, and mean absolute error are used as standards. In addition, a comparison between nineteen predictive models has been made. The results have proved the capability of linear regression and the gaussian process to restore the bus voltages in the distribution network accurately and quickly and to solve the shortening in the voltage dynamic response caused by the iterative nature of the heuristic algorithm.  
Three-phase four-wire shunt hybrid active power filter model with model predictive control in imbalance distribution networks Asep Andang; Rukmi Sari Hartati; I Bagus Gede Manuaba; I Nyoman Satya Kumara
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.pp5923-5937

Abstract

This paper presents a harmonic reduction and load imbalance model in a three-phase four-wire distribution network. This model uses a hybrid active power filter, a passive inductor and capacitor filter, and an active power filter in the form of a three-phase, four-leg connected grid inverter. The switching of the voltage source converter on this filter uses finite control set model predictive control (FCS-MPC). Control of this hybrid active power filter uses model predictive control (MPC) with a cost function, comparing the reference current and prediction current with mathematical modelling of the circuit. The reference current is taken from the load current by extracting dq, and the predicted current is obtained from the iteration of the voltage source converter (VSC) switching pattern. Each combination is compared with the reference current in the cost function to get the smallest error used as a power switching signal. Modelling was validated by using MATLAB Simulink. The simulation results prove a decrease in harmonics at a balanced load from 22.16% to 4.2% and at an unbalanced load, reducing the average harmonics to 4.74%. The simulation also decreases the load current imbalance in the distribution network. Reducing the current in the neutral wire from 62.01%-0.42% and 11.29-0.3 A.
Recognition of compound characters in Kannada language Sridevi Tumkur Narasimhaiah; Lalitha Rangarajan
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.pp6103-6113

Abstract

Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes.
Cloud data security and various cryptographic algorithms Yahia Alemami; Ali M. Al-Ghonmein; Khaldun G. Al-Moghrabi; Mohamad Afendee Mohamed
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.pp1867-1879

Abstract

Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.
A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality Ahmad Azhari Mohamad Nor; Murizah Kassim; Mohd Sabri Minhat; Norsuzila Ya'acob
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.pp6602-6613

Abstract

Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented reality (AR). Fifty papers from recent referred publications in the nuclear areas were reviewed and compared. Detailed comparison of ML techniques, parameters involved in the coolant system and AR design techniques were done. Multiclass support vector machines (SVMs), artificial neural network (ANN), long short-term memory (LSTM), feed forward back propagation (FFBP), graph neural networks-feed forward back propagation (GNN-FFBP) and ANN were used for the machine learning techniques for the nuclear reactor. Temperature, water flow, and water pressure were crucial parameters used in monitoring a nuclear reactor. Image marker-based techniques were mainly used by smart glass view and handheld devices. A switch knob with handle switch, pipe valve and machine feature were used for object detection in AR markerless technique. This study is significant and found seven recent papers closely related to the development of predictive maintenance for a research nuclear reactor in Malaysia.
The effect of changing the formation of multiple input multiple output antennas on the gain Majed Omar Dwairi; Mohamed Salaheldeen Soliman; Amjad Yousef Hendi; Ziad AL-Qadi
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.pp531-548

Abstract

In this paper, different 2×1 and 2×2 multiple input multiple output (MIMO) antennas were investigated with changing substrate shapes and changing the placing of the patches on the substrate, all the investigated antennas based on FR-4 substrate are characterized by , and loss , with a partial ground. The original antenna covered 3.4 to 13.5 GHz. The best simulation results of the proposed 2×1 MIMO antenna received for 2×1 inverted with high ultra-wideband (UWB) with bandwidth up to 40 GHz, the received maximum gain was up to 6.51 dB, with an average gain of more than the original single antenna at about +1.27 dB. The best of eight 2×2 MIMO antennas configurations that give good results were shown. The best-received gain compared with a single antenna gain were at 4.2 GHz about +2.73, +1.17, and +0.92 dB for plus-shaped, loop, and chair-shaped respectively. A comparison between the proposed MIMO antennas and other reported works were done. The proposed MIMO antennas give a good maximum gain and are suitable for different narrow bands within the UWB such as wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX), aeronautical radio navigation (ARN), International Telecommunication Union 8-GHz (ITU-8), and X-Band applications with the ability to give high gain without the need to increase the radiated power of the transmitter antenna.

Page 456 of 631 | Total Record : 6301

 454   455   456   457   458 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue