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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Mobile recommender system based on smart city graph Khalid Khoshnaw, Karwan Hoshyar; Abdulkarim Shwany, Zardasht Abdulaziz; Mustafa, Twana; Ismail, Shayda Khudhur
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1771-1776

Abstract

Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.
Applications of artificial intelligence with cloud computing in promoting social distancing to combat COVID-19 Mohammed Ghadhban Al-Hamiri; Hayder Fadhil Abdulsada; Laith A. Abdul-Rahaim
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1550-1556

Abstract

The emergence of Coronavirus disease 2019 (COVID-19) disease and its rapid spread around the world has serious impacts on people's lives in addition to its effects on many aspects, including the economic and educational sectors. Researches have proved that social distance is effective in combating COVID-19. Maintaining social distance is hard to be handled by humans especially in crowded areas such as airports and campuses. So, there is a need to apply a robust and proactive design to manage this process automatically and smartly. This paper presents a design system to fight COVID-19 by maintaining the social distance with effective monitoring for suspected cases. This has been done using cloud computing and a framework including Arduino (node microcontroller unit (NodeMCU)) with several sensors. The operational aspects of this design system using cloud computing have been discussed. Generally, NodeMCU has been involved in checking the conditions, comparison processing, and communication with the webserver. Moreover, the webserver has been used for determining the maximum number of persons allowed to enter. The results state that this design system is effective in combating COVID-19 through maintaining the social distance and collecting information about suspected cases. This system is valuable, dependable, and stable since the whole process is contactless.
Transfer learning with GoogLeNet for detection of lung cancer Muayed S AL-Huseiny; Ahmed Sattar Sajit
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1078-1086

Abstract

The use of computer algorithms has gained momentum in filling/assisting roles of specialists especially in early diagnosis scenarios. This paper proposes the employment of deep neural networks (DNN) to detect images with malignant nodules of lung computed tomography (CT). The method includes subjecting input images to a simple and fast pre-processing which isolates regions of interest (ROI), that’s the lungs dominated area, ridding the images of other surrounding tissues and artefacts. Centered and size normalized images are then fed to a deep neural network for training and validation. In this work transfer learning is used to readjust GoogLeNet DNN to learn this medical data. This includes allowing final layers of the DNN to evolve while restricting deep layers. In this setting, a rough, unprocessed dataset, the IQ-OTH/NCCD lung cancer dataset was used to train/validate the proposed algorithm. Experimental results show that this algorithm scores 94.38% accuracy, which outperforms benchmark method previously used with this dataset.
Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch Oussama Dahmane; Mustapha Khelifi; Mohammed Beladgham; Ibrahim Kadri
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1469-1480

Abstract

In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and brightness preserving bi-histogram equalization was also used (BBHE) to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained VGG19 as feature extractor and our designed convolutional neural network (CNN).
Sectoral dual-polarized MIMO antenna for 5G-NR band N77 base station Muhsin Muhsin; Afina Lina Nurlaili; Aulia Saharani; Indah Rahmawti Utami
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1611-1621

Abstract

Massive internet of things (IoT) in 5G has many advantages as a future technology. It brings some challenges such as a lot of devices need massive connection. In this case, multiple-input multiple-output (MIMO) systems offer high performance and capacity of communications. There is a challenge of correlation between antennas in MIMO. This paper proposes three-sectors MIMO base station antenna for 5G-New Radio (5G-NR) band N77 with dual polarized configuration to reduce the correlation. The proposed antenna has a maximum coupling of -16.90 dB and correlation below 0.01. The obtained bit error rate (BER) performance is very close to non-correlated antennas with bandwidth of 1.87 GHz. It means that the proposed antenna has been well designed.
Determining subject headings of documents using information retrieval models Evi Yulianti; Laksmita Rahadianti
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1049-1058

Abstract

Subject heading is a controlled vocabulary that describes the topic of adocument, which is important to find and organize library resources. Assigning appropriate subject headings to a document, however, is a time-consuming process. We therefore conduct a novel study on the effectiveness of information retrieval models, i.e.,language model (LM) andvector spacemodel (VSM), to automatically generate a ranked list of relevant subject headings, with the aim to give a recommendation for librarians to determine the subject headings effectively and efficiently. Our results show that there are a high number of our queries (up to 61%) that have relevant subject headings in the ten top-ranked recommendations and on average, the first relevant subject heading is found at the early position (3rd rank). This indicates that document retrieval methods can help the subject heading assignment process. LM and VSM are shown to have comparable performance, except when the search unit is title, VSM is superior to LM by8-22%. Our further analysis exhibits three faculty pairs that are potential to have research collaboration as their students’ thesis often have overlap subject headings: i) economy and business-social and political sciences, ii) nursing-public health and iii) medicine-public health.
Intelligent multimodal identification system based on local feature fusion between iris and finger vein Enas Abbas Abed; Rana Jassim Mohammed; Dhafer Taha Shihab
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp224-232

Abstract

Biometric identification systems, which use physical features to check a person's identity, ensure much higher security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single modal biometric identification system fails to extract enough features for identification. Another disadvantage of using only one feature is not always readable. In this article, a smart multimodal biometric verification model for identifying and verifying a person's identity is recommended based on artificial intelligence methods. The proposed model is identified the iris and finger vein unique patterns each individual to overcome many challenges such as identity fraud, poor image quality, noise, and instability of the surrounding environment. Several experiments were performed on a dataset containing 50 people by using many matching methods. The results of the proposed model were provided a higher accuracy of 98%, with FAR and FRR of 0.0015% and 0.025%, respectively.
Parameter selection in data-driven fault detection and diagnosis of the air conditioning system Noor Asyikin Sulaiman; Md Pauzi Abdullah; Hayati Abdullah; Muhammad Noorazlan Shah Zainudin; Azdiana Md Yusop; Siti Fatimah Sulaiman
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp59-67

Abstract

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.
Hardware implementation and performance evaluation of microcontroller-based 7-level inverter using POD-SPWM technique Hajar Chadli; Sara Chadli; Mohamed Boutouba; Mohammed Saber; Abdelwahed Tahani
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp120-131

Abstract

Renewable energy sources are considered as inexhaustible sources for the very long-term, as they come from natural processes that are constantly replenished. However, there are a number of challenges facing renewable energy technology adoption, like the grid connecting problems. One of the main challenges relates to the grid connecting problem is the power quality issues for power converter, such as harmonics, voltage stability, and frequency fluctuation. Hence, the inverter remains the first element to be built because of its undeniable advantages in alternative continuous conversion. However, it has some disadvantages such as high component count and complex control method. This paper presents the design and implementation of a new 7-level inverter architecture with only six switches. This architecture requires fewer components compared to other 7-level inverter topologies therefore, the overall cost, control technique complexity, and conduction losses are highly reduced. A digital phase opposition disposition sinusoidal pulse width modulation (POD-SPWM) strategy using the Arduino is adopted to improve the performance of the proposed multilevel inverter (MLI) which leads to further reduction in total harmonic sistortion (THD). In this paper, the proposed inverter is tested using Proteus software and Matlab Simulink. Finally, a laboratory setup of the proposed inverter was built to validate its workability by the experimental results.
New scaled algorithm for non-linear conjugate gradients in unconstrained optimization Ghada M. Al-Naemi; Ahmed H. Sheekoo
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1589-1595

Abstract

A new scaled conjugate gradient (SCG) method is proposed throughout this paper, the SCG technique may be a special important generalization conjugate gradient (CG) method, and it is an efficient numerical method for solving nonlinear large scale unconstrained optimization. As a result, we proposed the new SCG method with a strong Wolfe condition (SWC) line search is proposed. The proposed technique's descent property, as well as its global convergence property, are satisfied without the use of any line searches under some suitable assumptions. The proposed technique's efficiency and feasibility are backed up by numerical experiments comparing them to traditional CG techniques.

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