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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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Fully automated model on breast cancer classification using deep learning classifiers Mudhafar Jalil Jassim Ghrabat; Zaid Alaa Hussien; Mustafa S. Khalefa; Zaid Ameen Abduljabbar; Vincent Omollo Nyangaresi; Mustafa A. Al Sibahee; Enas Wahab Abood
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp183-191

Abstract

Deep learning models on the same database have varied accuracy ratings; as such, additional parameters, such as pre-processing, data augmentation and transfer learning, can influence the models’ capacity to obtain higher accuracy. In this paper, a fully automated model is designed using deep learning algorithm to capture images from patients and pre-process, segment and classify the intensity of cancer spread. In the first pre-processing step, pectoral muscles are removed from the input images, which are then downsized. The removal of pectoral muscles after identification may become crucial in classification systems. Finally, the pectoral musclesaredeleted from the picture by using an area expanding segmentation. All mammograms are downsized to reduce processing time. Each stage of the fully automated model uses an optimisation approach to obtain highaccuracy results at respective stages. Simulation is conducted to test the efficacy of the model against state-of-art models, and the proposed fully automated model is thoroughly investigated. For a more accurate comparison, we include the model in our analysis. In a nutshell, this work offers a wealth of information as well as review and discussion of the experimental conditions used by studies on classifying breast cancer images.
Influence of wind speed on rain-based attenuation at Ku-band for earth-satellite links in Nigeria Joseph Sunday Ojo; Isaac Olawale Sunday; Elijah Olusayo Olurotimi
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1529-1541

Abstract

The peculiarity of the tropical climate shows that when the quality of service (QoS) on Satellite-Earth propagation links is to be determined, the statistics of rain-based attenuation (RbA), storm speed, and their impacts are the important parameters to be considered, especially at frequencies above 10 GHz. This paper assesses the influence of storm speed in estimating RbA at Ku-band, based on 2-year rain rate data obtained using automatic weather station (AWS) and RbA beacon measurements in Nigeria. The rain rates based on the time series were employed to deduce the time series RbA based on the synthetic storm technique (SST) algorithm. The results show a seasonal pattern of rain rate that correlates with the SST-based RbA. The RbA generated closely follows suit with the beacon measurement, especially under low wind speed, and outperforms the international telecommunications union–radiocommunication sector (ITU-R) model based on the lowest metric measures. However, at a higher storm speed, the RbA generated deviated widely from the measured RbA values by about 16%. These results are crucial for figuring out what needs to be done to protect QoS in a tropical area where wind and rain are common.
Enhance work for java based network analyzer tool used to analyze network simulator files Jawad Alkenani; Khulood Ahmed Nassar
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp954-962

Abstract

The network performance measurement is important in computer networks, and performance measurement may not be effective for installation in peripheral devices resulting in the replacement of those devices and thus increasing cost. In light of this, it is better to have a simulation of the network to see its performance rather than the actual design. NS-2 is one of the most popular and widely used open-source network simulators in many organizations, which generates trace files during the simulation experience. The trace file contains all network events that can be used to calculate performance. Thus, NS-2 does not offer any visualization options for analyzing simulation results (trace files), which is the fundamental problem of trace file parsing difficulty. This paper provides a graphical user interface tool that enables researchers to quickly and efficiently analyze and visualize NS-2 trace files. This tool is a development of the JDNA tool, as it could not analyze more than one trace file at a time. In addition, this work can be a useful guide for network researchers or other programmers to analyze their networks and understand how to calculate network performance metrics.
Analysis of the current state of deepfake techniques-creation and detection methods Ashraf A. Abu-Ein; Obaida M. Al-Hazaimeh; Alaa M. Dawood; Andraws I. Swidan
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1659-1667

Abstract

Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algorithms and datasets used to build deepfakes, as well as the approaches presented to detect deepfakes to date. By reviewing the background of deepfakes methods, this paper provides a complete overview of deepfake approaches and promotes the creation of new and more robust strategies to deal with the increasingly complex deepfakes.
Evolution of automated learning techniques for combating COVID-19: an analysis Azheen Ghafour Mohammed; Eman Shekhan Hamsheen
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1635-1641

Abstract

It is now more than two years that the world is battling the tiny invisible virus, COVID-19. Since its appearance, it showered humankind with shock, fear, and death. In small words, this pandemic has paused human life in all its aspects and beauties. Governments, health industry researchers and laboratories have put all their efforts to achieve a universal goal that is, overcoming the crisis and putting an end to the pandemic. However, this goal was never achievable without the smart use of automated learning, artificial intelligence, machine learning and deep learning algorithms. This review paper presents a collection of the experimental research articles tackled using real-time official datasets from hospitals and governments. These datasets are processed using automated learning (AL) algorithms in order to find suitable solutions to most of the COVID-19 related problems. This paper presents the AL applications in a story telling manner, starting from the first phases of COVID-19, when doctors had no experience dealing with the disease and had difficulty in diagnosing it, then moving to the other phases like suggesting a medicine, drug repurposing, facial mask detection, fake news detection, vaccine development, pandemic management, post vaccine statistics and lastly post COVID-19 analysis.
Major depressive disorder diagnosis based on PSD imaging of electroencephalogram EEG and AI Ammar Falih Mahdi; Aseel Khalid Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp535-544

Abstract

One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral density (PSD) images, and then they were classified as healthy or MDD using a deep neural network for feature extraction and a machine learning (ML) classifier. When employing the proposed approach, the images formed from the PSD show a considerably improved performance in classification results.
Energy harvesting schemes for internet of things: a review Doaa Abbas Fadil; Riyadh Jabbar Al-Bahadili; Mohammed Najm Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1088-1094

Abstract

As the internet of things (IoT) grows quickly, more people are interested in making wireless, low-power sensors. IoT systems currently use wireless sensors to collect reliable and accurate data in areas like smart buildings, environmental monitoring, and healthcare. Wireless sensors have typically been driven by batteries, which, although allowing for low total system costs, can have a significant impact on the whole network's lifetime and performance. The solution to this problem is energy harvesting (EH) from the environment. An EH is a technique for converting energy from an environmental source, such as heat, light, motion, or wind, into electric power. This paper describes many types of EH systems as well as some technological issues that must be solved before IoT energy harvesting solutions can be widely used.
New methods for proportional-integral controller design for time-delay systems Aye Taiwo Ajiboye; Jayeola Femi Opadiji; Abdulrahman Olalekan Yusuf; Olusogo Joshua Popoola; Esther Toyin Olawole; Olalekan Femi Adebayo
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1437-1450

Abstract

The development of structured methods for proportional-integral (PI) controller design for systems with time delay are proposed in this article. Several PI controller design methods for time-delay systems have been reported. However, combining two or more methods to form new ones have not been given serious attention. The system stability region in the controller parameters space was determined by plotting the stability boundaries. In this study, the controller gains were first obtained using genetic algorithm (GA), weighted geometric center (WGC), and centroid of convex stability region (CCSR). Thereafter, these gains were combined by finding the centroids of lines joining any of the two gain locations, and triangle whose vertices are the location of the three gains in the convex stability region, thus yielding four additional methods, M1, M2, M3, and M4. Compared to a particular existing method, some of the proposed methods yield faster response speed at the expense of reference input tracking, while the reverse is the case for others. Any of the proposed methods (M1, M2, M3, and M4) can be selected depending on the system performance specifications.
Developing mobile game application for introduction to financial accounting Mohamed Imran Mohamed Ariff; Fuad Mohd Khalil; Rahayu Abdul Rahman; Suraya Masrom; Noreen Izza Arshad
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1721-1728

Abstract

The financial accounting subject is one of the core subjects that is essential for any accounting student. However, this subject is perceived as boring and difficult to comprehend particularly for students who lack in the accounting knowledge. The aim of this research paper is to present the adoption of the gamification learning concept in designing and developing a mobile game application to cultivate better understanding in the financial accounting subject. This mobile application was developed for Android operating system and was designed using the modified game methodology. Further, this mobile application was subjected to several testing phases using numerous participants. The results indicate the adoption of gamification has aided the students in understanding the financial accounting subject. Furthermore, the participants also indicated that learning using gamification has encouraged them to think critically which then allowed them to better comprehend the financial accounting subject. The development of this mobile game application also contributes to the gamification literature which is vastly used in learning, and it advantages in improving the understanding of how games can be adopted to foster better understanding in the financial accounting subject.
IoT-based drinking water quality measurement: systematic literature review Yulieth Carriazo-Regino; Rubén Baena-Navarro; Francisco Torres-Hoyos; Juan Vergara-Villadiego; Sebastián Roa-Prada
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp405-418

Abstract

Sustainable development throughout the world depends on several factors such as the economy, quality education, agriculture, industry, among others, but the environment is one of the most important. Industrialization and new land use plans have caused the proliferation of pollutants in water resources, which poses a serious public challenge. As outlined in the sustainable development goals (SDGs), innovative water quality monitoring methods are needed to ensure access to water, sustainable management and sanitation. In this sense, technologies are sought that contribute to the development and implementation of groundwater and surface water quality monitoring systems in real time, so that their parameters can be evaluated through descriptive analysis, in rural populations and areas of difficult access. Nowadays, the internet of things (IoT) and the development of modern sensors are more used, so this research reviews the latest technologies to monitor and evaluate water quality using the potential and possibilities of the IoT. The main contribution of this article is to present an overview of the state of the art of IoT applications and instrumentation for water quality monitoring, focusing on the latest innovations, in order to identify interesting and challenging areas that can be explored in future research.

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