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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 :
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Articles 64 Documents
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Search results for , issue "Vol 26, No 1: April 2022" : 64 Documents clear
Design a monitoring system for COVID-19 patients Hayder Ibrahim Hendi; Haider Hassan Mshali
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp304-309

Abstract

This paper considers to developing application software that can assist COVID-19 patients in-home quarantine to know their situations and call the emergence center when the patient needs it. It includes a smart band as well as an application on the smartphone, the smart band can determine blood oxygen levels, the temperature of the patient, environmental temperature and humidity, also daily activities that affect the decision to go to the hospital or stay at home. The core of the proposed project is using ontology and semantics web to process the data that coming from sensors (physiology and environment), and the information of patients stored in the database on the mobile application. The response depends on the dataset of affect sensors parameters and type of activity the patient at the time. There are three types of response to proposed program is (normal, alert, and emergency).
Improving the sub-image classification of invasive ductal carcinoma in histology images Khanabhorn Kawattikul; Kodchanipa Sermsai; Phatthanaphong Chomphuwiset
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp326-333

Abstract

Whole slide image (WSI) processing is a common technique used in the analysis process performed by pathologists. Identifying precise and accurate regions of cancerous in the tissue is an important process in the disease diagnosis modality. This work proposes an automated technique for identifying invasive ductal carcinoma (IDC) in histology images using. An image is divided into small non-overlapped patches (or image windows). Then, the task is to classify the image patches into different classes, i.e., i) IDC and ii) non-IDC. We employ a two-stage classification-based to classify the patches, as to identify IDC regions in the tissue. In the first stage (patch-level classification), image patch classification is carried out using a conventional handcrafted feature and deep-learning technique are explored. The second stage (post-processing) undergoes a refinement process, which considers the spatial relationships between the neighboring patches. This stage aims to amend some of miss-classified patches. Markov random field (MRF) is implemented in this stage to examine the relationships of the patches and their neighborhoods. The experiments are conducted on public dataset. The experimental results show the post-processing can improve the performance of the classification in the first stage using the handcrafted-based technique and deep learning.
Elastic net feature selected multivariate discriminant mapreduce classification Arunadevi Nakkiran; Vidyaa Thulasiraman
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp587-596

Abstract

Analyzing the big stream data and other valuable information is a significant task. Several conventional methods are designed to analyze the big stream data. But the scheduling accuracy and time complexity is a significant issue. To resolve, an elastic-net kernelized multivariate discriminant map reduce classification (EKMDMC) is introduced with the novelty of elastic-net regularization-based feature selection and kernelized multivariate fisher Discriminant MapReduce classifier. Initially, the EKMDMC technique executes the feature selection to improve the prediction accuracy using the Elastic-Net regularization method. Elastic-Net regularization method selects relevant features such as central processing unit (CPU) time, memory and bandwidth, energy based on regression function. After selecting relevant features, kernelized multivariate fisher discriminant mapr classifier is used to schedule the tasks to optimize the processing unit. Kernel function is used to find higher similarity of stream data tasks and mean of available classes. Experimental evaluation of proposed EKMDMC technique provides better performance in terms of resource aware predictive scheduling efficiency, false positive rate, scheduling time and memory consumption.
Prediction of of heart diseases utilising support vector machine and artificial neural network Alaa Khaleel Faieq; Maad M. Mijwil
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp374-380

Abstract

The heart, like a pump, is an organ about the size of a fist, mainly composed of muscle and connective tissue that functions to distribute blood to tissues. The heart is located under the rib cage, above the diaphragm between the lungs, slightly closer to the left. Sometimes a small, unexpected problem with the veins or the valves that supply the heart affects a person's life and can lead to death. Early diagnosis is essential to predict diseases that affect the human heart and lead people to live another period of life. In this context, the authors introduce two methods for early diagnosis of heart disease, the support vector machine and artificial neural network. The medical data is taken from the University of California Irvine (UCI) Machine Learning Repository database, and it contains reports of 170 people. The investigation results confirm that the optimal execution is the support vector machine technique. It gives high-accuracy prediction results. As for the performance of the forward propagation artificial neural networks (ANN) technique is acceptable.
Advanced control of a permanent magnet synchronous generator for a wind turbine Abdelkader Belkacem; Zinelaabidine Boudjema; Ghalem Bachir; Rachid Taleb
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp194-201

Abstract

This article presents an improved vector control scheme based on super twisting continuous sliding mode for a permanent magnet synchronous generator integrated in a dual roror wind turbine system. To augment the energy effectiveness of wind systems, several research has recently been realized by different researchers and in various technologies fields. The field of machine control occupied a large part of this research as the objective was always to find the most optimal control solution. Two main objectives are targeted in this work. The first goal is to develop the vector control performance of the permanent magnet synchronous generator by using second order continuous sliding mode controller, which is known for their robustness and ability to reduce chattering phenomenon. The second objective of this work is to use a dual rotor wind turbine in order to increase the energy efficiency of the wind power system used. The obtained simulation results showed the efficacy of the techniques used.
A novel optimization-based power quality enhancement using dynamic voltage restorer and distribution static compensator Madduluri Chiranjivi; Katragadda Swarnasri
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp160-171

Abstract

The power assurance for distinct purchaser in the nation must be guaranteed through electric power distribution (EPD) system, subsequently EPD must be reliable, and well facilitating with power quality (PQ) in distribution framework. It will be much significant to keep up with PQ in power distribution (PD) framework for service continuity. The PQ issue basically emerge in distribution framework are voltage swell, sag, harmonics, and disparities in power. In this manuscript, the PQ development in photovoltaic (PV) PD framework for IEEE 33 and 57 bus frameworks is deliberated as the aim. The improvement is satisfied with FACTS pay gadgets of dynamic voltage restorer (DVR) and distributed static compensator (DSTATCOM). The butterfly optimizer (BO) and gray wolf optimizer (GWO) framework is deliberated for suggested work that implies a switching gadget and it compares the yield signal of DSTATCOM and DVR. In this proposed system DVR is compensated with an advanced GWO and DSTATCOM with BO for optimal location of FACTS devices in IEEE 33 and 57 bus systems are discussed like in the conventional paper as 13th bus for IEEE 33 system & 30th bus for IEEE 57 system.
Rapid bacterial colony classification using deep learning Son Ali Akbar; Kawarul Hawari Ghazali; Habsah Hasan; Zeehaida Mohamed; Wahyu Sapto Aji; Anton Yudhana
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp352-361

Abstract

Bacterial colonies infection is one of the causes of bloodstream disease, and it can be a fatality. Therefore, medical diagnoses require fast identification and classification of organisms. Artificial Intelligence with deep learning (DL) can now be developed as a rapid bacterial classification. The research aims to combine deep learning and support vector machines (SVM). The ResNet-101 model of the DL algorithm extracted the image’s features using transfer learning then classified by the SVM classifier. According to the experimental results, this model had 99.61% accuracy, 99.58% recall, 99.58% precision, and 99.97% specificity. The technique presented might enhance clinical decision-making.
Performance comparison between fixed tilt angle and solar tracking systems at Basra governorate: A case study Ismaeil R. Alnaab; Harwan M. Taha; Zainab A. Abdulwahab; Mohammed Salah Al-Radhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp184-193

Abstract

Theoretical calculations and online system simulations using PVWatts and global solar atlas simulators, were conducted in this study to find the difference in solar power production between fixed solar power systems and tracking systems at Basra Governorate. The research included the analysis of geographical location and weather conditions and their effect on output power. The reliant power resource types and power generation of the southern region of Iraq as well as load demands were demonstrated and discussed in this research. Furthermore, the sun path, solar angles and solar radiations were considered in this study, in addition to the mathematical calculations of optimum tilt angles. The methodology used in this study was based upon theoretical and online measures of real-time weather factors, solar angles, solar radiations and model properties of the examined system. The results and factors of different systems including: peak sun hours per day, dc to ac derate factors, tilt angles, solar radiations and power production were compared to multiple similar research elements that were accomplished around the same region and some other countries. The study concluded that solar tracking system absorbs more radiations and produces an annual production of 15–30% higher than fixed tilt angle system.
Optimizing random forest classifier with Jenesis-index on an imbalanced dataset Joylin Zeffora; Shobarani Shobarani
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp505-511

Abstract

Random forest is an ensemble algorithm for machine learning. In decision trees, the splitting criteria is built on the prediction of the nodal points and formation of rules by Gini index and Information Gain. Gini index is a measure of inequality. Gini index does not take into consideration the structural changes in the dataset, and inaccurate data can distort the validity of the gini-coefficient. For data with the same feature but different outcomes, the gini-coefficient remained the same. The proposed method for attribute selection measure takes into consideration that there may be structural changes in the dataset overtime and it adapts to such expected changes and maintain the accuracy of the algorithm avoiding under-fitting and over-fitting. A dataset on myocardial infarctions was taken for the study and the results were promising.
Turbo polar code based on soft-cancelation algorithm Wallaa Yaseen Alebady; Ahmed Abdulkadhim Hamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp521-530

Abstract

Since the first polar code of Arikan, the research field of polar codes has been continuously active. Improving the performance of finite-code-length polar codes is the central point of this field. In this paper, the parallel concatenated systematic turbo polar code (PCSTPC) model has been proposed to improve the polar codes performance in a finite-length regime. On the encoder side, two systematic polar encoders are used as constituent encoders. While on the decoder side, two single iteration soft-cancelation (SCAN) decoders are used as soft-in-soft-out (SISO) algorithms inside the iterative decoding algorithm of the parallel concatenated systematic turbo polar code (PCSTPC). As compared to the optimized turbo polar code with SCAN and BP decoders, the proposed model has about 0.2 dB and 0.48 dB gains at BER=10(-4), respectively, in addition to 0.1 dB, 0.31 dB, and 0.72 dB gains over the TPC-SSCL32, TPC-SSCL16, and TPC-SSCL8 models, respectively. Moreover, the proposed model offers less complexity in comparison with other models, therefore requiring less memory and time resources.

Page 6 of 7 | Total Record : 64

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