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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 65 Documents
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Search results for , issue "Vol 30, No 3: June 2023" : 65 Documents clear
Knee-joint exoskeleton control system design using adaptive barrier function controller Amer B. Rakan; Mohammed Rashid Subhi; Ali H. Mhmood
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1440-1448

Abstract

Exoskeletons and wearable robots are mechatronic devices that are worn by an operator and fit closely to the body to improve daily activities. The adaptive sliding mode control (ASMC) based on the barrier function is proposed in this study to regulate the movement of the knee joint exoskeleton with friction. This controller is implemented without the requirement to know the system model uncertainty and disturbance bounded and does not require the use of the low pass for chattering elimination with keeping the controller’s performance. The suggested barrier method can be guaranteed the output variable’s convergence and keep it in a preset neighborhood of zero regardless of the disturbance’s upper bound, without overestimating the control gain. The simulation results show that the proposed performs well, the system angle following the target angular position with a modest pre-adjusted steady-state error. Furthermore, when compared to a typically strong and stable ASMC developed with the identical actuator; the obtained results reveal superior features. Concluded from the above this control method indicates the robustness of the proposed adaptive controller with barrier function against uncertainties and disturbances elimination.
Do clinical decision support systems for prescribing improve patient safety? a systematic literature review Sri Kusumadewi; Isnatin Miladiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1748-1761

Abstract

Clinical decision support systems (CDSS) are developed independently or connected to the electronic health record (EHR) or other computerized systems. The study begins with identification by searching the literature through the Google Scholar, PubMed and ScienceDirect databases. The search results obtained 5,595 articles. Forty-two articles were obtained, which were used further. Most of the research focus is on "CDSS development and evaluation". In terms of impacts, the most common is "reduce prescribing errors". One of the biggest problems reported was the presence of "alert fatigue," which was felt to be disturbing to doctors and pharmacists. CDSS must be supported by a method that is able to indicate the presence of drug-drug interactions (DDI). The use of alerts indicating the presence of a DDI should be interpreted using clinical judgment to determine the risks and benefits of a particular drug for a specific patient. The performance of CDSS is mostly reported to have been able to reduce prescribing errors, which in turn will improve patient safety. However, increased adherence to clinical protocols has not been widely reported. Complaints that are still quite a lot reported are the presence of "alert fatigue", which can interfere with effectiveness.
A novel hybrid feature extraction and ensemble C3D classification for anomaly detection in surveillance videos Vishnu Priya Thotakura; Purnachand Nalluri
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1572-1585

Abstract

Anomaly detection in several deep learning frameworks are recently presented on real-time video databases as a challenging task. However, these frameworks have high false positive rate (FPR) and error rate due to various backgrounds, motion appearance and semantic high-level and low-level features for anomaly detection through action classification. Also, extraction of features and classification are the major problems in traditional convolution neural network (CNN) on real-time video databases. The proposed work is a novel action classification framework which is designed and implemented on large video databases with high true positive rate (TPR) and error rate. In this framework, Kalman based incremental principal component analysis (IPCA) feature extraction method; C3D and non-linear support vector machine (SVM) classifier are used to improve the action prediction (anomaly detection) on the large real-time video databases. The proposed frame work shown new results of high computation performance than the traditional deep learning frameworks for action classification.
Efficient palm vein authentication encryption technique in wireless implantable medical devices Ahlam Almukhlifi; Saad M. Almutairi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1651-1658

Abstract

Implantable medical devices (IMD) are commonly utilized to treat chronic illnesses. Many IMD communicate in wireless mode using an external programmer, which raises security concerns. Security of IMD is a critical issue which assaults direct harm to patients. Many researches are carried out on IMD security and challenges when the patient is not in a critical situation. Still, it would be a major issue while the patient is unconscious. In this research, a novel scheme for emergency secure access control of IMD was proposed to improve the security of biometric-based IMD schemes. The proposed authentication scheme uses a combination of palm vein and zero-watermark to generate encrypted credential data for IMDs. Using quantitative assessment for evaluating images, such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the mean squared errors (MSE), the suggested framework is shown to be superior to existing methods. Two other study goals are improved efficiency and image quality at a lower computational cost.
Simulation and harmonic analysis of hybrid distributed energy generation based microgrid system using intelligent technique Jaspreet Kaur; Anita Khosla
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1287-1296

Abstract

Wind and solar photovoltaic (PV) based hybrid renewable energy generation are environmentally friendly and reasonable. This study describes a hybrid distributed energy generations (DEGs)-based microgrid framework where DC-DC converter, three-stage inverter with fuzzy logics control (FLC), and LC filter channel are coupled with the PV and wind energy. In India, wind and PV energies are affordable for hybrid power frameworks since they are ecological well-disposed and broadly accessible. Generally, these sources produce a fluctuating yield voltage that prompts harm to the working grid on a steady inventory. The proposed model of the hybrid-based framework is executed utilizing MATLAB/Simulink. A boost converter being associated with PV cluster is associated with the basic DC bus also a battery is used by a two-way DC to DC converter, and connected into the utility framework by a typical DC to AC inverter. Wind cluster and PV is linked with maximum power point tracking (MPPT) to produce the higher capacity to the grid, and the charging and discharging of the battery energy can be done to adjust the energy between DEGs generation and utility network. In this paper, various cases of harmonic analysis are executed on MATLAB-simulation and feasibility of proposed grid models and FLC-based intelligent control.
Robust features extraction from shape signature for fish images classification Ali Ahmed; Sherif Hussein; Younis Ibrahim Gali
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1740-1747

Abstract

Recently, the process of fish species classification has become one of the most challenging problems addressed by researchers. In this work, a robust scheme to classify fish images based on robust feature extraction from shape signatures is proposed. First, the image contour is fitted using one of the common approaches named radial basis function neural network (RBFNN) fitting to obtain image centroid. Afterward, prominent features from the shape signature are extracted. These features are representative of fish shapes because they can distinguish the characteristics of each class as well as being relatively robust to scale and rotation changes. Finally, for the classification process purpose, RBFNN is used again for image classification against one of the most commonly used classification techniques called support vector machine (SVM). The proposed paradigm has been applied to a standard fish dataset acquired from a live video dataset grouped into twenty-three clusters representing specific fish species. The resulting accuracy based on SVM and RBFNN was 90.41% and 98.04%, respectively.
A comprehensive survey on blockchain-based healthcare industry: applications and challenges Sara Ait Bennacer; Khadija Sabiri; Abdessadek Aaroud; Khalid Akodadi; Bouchaib Cherradi
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1558-1571

Abstract

Blockchain has attracted a lot of interest since its publication because to its unique characteristics of immutability, decentralization, smart contract, and consensus mechanism. Today’s healthcare systems are facing many issues in the era of digital health transformation and the growth of electronic health records. Blockchain has the potential to provide solutions to a variety of electronic health record (EHR)-related challenges, including data management, security, data sharing and patient privacy. This paper represents a blockchain-based healthcare industry survey; it includes many research publications in high-ranking scientific journals in recent years from 2016 to 2022. We adopt the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach and aspects. This work is based on five databases: Elsevier, Springer, IEEE Xplore, PubMed and MDPI. The number of studies included in this study was 56. We found that researchers attempted to use blockchain technology to assist patients and healthcare providers in diagnosis and data processing, as well as including multiple entities. Our study discusses the potential of blockchain technology, its roles and benefits in healthcare, aiming to solve several problems such as data management, data sharing, access control, data security and privacy, which are missed in the conventional healthcare system.
Classification of medical X-ray images using supervised and unsupervised learning approaches Ranjana Battur; Jagadisha Narayana
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1713-1721

Abstract

Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.
An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis Manu Yadakere Murthygowda; Ravikumar Guralamata Krishnegowda; Shashikala Salekoppalu Venkataramu
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1369-1380

Abstract

The uncontrolled outburst in population has led to crowd gatherings in various public places causing panic and disaster in certain unpleasant and extreme conditions. A study on the analysis of crowd accumulation has been carried out for various reasons that include management of crowd, design of a well-planned public space, the possibility for surveillance at every area and transportation systems. A lot of disasters also occurs due to uncontrollable crowd behaviour and poor crowd management. It could result in loss of property, fatalities or casualties. To avoid this, the conduct of a crowd of people has been studied and analyzed to control their movement and traffic. Hence, in this research work, integrated multi-level feature fusion (IMFF) framework is designed to predict the behaviour; further classification based on the local region is carried out to enhance the prediction. In the case of multi-level feature fusion; first level feature fusion utilizes the motion and appearance; second-level feature fusion utilizes the spatial connection and third-level utilizes the temporal connections. Further, the classification approach is integrated based on the local region is used to enhance the crowd behaviour prediction in terms of accuracy and faster. Moreover, performance evaluation is carried out considering the two distinctive datasets.
Distinguishing license plate numbers using discrete wavelet transform technology based deep learning Asma Abdulelah Abdulrahman; Fouad Shaker Tahir
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1771-1776

Abstract

Cars that violate the red light, and to increase the huge number of cars in violation, it is necessary to discover a system for identifying car plate numbers with the intervention of a computer, computer vision and neural networks segment and detail the number plates by designing regular algorithms to identify the number of license plates in violation. In this work, interest is in identifying the Iraqi car plate in order to know the place where the vehicle papers and the letters on which the vehicle depends and to know the location of the car were completed. The technique that was carried out in this work is to build new wavelets from polynomials by mathematical methods and discover a new algorithm using the MATLAB program to identify each number in the vehicle plate with a specific color by training a convolutional neural network (CNN) after analyzing the image using the new wavelets to identify the contents of the plate and good results have been reached. The accuracy level was reached with good values of up to 95%.

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