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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 9,138 Documents
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Mobilenet, inception ResNet and GoogleNet for epilepsy detection using spectrogram images Fatima Edderbali; Mohammed Harmouchi; Elmaati Essoukaki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp870-877

Abstract

Epilepsy is considered the most common cerebral disorder, around 1% of the worldwide population suffer from it. Recently, detection of epilepsy has attracted more and more attention. It has become a hastily increasing problem that can worsen their conditions which necessitate a specific and crucial attention where the symptoms can be an impaired awareness or motor symptoms. Besides that, the difficult process of manual inspection of electroencephalography electroencephalogram (EEG). This paper proposes using transfer learning models to detect both normal and epileptic brain activity and auto-classify signals from the brain. The models considered for this study are GoogleNet, MobileNet, and inception residual neural network inception ResNet. These models were associated with seven different classifiers such as discriminant. These classifiers were tested, analyzed and compared with each other. The efficiency of models is comparatively evaluated through result using multiple metrics. We therefore attained an accuracy of 96.53%, a precision of 97.18%, a false positive rate of 2.78% and an F1-score of 96.50%. Finally, comparison of the suggested approach with existing research shows that the performance of epilepsy classification has been markedly enhanced.
Multi-person interaction in collaborative virtual conference for Metaverse Yingying Li; Ajune Wanis Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1998-2006

Abstract

Virtual conferences have successfully solved the challenges of traditional face-to-face conferences that can’t be carried out smoothly due to issues such as venue, transportation, and the pandemic. More importantly, as virtual conferences continue to develop and mature, they provide a virtual environment for multi-person collaboration, which is impossible to achieve with traditional face-to-face conferences. This technological innovation is expected to bring significant cost savings to various fields such as medical care, education, and industry. This paper proposes a unity-based multi-party collaborative virtual conference model. The model takes automobiles as the theme, which can realize the design and modification of the appearance of the automobiles by multiple people in the same space, and also supports operations such as the disassembly and installation of the automobile engine. This model is designed to deepen our understanding of multi-person collaboration capabilities in virtual conferences, a feature that is expected to significantly improve teamwork efficiency.
Cervical cancer: empowering diagnosis with VGGNet transfer learning Joshi, Vaishali M.; Mulmule, Pallavi V.; Gandhi, Swati A.; Patil, Alaknanda S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp467-474

Abstract

This study addresses the critical issue of cervical cancer, which stands as the fourth most prevalent cancer among women. With early detection being pivotal for successful treatment, the research focuses on evaluating the effectiveness of deep learning-based models in cervical cancer detection. Leveraging the widely employed Papanicolaou (Pap) smear test, the study proposes a transfer learning approach, incorporating contrast limited adaptive histogram equalization for image enhancement. Convolutional neural network models, including AlexNet, visual geometry group (VGGNet)-16, and VGGNet-19, are employed to accurately distinguish between cancerous and non-cancerous cervical cell images. The evaluation metrics encompass accuracy, precision, sensitivity, specificity, F1-score, and the matthew correlation coefficient (MCC). Notably, the findings reveal the exceptional performance of the VGGNet-19 model, achieving an accuracy of 98.71%, sensitivity of 98.33%, and specificity of 99% for a single smear cell. This research marks a significant advancement in the application of deep learning for precise cervical cancer detection. The promising results underscore the potential of these models to enhance early diagnosis and contribute to improved treatment outcomes, thereby addressing a crucial aspect of women's health.
A study of routing-based distributed mobility management in supporting seamless data transmission in smart cities Sunguk Lee; Ronnie D. Caytiles; Byungjoo Park
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1067-1075

Abstract

The current trend of people having their multimedia-capable wireless and mobile devices roam within highly urbanized areas (i.e., smart cities) has led to the wide deployment of overlapping heterogeneous wireless network technologies. In this regard, various challenges have emerged, such as the number of mobile devices that connect or disconnect a wireless network domain, the diversity of time of their connections, frequent handovers as mobile devices frequently change their locations, the heterogeneity of wireless network technologies, cooperation challenges between the overlapping heterogeneous wireless network technologies, the increasing volume of multimedia traffic, security and privacy issues, and many more. This paper focuses on the deployment of a routing-based distributed mobility management (DMM) scheme to address the constraints and limitations of centralized wireless architectures for smart cities. The comparative analysis with centralized mobility management solutions shows significant alleviation in performance as to handover latency and packet losses, thus providing seamless handovers to maintain quality of service (QoS) for multimedia services.
Enhancing lung cancer disease diagnosis by employing ensemble deep learning approaches Manmath Nath Das; Niranjan Panda; Rasmita Rautray
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1766-1773

Abstract

Cancer is a disease that results from the unnatural proliferation of aberrant cells that infest the body’s healthy cells and spread throughout the body. Lung cancer is characterized by an imbalance in the cells of the affected organs, namely the lungs. The prediction of lung cancer at an early stage is very important, particularly in countries that are densely populated and have lower incomes. Clinically conventional approaches, such as blood tests and other types of treatments, are used by specialists. The age of artificial intelligence (AI) has begun, and today, it is feasible to construct a computer-aided diagnostic mechanism with the assistance of machine learning and deep learning algorithms. In this particular piece of research, one deep learning algorithm, an artificial neural network (ANN), has been investigated to determine whether or not lung cancer could be detected at an earlier stage. In addition to conventional ANN, ensemble ANN with weighted averaging and soft and hard voting ensemble techniques are also considered. In order to achieve this effectiveness, the state-of-the-art parameters for the proposed method using ANN are assessed and evaluated using the lung cancer dataset. The empirical analysis shows that hard voting-enabled ANN shows the highest accuracy at 97.47%.
FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter Abdoul Moumouni Harouna Maloum; Nicasio Maguu Muchuka; Cosmas Raymond Mutugi Kiruki
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp888-899

Abstract

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.
Electrical discharge reproduction in rod-barrier-plane system Benharat Samira; Belgacem Leila; Doufene Dyhia; Bouazabia Slimane; Haddad Abderrahmane; Sakmeche Mounir
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp1-10

Abstract

The present paper deals with new modeling to reproduce the electric discharge in the rod-plane air gap system with rubber insulating barrier under AC and impulse voltage. This model considers the randomness character of discharge evolution which is governed by the electric field. The discharges shape obtained by this model are compared with ones given by experimental tests. The established model reproduces correctly the forms of discharges obtained by experimental tests under AC voltage. It is found that the behavior of the electrical discharge depends not only on the dimension (thickness and width) of the insulating barriers but on its positions in the air gap as well. It is to highlight that the mode of applied voltage is of key importance barrier. Experimental investigation shows that the developed arc can evolve on 1 to 4 channels. The generated discharges in AC voltage distinguish by the formation of a multiple-channel arc. Whereas, the discharge under lightning impulse voltage found to progress in a single channel whatever the barrier position and dimensions. The model confirms that electric field is the most important factor in the behavior of the rod-insulating barrier-plane system submitted to high voltage.
Developing Bluetooth phonocardiogram for detecting heart murmurs using hybrid MFCC and LSTM Wahyu Nugroho, Dwi Oktavianto; Hikmah, Nada Fitrieyatul; A’alimah, Fathin Hanum; Oktavia, Nabila Shafa; Dwi Winarsih, Meitha Auliana; Elparani, Sirsta Hayatu; Rifqi Hananto, R. M. Tejo
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp878-887

Abstract

Cardiovascular disease is a leading global cause of mortality. Most stethoscopes still necessitate the use of tubing, which entails direct physical contact between the healthcare provider and patient. The stethoscope can serve as a means of transmission if it is utilized on individuals who have been diagnosed with airborne and droplet-borne infectious illnesses. A prototype was created to capture heart sounds using a Phonocardiography (PCG) device over website-based Bluetooth connectivity. This approach offers the benefits of being cost-effective, facilitating computer-aided diagnostics, and being wearable. In addition, the primary significance of this study resides in the identification of heart sound irregularities caused by cardio dynamic abnormalities of the heart valves, known as murmurs. The heart sound categorization process utilizes a machine learning model that involves extracting 25 Mel frequency cepstral coefficients (MFCC) as features. The model employs a hybrid approach combining convolutional neural network and long short-term memory (CNN-LSTM) techniques. The research findings indicate that the suggested model achieves an average accuracy rate of 95.9% over five distinct categories, i.e., normal, atrial stenosis, mitral regurgitation, mitral stenosis, and mitral valves prolapse. Further study can be conducted on hardware development by incorporating an infrared sensor at the fingertip of the stethoscope.
Single nucleotide polymorphism based on hypertension potential risk prediction using LSTM with Adam optimizer Lailil Muflikhah; Imam Cholissodin; Nashi Widodo; Feri Eko Herman; Teresa Liliana Wargasetia; Hana Ratnawati; Riyanarto Sarno
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1126-1139

Abstract

Recent healthcare research has focused a great deal of interest on using genetic data analysis to predict the risk of hypertension. This paper presents a unique method for accurately predicting the vulnerability to hypertension by utilizing single nucleotide polymorphism (SNP) data. We present a novel neural network design utilizing the adaptive moment (Adam) optimizer to describe the intricate temporal correlations in SNPs. The study used a dataset with carefully preprocessed SNP data from a broad cohort for model input. The long short-term memory (LSTM) network was methodically built and trained with hyper-parameter and fine-tuning using the Adam optimizer to converge on ideal weights. Our findings indicate encouraging predictive performance, highlighting the suggested methodology’s usefulness in determining hypertension risk factors. The result showed that the proposed method achieved stability in the performance of 89% accuracy, 96% precision, 88% recall, and 92% F1-score. Due to its higher accuracy and greater predictive power, our SNP-based LSTM methodology is superior to the conventional machine learning method. By providing a novel framework that uses genetic data to predict the risk of hypertension, this research makes substantial contribution to the field of predictive healthcare. This framework helps with early intervention and customized preventative efforts.
Graph attention-driven document image classification through DualTune learning Shilpa Shilpa; Shridevi Soma
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp278-289

Abstract

Document image classification is a challenging task due to the complexity of information contained within documents, including text, images, and their spatial arrangement. Deep learning has become a pivotal tool for extracting and learning complex patterns. However, conventional methods often grapple with integrating different data modalities and minimizing redundancy, leading to a need for more advanced and efficient deep learning strategies. This study presents a new approach to document image classification, named graph attention-driven with dual tune learning (GAD-DTL). GAD-DTL employs dual-tune learning and graph attention networks. The methodology creates semantic region embedding within document images, which incorporate both textual and spatial data. A key feature of this approach is the adaptive fusion layer, which integrates different modalities and uses a graph attention layer to capture context within each region. To minimize redundancy in learned features, we implement two distinct learning techniques, relational and non-relational learning. This approach enhances document image classification by ensuring invariant representation and minimal redundancy in features.

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