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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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Penguin search with Harris-Hawk optimization algorithm to improve clustering performance in wireless network Chitra Sabapathy Ranganathan; Rajeshkumar Sampathrajan
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.pp190-197

Abstract

Integrating optimal search algorithm concepts across the wireless core and cluster structure enables next-generation wireless networks to effectively provide reliable low-delay communications and connectivity for internet of things (IoT) devices. This article describes penguin search with the Harris-Hawk optimization algorithm (PHHO) to improve clustering performance in wireless networks. The penguin search optimization algorithm (PSO) algorithm computes the fitness value for feature selection from the database. Harris-Hawk optimization (HHO) algorithm to reduce the time and energy required for network transmission. This mechanism builds the clusters based on node communication range. The node direction, node mobility, node bandwidth availability, and energy parameters to decide the cluster head (CH) by applying the HHO algorithm. This approach uses a PSO algorithm fitness function to select the feature subset to minimize error and overhead in the network. Using a network simulator (NS)-3, this method assesses and chooses the most efficient way for data transmission, and the result is compared to a baseline mechanism.
Automated Alzheimer’s disease detection and classification based on optimized deep learning models using MRI Saini, Rashmi; Singh, Suraj; Semwal, Prabhakar
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.pp1333-1342

Abstract

Alzheimer’s disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies.
Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network Chembian, Woothukadu Thirumaran; Sankar, Krishna Murthi; Koteeswaran, Seerangan; Thinakaran, Kandasamy; Raman, Periyannan
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.pp323-334

Abstract

Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.
Bayesian K-means clustering based quality of experience aware multimedia video streaming Manjunatha Peddareddygari Bayya Reddy; Sheshappa Shagathur Narayanappa
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.pp612-621

Abstract

Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.
Machine learning based detection of DDoS attacks in software defined network Charulatha Kannan; Rajendiran Muthusamy; Vimala Srinivasan; Vivek Chidambaram; Kiruthika Karunakaran
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.pp1503-1511

Abstract

Nowadays, software defined networking (SDN) offers benefits in the area so fautomation, elasticity, and resource consumption. However, evidenceis there that SDN controller may undergo certain defeat for the network structure, particularly as the yare targeted by attacks like denial of service (DoS). Due to this network traffic has increased tremendously and attacked the server severely. To handle this issue, weused the Ryu controller and Mininet tool to identify and all eviate the DoS attack by the machine learning (ML) algorithm. Since ML is deemed as themain method for detecting peculiarities, the detection of DoS attacks was done through ML based classification. In this paper, several ML techniques were used to identify the DoS attack, and the traffic which is causing the attack has been dropped immediately to avoid congestion. The proposed work hasbeen simulated in Mininet and the results show that the proposed work detects DoS attacks well and achieves good accuracy.
Classifying flexible pavement defects using hybrid machine learning approach Jaykumar Soni; Rajesh Gujar
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.pp1072-1080

Abstract

The transportation infrastructure sector significantly impacts a country’s gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN). The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation.
CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease Navaneeth Bhaskar; Ratnaprabha Ravindra Borhade; Sheetal Barekar; Mrinal Bachute; Vinayak Bairagi
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.pp144-151

Abstract

An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach.
Study of BaTiO3-doped Bi2O3/ZnO varistor microstructure and its electrical characteristics Kharchouche, Faiçal; Malaoui, Yousra; Bouketir, Omrane
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.pp42-51

Abstract

This study presents the characterization and optimization of BaTiO3-doped ZnO-based varistors for electrical and electronic applications. The varistors were prepared using a conventional ceramic procedure and were sintered at a temperature of 1,000 °C with different concentrations of BaTiO3 (0 and 3 mol%) added to the Bi2O3/ZnO-based varistor composition (99.5 mol% ZnO and 0.5 mol% Bi2O3). The results showed that the addition of BaTiO3 led to the formation of various oxides and solid solutions, such as Bi12TiO20, BaTiO3, and (Bi2O3)0.80 (BaO)0.20. The dielectric constant and grain size decreased with increasing BaTiO3 content, while the non-linearity coefficient, electric fields (Eb) increased, and dielectric loss (Tanδ) decreased. The optimized varistor contains 2 mol% BaTiO3 and an electric field of 148.08 V/mm, which are superior to those of the BaTiO3/Bi2O3/ZnO-based varistor. During this study, we were able to observe that a slight addition of BaTiO3 will increase the breakdown voltage and the coefficient of nonlinearity and this will allow us to develop low-dimensional varistors and install them in the high-voltage domain.
Generative adversarial networks with attentional multimodal for human face synthesis Sowmya BJ; Meeradevi Meeradevi; Seems Shedole
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.pp1205-1215

Abstract

Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.
Independent swarm grid vehicle charging by a logical DC-DC converter with hybrid boundary conduction mode Karthikeyan Nagarajan; Kaliappan Esakkiappan; Dinakaran Kala Pandian; Dharmaprakash Ramasamy
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.pp1318-1326

Abstract

A modern and expanding civilization now takes for granted that future environments will be clean, efficient, and carbon neutral. Energy-efficient electrical loads, such as v ehicle charging now often u sed in schools, companies, and gated communities. Recent articles have focused more on Swarm smart grid technology as a way to transmit power from rooftop solar installations. A major issue with the system is the necessity for inverters and other conversio n equipment. The mechanism consumes part of the power generated by this process. It is possible to utilize the DC supply to power the v ehicle chargers in smart houses, although some tweaks may be necessary. The charging Vehicle is a common task that can sh are by different converter by swarm network. This study demonstrates the feasibility of using a swam nano grid t o interconnect buildings inside a campus or other large business or residential complex, as well as within a gated community. The electricity gr id is monitored by the swam nano grid system, which eliminates the need for costly equipment. The DC - DC converter was being controlled directly by the simple logic controller. M ATLAB Simulink simulates the controlled converter under load and shows the performance of a bidirectional DC-DC converter using waveforms.

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