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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 75 Documents
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Search results for , issue "Vol 13, No 5: October 2024" : 75 Documents clear
An image analysis technique for wheat head count detection using machine learning Kalluri, Ramadevi; Selvaraj, Prabha
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7255

Abstract

Deep learning (DL) techniques have significantly enhanced the potential for wheat head detection in recent times. The different development phases of canopy, genotype, wheat heads, and wheat head orientation provide considerable obstacles. The overlapping density of wheat heads and wind- induced picture blurring complicate wheat head recognition. This study describes an effective wheat head detection and counting method. Due to its high throughput in agricultural field analysis, remote sensing phenotyping has grown in popularity. Applying DL methods for image processing and other technological advancements has increased the scope for the quantitative evaluation of various crop traits. The ability to detect and characterize wheat heads in the industry is an important part of the wheat breeding process for selecting high-yielding cultivars. The proposed method uses the Mask region-based convolutional neural network (RCNN) framework to detect and classify the wheat ears. The complete detection task is done in three steps: region proposal generation, region of interest alignment, and mask generation. The global wheat head detection (GWHD) dataset is used for the experimental analysis of the dataset. The proposed method achieved an accuracy of 95.11% on the GWHD dataset, demonstrating its effectiveness in wheat head detection and classification tasks.
Addressing the complexities of postoperative brain MRI cavity segmentation–a comprehensive review P, Sobha Xavier; P K, Sathish; G, Raju
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7930

Abstract

Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase.
Comparative field assessment of grounding enhancement material for electrical earthing system Zhe Kang, Lim; Chun Lim, Siow; Muhammad, Usman; Aman, Fazlul; Nor, Normiza Mohamad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7303

Abstract

Grounding enhancement material (GEM) is used to lower the earthing resistance value of a given earthing system. In this paper, a commercially available GEM is experimented at the field alongside with Sodium Chloride, Copper II Sulphate and planting soil. The well established Wenner’s 4 pole method and fall of potential method was employed to measure the soil resistivity and earthing resistance respectively. It was found that the salts i.e., Sodium Chloride and Copper II Sulphate are superior in reducing the earthing resistance as reduction of more than 85% were observed. However, the commercial GEM has exhibited the most stable earthing resistance value over a period of 101 days, exhibiting the lowest standard deviation. This seems to suggest that the commercial GEM has superior moisture retention capability. This study also proven that Sodium Chloride can be dissolved by heavy downpour and replenishing it periodically is needed in a tropical country like Malaysia with regular thunderstorms and heavy downpours.
Enhanced building footprint extraction from satellite imagery using Mask R-CNN and PointRend NourEldeen, Ahmed; E. Wahed, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7443

Abstract

The extraction of building footprints from aerial photos and satellite imagery plays a crucial role in change detection, urban development, and detecting encroachments on agricultural land. Deep neural networks offer the capability of extracting features and provide accurate methods for detecting and extracting building footprints from satellite imagery. Image segmentation, the process of dividing an image into coherent parts, can be accomplished using two types: semantic segmentation and instance segmentation. Convolutional neural networks (CNN) are commonly used for both instance and semantic segmentation tasks. In this paper, we propose a hybrid approach to extracting building footprints from low-resolution satellite imagery using instance segmentation techniques. Our analysis demonstrates that the mask region-based CNN (R-CNN) architecture with a ResNet-34 backbone and PointRend head to improve the bounding-boxes and mask prediction achieves the highest performance, as evidenced by various metrics, including an average precision (AP) score of 83.39% and an F-1 score of 85.71%. This approach holds promise for developing automated tools to process satellite imagery, benefiting fields such as agriculture, land use monitoring, and disaster response.
Deblurring image compression algorithm using deep convolutional neural network Menassel, Rafik; Gattal, Abdeljalil; Kerdoud, Fateh
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7783

Abstract

There are instances where image compression becomes necessary; however, the use of lossy compression techniques often results in visual artifacts. These artifacts typically remove high-frequency detail and may introduce noise or small image structures. To mitigate the impact of compression on image perception, various technologies, including machine learning and optimization metaheuristics that optimize the parameters of image compression algorithms, have been developed. This paper investigates the application of convolutional neural networks (CNNs) to reduce artifacts associated with image compression, and it presents a proposed method termed deblurring compression image using a CNN (DCI-CNN). Trained on a UTKFace dataset and tested on six benchmark images, the DCI-CNN aims to address artifacts such as block artifacts, ringing artifacts, blurring artifacts, color bleeding, and mosquito noise. The DCI-CNN application is designed to enhance the visual quality and fidelity of compressed images, offering a more detailed output compared to generic and other deep learning-based deblurring methods found in related work.
Handover management in vehicle communication: applications, techniques, issues, and challenges: a review Qasim, Hamzah Hadi; Zainol Abidin, Husna; Afzal Che Abdullah, Syahrul
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7549

Abstract

Vehicle-to-everything (V2X) communication is an emerging technology that facilitates communication among vehicles and numerous environmental entities. However, it encounters certain challenges throughout the handover process. This study analyses the challenges and complexity of managing handover, specifically in maintaining uninterrupted connection and meeting the service criteria outlined in the 3GPP 5G new radio (NR) standard. Various applications of V2X technology that require handover management are explored, such as vehicle safety and traffic management, enhanced driver assistance, and autonomous driving. Furthermore, this paper illuminates the most recent developments in V2X communication, highlighting the significance of efficient handover management, resolving technical issues, based on the full potential of the use of V2X apps that contribute to the establishment of a transportation ecosystem that is characterized by enhanced safety, increased intelligence, and improved connectivity. This paper can be used as a starting point for thinking about how to improve C-V2X communication.
Prediction of mental illness using ensemble model and grid search hyperparameter optimization Kudlapura Shivaiah, Srinath; Krishnappa, Kiran; Kumar Boraiah, Naveen; Deepa Shenoy, Punjalkatte; Kuppanna Rajuk, Venugopal
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7309

Abstract

The early prediction of mental illnesses reduces the severity of the disease. The symptoms like poor concentration, unstable energy of the body, pressure, and loss of interest cause depression. A large-scale group decision making (LSGDM) method is proposed in this paper along with the ensemble classifier model by combining convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) for effective classification of depression, anxiety and stress (DAS) levels. The data is collected from the depression anxiety stress scale-42 (DASS-42) dataset for efficient classification and predictions of mental health problems. The min-max normalization is used to pre-process the data, and the feature selection is done for extracting informative features. The extracted features are provided as input to the ensemble classifier. The proposed LSGDM model maximizes the classification accuracy with the help of grid search CV hyperparameter tuning, and results in an accuracy of 98.88%, precision of 98.21%, recall of 99.62%, F1-Score of 98.90%, and MCC of 99.41%. The proposed LSGDM method gives superior results when compared to the existing machine learning (ML) based ensemble model, a principal component analysis along with modified fast correlation based filtering (PCA-mFCBF), and LSTM based RNN (LSTM- RNN).
A new approach to joint resource management in MEC-IoT based federated meta-learning Samafou, Faustin; Amine Adoum, Bakhit; Abba Ari, Ado Adamou; Marius Fidel, Faitchou; Moungache, Amir; Armi, Nasrullah; Mourad Gueroui, Abdelhakh
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7993

Abstract

MEC and IoT are rapidly expanding technologies that offer numerous opportunities to enhance efficiency and application performance. However, the huge volume of data generated by IoT devices, coupled with computational and latency constraints, poses data processing challenges. To address this within the MEC architecture, deploying computing servers at the network edge near IoT devices is a promising approach. This reduces latency and traffic load on the core network while improving the user experience. However, offloading computations task from IoT devices to MEC servers and efficiently allocating computing resources is a complex problem. IoT tasks may have specific requirements in terms of latency, bandwidth and energy efficiency, while computing resources and capacities maybe limited or shared between several users. We propose an approach called FedMeta2Ag, which we evaluate using the MNIST database. With 20 epochs, the training accuracy reached 91.5%, while the test accuracy achieved 92.0%. Performance consistently improved during the initial 20 iterations and gradually stabilized thereafter. Additionally, we compared the performance of our proposed model with existing methods, finding that our approach outperforms existing models in predicting performance more accurately. Thus, this approach effectively meets the demanding performance requirements of wireless communication systems.
Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware Shivaramu Banumathi, Chandini; Basavegowda Rajendra, Ajjipura
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7548

Abstract

The classification and analysis of malicious software (malware) has reached a huge development in the systems associated with the internet. The malware exploits the system information and takes off the important information of the user without any intimation. Moreover, the malware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect obfuscated and packed malware. To overcome these problems, the hybridized grasshopper optimization and cuckoo search (GOA-CSA) algorithm is proposed. The effective features are selected by the GOA-CSA algorithm which eases the process of classifying the malware. This research also utilized long short-term memory (LSTM)-softsign classifier to classify the malware. The malware samples are collected from the VXHeavens dataset which consists of malware samples from various software. The proposed model performance is estimated by using the performance metrics like accuracy, sensitivity, recall, and F1-score. The model attained better accuracy of 98.95% when the model is compared with other existing models.
Harnessing DBSCAN and auto-encoder for hyper intrusion detection in cloud computing Kaliyaperumal, Prabu; Periyasamy, Sudhakar; Periyasamy, Muthusamy; Alagarsamy, Abinaya
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8135

Abstract

The widespread availability of internet services has led to a surge in network attacks, raising serious concerns about cybersecurity. Intrusion detection systems (IDS) are pivotal in safeguarding networks by identifying malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, probe, remote-to-local, and user-to-root attacks. To counter these threats effectively, this research focuses on utilizing unsupervised learning to train detection models. The proposed method involves employing auto-encoders (AE) for attack detection and density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. By using preprocessed and unlabeled normal network traffic data, the approach enables the identification of unknown attacks while minimizing the impact of imbalanced training data on model performance. The auto-encoder method utilizes the reconstruction error as an anomaly detection metric, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determine cluster count, ensure scalability, and minimize false positives. Tested on standard datasets such as KDDCup99, UNSW-NB15, CICIDS2017, and CSE-CIC-IDS2018, this proposed model achieves accuracies exceeding 98.36%, 98.22%, 98.45%, and 98.51%, respectively. These results demonstrate the effectiveness of unsupervised learning in detecting and clustering novel intrusions while managing imbalanced data.

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