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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 9,199 Documents
 903   904   905   906   907 
A systematic evaluation of pre-trained encoder architectures for multimodal brain tumor segmentation using U-Net-based architectures Abbas, Marwa; Khalaf, Ashraf A. M.; Mogahed, Hussein; Hussein, Aziza I.; Gaber, Lamya; Mabrook, M. Mourad
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp850-859

Abstract

Accurate brain tumor segmentation from medical imaging is critical for early diagnosis and effective treatment planning. Deep learning methods, particularly U-Net-based architectures, have demonstrated strong performance in this domain. However, prior studies have primarily focused on limited encoder backbones, overlooking the potential advantages of alternative pretrained models. This study presents a systematic evaluation of twelve pretrained convolutional neural networks—ResNet34, ResNet50, ResNet101, VGG16, VGG19, DenseNet121, InceptionResNetV2, InceptionV3, MobileNetV2, EfficientNetB1, SE-ResNet34, and SE-ResNet18—used as encoder backbones in the U-Net framework for identification and extraction of tumor-affected brain areas using the BraTS 2019 multimodal MRI dataset. Model performance was assessed through cross-validation, incorporating fault detection to enhance reliability. The MobileNetV2-based U-Net configuration outperformed all other architectures, achieving 99% cross-validation accuracy and 99.3% test accuracy. Additionally, it achieved a Jaccard coefficient of 83.45%, and Dice coefficients of 90.3% (Whole Tumor), 86.07% (Tumor Core), and 81.93% (Enhancing Tumor), with a low-test loss of 0.0282. These results demonstrate that MobileNetV2 is a highly effective encoder backbone for U-Net in extracting tasks for tumor-affected brain regions using multimodal medical imaging data.
Vehicle recognition on indian roads using data augmentation and VGG-16 model K. L., Arunkumar; K. M., Poornima; Danti, Ajit; H. T., Manjunatha
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1177-1186

Abstract

In an advanced intelligent transportation system vehicle recognition and classi f ication is very significant. In current research trend, recognition of vehicles is done byusingmachinelearning (ML)andcomputervisiontechniques. Vehicle’s multi-view images or videos with different lighting conditions are annotated and given to the deep neural network to build an automated system to recognize the vehicles models. The augmentation of data can increase the number of sam ples in learning, with the small available datasets. Geometric transformations, brightness changes, and different filter operations are applied to the data through data augmentation. Furthermore, be orthogonal experiments we determine the optimal data augmentation method to obtain 96% accuracy in results. Detailed information is reported based on the classification of four different types of vehi cles and the results show that convolutional neural network with 16 layers deep techniques are effective in solving challenging tasks while recognizing moving vehicles.
Ensemble recursive feature elimination-based ensemble classification for medical diagnosis Ramanathan, Thirumalaimuthu Thirumalaiappan; Hossen, Md. Jakir; Al Mamun, Abdullah; Raja, Joseph Emerson
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp758-771

Abstract

The application of data mining techniques for the extraction of patterns from medical datasets is useful in the prediction of various diseases from the data of patients. An appropriate feature selection method is required for the medical datasets to give better results for the medical data mining process. In data preprocessing, feature selection is an important process that finds the most relevant features from the dataset. Considering all features of the medical dataset without using any feature selection process may sometimes lead to inaccurate results. Most of the medical datasets contain meaningless data that are not relevant to the data mining process. These data can be eliminated through the feature selection process. This paper presents an integration of an ensemble feature selection approach and an ensemble classification approach through a classifier called the ensemble recursive feature elimination-based ensemble classifier (ERFE-EC) for the classification of medical data. Four different medical datasets were used for testing the ERFE-EC method, which showed promising results.
Enhancing IoT security: a hybrid intelligent intrusion detection system integrating machine learning and metaheuristic algorithm Ghaleb, Sanaa A. A.; Mohamad, Mumtazimah; Ghanem, Waheed; Ngah, Amir; Yunus, Farizah; Alhadi, Arifah Che; Islam Siddique, MD Nurul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1040-1049

Abstract

The rapid proliferation of the internet of things (IoT) has introduced significant security and privacy challenges. As IoT devices often have limited computational power and memory, they are highly vulnerable to cyber threats. Traditional intrusion detection systems (IDS) struggle to operate efficiently in these constrained environments, necessitating more adaptive and optimized security solutions. To address these challenges, this study proposes an innovative IDS model, MSAMLP, which combines the moth search algorithm (MSA) with a multilayer perceptron (MLP) classifier. The objective is to enhance the classification accuracy of malicious and benign network traffic while maintaining computational efficiency. The model was evaluated using two widely recognized intrusion detection datasets, benchmarking its performance against existing IDS approaches. Experimental results indicate that MSAMLP outperforms conventional classification models, achieving high accuracy, improved detection rates, and reduced false alarm rates. Its adaptive learning capability ensures better anomaly detection in dynamic IoT environments. In conclusion, the proposed MSAMLP model demonstrates superior performance in securing IoT networks, offering an effective solution to mitigate evolving cyber threats. This research contributes to the advancement of IoT security by introducing a robust and scalable intrusion detection approach.
Image recognition using deep learning: a review Hassan, Osama M.; Gouda, Ashraf A.; Razek, Mohammed Abdel
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp953-967

Abstract

This paper presents a comprehensive review of recent advancements in image recognition, with a focus on deep learning (DL) techniques. Convolutional neural networks (CNNs), in particular, have significantly transformed this domain, enabling substantial improvements in both accuracy and efficiency across diverse applications. The review explores state-of-the-art methods, highlighting their practical implementations and the progress achieved. It also addresses key challenges such as data scarcity and model interpretability, offering perspectives on emerging opportunities and future directions. By synthesizing current trends with forward-looking insights, the paper aims to serve as a valuable resource for researchers and practitioners seeking to navigate and contribute to the evolving landscape of image recognition. Moreover, the paper examines critical challenges that persist in the field, such as transfer learning, data augmentation, and explainable artificial intelligence (AI) approaches. By synthesizing current trends with emerging innovations, the review not only maps the trajectory of progress but also highlights future directions and research opportunities. This synthesis aims to provide researchers, developers, and industry practitioners with a solid understanding of the dynamic and rapidly evolving environment surrounding image recognition technologies.
Hyperparameter optimization of convolutional neural network using grey wolf optimization for facial emotion recognition Munsarif, Muhammad; Saman, Muhammad; Ernawati, Ernawati; Santosa, Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp898-906

Abstract

Facial emotion recognition (FER) is a challenging task in computer vision with wide applications in areas such as human-computer interaction, security, and healthcare. To improve the performance of convolutional neural networks (CNN) in FER, a novel approach combining CNN with grey wolf optimization (GWO) was proposed to optimize key hyperparameters. The CNN-GWO model was fine-tuned by adjusting hyperparameters such as the number of convolutional layers, kernel size, number of filters, and learning rate. This model was evaluated using the CK+ dataset and achieved an accuracy of 90.97%, demonstrating its competitive performance compared to existing methods. The optimized hyperparameters included three convolutional layers, 35 filters, a kernel size of 5, a learning rate of 0.045990, a dropout rate of 0.4988, and a max pooling size of 3. These results confirm that GWO is effective in optimizing CNN for FER tasks, providing an efficient solution to enhance model accuracy. This approach shows promising potential for future FER applications, highlighting GWO as a valuable optimization technique for CNN architectures.
FPGA-based implementation of an S-Box cryptographic co-processor for high-performance applications Ahmed Nassim, Moulai Khatir; Zakarya, Ziani
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1167-1176

Abstract

The increasing demand for reliable cryptographic operations for securing current systems has given birth to well-advanced and developed hardware solutions, in this paper we consider issues within the traditional symmetric advanced encryption standard (AES) cryptographic system as major challenges. Additionally, problems such as throughput limitations, reliability, and unified key management are also discussed and tackled through appropriate hierarchical transformation techniques. To overcome these challenges, this paper presents the design and field programmable gate array (FPGA)-based implementation of a cryptographic coprocessor optimized for substitution box (S-Box) operation which is considered as a key component in many cryptographic algorithms such as AES. The architecture of the co-processor proposed in this article is based on the advanced characteristics of FPGAs to accelerate the S-Box transformation, improve throughput and reduce latency compared to software implementations. We discussed carefully the design considerations along with resource utilization, speed optimization, and energy efficiency. The obtained experimental results present significant performance improvements, the FPGA-based implementation ensured higher throughput and lower execution time compared to traditional CPU-based methods. We presented in this work the effectiveness of using FPGAs for the acceleration of cryptographic operations in secure applications which will therefore be a robust solution for the next generation of secure systems.
Monocular vision-based visual control for SCARA-type robotic arms: a depth mapping approach Chambi Tula, Diego; Challco, Bryan; Catari, Jonathan; Aguilar, Walker; Pari, Lizardo
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp801-813

Abstract

The accelerated growth of an increasingly automated industry requires the use of autonomous robotic systems. However, the use of these systems commonly requires an enormous amount of sensors. In this paper we evaluate the performance of a new system for visual control of a selective compliance assembly robot arm (SCARA) robotic arm using a monocular depth map that only requires one monocular camera. This system aims to be an efficient alternative to reduce the number of sensors in the robotic arm area while maintaining the effectiveness of traditional vision algorithms that use stereoscopic architectures of cameras. For this purpose, this system is compared with representative state-of the-art vision algorithms focused on the control of robotic arms. The results are statistically analyzed, indicating that the algorithm proposed in this research has competitive performance compared to state-of-the-art robotic arm visual control algorithms only using a single monocular camera.
Design and implementation of heterogeneous IoT wearables for multi-disease monitoring with OFDM-based spectrum allocation Boladale, Shittu Moshood; Oshiga, Omotayo Olabowale; Osanaiye, Opeyemi Ayokunle; Amuda, Abdulrasaq Olanrewaju; Odigbo, Abigail Chidimma; Araoye, Timothy Oluwaseun
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp667-677

Abstract

This research proposes a comprehensive and scalable architecture for intelligent healthcare monitoring, integrating heterogeneous wearable biosensors, edge computing, and bio-inspired optimization techniques employing an orthogonal frequency division multiplexing (OFDM)-based spectrum allocation strategy. The system continuously monitors key physiological parameters, including heart rate, electrocardiogram (ECG), blood glucose levels, body temperature, blood pressure, and respiratory rate, using low-power, biocompatible sensors with wireless communication capabilities. An edge computing layer performs real-time signal preprocessing (noise filtering, normalization, compression), significantly reducing latency and bandwidth demands. To optimize system performance, the walrus optimization algorithm (WOA), a novel metaheuristic inspired by walrus social and hunting behaviors, is employed. WOA is utilized to dynamically adjust critical parameters, including transmission power, modulation index, bandwidth allocation, and routing efficiency. Experimental results demonstrate notable improvements: signal-to-noise ratio (SNR) increased from 5 dB to over 31 dB, latency reduced from 10 ms to under 4 ms, and bit error rate (BER) was minimized to 8×10⁻⁶. Hybrid models incorporating WOA with machine learning (WOA-ANN, WOA-SVM) achieved spectral efficiencies up to 3.7 bits/s/Hz and energy efficiencies up to 22 bits/Joule. The proposed system supports reliable, real-time health data acquisition and transmission in both urban and remote healthcare environments. Its modular, power-efficient, and adaptive architecture demonstrates high potential for deployment in telemedicine, chronic disease management, and emergency response systems, establishing a robust foundation for next-generation smart healthcare infrastructure.
Accelerated framework for image compression and reconstruction based on compressive sensing M. Yousif, Tasneem; M. Ahmed, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1241-1250

Abstract

Image compression is a crucial field driven by advancements in communication and imaging technologies. Its primary goal is to achieve low bit rates while maintaining high-quality image reconstruction. Compression is essential in digital image processing, multimedia applications, and medical imaging. Various algorithms exist for image compression and reconstruction, each differing in efficiency. Compressive sensing (CS) algorithms, commonly used for radar data reconstruction, require iterative computations that demand significant processing power and time, limiting real-time applications. To overcome these challenges, this study proposes a parallel-pipelined processing approach to enhance compression and reconstruction efficiency. The method accelerates processing speeds, increases data throughput, and optimizes performance by reducing data size. The proposed approach divides image data into multiple parallel processing branches, significantly reducing computational cycles. This results in faster execution and improved real-time applicability. MATLAB simulations and field-programmable gate array (FPGA) hardware implementations have been conducted to validate the system’s effectiveness. The results demonstrate that the parallel-pipelined method significantly enhances efficiency compared to traditional approaches, making it suitable for applications requiring high-speed image processing, such as satellite imaging and medical diagnostics.

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