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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 35, No 3: September 2024" : 65 Documents clear
Detecting COVID-19 from chest X-ray images using machine learning and deep convolutional neural networks Vibhute, Amol D.; Patil, Chandrashekhar H.; Saini, Jatinderkumar R.; Patil, Harshali P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1786-1795

Abstract

The world was affected by a novel coronavirus in December 2019 that changed human life. Several types of research have been done, substantial scientific advances have been made, and millions of dollars have been spent on bringing scholars and scientists to one platform to end this critical pandemic. Ascertaining COVID-19 diagnoses in the initial stage of the pandemic was critical, specifically for patients with no manifestations. In this case, artificial intelligence-based systems were proposed to identify the virus at an earlier phase. Thus, the present study suggests a machine vision scheme to identify COVID-19 from chest X-ray images. Three machine learning approaches, such as logistic regression (LR), decision tree (DT), and random forest (RF), were implemented with more than 95% accuracy. The deep convolutional neural network (CNN) architecture was also proposed and implemented with a 99.99% detection rate. Therefore, the present work can effectively detect COVID-19 cases in the early stages.
Image segmentation of Komering script using bounding box Hamanrora, Muhammad Dio; Kunang, Yesi Novaria; Yadi, Ilman Zuhri; Mahmud, Mahmud
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1565-1578

Abstract

The development of deep learning technology is widely used for various purposes, including recognizing characters in a document. One of the scripts that can benefit from this deep learning technology is the Komering script, which is a local script in the South Sumatra region. However, there are challenges in reading documents written in this script, requiring a method to separate each character in a document. Therefore, there is a need for a technology that can automatically segment images of documents written in the Komering script. This research introduces an innovative technique for segmenting images of characters in documents that contain Komering script characters. The segmentation technique employs bounding box technology to separate each Komering script character, subsequently recognized by a pre-trained deep learning model. The bounding box approach imposes restrictions on the segmented object area. To recognize Komering characters, a deep learning model with a convolutional neural network (CNN) algorithm is employed.
A lightweight distributed ELM-based security framework for the internet of vehicles Karimy, Aziz Ullah; Reddy, Putta Chandrasekhar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1702-1709

Abstract

The fast growth of internet of vehicles (IoV) has created a new area of connectedness, with promising safety and efficiency in transportation. However, this advancement in vehicle technology has come with significant cybersecurity risks, specifically through control area network (CAN) protocol and other communication techniques within vehicles. This experimental study suggests a machine learning (ML) based security approach based on the extreme learning machine (ELM) algorithm to address these challenges. Unlike customary neural networks, ELM is known for its fast processing, minimal training time, and high accuracy, making it preferably suitable for dynamic IoV environments. The methodology involves data preprocessing, feature selection, and employing ELM for attack classification; the algorithm’s performance is evaluated using CARHacking, NSL-KDD, and EdgeIIoT datasets. We also examine the significance of distributed processing to enhance the computational efficiency of the model, obtaining 89% accuracy in 3 ms run-time for external networks, and 83% accuracy with 9 ms run-time for intra-vehical networks. This newly proposed security mechanism using ELM shows very accurate results in detecting intrusions with a high recall rate and reduced computation time through distributed processing.
A high efficiency boost converter topology with least component count Swargiary, Rikhit; Deva Sarma, Kaushik Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1404-1411

Abstract

This paper presents a design and analysis of novel DC-DC boost converter with a least component count. The proposed converter produces high DC gain voltage in comparison to some recently presented high voltage DC-DC converter. Here one switch, one inductor, two capacitors and one diode are used to achieved a high voltage gain without compromising efficiency of converter. The converter’s performance is evaluated using theatrical, simulation and experimental methods, with results indicating a four times of input voltage and a fast-transient response at various duty cycles is achieved. Due to its low component count the proposed converter is compact and hence it offers an effective solution for various power applications.
Cell nuclei image segmentation using U-Net and DeepLabV3+ with transfer learning and regularization Koishiyeva, Dina; Sydybayeva, Madina; Belginova, Saule; Yeskendirova, Damelya; Azamatova, Zhanerke; Kalpebayev, Azamat; Beketova, Gulzhanat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1986-2000

Abstract

Semantic nuclei segmentation is a challenging area of computer vision. Accurate nuclei segmentation can help medics in diagnosing many diseases. Automatic nuclei segmentation can help medics in diagnosing many diseases such as cancer by providing automatic tissue analysis. Deep learning algorithms allow automatic feature extraction from medical images, however, hematoxylin and eosin (H&E) stained images are challenging due to variability in staining and textures. Using pre-trained models in deep learning speeds up development and improves their performance. This paper compares Deeplabv3+ and U-Net deep learning methods with the pre-trained models ResNet-50 and EfficientNetB4 embedded in their architecture. In addition, different regularization and dropout parameters are applied to prevent overtraining. The experiment was conducted on the PanNuke dataset consisting of nearly 8,000 histological images and annotated nuclei. As a result, the ResNet50-based DeepLabV3+ model with L2 regularization of 0.02 and dropout of 0.7 showed efficiency with dice coefficient (DCS) of 0.8356, intersection over union (IOU) of 0.7280, and loss of 0.3212 on the test set.
Enhancing stochastic optimization: investigating fixed points of chaotic maps for global optimization Rani, Gaddam Sandhya; Jayan, Sarada; Alatas, Bilal; Rajamanickam, Subramani
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1817-1834

Abstract

Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems.
Improvement of electromagnetic torque of BLDC motor for electrical cutter application Kahar, Muhammad Izanie; Raja Othman, Raja Nor Firdaus Kashfi; Khamis, Aziah; Karim, Kasrul Abdul; Abdul Shukor, Fairul Azhar; Ab Ghani, Ahmad Fuad; Rejab, Rofizal Mat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1412-1425

Abstract

As the advancement of brushless direct current (BLDC) motor is rising, it has been an advantage to use the motor for a wide range of applications. Its robustness and torque development have benefited small applications, such as the agriculture cutter. However, dropping performances of conventional BLDC are affected by the shape of the rotor that has unused magnetic flux. Therefore, this research aimed to analyze the electromagnetic torque by reducing the unused flux from an electromagnetic point of view. Two BLDC models with different slot-pole numbers and rotor types were modeled and simulated with equal permanent magnet volume, and magnetomotive force (MMF). Finite element method (FEM) software was used to compute back electromotive force (BEMF), cogging torque, electromagnetic torque, and magnetic flux density of the BLDC models. As a result, 9/8 slot-pole with zero ferromagnetic underneath the permanent magnet had the highest BEMF and torque produced compared to the conventional type, with a percentage difference of 27%. In conclusion, this research presents the motor that had an improvement of electromagnetic torque for electrical cutter application.
Reviewing approaches employed in Arabic chatbots Bouhlali, Abdelmounaim; Elmansori, Adil; El Mhouti, Abderrahim; Fahim, Mohamed; Boudaa, Tarik
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1751-1764

Abstract

The field of chatbots has witnessed a remarkable evolution in recent years, marked by a transition from simplistic rule-based structures to sophisticated systems employing advanced natural language processing (NLP) techniques. While most languages benefit from NLP support, the majority of chatbot research and development has been conducted in English, leaving a notable scarcity of comparable works in Arabic. This scarcity is attributed to the myriad challenges posed by the linguistically intricate nature of Arabic, encompassing orthographic variations and diverse dialects. This study systematically reviews articles that represent implementations of Arabic chatbots, revealing a discernible shift from rule-based frameworks to the predominant adoption of machine learning (ML) and deep learning (DL) methods. The results highlight the dynamic trajectory of chatbot technology, with a notable emphasis on the pivotal role of DL, as evidenced by a significant peak in 2023. Looking forward, the study anticipates a more sophisticated future for chatbot development, driven by ongoing advancements in artificial intelligence (AI) and NLP, offering valuable insights into the current state of Arabic chatbot research and laying the foundation for continued exploration in this evolving and dynamic field.
Design of a tunable center frequency and small size cavity bandpass filter by separating capacitor-loaded resonators Nguyen, Van Son; Dang, Hoang Anh; Tran, Van Dung; Pham, Cao Dai; Phan, Thi Bich; Nguyen, Van Trung; Dai, Xuan Loi; Huy, Long Tran
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1456-1467

Abstract

This paper presents a tunable center frequency and small size cavity bandpass filter design method. In this method, a capacitor-loaded open terminal coaxial resonator is employed to reduce the size of cavity filters. The resonator is designed and fabricated separately into two parts to achieve the flexible operating frequency purpose. The first part is called the base of the resonator which is simply a pillar and directly fabricated integrally with the cavity housing. The second part called the hat of the resonator is the main part causing the load capacitance in cavity filters. By using different heights of the base part or/and different shapes and sizes of the hat, the operating frequency of cavity filters can be changed flexibly. This method not only reduces the difficulty and cost of cavity filter processing but also makes cavity filters reconfigurable. To demonstrate the effectiveness of the method, a cavity filter sample with a center frequency of 3.45 GHz and a bandwidth of 80 MHz was designed, fabricated, and measured. The measured results show that the insertion loss was smaller than 1.33 dB in the whole bandwidth, one zero-point at 3.350 GHz reaching -68 dB, the rejection at 3.550 GHz was -41 dB, unloaded Q was 5,898, and the dimension of the filter was 128 mm×86 mm×23 mm.
Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis Manoharan, Thiyagarajan; Velvizhi, Ramalingam; Juluru, Tarun Kumar; Kamal, Shoaib; Mallick, Shrabani; Puliyanjalil, Ezudheen
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1670-1679

Abstract

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL.

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