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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
 837   838   839   840   841 
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.
A hybrid classification approach for automatically recognizing COVID-19 using deep transfer learning using chest radiographs Pinjara, Murthuja; Babu G., Anjan
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1605-1612

Abstract

Coronavirus 2019 causes COVID-19, a worldwide epidemic. It endangers millions globally. Early illness detection improves recovery and control. X-ray image processing is used to categorise and identify COVID-19 in the present study. Preprocessing, feature extraction using local binary pattern (LBP) and edge orient histogram (EOH), and classification utilising K-nearest neighbour (KNN), Navie Bayes, support vector machine (SVM), and transfer learning convolution neural networks (CNNs) are some of the stages that are implemented in the process. Other phases in the process include preprocessing, feature extraction, and preprocessing. LBP+KNN, EOH+KNN, LBP+SVM, EOH +SVM, CNN+LBP, and CNN+EOH are the outputs derived from the combinations of feature extraction operators and classifiers. Other possible outcomes are CNN+EOH and CNN+LBP. A total of 4,000 pictures were used as the basis for conducting an analysis of the performance of six different models. In order to train the models, 10-fold cross-validation was used, and their accuracy was measured accordingly. The evaluation results indicate a high level of accuracy in diagnosis, ranging from 90.2% to 97.56%. The CNN+LBP and CNN+EOH models have demonstrated superior performance compared to other models, achieving average accuracies ranging from 96.66% and 98.54%.
Efficient and secure data transmission: cryptography techniques using ECC Alhaj, Abdullah Ahmad; Alrabea, Adnan; Jawabreh, Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp486-492

Abstract

Data transmission has become a crucial aspect of our daily lives in the current digital age. However, this transmission comes with the risk of security breaches, which can result in data theft and unauthorized access. This issue can be addressed by using cryptographic techniques such as elliptic curve cryptography (ECC). In comparison to other cryptosystems, ECC is a potent cryptographic tool that provides high levels of security with comparatively reduced key sizes. This paper discusses the use of ECC in efficient and secure data transmission. It provides a comprehensive overview of ECC, including its mathematical background and how it can be applied to encryption and decryption processes. The paper also presents a comparison of ECC with other cryptographic techniques and highlights its advantages, including its resistance to attacks and efficiency in resource-constrained environments. Finally, the paper discusses the implementation of ECC in real-world scenarios and its potential to revolutionize secure data transmission.
Predictive analytics on crop yield using supervised learning techniques Okesola, Julius Olatunji; Ifeoluwa, Olaniyi; Ajagbe, Sunday Adeola; Okesola, Olubunmi; Abiodun, Adeyinka O.; Osang, Francis Bukie; Solanke, Olakunle O.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1664-1673

Abstract

Agriculture is one of Nigeria’s most important economic activities but with climate change is a threat to crop production and a significant impact on the national economy as unforeseen scenarios can cause a drop in crop yield. Machine learning algorithms are now being considered as decision support tools for crop yields prediction and weather forecasting. Maize is the crop selected in this study, and a stochastic gradient model of five popular regression algorithms was evaluated. The prediction system is written in Python programming language and linked to a web-based interface for ease of use and effectiveness. Using performance metrics, the result shows that stochastic gradient descent (SGD) performed best with lower error rates and better R2_score value of 0.98505036. This crop yield prediction system (CYPS) is able to predict the yield of the crop which will help farmers and analysts in decision-making. It will also help industries that make use of the agricultural product in strategizing the logistics of their business.
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.
Ultra-miniaturized dual-band implantable antenna for retinal prosthesis Bousrout, Abdelmouttalib; Khabba, Asma; Ibnyaich, Saida; Mazri, Tomader; Habibi, Mohamed; Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp760-776

Abstract

This article presents two miniaturized antennas designed for retinal prosthesis devices, aimed at enhancing vision for blind individuals with functional optic nerves. The implantable antenna is 2.2 mm wide, 2.15 mm tall, and 0.78 mm thick. It works in the ISM bands but is small because it uses slot incorporation and high-permittivity substrates. High-frequency structure simulator (HFSS) electromagnetic simulations show great performance, with a 16.66% impedance bandwidth at 2.4 GHz and a 10.34% bandwidth at 5.8 GHz. The peak gain values are -27.76 dB at 2.4 GHz and -16.40 dB at 5.8 GHz. We have also developed an extraocular antenna for telemetry and energy transfer, with dimensions of 36×36×1.6 mm3 . Validation through CST calculation software confirms the efficacy of both antenna designs. Implantable antennas hold significant promise in biomedical antenna research, demonstrating capabilities conducive to retinal implantation and offering potential advancements in vision restoration technology.
Utilizing minimum spanning trees for effective mobile sink routing in wireless sensor networks Taleb, Anas Abu; Odeh, Ammar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1938-1949

Abstract

With many practical applications, wireless sensor networks (WSNs) represent an important field of study. Real-world applications of WSNs include smart home automation, healthcare, agriculture, industrial automation, and environmental monitoring. WSNs present countless chances for creative solutions across various industries as they develop and become more sophisticated. But because they are unattended, we must devise ways to make them work better without using the sensor nodes’ most important resource—battery power. A unique sink mobility model from a deployed WSN is proposed in this paper, based on constructing a minimal Spanning tree. The proposed approach derives a controlled movement model for the mobile sink based on minimal spanning tree (MST) features. Consequently, fixed nodes will be scheduled and visited to save routing overhead and improve network efficiency. Using the properties of the minimal spanning tree, the moving sink node can visit immobile sensor nodes, which is the most effective approach to gather data and send it to the base station. The effectiveness of WSNs was examined when implementing this mobility model, and we used the NS-2 simulator to run simulations to assess how efficiently the suggested strategy performed. Our findings demonstrate that WSN performance can be significantly enhanced by implementing the proposed method.
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.
Scientific landscape on opportunities and challenges of large language models and natural language processing O. Roxas, Rachel Edita; C. Recario, Reginald Neil
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp252-263

Abstract

This paper conducted a systematic review of Scopus-indexed publications on large language models (LLMs) and natural language processing (NLP) extracted in October 2023 to address the dearth of literature on their opportunities and challenges. Through bibliometric analysis, from the 1,600 relevant documents, the study explored research productivity, revealing both opportunities and challenges spanning research and real-world applications in education, medicine, and health care, citations, and keyword co-occurrence networks. Results highlighted distribution patterns and dominant players like Google LLC and Stanford University. Opportunities such as technological development in generative artificial intelligence (AI), were contrasted with challenges such as biases and ethical concerns. The intellectual structure analysis revealed prominent application areas in health and education and also emphasized issues such as AI divide and human-AI partnership. Improvement on the technology performance of LLM and NLP remains to be a challenge. Recommendations include further exploration of open research problems and bibliometric studies using other research databases given the research bias towards Scopus-indexed English publications.
A low-cost localization method in autonomous vehicle by applying light detection and ranging technology Kannan, Raju Jagadeesh; Amru, Malothu; Muthumarilakshmi, Surulivelu; Jeyapriya, Jeyaprakash; Aghalya, Stalin; Muthukumaran, Dhakshnamoorthy; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1739-1749

Abstract

The autonomous platform uses global positioning system (GPS) to localize the vehicle. In addition, light detection and ranging (LIDAR) and the high precision camera help to identify the turns in the road. The proposed system can help to determine the road turns with higher accuracy without utilizing LIDAR and high-precision camera technology. This research aims to implement a cost-effective simultaneous localization system that can reduce the cost by half for any autonomous vehicle. The existing system is more complex due to the inclusion of LIDAR technology. In contrast, the proposed approach uses beacon communication between vehicles and infrastructure and long-range (LoRa) for vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) communication. The simulation result illustrates that the proposed approach provides better performance.

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